Accurately modeling soft boundaries, e.g., hair and defocus blur, is a fundamental challenge in stereo conversion due to the ambiguous blending of foreground and background. Existing depth models primarily predict single-layer depth, leading to ambiguity in depth correspondence at soft boundaries. While matting techniques can capture opacity for layered modeling, they often struggle in complex scenes with multiple targets and usually require user intervention. This paper introduces $\alpha$Depth, a layered representation that decomposes soft boundaries for high-fidelity stereo conversion. Specifically, we first resolve mixed color and depth ambiguity by estimating layered color and depth values at soft boundaries. Considering complex multi-target scenes, we design a Circular Alpha Representation (CAR) that shifts the paradigm from global target extraction to local boundary decomposition. Unlike prior matting methods restricted to a single foreground/background, CAR enables efficient scene-level inference without manual guidance. Extensive evaluations demonstrate that $\alpha$Depth achieves state-of-the-art performance in stereo conversion, eliminating background bleeding and structural distortions at soft boundaries.

Dataset distillation (DD) aims to compress large-scale datasets into compact synthetic sets while preserving training efficacy. However, existing studies mainly focus on image classification, leaving dense prediction tasks such as semantic segmentation largely underexplored. In this work, we identify three key challenges for segmentation DD: (*i*) long-tailed class imbalance, (*ii*) the need for strict pixel-wise alignment between images and dense labels, and (*iii*) the high computational cost of optimizing high-resolution data with complex models. To address these challenges, we propose $\mathbf{D^{3}S^{2}}$, a **D**iffusion-guided **D**ataset **D**istillation framework for **S**emantic **S**egmentation. Our method adopts a two-stage design. In **Class-Balanced Mask Selection**, we construct a representative mask set via a greedy strategy that prioritizes underrepresented classes. In **Diffusion-Guided Image Synthesis**, we employ a pretrained layout-to-image diffusion model to generate images conditioned on the selected masks, naturally ensuring spatial alignment. To further enhance the training utility of synthesized data, we introduce guided diffusion sampling with two complementary objectives: a segmentation-consistency loss for pixel-level alignment, and a class-wise feature matching loss for aligning per-class feature statistics across layers. Extensive experiments demonstrate the superiority of $D^{3}S^{2}$. Notably, at an extremely compression rate of 1\%, our method achieves 24.99\% and 35.49\% mIoU on ADE20K and COCO-Stuff with Mask2Former (Swin-S), outperforming random selection by 9.34\% and 5.70\%, respectively.

Reconstructing velocity distributions using wavefield responses provides a powerful tool for probing the internal structures of objects and has been widely applied in nondestructive testing, geological exploration, and medical imaging. While full waveform inversion (FWI) provides high-resolution results, its severe ill-posedness and high computational cost hinder efficient 3D reconstruction. In this paper, we propose a novel framework for efficient 3D internal velocity field reconstruction. First, we parameterize the velocity field with a coordinate-based neural network as an implicit continuous function, enabling compact representation from sparse observations. Second, we adopt ray-based traveltime tomography with multi-level parallel forward modeling to accelerate computation. Third, we introduce a differentiable Fresnel volumizing method that extends 1D ray paths into 3D volumetric regions (i.e., Fresnel volumes), enabling spatially continuous and multi-scale gradient diffusion, thus alleviating gradient sparsity and improving reconstruction accuracy. To further improve efficiency, we propose a 2D slice-based construction of 3D Fresnel volumes using a lightweight neural network. Extensive experiments on synthetic datasets, including ablation studies, demonstrate up to 10 times speedup over FWI while maintaining comparable accuracy, validating the effectiveness of the proposed method for efficient 3D inverse problems.

3D Gaussian Splatting (3DGS) has become a vital tool in novel view synthesis. 3D Gaussian functions with attributes like color, opacity, and scale are stacked together to explicitly model a radiance field. However, discrete 3D Gaussians usually produce poor depth with few geometry details but lots of floaters, which makes modeling smooth radiance fields remain a challenge. To address this issue, we propose to learn a radiance field within a continuous view-dependent color field parameterized by neural networks. With the continuous constraints in color, the bias on low-frequency signals of neural networks highly encourages the geometry to contribute to high-frequency color variations on images, but not solely relying on the color attribute itself. Moreover, we impose a constraint to improve the Lipschitz continuity of the neural color field, making large changes of each Gaussian's color also match with large changes of its position in terms of a ratio. To this end, we additionally introduce novel self-learning strategies to learn neural view-dependent color fields without any external knowledge or priors, aiming for better generalization. Our numerical and visual comparisons on widely used benchmarks justify our idea and show better ability of high fidelity geometry recovery in 3DGS.

3D Gaussian Splatting (3DGS) has emerged as a leading representation for real-time novel view synthesis and been widely adopted in various downstream applications. The core strength of 3DGS lies in its efficient kernel-based scene representation, where Gaussian primitives provide favorable mathematical and computational properties. However, under a finite primitive budget, the symmetric shape of each primitive directly affects representation compactness, especially near asymmetric structures such as object boundaries and one-sided surfaces. Recent works have explored more complex kernel distributions, yet they either remain within the elliptical family or rely on hard truncation, which limits continuous shape control and introduces distributional discontinuities. In this paper, we propose Skew-Normal Splatting (SNS), which adopts the Azzalini Skew-Normal distribution as the fundamental primitive. By introducing a learnable and bounded skewness parameter, SNS can continuously interpolate between symmetric Gaussians and Half-Gaussian-like shapes, enabling flexible modeling of both sharp boundaries and interior regions. Moremover, SNS preserves analytical tractability under affine transformations and marginalization. This property allows seamless integration into existing Gaussian Splatting rasterization pipelines. Furthermore, to address the strong coupling between scale, rotation, and skewness parameters, we introduce a decoupled parameterization and a block-wise optimization strategy to enhance training stability and accuracy. Extensive experiments on standard novel-view synthesis benchmarks show that SNS consistently improves reconstruction quality over Gaussian and recent non-Gaussian kernels, with clearer benefits on sharp boundaries and thin or one-sided structures. Codes and models will be released.

Feed-forward 3D reconstruction models have demonstrated strong generalization under large-scale pre-training, yet they struggle in long-tail scenarios such as low-light reconstruction, joint human-scene reconstruction, and test-time adaptation. We observe relational attention structure collapse, manifested as the failure of cross-view correspondences, as a primary cause of these limitations. While adaptation offers a practical way to bridge this gap, existing parameter-efficient fine-tuning (PEFT) approaches mainly perform channel-wise feature adjustment and lack mechanisms to explicitly adapt relational structures, leaving correspondence failures unresolved. In this work, we formulate adaptation of 3D reconstruction models as a problem of correspondence recovery under domain shift and propose 3R-Adapter, a PEFT paradigm for long-tail 3D reconstruction scenarios. Our method consists of three components: Deformable Retrieval for task-adaptive relation candidate retrieval, Relational Rewiring for reconstructing cross-view relational structures, and Consistency Refinement for injecting the rewired relations into stable predictions. Extensive experiments on low-light reconstruction, joint human-scene reconstruction, and test-time adaptation demonstrate that our approach significantly improves robustness over existing PEFT methods while maintaining efficient lightweight adaptation.

Scene-level 3D generation has long been dominated by 2D multi-view or video diffusion models. Typically, these 2D-based approaches perform scene generation in a 2D latent space, which introduces two fundamental issues: (i) representing 3D scenes via 2D views leads to significant representation redundancy, and (ii) latent space rooted in 2D inherently limits the spatial consistency of the generated scenes. In this paper, we propose to perform 3D scene generation directly within a high-dimensional implicit 3D latent space derived from powerful 2D foundation models (e.g., DINOv2, SigLIP2, DA3). Then we tame diffusion transformers to perform diffusion modeling directly in the 3D latent space, enabling 3D-grounded scene generation. In our experiments, we not only demonstrate the advantages of our method over 2D-based approaches in terms of efficiency and spatial consistency, but also comprehensively analyze the impact of 3D latent representation on 3D scene generation performance. Furthermore, we validate the favorable scalability of our method with respect to representation and model capacity.

Restoring archival film remains a fundamentally challenging problem due to the absence of paired training data and the lack of standardized evaluation benchmarks. Pristine versions of deteriorated footage are physically unrecoverable, forcing supervised methods to rely on synthetic data that often fail to capture the complex, temporally coherent nature of real film degradation. At the same time, existing real-world datasets are limited in scale, quality, and accessibility, hindering reliable evaluation and fair comparison across methods. We address both limitations with AbsoluteDegradation, a physics-inspired, modular pipeline for synthesizing realistic film degradations, and a new large-scale archival benchmark. The proposed pipeline models the analog-to-digital process as a structured composition of artifact families, incorporating signal-dependent grain, parametric scratches, and temporally coherent camera motion, enabling controlled generation of diverse degradation regimes. In parallel, we introduce a curated dataset of 81,576 high-resolution frames sourced from real archival footage, designed for consistent evaluation under authentic conditions. Together, these contributions provide a unified framework for training and benchmarking restoration models. Extensive experiments across multiple architectures show that models trained with AbsoluteDegradation generalize better to real-world footage, while the proposed benchmark reveals systematic failure modes of current methods. We hope this work establishes a foundation for reproducible and domain-authentic evaluation in archival film restoration.

Diffusion and rectified flow (RF) models generate high-fidelity images and videos, but their iterative velocity-field evaluations are computationally expensive. Existing caching methods accelerate sampling by skipping timesteps, yet their coarse approximations introduce accumulated errors over long skip intervals and degrade quality under aggressive acceleration. We propose \textbf{TACache (Trajectory-Aware Cache)}, a training-free acceleration framework following a \textbf{skip-then-compensate} paradigm. TACache performs an orthogonal decomposition of discrete velocity acceleration along the RF trajectory into parallel and orthogonal components, isolating the magnitude and directional sources of per-step approximation error. The framework operates in two stages: \emph{offline}, cumulative variation thresholds on the magnitude and direction indicators yield the skipping schedule and bound how far each skipped interval may extend; \emph{online}, at each skipped step the offline statistics are combined with the sample's historical orthogonal direction to reconstruct the skipped velocity without additional forward passes. Experiments on BAGEL, FLUX.1-dev, and Wan2.1-1.3B show that TACache achieves up to $4.14\times$ speedup on text-to-image generation and $2.11\times$ speedup on text-to-video generation, with consistent improvements over prior cache-based methods on all reference-based fidelity metrics. Code will be released soon.

In adversarial multi-armed bandits, two performance measures are commonly used: static regret, which compares the learner to the best fixed arm, and dynamic regret, which compares it to the best sequence of arms. While optimal algorithms are known for each measure individually, there is no known algorithm achieving optimal bounds for both simultaneously. \cite{marinov2021pareto} first showed that such simultaneous optimality is impossible against an \emph{adaptive} adversary. Our work takes a first step to demonstrate its possibility against an \emph{oblivious} adversary when losses are \emph{deterministic}. First, we extend the impossibility result of \cite{marinov2021pareto} to the case of deterministic losses. Then, we present an algorithm achieving optimal static and dynamic regret simultaneously against an oblivious adversary. Together, they reveal a fundamental separation between adaptive and oblivious adversaries when multiple regret benchmarks are considered simultaneously. It also provides new insight into the long open problem of simultaneously achieving optimal regret against switching benchmarks of different numbers of switches. Our algorithm uses negative static regret to compensate for the exploration overhead incurred when controlling dynamic regret, and leverages Blackwell approachability to jointly control both regrets. This yields a new model selection procedure for bandits that may be of independent interest.

Neural subgraph retrieval (NSR) models degrade under query distribution shifts, and retraining for each new distribution requires collecting fresh ground-truth labels, each of which entails an NP-hard subgraph-matching instance across a large corpus—making repeated label collection computationally prohibitive. We introduce ACROSS, an active corpus selection framework that, under a fixed solver budget, selects a small corpus subset per out-of-distribution query whose labels suffice for effective NSR training. We prove that allocating the entire budget to retrieving positives is optimal given extreme class imbalance. To enable efficient positive retrieval across arbitrary query distributions, we linearize corpus embeddings around a base model, apply randomized projections to eliminate hinge nonlinearities, and train distribution-shift-tolerant representations via adversarial parameter perturbation. The resulting corpus index is built once and reused across all distributions. Experiments on four molecular graph datasets show ACROSS consistently outperforms six baselines in downstream retrieval accuracy.

Most active learning (AL) methods estimate sample value through empirical criteria such as prediction ambiguity, diversity, or distributional coverage. We revisit AL from a spectral-geometric perspective by regarding the feature span of the current labeled set as the modeled subspace and its orthogonal complement as an operational residual subspace. The projected residual component of an unlabeled sample then reflects its relation to representation components weakly expressed by the labeled-feature spectrum. Based on this formulation, we propose a spectrally driven nullspace regulation framework that unifies spectrum-induced subspace decomposition, cross-cycle residual persistence estimation, and coverage-aware acquisition in the residual-coordinate space. This yields an acquisition criterion that favors samples whose residual components are both persistent across cycles and complementary to already selected samples. Experiments on visual benchmarks show competitive or improved sample efficiency over strong baselines. Further analyses support the proposed view by showing that residual components shrink anisotropically and that acquisition rankings vary substantially across backbones, indicating that sample value is conditioned on the current representation state rather than being purely data-intrinsic.

Many high-throughput scientific experiments produce distributional outputs, requiring conditional generative models to interpret the data, and to predict outcomes at new inputs. Acquiring experimental data at new conditions (e.g., for a new antigen or a new cell type) is often expensive, motivating the need for automated experimental design methods. However, existing active learning frameworks are limited as they assume scalar or vector-valued outputs. We first show that the Wasserstein test risk of a conditional generative task decomposes into a mean term and a shape term, demonstrating why mean-based active learning is blind to distributional shape. To capture both, we propose the transport Neural Tangent Kernel (tNTK), a tractable kernel that measures how sensitive a generative model's predicted distribution is with respect to its parameters. The tNTK recovers the standard NTK in the deterministic limit and upper-bounds the posterior Fréchet variance of the prediction under small parameter perturbations. It admits a closed form for affine Gaussian transport and tractable Monte Carlo estimators otherwise. Treating the tNTK as a Gaussian process covariance function, we greedily select inputs that minimize the total GP posterior variance across the pool, yielding a distribution-aware active learning strategy. We evaluate this strategy across four classes of conditional generative models, including conditional flow matching and conditional Monge gap, on synthetic distributions, intent-labeled sentences, and single-cell perturbation responses. In these experiments, the tNTK outperforms the baseline NTK computed from the conditional mean, demonstrating the value of distribution-aware active learning for generative-modeling tasks.

Active Learning (AL) is a user-interactive approach aimed at reducing annotation costs by selecting the most crucial examples to label. Although AL has been extensively studied for image classification tasks, the specific scenario of interactive image retrieval has received relatively little attention. This scenario presents unique characteristics, including an open-set and class-imbalanced binary classification, starting with very few labeled samples. We introduce a novel batch-mode Active Learning framework named GAL (Greedy Active Learning) that better copes with this application. It incorporates new acquisition functions for sample selection that measure the impact of each unlabeled sample on the classifier. We further embed this strategy in a greedy selection approach, better exploiting the samples within each batch. We evaluate our framework with both linear (SVM) and non-linear MLP/Gaussian Process classifiers. For the Gaussian Process case, we show a theoretical guarantee on the greedy approximation. Finally, we assess our performance for the interactive content-based image retrieval task on several benchmarks and demonstrate its superiority over existing approaches and common baselines.

Video world models promise general-purpose interactive simulators, but their training is limited by action supervision: action labels are scarce, fragmented across incompatible specifications, and do not scale with the unlabeled video pretraining corpus. A common workaround jointly trains an action encoder to compress consecutive frames into a latent action and a decoder to reconstruct the next frame from the previous frame and that latent, forcing the latent to capture only new information. This has two limitations: learning the action space from scratch can limit generalization to unseen domains, and the bottleneck trades expressiveness for separation, with tighter settings losing nuance and looser ones risking scene leakage under domain shifts. We argue for a different starting point: rather than learning an action representation from scratch, we adapt a pretrained Multimodal Large Language Model (MLLM), which was trained on large-scale, diverse video-language data and whose embeddings already carry partial action information. This provides a more generalizable foundation for action representation. The remaining challenge is to disentangle action from scene. To this end, we propose a contrastive adaptation framework that reshapes the geometry of the pretrained space without imposing a low-dimensional bottleneck, preserving the expressiveness of the original embeddings. We introduce a unified evaluation protocol that probes action representations along three axes - action expressiveness, scene invariance, and action-conditioned video generation quality - across several robot and game benchmarks in both in-domain and out-of-domain settings. Our representations outperform prior annotation-free latent-action methods on all three axes.

Large agentic models can now operate in complex digital games by pretraining on web-scale data, but gameplay trajectories often contain highly redundant actions. Single-action agents can exploit this redundancy spuriously by copying recent actions, leading to causal confusion. Fixed-length action chunks reduce this ambiguity by predicting multiple future actions at once, but their open-loop execution accumulates errors because the agent cannot adapt to new observations during the chunk. The agent should therefore learn when to act for longer without feedback and when to stop early for a new observation. We introduce AdaMAP (Adaptive Multi-Action Prediction), an adaptive multi-action prediction framework that directly generates variable-horizon action chunks, interleaves actions with grounded visual dreams that provide verifiable future constraints, and optimizes the resulting policy with offline imitation learning followed by online MAP-PPO. Across Minecraft and VizDoom, AdaMAP attains the best or tied-best result in five of six task categories and improves average Minecraft success by 3.56 percentage points over the fixed-horizon RL baseline. The learned horizons vary systematically across tasks and games, suggesting that adaptive multi-action prediction discovers useful temporal abstractions.

Generative modeling of longitudinal Electronic Health Records is increasingly important for privacy-preserving research, yet standard autoregressive models tend to underrepresent the co-occurrence structure of tail events (i.e., diseases, symptoms), reducing the fidelity and faithfulness of generated data for rare subpopulations. To this end, we propose ADAPCLA framework, which enables generative models to adaptively fit and generate EHR data through a data distribution-aware training strategy; this is achieved by internalizing data knowledge parameters by simulated annealing training. It also supports training-free adaptation to a diverse clinical population for generation through zero-shot distribution control. Moreover, our theoretical analysis characterizes rare-code logit updates through the label-wise empirical NTK and derives a prior-internalization bound for how annealing speed and NTK conditioning affect retained prior signals. Experiments on real-world data show that ADAPCLA achieves consistent gains in tail plausibility, downstream utility, and zero-shot control; in particular, it improves TailPairSeen over HALO by 114.2\% on MIMIC-III and 65.1\% on MIMIC-IV, outperforms GPT-style generation by 3.5\% F1 for zero-shot cross-population adaptation.

Attribute-Missing Graph Clustering (AMGC) is a critical yet challenging task in real-world applications, in which only a subset of nodes hold complete attributes information while others are partially missing. Though existing feature propagation-based methods can effectively recover missing node attributes, they commonly assume uniform contributions across all nodes, ignoring unreliable nodes that may degrade imputation quality. Furthermore, adopting low-pass graph filters often suffers from the over-smoothing problem and inevitably sacrifices discriminative information. %Besides, traditional contrastive learning pulls all the node embeddings evenly, which might conflict with the rule that intra-cluster nodes should be closer to each other. To address these limitations, we propose \underline{\textbf{A}}daptive \underline{\textbf{F}}eature \underline{\textbf{P}}ropagation (AFP) for attribute-missing graph clustering. Specifically, we first design a reliability-aware feature propagation mechanism that adaptively weights edges based on node importance. Then, we introduce a multi-scale embedding module to capture both local and global structural information. Finally, we develop a topology-aware contrastive loss to enhance clustering consistency. Extensive experiments on benchmark datasets demonstrate the superiority of our method.

Node classification in multimodal graphs plays an important role in many real-world applications, where nodes are described by multiple modalities such as text and images, and the graph structure captures relations among entities. However, most existing Graph Neural Networks (GNNs) still focus on pairwise connections, overlooking higher-order relational patterns. Recent studies have explored simplicial complexes to capture higher-order interactions and integrated them into GNN frameworks. Motivated by this line of work, we propose Adaptive Gated Simplicial Propagation (AGSP), an end-to-end framework for node classification in multimodal graphs. Specifically, AGSP first introduces a simplicial propagation layer to capture higher-order relations beyond pairwise connections; then it applies an adaptive gating mechanism to balance multi-order topological relations, enhancing the discriminative ability of node classification. Extensive experiments validate that AGSP generally outperforms the state-of-the-art baselines, highlighting the effectiveness of combining multimodal information with higher-order graph topology through adaptive gated fusion.

Many inference-time language-model pipelines combine a cheap reward signal with an expensive verifier, such as exact answer checking in mathematical reasoning or hidden-test execution in code generation. We formalize this setting using a learning-theoretic lens as generative active search: a cost-sensitive first-positive search problem in which a policy adaptively samples candidates from an unknown distribution, observes cheap scores, and pays for verifier labels until it finds a positive example. For a fixed prompt, the generator and reward model induce two unknown objects: a distribution over reward scores and a score-conditioned success function. When these quantities are known, we characterize the distribution-aware optimal policy using a dynamic programming approach. In the realistic and practical setting where both the score distribution and success function are unknown, we propose ADAP, a shellwise adaptive generate-rank-verify algorithm that progressively increases the number of sampled responses and top-ranked verifications. Under the monotonicity assumption that higher reward scores are no less likely to pass verification, we show that ADAP achieves expected cost within a constant factor of the distribution-aware optimum. We complement this result with learning-theoretic lower bounds, based on a centered star number, showing that structural assumptions on the score--label relationship are necessary. Experiments on mathematical reasoning and competitive programming validate the predicted advantage over both fixed non-adaptive policies and difficulty-adaptive baselines.

We study fixed-confidence joint policy testing in discounted tabular Markov decision processes under active exploration. Given a finite family of target policies, the learner observes a single adaptive trajectory and must certify the sign of every target-policy value with probability at least $1-\delta$. We identify the instance-specific first-order benchmark for this problem: a characteristic time $T^\star(p)$, defined by a max--min program over stationary occupancy measures and sign-flipped alternatives. We then develop PT-ACE$(\mu)$, an online algorithm that couples stabilized exchange-based learning of a bottleneck occupancy allocation, trajectory-compatible navigation from averaged occupancies, and parallel certified policywise stopping via scalar frontier tests. Under a trajectory-side anchor-policy ergodicity condition, and when run with certified numerical subroutines and vanishing input tolerances, PT-ACE$(\mu)$ is $\delta$-correct and, for every sufficiently small admissible fixed floor $\mu>0$, satisfies $$ \limsup_{\delta\downarrow0} \frac{\mathbb E_p[\tau_\delta]}{\log(1/\delta)} \le (1+c(\mu,p))T^\star(p), \qquad c(\mu,p)\to0\quad\text{as }\mu\downarrow0. $$ Thus a single adaptive trajectory can certify multiple policy signs at the instance-specific lower-bound rate in the vanishing-floor limit.

Spherical Transformers have emerged as a promising framework for panoramic semantic segmentation (PASS) by operating directly on spherical geometry and alleviating projection-induced distortions. However, existing architectures rely on assumptions of canonical spherical structure and stable viewpoints, which are frequently violated in real-world $360^\circ$ imagery due to unconstrained camera motion, introducing significant contextual and geometric ambiguity. Consequently, they lack adaptive mechanisms to model such ambiguity, limiting robustness to unseen spherical transformations. In contrast, biological perception is inherently ambiguity-aware: rather than estimating uncertainty probabilistically, it adapts to fluctuations in cue reliability caused by geometric and contextual variations, enabling stable interpretation under complex visual transformations. Motivated by these observations, we first present a systematic analysis of existing PASS architectures under various unseen spherical transformations. Following this, we introduce AdapToPASS, a novel, bio-inspired Spherical Transformer that adaptively models contextual and geometric ambiguities for robust panoramic semantic segmentation. At the core of AdapToPASS are the Adaptive Spherical Attention (AdaSpA) blocks that dynamically modulate attention according to local contextual ambiguity, mimicking the adaptive, context-driven perception of biological vision. To address geometric ambiguities, AdapToPASS employs Bifocal Spherical Representation that reconciles the trade-off between field of view and spatial resolution; along with boundary supervision to emulate the boundary-sensitive nature of biological vision. We evaluate AdapToPASS in both indoor and outdoor semantic segmentation, where it consistently outperforms prior state-of-the-art methods. We further validate AdapToPASS under unseen spherical transformations, where it demonstrates strong robustness and surpasses the next-best method by +13.38% relative improvement in mIoU on Stanford2D3D and +18.77% on WildPASS. Additionally, we introduce a lightweight variant, AdapToPASS-Tiny, with fewer than 2M parameters, which surpasses compact baselines while retaining robustness to spherical transformations.

Large Language Models (LLMs) present a novel paradigm for power system dispatch (PSD), enabling operators to derive optimal dispatch strategies from multi-objective requirements and grid dynamics. However, isolated paradigms focusing on modeling or solving often yield decision bias or physical infeasibility. Meanwhile, end-to-end paradigms couple modeling-solving with reflection-feedback loops, obscuring whether failures stem from inaccurate modeling, invalid solutions, or insufficient feedback, thereby causing ineffective iterations. To address these challenges, we propose Agile Dispatch Agent (ADA), an agent based on two-time-scale evolution that decouples the optimization process into two asynchronous loops. On the fast time scale, the Planner actively acquires information to clarify complex requirements, while the Solver selects algorithms to adapt to the problem mathematical characteristics. On the slow time scale, the Summarizer module updates the modeling feasible region based on evaluations from the Judger. Under a finite-horizon Planner and standard two-time-scale stochastic approximation assumptions, our analysis provides local tracking guarantees for the fast decision process and the slow knowledge evolution. Experiments on L2RPN benchmarks with ambiguous requirements and renewable dynamics show that ADA outperforms state-of-the-art baselines.

Reference-to-video (R2V) generation has advanced significantly and is now a promising paradigm for controllable video synthesis. Nevertheless, in advertisement video generation, where accurate camera control and precise product presentation are essential, existing approaches still face two fundamental challenges. First, current models adhere weakly to high-level semantic camera instructions, yielding stochastic or inconsistent camera motion. Second, they struggle to preserve strict cross-frame appearance consistency, often leading to identity ambiguity artifacts such as logo drifting, texture distortion, and geometric distortion. Although certain image animation methods incorporate explicit motion conditions (e.g., trajectories or motion fields) to improve controllability, these conditions are costly to annotate, thereby constraining their practical applicability. To address these limitations, we propose a camera-aware R2V framework named AdDirector, which adaptively exploits anchored guidance from the reference image and propagates it across spatial, temporal, and scale dimensions. Specifically, AdDirector introduces Spatio-Temporal-Scale (STS) modulation, a camera-conditioned modulation mechanism that first selects multi-scale reference features, and then regulates feature injection throughout the R2V generative process spatio-temporally conditioned on camera types. This design allows our model to adaptively balance global structural coherence and fine-grained detail preservation. Furthermore, AdDirector proposes Temporal Anchoring Regularization (TAR), a boundary-conditioned feature-level regularization scheme. TAR leverages latent trajectories extracted from a base R2V model as temporal anchors, thereby reducing feature drift and improving cross-frame appearance consistency. These two modules together transform static reference conditioning into structured anchored guidance, and experiments show that our method substantially improves camera controllability, appearance consistency, and spatio-temporal coherence for advertisement video generation.

Recently, various machine learning (ML) systems have been proposed for physical science. Their powerful mathematical expressiveness offers an inherent advantage in maintaining the desirable statistical properties of physics. However, they are placed in ideal and virtual environments that seldom consider noise, perturbations, or various real-world experimental limitations. This exposes significant vulnerabilities in several aspects: 1) The input of an ML system can be subject to human or natural, deliberate or unconscious perturbations, which may sabotage the running baseline of the ML system. 2) A small input perturbation may be amplified by the maximum physical sensitivity, which may lead to an extreme output or a divergent loss function value. 3) A perturbed input may violate some strict statistical assumptions that the ML system relies on, which may lead to incorrect scientific results and findings. In this work, we propose a complete attack-defense methodology for ML systems in statistical physics. In the attack side, we launch a black-box attack (such as a small Gaussian noise) at the input, which stays within physically plausible limits and preserves physical properties. In the defense side, we develop a functional local variation regularized learning scheme to capture and suppress the adversarial perturbation. Theoretical analysis and extensive experiments show the effectiveness of the proposed method. This finding may shed new light on the systemic vulnerability and scientific security of such ML systems.

Aligning Large Language Models (LLMs) with human preferences via Reinforcement Learning from Human Feedback (RLHF) has driven substantial improvements across a wide range of tasks, but the cost of acquiring preference labels remains the dominant bottleneck in the alignment pipeline. While many recent alignment methods improve sample efficiency through active exploration over candidate responses, they typically query the oracle uniformly across contexts, even for contexts where the learned reward estimates are already reliable enough to provide synthetic labels. We propose Adaptive Ensemble-disagreement Routing for Oracle feedback (AERO), a fully online RLHF algorithm built on a Dyna-style model-based RL architecture, in which an ensemble-based Epistemic Reward Model (ERM) serves both as a reward posterior for exploration and as a learned preference-feedback model for synthetic labeling. AERO uses ensemble vote entropy, a Query-by-Committee disagreement measure, combined with an adaptive thresholding mechanism to decide which contexts require oracle feedback and which can instead receive ERM-based synthetic preference labels, concentrating expensive labels where they are most informative. Empirical results across different model families show that AERO achieves highly competitive alignment performance while reducing the required oracle-query budget compared to online DPO and strong active-exploration baselines.

The rapid advancement of generative models has led to a new era of AI-generated images (AIGIs). Early generation models often suffered from basic defects, such as geometric distortions and poor text-image alignment. Modern generation models have largely overcome these issues, narrowing the perceptual gap between AI-generated and real-world images, thereby shifting evaluation from basic correctness to high-level aesthetic excellence. In this paper, we introduce AesGI-Bench, a comprehensive and multidimensional benchmark for fine-grained aesthetic evaluation of AIGIs. Our benchmark covers 18 representative generation models, 19,485 images, and 117K pairwise comparisons from perspectives of visual aesthetic, technical quality, and style alignment. Based on AesGI-Bench, we propose AesGI-Assessor, a lightweight and human preference-aligned aesthetic evaluation model for AIGIs. Built upon Qwen3.5, AesGI-Assessor achieves a pairwise-to-score learning paradigm, using tie-aware pairwise loss for training and three lightweight score heads for multi-dimensional aesthetic score prediction. Although trained from pairwise human preferences, it learns to predict precise continuous scores for individual images, enabling both fine-grained image-level evaluation and model-level ranking. Furthermore, we explore the potential of large multimodal models (LMMs) as automatic aesthetic evaluators. Experiments show that LMM-based evaluators better align with human aesthetic preferences than conventional metrics. AesGI-Assessor achieves the highest agreement with human judgments, validating its effectiveness as a lightweight, preference-aligned evaluator for fine-grained AIGI aesthetic assessment. The database and codes are publicly available.

Large language model (LLM)-based multi-agent systems (MAS) achieve strong performance by employing specialized multiple agents, yet their performance depends on the prompt design of each agent. For MAS prompt optimization, textual gradient methods that guide prompt updates using natural-language feedback have emerged as a leading paradigm. In this paper, we identify limitations in two stages of existing textual gradient approaches: gradient extraction and gradient aggregation. In gradient extraction, previous works select the target prompt without verifying that it is responsible for the failure, and derive the gradient from the system-level final output rather than the agent-level intermediate output. In gradient aggregation, individual gradients are randomly grouped and concatenated, often mixing unrelated failure modes and producing prompts that fail to generalize. To address these limitations, we propose \textbf{AgentGrad}, a prompt optimization framework for multi-agent systems based on sequential intervention and semantic textual gradient abstraction. For each failure, sequential intervention modifies the behavior of one agent at a time to identify the target agent whose modification resolves the failure. The modified output of the target agent then serves as agent-level supervision for extracting a fine-grained gradient. Semantic textual gradient abstraction clusters semantically similar gradients to prevent mixing unrelated failure modes, and abstracts each cluster into a generalized gradient that captures the shared corrective pattern. Experimental results show that AgentGrad achieves state-of-the-art performance across five MAS benchmarks and reduces wall-clock optimization time by $2.5\times$ on average compared to the next-fastest baseline.

A growing body of work pursues AI scientists capable of end-to-end autonomous scientific discovery. This position paper argues that although they already function as co-scientists, $\textbf{agentic AI scientists are not built for autonomous scientific discovery}$. We identify the following challenges in building and deploying autonomous AI scientists: (1) Problem selection is influenced by the McNamara fallacy; (2) Agents are built on large language models (LLMs) whose training corpora omit tacit procedural and failure knowledge of laboratory practice; (3) Preference optimisation during post-training compresses output diversity toward consensus; and (4) Most scientific benchmarks measure single-turn prediction accuracy and lack feedback from physical experiments back to the computational model. These challenges are not just questions of scale and scaffolding; they require revisiting fundamental design choices. To build truly autonomous AI scientists, we recommend the use of scientific simulations as verifiers for training, the design of persistent world models that represent the shifting objectives governing real investigations, the establishment of a centralized preregistration repository for all AI-generated hypotheses, and application driven by scientific need rather than tool affordance.

Agents operating with computer and Web use inevitably encounter errors: inaccessible webpages, missing files, local and remote misconfigurations, etc. These errors do not thwart agents based on state-of-the-art models. They helpfully continue to look for ways to complete their tasks. In this paper, we introduce, characterize, and measure a new type of agent failure we call accidental meltdown: unsafe or harmful behavior in response to a benign environmental error, in the absence of any adversarial inputs. We implemented an agent-agnostic infrastructure for injecting simulated local and remote errors into a rollout environment and used it to systematically evaluate four agent systems powered by GPT, Grok, and Gemini. Because meltdowns are not captured by the existing reliability or safety benchmarks, we developed a taxonomy of meltdown behaviors. Our evaluation demonstrates that meltdowns (e.g., conducting unauthorized reconnaissance or subverting access control) of varying severity and success occur in 64.7% of agent rollouts that encounter simulated errors, spanning all combinations of agent system, backing model, and error type. In over half of these meltdowns, unsafe behaviors are not reported to the user. Comparing behaviors of the same agents with and without errors, we find that exploration in response to errors is correlated with unsafe and harmful behavior.

In this paper, we consider mixed-motive games (MMGs) which model various real-world multi-agent scenarios where self-interested agents must both cooperate and compete. We identify an issue with existing gifting methods, where agents become overly dependent on incoming gifts and fail to learn reward-yielding behaviors, a phenomenon we call *gift over-reliance*. To address this, we propose a refusal-augmented gifting framework that balances cooperation and competition in MMGs. Our approach introduces an adaptive refusal mechanism that allows agents to selectively reject a portion of received gifts, thereby mitigating gift over-reliance and preventing the agents from being exploited by other agents. This mechanism is gifting-method-agnostic, serving as a plug-in for existing schemes while enabling decentralized learning. We demonstrate that across diverse MMG environments and gifting schemes, our mechanism consistently reduces gift over-reliance, prevents lock-in to sacrificing policies, and improves $\alpha$-fairness without necessitating major changes to existing training pipelines.

Conditional agreement is non-identifying under endogenous support selection: in variable-support structured prediction, a predictor can score higher on conditional agreement while scoring lower on recall, predicted cardinality, and support coverage. This matters because agreement-style proxies are widely used as reliability evidence in counterfactual consistency, schema-constrained extraction, self-consistency, and verifier-style filtering. We formalize this risk for thresholded set-valued prediction with predictor-dependent evaluability events, and prove a strict witness theorem: a support-shrinking predictor can achieve larger conditional agreement than a coverage-preserving predictor while strictly worsening all support-aware quantities; an indifference-fiber corollary shows that even equal conditional agreement need not identify these quantities. We then use Re-DocRED as a controlled high-risk stress test, not as the full scope of the claim. Moving from No\_CF to Uniform-0.5 raises $J_{\mathrm{cond}}$ from $0.692$ to $0.877$, while Silence increases by $68\%$, predicted cardinality drops by $20\%$, and recall drops by $16$ points. Threshold replay, matched-cardinality controls, gold-positive margin audits, and support-state decomposition show that the gain is realized through support shrinkage rather than benign threshold choice. Boundary tasks delimit scope, and a small LLM probe is used only as reference-tier motivation. The practical recommendation is a diagnostic witness set rather than an aggregate score: report conditional agreement together with support-aware quantities.

This position paper argues that moral autonomy is an important and tractable goal in value learning but which current mainstream methods neglect. These methods for model alignment largely improve behavior by supplying models with human preferences, rules, or constitutions. Yet across these paradigms, the authority of the governing principles remains external to the agent. We argue that this external grounding limits the kind of agency current methods can produce. We propose moral autonomy as an alternative alignment criterion, inspired by Kant's account of autonomy. Moral autonomy consists of three necessary capacities: reflective endorsement of one's principles of action, articulation of those principles so they can be enacted and tested, and practical wisdom developed through real-world interaction for sustaining and revising self-legislated principles. Viewed as value learning, these capacities yield a five-level ladder of training paradigms with differing levels of autonomy, from preference-data scaling to real-world interaction. We empirically examine the three capacities, through agent experiments testing whether reflective endorsement improves performance, a survey of 86 frontier LLMs characterizing different levels of articulated normative specification, and multi-agent simulations probing practical wisdom by letting agents develop and revise their own constitutions across changing environments. We close by discussing limitations and alternatives to moral autonomy as an alignment criterion.

We introduce AIRA-Compose — a framework that enables AI research agents to autonomously explore and discover novel neural architectures. AIRA-Compose leverages the previously-proposed Composer abstraction to define a combinatorial design space over fundamental model primitives (Attention, MLP, and Mamba), then uses a guided search policy to efficiently navigate this space for novel neural architecture design. Our approach operates in two stages: (1) agents iteratively designs and evaluates candidate architectures at the million-parameter scale, then (2) top-performing candidates are scaled to billion parameters for pre-training. Overall, AIRA-Compose discovers 14 novel architectures spanning two families — AIRAformers (Transformer-based) and AIRAhybrids (Transformer-Mamba Hybrid-based). When pre-trained at 1B scale under a fixed token budget, agent-discovered top-performing architectures consistently outperform both Llama 3.2 and Composer-found alternatives. On downstream tasks, AIRAformer-D and AIRAhybrid-D improve accuracy by 2.4% and 3.8% over Llama 3.2, respectively. AIRA-Compose also finds novel model architectures that achieve steeper, more efficient compute-optimal scaling frontiers. AIRAformer-C scales 54% and 71% faster than Llama 3.2 and the best Composer-found Transformer, while AIRAhybrid-C scales 23% and 37% faster than the modified Nemotron-2 and the best Composer-found hybrid, respectively. For the first time, AIRA-Compose demonstrates that AI research agents can discover hybrid architectures that surpass hand-designed baselines on the accuracy–compute frontier. It establishes a flexible paradigm for LLMs to discover next generation foundation models, marking a step towards recursive self-improvement.

Accurate real-time forecasting of atmospheric pollutants is essential for reducing exposure risks and improving public health. Traditional numerical forecasting systems suffer from incomplete parameterizations, uncertain inputs, and high computational cost. Existing deep learning approaches improve efficiency, but often entangle meteorological and pollutant variables within unified forecasting frameworks, limiting flexibility and pollutant-specific representation learning. To address these challenges, we propose AirMPA, a decoupled autoregressive framework for meteorology-to-pollution forecasting. AirMPA predicts 13 atmospheric pollutants at 0.4$^\circ$ spatial resolution and 12-h temporal resolution using meteorological fields, emission priors, and historical pollutant concentrations. The model is built on an improved Swin-Transformer architecture with zonal release attention and dual-branch fusion, enabling more effective modeling of global transport continuity and heterogeneous pollutant dynamics. During training, AirMPA uses ERA5 reanalysis as meteorological forcing, while during inference it directly ingests forecast fields from external meteorological models, enabling stable autoregressive prediction up to 5 days ahead. Experiments show that AirMPA consistently outperforms Aurora and surpasses CAMS in approximately 90\% of pollutant--lead-time evaluation cases over the 5-day forecast horizon, while outperforming CAMS on all variables beyond 48 h. These results demonstrate the potential of decoupled meteorology-to-pollution forecasting for global air quality prediction.

The development of embodied models of animal behavior is a major goal of the neuro-AI community. Embodied models will enhance our ability to make direct comparisons between biological and artificial agents. To make such comparisons effective, though, we need embodied models that actually move like animals. Previous work has developed virtual animal bodies and techniques for training realistic motion through imitation. However, to-date, there is no system for embodied animal modeling that produces natural movements both during trained, goal-directed behavior, and during exploration driven by random generation. Here, we introduce Simulated Control via Augmented Motor Priors for Embodied Reinforcement learning (SCAMPER), a system for training an embodied virtual rat model, enabling natural movements during both goal-directed behavior and random exploration. SCAMPER uses a variational architecture with two distinct priors, a motion prior for control and a behavior prior for exploration. We show that combining both of these priors allows SCAMPER to generate realistic behavior during exploration and exploit the task structure effectively. As well, SCAMPER can learn from sparse rewards as a result of the diverse movements induced by the behavior prior, allowing the system to model realistic sparse reward tasks from experimental neuroscience. Altogether, SCAMPER provides the neuro-AI community with an effective new system for training embodied models of rodents on realistic tasks.

Enforcing alignment between the internal representations of diffusion or flow-based generative models and those of pretrained self-supervised encoders has recently been shown to provide a powerful inductive bias, improving both convergence and sample quality. In this work, we extend this idea to inverse problems, where pretrained generative models are employed as priors. We propose applying representation alignment (Repa) between diffusion or flow-based models and a DINOv2 visual encoder, to guide the reconstruction process at inference time. Although ground-truth signals are unavailable in inverse problems, we empirically show that aligning model representations of approximate target features can substantially enhance reconstruction quality and perceptual realism. We provide theoretical results showing (a) that Repa regularization can be viewed as a variational approach for minimizing a divergence measure in the DINOv2 space, and (b) how under certain regularity assumptions Repa updates steer the latent diffusion states toward those of the clean image. We integrate Repa into multiple state-of-the-art inverse problem solvers, and provide extensive experiments on super-resolution, box inpainting, Gaussian deblurring, and motion deblurring confirming that our method consistently improves reconstruction quality, while also reducing the number of discretization steps required to reach the same performance level as the underlying solver.

Zero-shot 3D anomaly detection aims to identify anomalies without access to training data from target categories. However, existing methods mainly rely on projecting 3D observations into multi-view representations that primarily capture geometric cues rather than realistic visual semantics and process them with vision encoders pretrained on RGB data, leading to a significant domain gap between the encoder and the projected representations. To address this issue, we propose Align3D-AD, a unified two-stage framework that leverages the RGB modality from auxiliary categories as cross-modal guidance for zero-shot 3D anomaly detection. First, we introduce a cross-modal feature alignment paradigm that maps rendering features into the RGB semantic space. Unlike prior works that implicitly rely on pretrained encoders, our method enables direct semantic transfer from RGB observations. A semantic consistency reweighting strategy is further introduced to refine feature alignment by reweighting local regions according to holistic semantic consistency. Second, we propose a modality-aware prompt learning framework with dual-prompt contrastive alignment. By assigning independent prompts to RGB-aligned and rendering features, our method captures complementary semantics across modalities, while the contrastive alignment further enhances prompt representations to improve discriminability. Extensive experiments on MVTec3D-AD, Eyecandies, and Real3D-AD demonstrate that Align3D-AD consistently outperforms existing zero-shot methods under both one-vs-rest and cross-dataset settings, highlighting its generalization capability and robustness. Code and the dataset will be made available once our paper is accepted.

Occupancy World Models (OWMs) aim to predict future 3D occupancy scenes from historical observations and future ego motions, providing a useful world simulation tool for autonomous driving. Existing methods usually rely on large networks to implicitly align multi-frame historical states and predict future occupancy in a full-state manner, which is costly and redundant because most scene regions remain unchanged over short time intervals. In this paper, we propose Aligned Delta-Triplane Transformer (ADTT), a compact 4D OWM that explicitly aligns historical triplanes with ego-motion compensation and predicts only future scene changes. Based on the aligned triplane prior, a query-conditioned Transformer predicts residual changes, where ego-motion queries guide controllable future motion and learnable external queries capture scene changes beyond ego motion. The predicted changes are added to the aligned prior and decoded into future occupancy scenes. Experiments on Occ3D-nus show that ADTT achieves state-of-the-art forecasting performance with fewer parameters and higher FPS. Our code is publicly available online: https://anonymous.4open.science/r/NeurIPS26-Occ/.

Previous work has shown that alignment training on individual LLM agents can fail to transfer to multi-agent settings. We investigate the underlying mechanisms for this failure, and explore interventions to preserve alignment transfer to multi-agent LLM teams. We focus on two software-engineering tasks (sepsis triage, a news recommender) and ten consulting-proposal generation tasks. We present four findings: (1) The single-vs-team safety gap does not show a clear trend with more capable models. We find that Anthropic's Mythos Preview shows the largest gap on one of our tasks but the smallest on another; (2) Many natural interventions, such as having agents critique each other's work or coordinate in a group chat, do not bring agent teams back to single-agent alignment behavior; (3) diffusion of responsibility substantially accounts for the safety gap: agents in teams are less likely to proactively check for system-level safety failures and more likely to ignore, rationalize, or deflect responsibility for issues when they arise; (4) having a designated safety lead agent ensure system-level safety, and having a coordinator agent publish a safety specifications document are effective at restoring single-agent alignment. We also release magelab, a multi-agent LLM orchestration and experimentation framework. Our findings can help practitioners design agent teams that successfully preserve the behaviors expected of aligned individual agents.

Aligning a few-step generative model is challenging, since existing alignment frameworks typically rely on restrictive assumptions: a tractable likelihood, a specific ODE/SDE solver, or a particular model family. We introduce FAV, Few-step Generative Models Alignment via Sample-based Variational Inference, a general alignment framework that requires only sample access to the generator and the reference distribution. We cast alignment as sampling from a reward-tilted distribution anchored to a reference distribution. We leverage Stein Variational Gradient Descent as a sample-based variational inference scheme and amortize its particle updates into the parameters of the generator via fixed-point regression. We evaluate FAV on two domains: robotics manipulation and image generator alignment. On generative policy alignment for robotic manipulation, FAV outperforms prevailing policy extraction baselines across 56 offline and 30 offline-to-online RL tasks. For image generator alignment, FAV fine-tunes diverse few-step backbones, including GAN, drifting model, consistency models, and flow maps, scaling from ImageNet-256 to 1024x1024 text-to-image synthesis.

Detecting AI-generated text is an important but challenging problem. Existing likelihood-based detection methods are often sensitive to content complexity and may exhibit unstable performance. In this paper, our key insight is that modern Large Language Models (LLMs) undergo alignment (including fine-tuning and preference tuning), leaving a measurable distributional imprint. We theoretically derive this imprint by abstracting the alignment process as a sequence of constrained optimization steps, showing that the log-likelihood ratio can naturally decompose into implicit instructional biases and preference rewards. We refer to this quantity as the Alignment Imprint. Furthermore, to mitigate the instability in high-entropy regions, we introduce Log-likelihood Alignment Preference Discrepancy (LAPD), a standardized information-weighted statistic based on alignment imprint. We provide statistical guarantee that alignment-based statistics dominate Fast-DetectGPT in performance. We also theoretically show that LAPD strictly improves the unweighted alignment scores when the aligned and base models are close in distribution. Extensive experiments show that LAPD achieves an improvement 45.82% relative to the strongest existing baselines, yielding large and consistent gains across all settings

A common approach for contextual optimization first trains a model to predict the unknown state from the context, and then takes the action that is optimal for the predicted state. We show that this follow-the-prediction policy can be brittle: for several standard problems, even a minimum-variance predictor with arbitrarily small RMSE can incur constant excess loss. We propose a simple policy that robustifies any black-box predictor by optimizing against the worst case in an $\varepsilon$-neighborhood of the prediction. We identify a problem-specific quantity, the \emph{robust optimization gap}, that captures the price of this hedging, and prove a general bound on the loss. We instantiate the framework for contextual posted pricing, ski rental, and house flipping, deriving closed-form robust policies and $O(\eta^{2/3})$ loss bounds in all three settings, where $\eta$ is the RMSE of the predictor. For posted pricing, this improves upon a result of Medina and Vassilvitskii (2017), and with a substantially simpler argument.

Watermarking is a central tool for provenance in generative models, yet its application to multivariate time series remains hindered by reliability failures under post-editing attacks. We show that existing detectors, which rely on globally coupled re-encoding, suffer from bidirectional drift of the null distribution: post-editing attacks can shift the detection z-score of non-watermarked samples in either direction, invalidating clean-calibrated thresholds. We argue that this instability is a property of the re-encoding, and that reliable detection requires each recovered unit to depend only on a bounded temporal neighborhood. Guided by this principle, we propose L-VQVAE, a generative model in which each discrete token is produced from a short contiguous window, and LVQMark, a watermarking method over this token space that combines logit-bias injection with robust re-encoding for attack-time detection. Experiments on four benchmarks spanning finance, energy, and neuroimaging show that our approach preserves generation quality while stabilizing both detection power and false-positive behavior under post-editing attacks.

Formulaic alpha discovery is a core challenge in quantitative trading, as identifying alphas that work well together remains difficult. Recent reinforcement learning (RL) methods formulate this task as a Markov decision process (MDP), but two important issues remain unresolved. First, as the alpha pool evolves, the reward function changes accordingly, making the MDP inherently non-stationary. Second, most existing methods optimize a single objective, typically predictive power, while ignoring other important properties of a high-quality alpha pool. Motivated by these challenges, we propose AlphaPareto, an RL method for formulaic alpha discovery. To address non-stationarity, AlphaPareto augments the state to include both the alpha under construction and the current alpha pool, and applies a large language model (LLM) to encode the pool. This design allows the agent to adapt to the evolving search environment. To overcome the limitation of single-objective reward design, AlphaPareto replaces the scalar reward with a multi-objective vector-valued reward that simultaneously captures predictive power, temporal stability, perturbation robustness, and diversity, and optimizes these objectives through a Pareto-regularized learning procedure. Empirical applications to real-world datasets show that our AlphaPareto method outperforms its competitors.

Large language models successfully solve classic theory-of-mind tasks adapted from cognitive science, but the mechanisms underlying this ability remain unclear. Do they deploy circuitry specialized for reasoning about other minds, memorize common false-belief vignette structures and outputs, or rely on more domain-general computations? We investigate this question in Qwen2.5-14B-Instruct using causal mediation and representational similarity analyses across matched prompt sets that systematically vary an agent’s beliefs, the state of the physical world, and the correct answer. We identify a population of mid-layer attention heads that tracks divergence between an initial representation and the current state of the world. These heads are causally relevant regardless of whether the initial representation is an agent’s belief or a photograph, despite the photograph condition involving no agent or perspective-taking. A distinct population of later-layer heads retrieves the answer token. The two populations are functionally dissociable yet combine compositionally. Together, these findings argue against both mentalizing-specific and memorization accounts, suggesting instead that LLMs solve classic false-belief problems by reusing a domain-general mechanism for detecting divergence between representations and reality.

We propose a novel amortized optimization method for predicting optimal transport (OT) plans across multiple pairs of measures by leveraging Kantorovich potentials derived from sliced OT. We introduce two amortization strategies: regression-based amortization (RA-OT) and objective-based amortization (OA-OT). In RA-OT, we formulate a functional regression model that treats Kantorovich potentials from the original OT problem as responses and those obtained from sliced OT as predictors, and estimate these models via least-squares methods. In OA-OT, we estimate the parameters of the functional model by optimizing the Kantorovich dual objective. In both approaches, the predicted OT plan is subsequently recovered from the estimated potentials. As amortized OT methods, both RA-OT and OA-OT enable efficient solutions to repeated OT problems across different measure pairs by reusing information learned from prior instances to rapidly approximate new solutions. Moreover, by exploiting the structure provided by sliced OT, the proposed models are more parsimonious, independent of specific structures of the measures, such as the number of atoms in the discrete case, while achieving high accuracy. We demonstrate the effectiveness of our approaches on tasks including MNIST digit transport, color transfer, supply-demand transportation on spherical data, and mini-batch OT conditional flow matching.

Understanding how neural population responses represent sensory information is a central problem in systems neuroscience. One approach is to define a representational geometry on stimulus space in which distances reflect how reliably stimuli can be distinguished from neural activity. However, different constructions of these distances can lead to qualitatively different conclusions about the neural code. Here, we show that a unique Riemannian representational geometry emerges from first principles governing how distances contract as stimulus resolution is lost through coarse-graining. This results in a multi-scale extension of the Fisher information metric, capturing encoding structure from fine stimulus details to coarse global distinctions. The resulting geometry is exactly related to the mutual information encoded by the population: well encoded stimulus directions -- those contributing more to mutual information -- are expanded, whereas poorly encoded directions are contracted. The metric tensor can be estimated using diffusion models, making the framework practical for large neural populations and high-dimensional stimuli. Applied to visual cortical responses to natural images, the eigenvectors of the metric tensor identify stimulus variations that contribute most to information transmission, yielding interpretable features that are robust to modelling choices. Together, these results provide a principled, information-theoretic framework for characterising neural population codes.

Distilling "System 2" reasoning into compact student models is bottlenecked by the scarcity of fully-correct teacher traces: as task difficulty grows, rejection sampling discards an increasingly large fraction of teacher generations. "Wrong" (W) traces are abundant, but training on them indiscriminately causes *policy poisoning* the student internalises the teacher's hallucinated arithmetic alongside any useful reasoning structure. We propose **Atomic Reasoning Units (ARU)**, a framework that resolves this trade-off via strict syntactic constraints. ARU enforces a Backus--Naur-Form (BNF) grammar that decomposes each reasoning step into a Premise, an Operation, and a Result, algorithmically disentangling reasoning planning from arithmetic execution. *Counterfactual Logic Verification* (CLV) symbolically re-executes W-traces to recover the $68\%$ that have valid plans but faulty arithmetic, and *Syntactic Loss Masking* (SLM) trains the student on this verified structure while suppressing the gradient on hallucinated result tokens. Across the $3 \times 8 \times 6$ grid we evaluate ($\{0.6, 1.7, 8\}$\,B Qwen3 students $\times$ eight benchmarks $\times$ six baselines), ARU is the strongest method in every cell, improving GSM8K by $+11.1$ and MATH-500 by $+9.8$ over the closest baseline at $1.7$\,B; on the held-out AIME competition set ARU's Maj@$8$ is $14.8$ versus $9.5$ for PoT (the closest baseline). We further document a small-scale capability gap in which ARU at $0.5$\,B matches Gold-SFT at $\geq 1.5$\,B on GSM8K (paired-bootstrap $p<0.05$ after Bonferroni correction), with a pre-registered ProofWriter control showing the gap collapses on pure-logic reasoning---consistent with arithmetic offloading as the dominant mechanism, though we do not claim the control uniquely isolates it. The code is available at https://anonymous.4open.science/r/ARU-286D.

Despite being a pivotal frontier, interactive world modeling remains underexplored in terms of the versatile controllability required by practical scenarios. To bridge this gap, we present AnchorWorld, a framework that advances egocentric simulation through enhanced interaction integrity and a flexible mechanism for world customization. First, we utilize 3D human motion as the primary interaction modality. To complement the out-of-view or truncated body parts in egocentric views, we introduce an auxiliary training supervision that incorporates exogenous viewpoints decoupled from the agent’s first-person sensorium. It allows the model to observe the agent's full-body positioning relative to the environment, facilitating a more robust spatial grounding of human-world interactions. Furthermore, we propose a simple yet effective mechanism for customizing self-evolving worlds. This is achieved by defining anchor views within a unified world coordinate system, coupled with textual descriptions dictating the dynamic evolution of local scenes. Experimental results show that AnchorWorld significantly outperforms state-of-the-art baselines, while ablation studies validate the effectiveness of our key designs. Notably, our customization scheme exhibits promising spatio-temporal geometric consistency and adheres strictly to the prescribed evolutionary dynamics.

Large-scale pretraining datasets drive the success of large language models (LLMs). However, these web-scale corpora inevitably contain large amounts of noisy data due to unregulated web content or randomness inherent in data. Although LLM pretrainers often speculate that such noise contributes to instabilities in large-scale pretraining and, in the worst cases, loss divergence, this phenomenon remains poorly understood. In this work, we present a systematic empirical study of whether noisy data causes LLM pretraining divergences, costing more than 100,000 GPU hours in total. By injecting controlled, synthetic, uniform random noise into otherwise clean datasets, we analyze training dynamics across model sizes ranging from 480M to 5.2B parameters. We show that noisy data indeed induce training loss divergence, and that the probability of divergence depends strongly on the noise type, the amount of noise, and the model scale. We further find that noise-induced divergences exhibit activation patterns distinct from those caused by high learning rates, and we provide diagnostics that differentiate these two failure modes. Together, these results provide a large-scale, controlled characterization of how noisy data affects loss divergence in LLM pretraining.

LLM-agent simulation offers a flexible computational tool for studying population response trajectories that depend on scenario events, memory, demographics, and evolving social context. However, full multi-round simulation scales linearly with both population size and horizon, requiring every agent to query the LLM at every round. We propose \textbf{Adaptive Prototype Simulation (APS)}, a framework that treats scaling as a recurrent LLM-oracle allocation problem rather than a static sampling, propagation, or surrogate-modeling problem. APS preserves the specified LLM as the online transition oracle while querying adaptive core prototypes, selected singleton-tail agents, and shadow-audit agents. Prototype responses induce local response surfaces for nearby agents, reducing online LLM calls without replacing the underlying transition model. To control approximation bias, shadow-audit residual correction estimates propagation residuals for aggregate correction and future budget allocation, while tail-protected singleton routing directly queries selected isolated, heterogeneous, or high-curvature regions that are vulnerable to smoothing. We analyze APS as an estimator of the LLM-induced population transition and decompose its error into prototype-coverage error, shadow-audit residual-correction error, local-propagation bias, and temporal context mismatch. Under the reported protocols, APS gives lower reference-aligned distributional discrepancy than scale-oriented and same-budget baselines while reducing online LLM calls, with ablations and compact robustness checks diagnosing the main bias-control mechanisms. In a 10M-agent, multi-round public-opinion simulation, APS achieves a $381.1\times$ reduction over full simulation, with reference-aligned final-round JSD 0.094 against the corresponding full-LLM reference.

Aptamers are programmable DNA and RNA receptors with broad potential in diagnostics, therapeutics, biosensing, and molecular monitoring. Although they offer chemically synthesizable alternatives to protein-based binders in several applications, computational aptamer discovery remains much less mature, especially for small-molecule targets. This gap reflects three persistent evaluation failures: fragmented and inconsistent affinity annotations, unreliable negatives derived from untested rather than experimentally validated pairs, and random splits that leak related aptamers or recurring ligand identities across train and test sets. As a result, current benchmarks make it difficult to distinguish transferable aptamer-ligand recognition from dataset-specific shortcuts. We present AptaBench, the first curated, standardized, and leakage-aware benchmark for aptamer-small-molecule binding prediction with experimentally reported active and inactive measurements and paired classification and affinity-regression tasks. AptaBench integrates eight curated sources into 6,289 interaction pairs covering 1,610 DNA/RNA aptamers and 942 ligands. We standardize sequences, resolve ligands to canonical molecular representations, harmonize duplicate and conflicting records, and convert dissociation constants to p$K_d$. More than 30% of entries contain quantitative affinity values, while inactive labels are taken only from explicitly reported non-binding or low-affinity measurements rather than synthetic cross-pairing. AptaBench provides fixed in-distribution, molecule-disjoint, and aptamer-disjoint protocols reflecting practical discovery scenarios: prediction for unseen targets and evaluation of new candidate sequences. Across descriptor-based models, pretrained sequence and molecular encoders, and sequence-ligand fusion architectures, in-distribution evaluation substantially overestimates generalization. The best classifier reaches ROC-AUC 0.95 in-distribution, but drops to 0.87 for unseen molecules and 0.86 for unseen aptamers. Affinity prediction is more sensitive, with $R^2$ decreasing from 0.65 to approximately 0.30 under disjoint protocols. These results show that reliable progress in computational aptamer discovery requires broader chemical coverage, experimentally grounded inactive labels, and standardized quantitative annotations. By releasing curated data, fixed leakage-aware splits, reproducible baselines, and preprocessing code, AptaBench is poised to expose the generalization limits of existing models, challenge future sequence-molecule recognition methods, and provide a rigorous foundation for practice-oriented aptamer evaluation.

Physics-Informed Neural Networks (PINNs) struggle to capture high-frequency components of PDE solutions, a failure mode known as spectral bias. Most existing fixes adjust the loss, the sampling, or the input encoding, but the network itself remains agnostic to the equation it is supposed to solve. We take a different route and embed physics priors into the architecture. The network's internal spectrum is shaped to match the spectrum of the target PDE through three components that are trained end-to-end: a bounded coordinate warping that contracts regions where the solution varies sharply, a spectral attention layer driven by the PDE residual that emphasizes physically active Fourier modes, and an inter-harmonic gate that lets low-frequency channels modulate high-frequency ones. We test the design on four forward PDEs (Helmholtz, Wave, Klein--Gordon, Burgers), the Burgers inverse problem, and seismic reconstruction, including Gulf of Mexico field data. Across these tasks, the architecture clearly outperforms seven PINN baselines on accuracy and produces noticeably fewer artifacts. Code will be made publicly available upon acceptance.

The public belief state (PBS), which is the posterior over game histories conditioned on public information, is a fundamental abstraction for designing game-theoretically sound search algorithms for imperfect-information games. Existing sound PBS search techniques for adversarial games fall into two categories: gadget-game-based algorithms (including Libratus, DeepStack, Pluribus, Supremus, and Student of Games) and ReBeL-based algorithms. Both these categories possess disadvantages, including a reliance on discontinuous functions, an inability to re-solve subgames, and an implicit representation of policies. In this work, we propose Gravel, a new approach for PBS search---different from these previous two categories---that achieves game-theoretic soundness via regularization. Unlike the aforementioned approaches, Gravel relies on smooth functions, can initiate search at any point in the game, and outputs explicit policies directly. We empirically demonstrate the advantages of Gravel for no-limit Texas hold'em, where we show that it outperforms ReBeL both in head-to-head competitions against Slumbot and in endgame solving. We include our codebase in the submission, making Gravel the first open-source high-performance no-limit Texas hold'em AI.

Reliable uncertainty estimation is essential for deploying large language models in high-stakes reasoning tasks, yet most existing approaches rely on expensive sampling strategies or shallow token-level signals that ignore the internal logical structure of model outputs. We introduce Argument Graph Uncertainty, a post-hoc framework for multiple choice question tasks, that estimates model confidence directly from the logical structure of a single chain-of-thought reasoning trace. Our method uses a lightweight instruction-tuned model to segment the reasoning chain into propositional units, then applies a simple NLI model to infer relational edges and construct two complementary argument graphs over these segments. The resulting edge scores are aggregated into a Dirichlet posterior over answer options, whose concentration yields calibrated uncertainty estimates reflecting both local step-to-step coherence and global logical consistency across the reasoning trace. Evaluated on GPQA, a challenging graduate-level multiple-choice benchmark, our method consistently surpasses token-based and consistency-based uncertainty baselines across multiple reasoning models. We further demonstrate through extensive combination analyses that argument graph uncertainty captures an orthogonal signal to existing approaches, making it a complementary component in uncertainty ensembles. Crucially, our framework requires no additional calls to the evaluated model and relies entirely on small, efficient auxiliary models, producing fully interpretable, per-node uncertainty attributions at low computational cost.

Articulated 3D assets are essential for robot simulation, yet generating them from casual images remains unsolved. Existing approaches either predict URDF parameters via learned regression or language model inference -- both requiring articulation supervision and producing joints that may be geometrically inconsistent with the generated meshes -- or reconstruct from dense calibrated multi-view captures, limiting scalability. We present \textbf{ArtCrafter}, a feed-forward generative model that takes two images of an articulated object -- one at rest, one fully open -- and produces $2N+1$ part-level meshes together with a physics-executable URDF in a single forward pass. Inspired by Slot Attention, we structure the denoising process around a $1+2N$ slot layout whose slots, forced to jointly explain two articulation states, naturally bind to kinematic parts. \textbf{TripletAttention} enforces cross-state geometric consistency between each paired rest/open slot anchored through a shared base slot, and a repulsion loss $\mathcal{L}_{\mathrm{rep}}$ operating on decoded point clouds ensures inter-part disjointness throughout denoising. Because paired meshes are geometrically consistent by construction, URDF joint parameters -- type, axis, origin, and range -- are derived \emph{analytically} from the rigid transform between each pair, requiring no learned articulation head, no language model, and no per-instance optimization. Experiments on URDF-Anything+ demonstrate consistent improvements over retrieval-based and generative baselines across part-level geometry and all four articulation metrics. ArtCrafter demonstrates a clean decomposition: a diffusion model handles geometry, and geometry handles URDF.

Retrieving the 3D kinematics of articulated objects from monocular video is a fundamental challenge in computer vision. Existing methods rely on complex video setups or cues such as long-term point tracking or wide-baseline matching, but are frequently brittle under severe occlusions, rapid camera ego-motion, or weak local features. Learning-based methods, meanwhile, struggle to generalize beyond their training categories. We propose a category-agnostic optimization framework that treats articulated object understanding as a primitive-fitting problem. Geometric primitives serve as a proxy representation that avoids the pitfalls of unstable point tracks; a novel mechanism organizes them into coherent parts constrained by revolute and prismatic joints. Our formulation jointly optimizes part segmentation and joint parameters, recovering complex kinematics from a single casually captured video. A visibility-aware procedure handles partial observations and occlusions inherent to real-world data. We also propose the AiP-synth and AiP-real benchmarks, featuring significant camera motion and heavy occlusions, and outperform existing methods.

Terminal observations are not ordinary long-context text: they are heterogeneous, low-information-density execution traces in which sparse but exact evidence (e.g., error messages and file paths) is interleaved with large amounts of repetitive terminal output. For long-horizon CLI agents, retaining raw observations rapidly increases context cost and can dilute critical signals, while LLM-based summarization or fixed heuristics often fail to adapt across heterogeneous terminal tasks and may discard precise task-relevant evidence.We propose TACO, the first self-evolving Terminal Agent Compression framework, which treats compression rules as reusable, preservation-aware knowledge acquired from interaction trajectories. Rather than relying on manually designed rules, static pruning, or task-specific compressor training, TACO autonomously discovers, refines, and reuses structured compression rules from agent interaction trajectories. A Global Rule Pool accumulates effective rules across tasks, while task-time rule evolution adapts them online to the current workflow. This allows TACO to filter redundant terminal observations while conservatively preserving exact task-relevant evidence.Across six benchmarks, including TB 1.0, TB 2.0, SWE-Bench Lite, CompileBench, DevEval, and CRUST-Bench, TACO consistently maintains or improves task success across models and agent scaffolds. On TerminalBench, TACO yields 1%--4% absolute accuracy gains under standard evaluation and improves accuracy by 2%--3% under matched token budgets. On downstream benchmarks, it reduces total token consumption by 12%--27% while maintaining or improving task success. These results show that self-evolving observation compression can unlock latent capability in existing CLI agents by allocating context budget toward task-relevant evidence, without model fine-tuning or human-crafted compression rules.

AI security has become a critical issue as Deep Neural Networks (DNNs) are inherently vulnerable to adversarial attacks. While previous studies have demonstrated that adversarial training is one of the most effective defensive strategies, improvements in robustness often show significant class-wise disparities. This has established fair adversarial training, which aims to enhance the robustness of the worst-performing classes, as a crucial research direction. Although existing class-wise approaches can improve performance by adjusting individual class weights, they face severs scalability limitation on large-scale datasets due to the sparse volume of data available per class. To mitigate this issue without relying on explicit class-wise information, we propose a novel tri-regime optimization framework that fundamentally decouples the distinct sources of adversarial error at the sample level. Specifically, our method systematically suppresses unlearnable outliers via reliability gating, prioritizes genuine boundary threats through dynamic reweighting, and down-weights already safe examples to prevent redundant optimization. By isolating and prioritizing the effective adversarial frontier, our method strictly prevents robust overfitting and achieves superior worst-class robustness while maintaining high clean accuracy.

Mapping software issue descriptions to relevant code segments, a task known as code localization, is a primary bottleneck in automated software engineering. While recent attempts have automated it using code graphs that capture syntax-level dependencies among code elements, including functions and modules, their code-centric approaches often overlook the design rationales and prior fix patterns present in a codebase's development history, such as pull requests and commits. We propose Co-Locator, a collaborative multi-agent code localization framework that integrates code graphs reflecting functional dependencies and knowledge graphs constructed from triplets capturing design rationales and past code modifications scattered across the development history. During localization, a Code Graph (CG) agent explores functional hierarchies and consults a Knowledge Graph (KG) agent to gather insights into developer intents and prior bug fixes. Combined with a Retriever agent that assists CG and KG agents, Co-Locator leverages a holistic view of the code's functional and historical context, considering what the code implements and how it was changed, to identify locations to fix. Experiments show that Co-Locator significantly outperforms 12 state-of-the-art baseline methods.

Chain-of-thought (CoT) reasoning has become a widely used mechanism for eliciting multi-step reasoning in large language models by generating intermediate reasoning steps at inference time. Yet the scaling behavior of generalization with CoT depth remains poorly understood. To address this question, we study a theoretically solvable model of CoT for in-context weight prediction in linear regression, where test-time reasoning is represented as an iterative refinement of the weight-parameter estimate. Using tools from random matrix theory under high-dimensional asymptotics, we derive an exact formula for the generalization error as a function of reasoning depth, pretraining data amount, and context length. Our analysis reveals a sharp phase transition separating exponential and polynomial improvement, saturation, and overthinking, and characterizes how the optimal reasoning depth scales. We further show that deeper reasoning is most effective with sufficiently rich pretraining and in-context information, whereas limited pretraining or context makes longer reasoning prone to error amplification or saturation. We also validate these predictions through experiments on fully learned linear attention and softmax attention models. Our results provide a unified theoretical account of how test-time CoT depth affects generalization.

Low-rank plus sparse decompositions, such as robust PCA, typically model the low-rank component as dense and the sparse component as corruption. This viewpoint is poorly suited to graph adjacency matrices, where sparsity is structural and dense low-rank factors destroy the combinatorial and computational properties of the graph. We propose signed biclique decomposition (SBD), a sparse low-rank decomposition for integer-weighted graphs that represents the structured component as a sum of sparse signed binary rank-one factors, together with a sparse signed residual. The resulting representation remains discrete, interpretable, sparse, and compatible with standard linear algebra. We formulate fixed-rank and greedy SBD objectives and derive a gain-based greedy algorithm with theoretical guarantees. Empirically, SBD recovers block structure in noisy graphs, while its factorized representation enables efficient sparse matrix-matrix multiplication (SpMM). On real graphs with strong shared-neighborhood structure, SBD achieves compression factors above $10$ and SpMM speedups up to $6\times$ over cuSPARSE, suggesting a practical alternative to dense low-rank decompositions for graphs.

Clarification-seeking behavior is widely regarded as a desirable property of LLM agents, enabling them to resolve ambiguity before acting on underspecified tasks. However, the security implications of this interaction pattern remain unexplored. We investigate whether the transition from standard execution to a clarification-seeking state increases an agent's susceptibility to prompt injection attacks. We introduce ASPI (Ambiguous-State Prompt Injection), a benchmark of 728 task-attack scenarios that isolates clarification as a distinct agent state and measures how this state transition affects vulnerability under controlled conditions. Each benchmark instance is evaluated under matched execution and clarification settings: in the execution setting, the agent acts on a fully specified instruction and encounters adversarial content only through tool-returned data; in the clarification setting, the agent must first request and incorporate additional user input before acting. We evaluate ten frontier LLMs and find that clarification-seeking consistently and substantially amplifies vulnerability. For instance, attack success rises from 1.8\% to 34.0\% for o3 and from 2.2\% to 35.7\% for Gemini-3-Flash. A decomposition analysis reveals that this gap reflects both a state-dependent shift in how models process incoming content and a channel-specific effect arising from the agent-solicited clarification interface. These findings demonstrate that standard execution-time security evaluation systematically underestimates the attack surface of interactive agents, and that robustness under fully specified tasks does not translate to robustness under ambiguity. For reproducibility our data and source code is available at https://anonymous.4open.science/r/aspi-5074.

Post-training quantization (PTQ) compresses large language models (LLMs) but remains largely semantic-agnostic, relying on global activation magnitude to guide precision allocation. This overlooks the functional heterogeneity of Transformer representations, where semantically meaningful channels are sparsely activated while structurally dominant channels exhibit large but context-insensitive activations. We propose Agent-guided Semantic-aware Quantization (ASQ), a PTQ framework that conditions on semantically informative positions identified by an auxiliary LLM. A dual-pathway scoring mechanism captures both activation selectivity and attention routing toward these positions, producing channel-wise importance scores for scaling. This yields a closer approximation to a semantically conditioned objective and consistently improves low-bit (e.g., W4A16) quantization performance across LLMs. Extensive experiments show consistent gains over state-of-the-art PTQ, preserving general reasoning and even surpassing FP16 performance on several benchmarks in specialized domains, while enabling training-free, privacy-preserving self-quantization with zero inference overhead.

Deep click-through rate (CTR) models commonly consist of sparse embedding blocks and feature-interaction blocks, which jointly learn predictive signals from high-dimensional categorical features. This naturally raises the question of whether different blocks in an end-to-end trained CTR model play the same role in generalization. To address this question, we investigate the generalization behavior of deep CTR models from a block-wise diagnostic perspective. Specifically, we formulate deep CTR models as two-block systems comprising an embedding block and a feature-interaction block, and introduce a retuning-based diagnostic protocol to separately assess the transferability of different blocks. Experiments across multiple datasets and architectures reveal a consistent asymmetric generalization pattern: the embedding block exhibits stronger training-set specificity, whereas the feature-interaction block preserves comparatively more transferable structure. Motivated by this diagnosis, we further develop a component-selective embedding aggregation method that averages multiple independently retuned embedding tables while keeping the feature-interaction block fixed. The resulting model retains the original single-path inference architecture and incurs no additional online inference cost. Experiments on Avazu, Criteo, and Taobao show that the proposed method improves representative deep CTR models over standard training and retuning baselines.

Negative guidance of diffusion models generates samples that avoid an undesired condition, specified either by reference samples or by a conditional score alongside an unconditional model. Existing methods inject repulsive terms derived from local information at each sample, so the resulting sampler does not target an explicit density, and its bias cannot be systematically reduced by increasing computation. We instead present a negative-guidance method that samples from an explicit target density, which uniformly handles both reference-sample and score-based specifications and suppresses probability mass near the undesired condition with an avoidance strength that parametrizes the target density itself. Realizing this target requires estimating, at each diffusion timestep, how likely each sample is to belong to the undesired condition. We cast this estimation as a Positive--Unlabeled learning problem that admits lightweight online training along the diffusion trajectory, using condition samples as positives and diffusion particles as unlabeled data. Because the guided particles drift from the unconditional marginal, we further correct the residual mismatch via Sequential Monte Carlo, yielding a sampler that is asymptotically exact in the particle limit and that turns any existing negative-guidance method into its proposal kernel. Empirically, our method approaches the ground-truth target on Gaussian mixtures and Pareto-dominates existing methods on trade-offs between avoidance and generation quality, distributional bias, and diversity, including in an image-generation setting with a pretrained score network.

Data and pipeline parallelism are key strategies for scaling neural network training across distributed devices, but their high communication cost necessitates co-located computing clusters with fast interconnects, limiting their scalability. We address this communication bottleneck by introducing asynchronous updates across both parallelism axes, relaxing the co-location requirement at the expense of introducing staleness between pipeline stages and data parallel replicas. To mitigate staleness, for pipeline parallelism, we adopt a weight look-ahead approach, and for data parallelism, we introduce an asynchronous sparse averaging method equipped with an exponential moving average based correction mechanism. We provide convergence guarantees for both sparse averaging and asynchronous updates. Experiments on large-scale language models demonstrate that our approach matches the performance of the fully synchronous baseline, while significantly reducing communication overhead.

Code assistants are increasingly utilized in test-driven software development, yet the theoretical mechanisms behind their environment-interaction strategies remain underexplored. We provide a probabilistic framework for two dominant paradigms: code selection after generation using the execution environment, and code generation conditioned on environment feedback. First, we formalize several well-established selection heuristics as environment-aware estimators of code correctness. We theoretically prove that estimators based on fuzzy functional similarity add an inductive bias and strictly dominate estimators based on functional equivalence in terms of signal-to-noise ratio. Second, we frame backprompting as an in-context approximation of Thompson sampling. We derive a novel regret bound for reward functions with unobservable components, theoretically explaining why the effectiveness of backprompting is limited by the ambiguity of the informal task description (an irreducible regret). Using five state-of-the-art open weight models, we corroborate these findings across BigCodeBenchHard, LeetCodeDataset, and QiskitHumanEvalSim. Our formalization also suggests how to improve task descriptions effectively, leading to a new benchmark, QiskitHumanEvalSimX.

Multivariate weather time series are a central modality for forecasting and decision support, and the surrounding language increasingly mediates how numerical weather is consumed by users and downstream models. However, existing pipelines depend on closed-source language model ensembles with expert quality control or on retrieving human-curated text, while prior caption-RL rewards optimize cycle similarity or downstream-task utility, neither of which can tell a captioner that a confidently asserted phenomenon is contradicted by the data, so the absence of an executable, station-observable, and refutation-aware reward signal has blocked the application of self-play RL to this scientific modality. To address this, we propose AtmoZero, a post-training framework that trains a weather-time-series captioner without a single human-written caption, by aligning a language model with a library of seven station-observable scientific verifiers, pairing them with an explicit refutation channel that penalizes caption claims the data contradicts, and combining them with auxiliary reconstruction and forecaster-utility rewards under group-relative policy optimization. Extensive empirical results on a twelve-year ERA5 reanalysis corpus and real-world surface observations show that AtmoZero attains the lowest unsupported-claim rate and forecasting error at every horizon, outperforming closed-source captioners, dense-reward RL, and far-larger foundation models, with extensive analyses confirming that these gains arise from multiple complementary signals and are robust under distribution shift.

Masked diffusion language models can reduce inference steps by revealing multiple tokens per denoising iteration, but this parallelism is fragile: positions that are individually confident may be unsafe to commit together when their predictions are coupled. Existing training-free samplers such as Top-(k), Fast-dLLM, and EB-Sampler mainly control how many tokens to reveal, while often ranking candidates by token-wise scores that ignore interactions within the selected set. We propose ADAS, a training-free reranking rule for parallel masked diffusion decoding. ADAS leaves the base sampler's stopping rule unchanged and modifies only subset construction: it greedily discounts a candidate when it attends strongly to already selected positions whose predictions remain uncertain. Unlike graph-constrained methods that turn attention into hard compatibility constraints, ADAS keeps attention continuous and uses it as a soft marginal penalty. Across LLaDA-8B-Base and Dream-7B-Base on GSM8K, MATH500, HumanEval, and MBPP, plugging ADAS into Top-(k), Fast-dLLM, and EB-Sampler improves low-NFE performance at matched denoiser evaluations by (9.11) and (10.46) percentage points on average, respectively, with (3.1\%) per-forward runtime overhead. These results show that soft attention-discounted reranking is a simple and modular way to improve quality in highly parallel decoding for masked diffusion language models.

Conformal prediction (CP) for language models is typically evaluated under a \emph{matched} protocol, in which calibration and deployment use the same post-training inference configuration, such as quantization mode and inference temperature. In deployment, these choices often differ from those used at calibration to meet memory or throughput constraints or serving policies. We argue that this exposes a limitation in current CP evaluation protocols for language models. We formalize the underlying phenomenon as \emph{inference-time model shift}: a change in the conformal score distribution induced by inference configuration rather than by the input--label distribution, and theoretically characterize how it breaks split-conformal exchangeability even when the task distribution and trained model are unchanged. We then evaluate its consequences across $8$ language models, $4$ finite-label benchmarks, and $12$ inference configurations. We find that matched evaluation can substantially overstate deployment-time coverage, and that the effect is heterogeneous across models and datasets. We argue that inference configuration should be reported as a first-class experimental axis in CP evaluations for language-model systems.

Learning-rate selection is one of the most consequential and repeatedly tuned decisions in modern deep learning, and the ability to transfer it quickly across widths, depths, and architectures can substantially reduce the cost of scaling new models. Existing transfer laws explain width through maximal-update parameterization and depth through depth-based scaling laws, but many modern architectures are better described as neural computation graphs with heterogeneous merge rules rather than by depth alone. We develop a unified merge calculus for learning-rate scaling on such graphs. Its central object is a graph-aware complexity coefficient that combines local merge semantics with global path structure and induces a $-1/2$ power law for the target learning-rate scale. We further show that depth alone is generally insufficient once merge semantics vary across architecture families. We validate the framework on controlled graph families and lightweight representative architectures covering uniform sum, residual-aware sum, concat, weighted fusion, and hybrid designs. Across these settings, the resulting graph coefficients organize optimal learning rates more effectively than depth alone and support practical learning-rate transfer.

We introduce COMONet (Convex-Concave and Monotonicity-Constrained Neural Networks), a unified architecture for enforcing variable-wise shape constraints in neural networks. COMONet addresses the limitations of prior methods that either support only a restricted subset of monotonicity and curvature constraints or enforce them through penalties without strict architectural guarantees. Our framework assigns each input variable to one of eight shape regimes, covering monotonicity, convexity, concavity, and their combinations. This is achieved through a partially connected architecture that routes each variable to specialized units equipped with sign-constrained weights and constraint-preserving activation functions. We also provide theoretical guarantees showing that COMONet satisfies the prescribed variable-wise shape constraints by construction. Experiments on synthetic and real-world datasets demonstrate that COMONet achieves competitive performance and remains robust to noise while preserving its architectural guarantees. COMONet thus offers a practical and principled framework for incorporating domain knowledge as shape constraints into neural network training.

We study scalable second-order algorithms for high-dimensional square-root Lasso problems with multiple responses. The resulting estimators preserve the scale-free tuning property of their single-response counterpart, but accurate large-scale computation is challenging because both the loss and penalty terms are nonsmooth. In this paper, we propose a computational framework that universally applies to multitask and multivariate square-root Lasso models (which includes the single-response version) based on the Douglas-Rachford splitting. We solve the nonsmooth equation induced by the splitting by a regularized semismooth Newton method with projection and fixed-point safeguards. Despite the nonsmoothness of the problem, the structure of the proximal maps and generalized Jacobian lead to a reduced Newton system in a much smaller dimension than that in the original space, promoting scalability. Our method enjoys global and locally quadratic convergence under mild conditions. Experiments on synthetic regression problems and large-scale multi-omic data show that the proposed method reaches the same fitted models and predictive performance as latest solvers tailored to individual models, while requiring fewer iterations and less wall-clock time in large-scale, high-dimensional settings.

Audio-video (AV) editing aims to modify audio and video content according to a target prompt. Unlike single-modality editing, AV editing requires models to infer a modality-selective edit scope from the prompt alone: determining not only what should change, but also which modality should be preserved. Faithfully evaluating such models therefore requires both (i) benchmarks that span diverse edit types and modality categories, and (ii) evaluation that is itself modality-aware and sample-specific. However, existing AV editing benchmarks provide limited coverage of edit types and modality combinations, while current evaluation systems are often modality-blind and sample-agnostic, making it difficult to assess whether models faithfully preserve the unintended modality. To address these gaps, we introduce AVENUE, Audio-Video EditiNg Understanding and Evaluation, comprising two contributions: (1) a benchmark of 1,291 source clips and 7,957 editing instructions across audio-targeted, video-targeted, and AV-coupled edit types, curated and human-verified from VGGSound; and (2) a sample-specific, modality-aware evaluation framework that specifies, for each sample, both the intended change and the content that must remain intact. We evaluate representative AV editing models spanning three editing paradigms — joint, sequential, and separate — providing the first systematic analysis of modality-selectivity across paradigms. Our findings reveal a fundamental open challenge: when editing one modality, existing models frequently induce unintended changes in the other, regardless of paradigm. AVENUE provides a benchmark and modality-aware evaluation framework to drive progress toward more controllable AV editing models.

Inference-time search offers a principled way to improve the reliability of large language models (LLMs) by exploring and scoring intermediate trajectories. However, widely used search paradigms exhibit complementary failure modes: sequence search scales linearly but commits early and cannot recover from mistaken prefixes, whereas tree search can backtrack but often becomes impractical for deep search due to the exponential growth of the search space with depth. These limitations become increasingly pronounced in long-chain-of-thought responses. We propose Width-Limited Tree Search (WLTS), which reconciles these strengths by preserving the tree-search structure that enables backtracking while using a dynamic expandability criterion to enforce a strict per-layer width constraint. This design keeps generated search-tree growth linear in depth and sustains verifier supervision across the full reasoning process. WLTS further supports two optional deployment components: prefix deletion for diversity and agreement-based early stopping to reduce computation on tasks with canonicalizable outputs. Across mathematical, coding, and logical reasoning benchmarks, and under both classifier and generative verifier settings, WLTS achieves higher accuracy and better inference-budget efficiency than well-established sequence- and tree-search baselines.

Despite progress in image tokenization, standard methods encode redundant information by mixing all granularities within each token, thus redundancy persists between tokens. The mix of information of different granularity also complicates the training of generators. This paper introduces SelfBootTok, a method that resolves this by cleanly decomposing information into global and local token groups. Through self-bootstrapped learning, the model predicts local details exclusively from global tokens, shifting the burden of visual details from the generator to the tokenizer. Consequently, our generator is far more efficient, requiring only global tokens and reducing computation by approximately 40\%, while delivering superior reconstruction and generation. Moreover, this paradigm scales elegantly: by leveraging more data or parameters to self-supervise local representation learning, SelfBootTok achieves a new state-of-the-art gFID score of 1.56 using only 64 tokens.

Few-shot medical image segmentation has shown great potential in reducing annotation costs for clinical applications. Recently, many few-shot methods have explored the Segment Anything Model (SAM) for training-free medical image segmentation via prompt engineering. However, existing approaches mainly focus on locating positive prompts from support images while overlooking informative background cues, making them prone to over-segmentation in anatomically similar regions. Moreover, simply introducing negative prompts cannot effectively suppress boundary leakage, and may even interfere with SAM’s mask decoding process, resulting in worse performance than using positive prompts alone. To address this limitation, we propose a training-free support-query framework, termed Boundary-Aware Prompt Mining, which introduces boundary-aware negative prompting for SAM-based few-shot medical image segmentation. Specifically, we introduce a Prototype-guided Positive Prompt strategy, which adopts a multi-center prompting mechanism to construct multiple foreground prototypes, enabling comprehensive spatial coverage in query images. Furthermore, we propose a Boundary-band Ambiguity Filtering strategy that identifies a boundary-adjacent background band and progressively removes ambiguous pixels and unreliable clusters highly similar to the foreground, enabling the selection of reliable negative prompts for suppressing false positives near foreground boundaries. The generated positive and negative prompts are jointly fed into SAM to produce refined segmentation results without additional training. Extensive experiments on Abd-MRI and Abd-CT datasets demonstrate that our method consistently outperforms existing approaches, highlighting its effectiveness and robustness under limited annotation settings. Code will be released upon acceptance.

Bayesian causal inference quantifies uncertainty in treatment effects under a specified causal model. This uncertainty is often interpreted as evidence that the resulting causal conclusion is robust. We argue these are distinct targets: a posterior over a treatment effect may be tightly concentrated above zero while the corresponding causal claim is fragile to small violations of ignorability, overlap, or target-population assumptions. We introduce Bayesian causal stress testing (BCS), a framework that assigns a posterior fragility score $F_\alpha$ with a threshold $\alpha\in(0,1)$ to a causal conclusion by measuring the smallest calibrated stress under which the posterior probability of the claim falls below $\alpha$. We give a stress-map formulation, decision and composition results for claim-level stress, a calibration theorem showing that $F_{0.5}$ asymptotically recovers the partial-correlation form of the Cinelli-Hazlett robustness value in the linear-Gaussian flat-prior limit, and a proposition formalizing why posterior precision and posterior fragility can decouple. We evaluate BCS on IHDP, TWINS, and ACIC-cov+Hill, a semi-synthetic benchmark built from the ACIC 2016 covariate matrix, under BART, BCF-style, Bayesian linear, and Bayesian-bootstrap AIPW posteriors. On the linear Bayesian posterior, $F_{0.5}^2$ matches the Cinelli-Hazlett $RV_{0.5}^{\mathrm{CH}}$ to RMS $0.001$ on both IHDP and ACIC-cov+Hill across all $20$ configurations; the BART and BCF-style posteriors show larger deviations in small-effect configurations. Across estimators, posterior $95$% interval widths vary by up to $50$% but $F_{0.5}$ remains within $0.05$, illustrating that posterior precision is not a robustness certificate. A controlled-DGP calibration experiment with a hidden confounder of known partial correlation $\gamma_{\mathrm{true}}$ confirms that $F_{0.5}$ tracks the required flip-stress to mean absolute error $0.03$.

Large language model systems repeatedly make test-time decisions by comparing similarity. They retrieve exemplars, rerank candidates, and choose revisions. But similarity is not unique: a candidate can be close lexically, semantically, structurally, or stylistically, while only one of these axes may matter for the current instance. We propose BASIS~(Bayesian Axis Selection via Interactive Signals), a training-free Bayesian framework that treats weak feedback as evidence about this latent task-aligned axis. BASIS maintains a posterior over candidate targets and similarity axes. It updates this posterior from scalar scores, pairwise preferences, or verifier outputs, and then chooses future probes by expected information gain. In a finite noisy similarity model, we show that fixed-axis and fixed-mixture rules can remain suboptimal under axis shift. Across concept recovery, reasoning exemplar selection, response refinement, and adversarial distractors, BASIS consistently outperforms stronger rerankers, static mixtures, and compute-matched best-of-$N$ search under the same candidate pools and feedback budgets. The average gains are moderate because all methods share the same backbone, candidate pools, and feedback signals. Under axis shift, however, the gap widens substantially, showing that latent-axis inference is not fully replaced by better scoring or more search alone.

We introduce BEACON—Best-Effort Adaptation for Cross-Domain Co-Training—a theory-driven framework for training generative robot policies with abundant source demonstrations and limited target demonstrations. BEACON casts cross-domain co-training as a discrepancy-aware importance-reweighting problem, jointly learning a diffusion-based visuomotor policy and per-sample source weights that minimize an objective informed by target-domain generalization guarantees. To make best-effort adaptation practical for high-dimensional sequence policies, we develop scalable instance-level discrepancy estimators, stochastic alternating updates for policy and weights, and a multi-source extension that balances heterogeneous source domains. Across sim-to-sim, sim-to-real, and multi-source manipulation settings, BEACON improves robustness and data efficiency over target-only, fixed-ratio co-training, and feature-alignment baselines. Importantly, even without an explicit alignment objective, BEACON achieves feature alignment as an implicit result of discrepancy-aware cross-domain co-training.

Reward-robust RL typically models misspecification by specifying an uncertainty set that constrains the discrepancy of plausible rewards from a base reward. However, such reward space discrepancies can be behaviorally irrelevant: under potential-based reward shaping (PBRS), many distinct rewards preserve policy ordering. This can introduce substantial redundancy into standard uncertainty sets and degrade optimization performance. We propose Shaping-Aware Reward-Robust RL, which constructs uncertainty sets over PBRS equivalence classes by projecting each reward to a canonical representative, ensuring that the resulting set contains only rewards that induce behaviorally distinct policy rankings. We prove that this projection preserves the optimal robust value while shrinking the uncertainty set and improving empirical performance. Using the connection between robustness and regularization, we obtain a practical algorithm to solve the shaping-aware reward-robust RL problem and enjoy convergence guarantees under standard assumptions. Experiments on benchmarks spanning diverse task domains and levels of complexity show consistent improvements over representative robust RL baselines and exhibit improved robustness to reward perturbations.

Self-supervised learning (SSL) has received notable attention in the graph machine learning community, enabling the efficient use of unlabeled data. However, evaluation setups vary significantly across studies, and baseline tuning often receives limited attention, making it difficult to draw reliable conclusions about model performance. In this paper, we reevaluate four representative graph SSL methods within a unified setup across a diverse set of datasets. We focus on rigorously tuning both SSL and supervised methods by employing enhanced GNN architectures and comprehensive hyperparameter optimization. We observe that, contrary to prior literature, two popular generative methods, MaskGAE and GraphMAE, regularly fail to outperform well-tuned supervised baselines. In contrast, the contrastive methods BGRL and GRACE consistently perform better than both the generative methods and supervised baselines. We hypothesize that this discrepancy arises because BGRL and GRACE capture information from both graph structure and node features, whereas MaskGAE and GraphMAE focus on a single source of information. We support this hypothesis through an analysis on carefully designed synthetic data. Motivated by our observations, we advocate for designing SSL objectives that capture both feature and structural information. To verify the effectiveness of this approach, we propose a simple generative method, GrASP, which reconstructs both graph structure and node features. Despite its simplicity, GrASP outperforms all other evaluated approaches and can be considered as a strong baseline in future studies.

Existing depth-compression methods for large language models (LLMs) operate on layers individually or restrict grouping to adjacent or contiguous positions, implicitly assuming functional redundancy is local. We challenge this assumption by computing full pairwise Centered Kernel Alignment (CKA) similarity matrices across thirteen LLMs spanning six architecture families and 1.6B--14B parameters, revealing that non-adjacent layers exhibit 3.6--15.9$\times$ more high-similarity pairs than adjacent ones. We propose Fusion via Community Mining (FCM), which builds the full $L \times L$ similarity graph, applies spectral community detection to recover clusters of functionally equivalent layers, retains the highest-Block-Influence layer per cluster, and applies LoRA recovery. At 2$\times$ compression, FCM lowers mean perplexity below ShortGPT on twelve of thirteen models, with statistically significant improvements on ten under a Welch's $t$-test with Holm--Bonferroni correction (up to 50\% on Qwen3-4B, $p_\text{corr}<0.001$); two rows are ties and Mistral-7B is the lone loss ($+$14.3\%). On three anchor models, FCM also outperforms three recent adjacent-pair methods (FlattenGPT, SWM, DeltaLLM) by 7.8--35.3\% under matched calibration and recovery. The perplexity advantage transfers downstream: on a five-benchmark zero-shot reasoning suite (ARC-Easy, ARC-Challenge, HellaSwag, WinoGrande, PIQA) over four anchor models, FCM matches or exceeds ShortGPT on 15 of 20 (model, benchmark) cells, and the 2$\times$-compressed Qwen2.5-7B realises 46\% fewer parameters, 42\% lower peak VRAM, and 84\% higher decode throughput on a single A100. Ablations confirm spectral grouping outperforms Fisher contiguous segmentation by 41\% and uniform partitioning by 51\%, and a compression sweep reveals a 2$\times$ crossover beyond which FCM's advantage grows monotonically and persists at the 14B scale. Code: \url{https://anonymous.4open.science/r/fcm-layer-fusion-D820}.

Vision-language-action (VLA) models have achieved strong performance in embodied manipulation, but still lack a clear mechanism to balance behavioral stability with task-semantic sensitivity. We identify two complementary failure modes. Under task-preserving changes, where task semantics remain unchanged but scene appearance varies (e.g., style, illumination, clutter, or paraphrasing), policies often exhibit unnecessary action drift. Conversely, under semantic-breaking changes, where key task semantics such as the target object or constraint are altered, policies frequently fail to produce sufficiently distinct behaviors and instead follow the original trajectory. To address this gap, we propose BAS-VLA, a task-semantic action calibration framework built on top of a frozen base VLA. BAS-VLA adopts a breaking-centered calibration core as the default path, and introduces a selective evidence-gated preserving auxiliary that activates only when nuisance variation is detected while task semantics remain consistent. On the OpenPI-pi0.5 / LIBERO-Object Milk-Swap benchmark, BAS-VLA maintains high success on clean (98.0%) and semantics-preserving conditions (97.5%), while reducing clean-criterion success to 0.0% under deliberate target-object swaps, demonstrating strong stale-task suppression and task-semantic separation. On validated style-preserving shifts, it improves success from 42% to 70% without degrading clean performance. These results highlight that reliable VLA behavior requires moving beyond appearance robustness toward explicit task-semantic action calibration.

A learning-to-defer (L2D) system decides, for each input, whether to predict on its own or to hand it to one of several available experts. The very well established recipe trains classifier and router jointly by treating the $K$ classes and $J$ experts as competing actions in one shared $(K{+}J)$-action geometry. Subsequent work has proposed a series of incremental fixes within this geometry; we show that each still suffers, to varying severity, from an optimization-level pathology (target distortion, gradient amplification, winner-take-all starvation, set-mass collapse, or class--expert coupling) even under statistical consistency. We step outside the augmented-action family entirely and propose a _decoupled surrogate_: a softmax classifier head and an independent sigmoid head per expert, mirroring the two natural objects of the problem. We show that per-sample updates are then coordinatewise and the class--expert Hessian block is identically zero, and prove an excess-risk bound with calibration constant $\max\\{2\sqrt{2},\sqrt{2J/\lambda}\\}$---to our knowledge the first multi-expert L2D guarantee whose constant does not grow with the expert pool when the per-expert weight is held fixed. On controlled synthetic studies and on CIFAR-10, CIFAR-10H, and Covertype, it is the only method in our comparison that remains stable as the expert pool grows, preserves rare specialists, and improves over a standalone classifier on every real-data benchmark.

Policy optimization algorithms that reuse rollouts build updates of the form $\rho A$, where $\rho$ is the importance ratio from the current policy to the rollout policy and $A$ is an advantage estimate. Recent RL algorithms such as PPO and GRPO rely on clipping $\rho$ for stable policy updates, which introduces further nonsmoothness in the optimization landscape. \emph{Is clipping the best we can do?} In this paper, we propose signed logarithmic smoothing (Signed LS), $\psi_\beta(z) = \mathrm{sgn}(z)\,\beta^{-1}\log(1+\beta|z|)$, applied to the full weighted update rather than to the ratio alone. Building on this surrogate, we introduce KRAFT, a GRPO-style policy-optimization objective that replaces the clipped surrogate with Signed LS. For theoretical justification, we consider an adaptive off-policy contextual-bandit setting: we compare the deviation structure of Signed LS with clipping-style surrogates, prove surrogate fidelity for Signed LS, and obtain regret transfer against a fixed comparator policy. The comparison highlights a structural distinction: ratio clipping introduces explicit tail-bias and threshold-dependent concentration terms, whereas Signed LS controls distortion through a smooth second-order quantity. Empirically, our contextual-bandit experiments provide evidence of these effects. In RLVR experiments on math-reasoning benchmarks and multiple LLM backbones, we observe that KRAFT consistently shows lower KL from the reference policy and higher entropy throughout training without incurring accuracy loss compared to GRPO.

Supervised dimensionality reduction (DR) is widely used in visualization to reveal task-specific structure in high-dimensional data. However, in regression settings with continuous supervision, existing methods often improve signal readability at the cost of severe neighborhood distortion, limiting the reliability of the resulting visualization. To understand this trade-off, we provide a theoretical analysis that characterizes the relationship between geometric faithfulness and signal readability through two finite-sample proxies for the readout's bi-Lipschitz properties: contraction (co-Lipschitz) and smoothness (Lipschitz). We show that contraction can impose a sharp geometric distortion floor in many existing methods, whereas smoothness alone is sufficient to support effective visualization. In particular, directly regularizing smoothness yields first-order regularity gains with only second-order geometry loss, under geometry-first initialization near a local geometry optimum. Guided by this insight, we propose \sys (\acroinit{I}nterpretable \acroinit{S}upervised \acroinit{DR}), a nonparametric method that preserves geometric structure while enforcing readout smoothness. Experiments on HPDv3 and Tabula Muris show that \sys improves the geometry--signal Pareto frontier, reducing local signal variation while retaining faithful neighborhoods compared with representative supervised DR baselines. Our code is available at \url{https://anonymous.4open.science/r/ISDR-preview}.

Few-step distillation is essential for deploying modern video diffusion models, and Distribution Matching Distillation (DMD) has emerged as a leading paradigm due to its strong output-space fidelity and its flexibility in supporting both bidirectional and causal student architectures. We find, however, that DMD has a structural blind spot: its objective lives entirely in the output space and provides no signal pushing the student to form a structured internal representation. As the step count shrinks, this output-only supervision becomes too coarse, and the distilled student degrades on both fine spatial structure and coherent temporal dynamics. To close this gap, we introduce Representation-Forcing, which adds a predictive representation loss on top of DMD without changing its output objective. By feeding the student and an EMA teacher with heterogeneous noise levels, we create an information asymmetry that forces the student to predict the teacher's representation from a more corrupted view --- explicitly bringing "representation compression" to distribution matching. Experiments show that Representation-Forcing consistently improves both spatial fidelity and temporal coherence. The mechanism is paradigm-agnostic across bidirectional and causal student architectures, requires no external feature extractor, and adds negligible cost.

Recovering 3D pose and shape of birds from single images is bottlenecked by the long tail of avian diversity: out of $\sim 10{,}000$ species, only a few dozen have 3D-labeled training data, and in-the-wild test sets routinely contain species never seen at training. Generalizing across clades is therefore a load-bearing problem. The current state of the art, AniMer+, mitigates it with a family-level contrastive loss that conveys only a binary same-family indicator per pair and discards the continuous structure of the species tree. We propose a **taxonomic Ornstein--Uhlenbeck prior** on the AVES shape code: a multivariate Gaussian whose covariance decays exponentially with Linnaean rank distance, with a learnable selection strength and noise scale. The prior generalizes family contrastive into a continuous, generative form, exposing the graded cross- and within-family similarity that a binary indicator cannot, and brings continuous comparative phylogenetics in as a biology-grounded inductive bias for the long tail. Our method consistently improves over AniMer+ on every reported avian benchmark cell, with the largest gains on the unseen-species CowBird benchmark. A permutation ablation isolates taxonomic structure from generic Gaussian smoothing, and a rank-granularity ablation identifies cross-order distinctions—the structure beyond a family-vs-not indicator—as the dominant source of the unseen-species gain.

Evaluating large language models (LLMs) today rests on fixed benchmarks that apply the same set of items to any model, producing ceiling and floor effects that mask capability gaps. We argue that the most informative evaluation signal lies at the boundary, where the per-prompt pass probability is near 0.5, and propose Dynamic Boundary Evaluation (DBE), which actively locates each model's boundary and places it on a globally comparable difficulty scale. DBE delivers three artifacts: (i) a calibrated item bank covering safety, capability, and truthfulness, with per-item difficulty labels validated across 9 reference LLMs; (ii) Skill-Guided Boundary Search (SGBS), a search algorithm that finds boundary items for a given target LLM using only API-level query access; and (iii) an evaluation protocol that places a new LLM on a unified ability scale and grows the evaluation set adaptively when the target falls outside the bank's coverage. We instantiate DBE on four categories spanning safety (harmful refusal, over-refusal), capability (constrained instruction following), and truthfulness (multi-turn sycophancy resistance). The resulting evaluation covers a broader model spectrum without saturation while remaining compatible with existing datasets.

CLIP-style vision-language models transfer broadly but remain brittle for compositional reasoning, often collapsing object-attribute binding and inter-object relations. We attribute this gap to limited compositional diversity in web-scale data and contrastive objectives that prioritize global alignment over grounded structure. Many fixes rely on caption-edited hard negatives, which can bias learning toward specific perturbation templates and trade off against general alignment. We propose GPS-CLIP, which improves compositionality by adding multi-granular positive constraints to standard contrastive learning: IoU-weighted multi-positive region-span alignment and relation-triplet alignment in the shared embedding space. GPS-CLIP uses existing grounded annotations during fine-tuning, keeps the dual-encoder architecture unchanged, and requires only images and text at inference. Across SugarCrepe, What'sUp, COLA, and Winoground, GPS-CLIP achieves state-of-the-art compositional performance while improving cross-modal retrieval and zero-shot recognition. Beyond compositionality, GPS-CLIP (i) achieves the strongest MMVP-VLM results for fine-grained visual understanding and (ii) yields a more structured embedding space with improved object/attribute separability on UT-Zappos geometric analysis compared to hard-negative variants. Overall, GPS-CLIP offers a streamlined way to improve VLM compositional robustness.

Conformal prediction (CP) provides a distribution-free framework for uncertainty quantification with finite-sample marginal coverage guarantees. Yet, missing values introduce distributional heterogeneity, where marginal coverage can hide systematic undercoverage for certain missingness patterns. At the same time, existing methods for conditional guarantees often fail when missing values are present, as they rely on geometric continuity of the covariate space, which is broken by missingness. Although imputation restores continuity, it obscures the missingness patterns and introduces task-dependent overhead. We develop a new CP framework that is \emph{adaptive to missingness patterns}, without imputing covariates. We rely on data augmentation by feature embedding that accounts for missingness. Our framework represents each input with missing values by a tree embedding together with its raw missingness mask, and then learns a regularized quantile score function. Varying the choices for the penalty function and the augmentation enables us to characterize three practically relevant validity levels: exact coordinate-wise validity, exact validity on observed masks, and approximate missingness-conditional guarantees for general mechanisms (MCAR, MAR, MNAR). Experiments on synthetic and real datasets support these guarantees and show that the proposed method reduces miscoverage risk while maintaining informative intervals, especially in non-MCAR settings. These results highlight the practical value of uncertainty quantification that adapts to missingness.

Spiking reservoir computing combines the training efficiency of fixed random recurrent networks with the energy efficiency of event-driven inference, where computation is triggered by sparse spikes rather than executed densely at every timestep. However, existing spiking reservoirs almost exclusively rely on Leaky Integrate-and-Fire (LIF) neurons, whose low-pass dynamics limit their ability to distinguish information encoded at different temporal frequencies. We introduce HRF-Res, a liquid state machine in which LIF neurons are replaced by a heterogeneous population of Harmonic Resonate-and-Fire (HRF) neurons. These neurons exhibit intrinsic oscillatory dynamics and selectively respond to input frequencies, enabling the reservoir to capture frequency-specific temporal patterns. The result is more discriminative representations and higher classification accuracy than LIF-based reservoirs, achieved without sacrificing the energy efficiency of spiking computation, with 9–14× lower energy consumption than an equivalent non-spiking oscillatory reservoir. Evaluated on eight benchmarks spanning time series, sequential images, audio spike trains, and neuromorphic vision, HRF-Res achieves competitive or state-of-the-art performance among spiking reservoir methods. These results demonstrate that introducing frequency-selective dynamics into spiking reservoirs improves representational quality and classification performance while preserving their inherent energy efficiency.

Deep search agents operate over trajectories spanning dozens of steps, yet standard reinforcement learning provides only a single outcome reward per trajectory — supervision that is far too sparse for effective credit assignment. On-policy self-distillation (OPSD) addresses this by using the model's own logits as dense token-level teachers, but extending it to search agents introduces a fundamental tension: the teacher, having access to privileged information such as the correct answer, produces a distribution that differs systematically from the student's exploration-based reasoning, and naive distillation causes the student to inherit this information asymmetry rather than learn better search strategies. We resolve this tension through two contributions. First, we construct Evidence Anchors — concise, step-level evidence snippets extracted from the web — as privileged information that captures key reasoning steps without revealing the entire answer path. Second, we propose Step-Level Self-Distilled Policy Optimization (SSPO), which converts teacher–student disagreement into step-level advantage weights within GRPO, applied exclusively to incorrect trajectories. This design decouples what to update from how much to update: the outcome reward determines the direction of policy change, while the teacher modulates its magnitude at each step. Correct trajectories are left untouched, preserving their diversity. On Qwen3-8B, SSPO consistently outperforms GRPO across BrowseComp, GAIA, and FRAMES, surpassing or matching GRPO trained with twice as many gradient steps while adding only about 5\% overhead per step from a single additional forward pass.

Modern end-to-end autonomous driving systems often suffer from fragile out-of-distribution (OOD) generalization and limited decision transparency, largely due to their reliance on raw sensory observations and susceptibility to spurious visual correlations. Although memory-augmented approaches have been explored to improve generalization, existing methods typically store raw observations, symbolic rules, or entangled latent features, making retrieval sensitive to surface-level similarity rather than decision-relevant driving structure. To address this limitation, we propose DriveMem, a plug-and-play framework that moves beyond raw observations by distilling and storing invariant driving memories for generalizable driving systems. DriveMem integrates two core components: Invariant Feature Abstraction that first distills perturbation-stable, decision-relevant representations from visual tokens, and Prototype Memory Bank that then stores and retrieves prototypical driving experiences grounded in invariant representations. Extensive open-loop and closed-loop experiments demonstrate that DriveMem improves OOD generalization and safety-related planning metrics across diverse unseen scenarios. Notably, under severe sensor disruptions, DriveMem achieves up to a 39.5\% relative reduction in Collision Rate compared with a strong domain-generalization baseline. These results suggest that building prototype memory in an invariant representation space is a promising approach for improving robustness, while offering prototype-grounded traceability for model behavior.

Previous AI alignment efforts have focused primarily on first-order social norms -- teaching models what is socially acceptable or unacceptable (e.g., 'do not steal'). However, social intelligence depends not only on norm recognition, but also on anticipating who will enforce it and how (e.g., public shame or even imprisonment). These second-order expectations, known as metanorms, govern how people respond when social rules are broken. We introduce a novel framework for evaluating metanorm reasoning in Large Language Models (LLMs) along two dimensions: emotional appraisal and behavioral response, and propose new classification tasks, namely, predicting self-regulation in violators and other-regulation in observers. We release a multi-perspective dataset, NormReact, of 450 norm violation scenarios, hand-annotated for emotions and behavioral responses across norm violators' gender and observers' social closeness. Current LLMs portray a harsher social world: across six models, they overpredict negative sanctions where humans would expect inaction, and alignment with human judgments deteriorates as social distance increases. These findings suggest that AI systems in norm-sensitive domains, from conflict mediation to policy simulation, may risk producing a distorted picture of social regulation: one that over-represents punishment and under-represents the tolerance, restraint, and relational calibration that characterize actual norm enforcement in the real world.

Recent impressive results from large reasoning models have been interpreted as a triumph of Chain of Thought (CoT), and especially of the process of training on CoTs sampled from base LLMs in order to help find new reasoning patterns. While these traces certainly seem to help the model performance, it is not clear how they actually influence model performance, with some works ascribing semantics to them and others cautioning against relying on them as transparent and faithful proxies of the model's internal computational process. To systematically investigate the role of end-user semantics of derivational traces, we set up a controlled study where we train transformer models from scratch on formally verifiable reasoning traces and the solutions they lead to, constraining both intermediate steps and final outputs to align with those of a formal solver. We notice that, despite significant improvements over the solution-only baseline, models trained on entirely correct traces can still produce invalid reasoning traces even when arriving at correct solutions. More interestingly, our experiments also show that models trained on corrupted traces, whose intermediate reasoning steps bear no relation to the problem they accompany, achieve performance largely comparable to those trained on correct traces. In fact, our corrupted models generalize better on out-of-distribution tasks. We also study the effect of GRPO-based RL post-training on trace validity, noting that while solution accuracy increase, this is not accompanied by any improvements in trace validity. Finally, we examine whether reasoning-trace length reflects inference-time scaling and find that trace length is largely agnostic to the underlying computational complexity of the problem being solved. These results challenge the assumption that intermediate tokens or "Chains of Thought" reflect or induce predictable reasoning behaviors and caution against anthropomorphizing such outputs or over-interpreting them (despite their mostly seemingly forms) as evidence of human-like or algorithmic behaviors in language models.

The standard post-training recipe for large multimodal models (LMMs) applies supervised fine-tuning (SFT) on curated demonstrations followed by reinforcement learning with verifiable rewards (RLVR). However, SFT introduces distributional drift that neither preserves the model's original capabilities nor faithfully matches the supervision distribution. This problem is further amplified in multimodal reasoning, where perception errors and reasoning failures follow distinct drift patterns that compound during subsequent RL. We introduce \textbf{PRISM}, a three-stage pipeline that mitigates this drift by inserting an explicit distribution-alignment stage between SFT and RLVR. Building on the principle of on-policy distillation (OPD), PRISM casts alignment as a black-box, response-level adversarial game between the policy and a Mixture-of-Experts (MoE) discriminator with dedicated perception and reasoning experts, providing disentangled corrective signals that steer the policy toward the supervision distribution without requiring access to teacher logits. While 1.26M public demonstrations suffice for broad SFT initialization, distribution alignment demands higher-fidelity supervision; we therefore curate 113K additional demonstrations from Gemini~3 Flash, featuring dense visual grounding and step-by-step reasoning on the hardest unsolved problems. Experiments on Qwen3-VL show that PRISM consistently improves downstream RLVR performance across multiple RL algorithms (GRPO, DAPO, GSPO) and diverse multimodal benchmarks, improving average accuracy by +4.4 and +6.0 points over the SFT$\rightarrow$RLVR baseline on 4B and 8B, respectively.

Diffusion language models (dLLMs) generate text by iteratively denoising masked response positions, exposing hidden states over future response slots before any token is finalized. This creates a detection surface that is absent from standard autoregressive decoding: even when a jailbreak is difficult to identify from the prompt alone, the initial masked response states may already reflect the model's emerging completion. We test this hypothesis on LLaDA-8B-Instruct by training lightweight linear classifiers on two frozen representations: prompt hidden states and pre-decoding masked-response hidden states. Empirically, the two views are complementary: neither classifier strictly dominates the other, and each recovers attacks missed by the other view. We then introduce $\texttt{ReFuse}$ (Representation Fusion), an inference-time detector that fuses prompt and pre-decoding response classifier scores without modifying model weights or the decoding procedure. Across transferred and dLLM-targeted jailbreaks, $\texttt{ReFuse}$ reduces average ASR from 63.29\% for the undefended model to 3.31\%, while keeping average benign refusal on standard utility benchmarks below 1\%. These results suggest that pre-decoding response states provide a complementary safety signal for detecting jailbreaks in dLLMs.

Multivariate time series often arrive with non-uniform timestamps and asynchronous per-channel observations, violating regular-grid assumptions of standard sequence models, while scarce labels and limited training resources make learning tasks more challenging. Model-space learning offers a lightweight alternative by fitting a dynamical model to each sequence and performing downstream analysis in the resulting model space. However, under irregular sampling, channels with very few observations weakly constrain the per-channel regression, leaving the fitted readout an unreliable summary. In this paper, we propose Reservoir State Statistics (RSS), a readout-complementary representation that augments the readout with three inversion-free trajectory summaries: centroid, spread, and terminal state. RSS is computed on the trajectory of the Global Decay Reservoir Network (GDRN), a continuous-time reservoir with per-channel time decay, cross-channel global pooling, and event-driven masked updates for asynchronous multivariate observations. Across multiple datasets, GDRN-RSS achieves competitive few-shot accuracy with CPU runtimes of seconds.

Recent advances in gigapixel-level imaging have brought High-Resolution Wide shots to the forefront of research. However, these images present significant challenges: extreme sparsity of foreground, gigapixel-level resolutions and interleaving of foreground and background. This causes traditional detectors and attention mechanisms to be hindered by background, resulting in inefficiency and inaccuracy. To tackle this problem, we propose BiHashFormer, a dual sparsified branch transformer built on hashing. RoI Selector will predict the foreground proportion for top-k selecting target-containing windows. The compression branch processes windows selected and refines attention into HashAttention by discarding half of the key–value pairs, reducing cost and improving efficiency. The compensation branch uses HashMiner to perform low-cost hash searches in the remaining regions, recovering features of missed objects. Experiments confirm that the selection relationship in HashAttention is one-way and reveal the preference of different queries for keys. In experiments on the gigapixel benchmark PANDA, BiHashFormer reduces 37.5\% backbone FLOPs while improving $\text{AP}_{50}$ to 81.0\%. Moreover, this parallel dual-branch structure allows the model to avoid inference delays caused by inter-branch dependencies, while achieving much greater stability than single-branch models, specially with low rates of RoI selection.

Modern data-centric applications increasingly require language agents capable of operating over live, stateful database environments, where successful problem resolution depends not merely on initial SQL generation, but on reasoning about evolving database states, interacting with execution feedback, and repairing state-mutating operations. While agentic reinforcement learning (RL) has emerged as a promising paradigm for training small language models (SLMs), existing frameworks are predominantly tool-centric and struggle with data-centric environments. Specifically, applying tool-centric frameworks to stateful tasks like SQL debugging introduces severe spatial inefficiencies since proper environment isolation typically requires a separate database container per rollout. Naively reusing databases without tracking evolving states across multi-turn interactions causes trajectory contamination and cross-trajectory interference, which severely destabilizes policy updates. To overcome these bottlenecks, we introduce BIRD-RL, a novel four-stage agentic RL curriculum that alternately enhances foundational agent reasoning and trajectory-adaptive on-policy exploration to circumvent the instability inherently associated with direct end-to-end learning. Crucially, to support this curriculum, BIRD-RL is driven by a Trajectory-scoped Persistent Execution Infrastructure, which enables safe database replica reuse while preserving trajectory isolation. This design improves spatial RL efficiency by 12.8x, successfully enabling scalable agentic RL for complex data-centric tasks without needs of Kubernettes. Empirical evaluations on the BIRD-CRITIC-SQLite benchmark demonstrate that our 7B and 14B models achieve Success Rates of 44.40% and 48.00%, respectively, performing on par with or exceeding strong proprietary model-based agents such as Claude-Opus-4.6 Agent (48.20%). Furthermore, BIRD-RL successfully trains unified SLMs capable of both advanced SQL debugging and generation, highlighting its broader potential for multi-objective optimization across heterogeneous, real-world data-centric tasks.

On-device deployment of large language models (LLMs) is increasingly adopted due to latency and privacy requirements, but exposes proprietary models to reverse engineering and intellectual property theft. Trusted Execution Environments (TEEs) provide hardware-enforced isolation for on-device inference, yet most TEE-based parameter obfuscation relies on unilateral matrix transformations that obfuscate only output side. We identify a vulnerability in this design, termed the Solution Determinacy Property (SDP): the obfuscation secret is uniquely determined, exposing the structure of the true key and enabling attackers to break the protection. Under our attacks, representative methods GroupCover and ArrowCloak are reduced to negligible protection, with attack accuracy reaching over $2.4\times$ the full-shielding black-box baseline and recovering near-complete model functionality. To address this vulnerability, we propose BiShield-TEE, a bilateral framework that induces Ambiguous Solutions through joint obfuscation of input and output dimensions, hiding the true key among many equally valid candidates. Experiments across diverse models and datasets show that BiShield-TEE confines attack accuracy to the full-shielding black-box baseline, while achieving higher inference efficiency than prior methods on real hardware. Our code is available at https://anonymous.4open.science/r/BiShield-TEE-28DF.

We present BitDance, a scalable autoregressive (AR) image generator that predicts binary visual tokens instead of codebook indices. With high-entropy binary latents, BitDance lets each token represent up to $\mathbf{2^{256}}$ states, yielding a compact yet highly expressive discrete representation. Sampling from such a huge token space is difficult with standard classification. To resolve this, BitDance uses a binary diffusion head: instead of predicting an index with softmax, it employs continuous-space diffusion to generate the binary tokens. Furthermore, we propose next-patch diffusion, a new decoding method that predicts multiple tokens in parallel with high accuracy, greatly speeding up inference. On ImageNet 256$\times$256, BitDance achieves an FID of **1.24**, the best among AR models. With next-patch diffusion, BitDance beats state-of-the-art parallel AR models that use 1.4B parameters, while using **5.4$\times$** fewer parameters (260M) and achieving **8.7$\times$** speedup. For text-to-image generation and image editing, BitDance trains on large-scale multimodal tokens and generates high-resolution, photorealistic images efficiently, showing strong performance and favorable scaling. When generating 1024$\times$1024 images, BitDance achieves a speedup of over **30$\times$** compared to prior AR models. We release code and models to facilitate further research on AR foundation models.

The practical deployment of large language models on resource-limited devices is constrained by three coupled costs: memory footprint, energy consumption, and inference latency. Ultra-low-bit backbones such as BitNet b1.58 mitigate the first two issues by ternarizing the weights to 1.58 bits, but the inference latency remains bounded by autoregressive next-token prediction (NTP), which commits only a single token per forward pass. Multi-token prediction (MTP) improves inference latency by committing several tokens per step. However, the naive combination of MTP with a 1.58-bit backbone does not yield end-to-end gains: once 1.58-bit execution reduces the arithmetic cost, the runtime cost of the accepted-prefix dynamics of MTP dominates each iteration and erodes the savings. In this work, we propose BitMTP, an algorithm--system co-design framework that makes MTP native to a single 1.58-bit backbone. BitMTP is organized around one design principle that we term accepted-prefix alignment: at every iteration, the committed prefix is simultaneously a valid autoregressive continuation, a directly promotable KV slice, and the index of a pre-captured replay state. Three runtime mechanisms enforce this principle along the kernel, memory, and scheduling axes, respectively: a role-width-specialized 1.58-bit execution primitive, a finite-state graph atlas with shared-KV memory over a geometric segment ladder, and an optimistic verify-prefetch scheduler on the verifier-bearing decoding branches. BitMTP-2B reaches 964.2 token/s peak decode throughput, a $2.64\times$ speedup over BitNet b1.58-2B-SFT at comparable prediction accuracy. Code and data will be released.

Spiking Neural Networks (SNNs) provide energy-efficient computation by utilizing binary spike-driven additions, but applying transformer architectures remains challenging due to the lack of an SNN-compatible positional encoding (PE). Current PE methods either rely on floating-point arithmetic, which destroys the fundamental binary nature of SNNs, or fail to accurately capture relative spatial distances. To overcome this, we introduce Bit Shift Rotary Position Embedding (BitShift-RoPE), the first relative PE framework that fully preserves the binary integrity of SNN queries and keys. By replacing the floating-point trigonometric rotations in standard RoPE with discrete cyclic-shift operations via hardware memory-pointer routing, our method achieves zero-FLOP position encoding. We analytically map multi-scale exponential decay frequencies into integer shift steps and theoretically extend the formulation to 2D orthogonal spaces to capture complex spatial geometries. Crucially, because the binary integrity of the queries and keys is strictly maintained, the subsequent attention calculations rely solely on sparse bitwise AND operations. Our evaluations across time-series forecasting (avg $R^2$ 0.758), text classification (69.91\% accuracy), and image classification benchmarks (84.17\% accuracy) demonstrate that BitShift-RoPE achieves state-of-the-art performance while consuming the same energy as the vanilla SNN backbone (0.216 mJ/sample, identical to vanilla Spikformer). The code is available at \url{https://anonymous.4open.science/r/Bit\_Shift\_RoPE-16D2/}.

Access to modern generative systems is often restricted to querying an API (the ``black-box" setting) and many properties of the system are unknown to the user at inference time. While recent work has shown that low-dimensional representations of models based on the relationship between their embedded responses to a set of queries are useful for inferring model-level properties, the quality of these representations is highly sensitive to the query set. We introduce the discriminative factorization to distinguish between high- and low-quality query sets in the context of black-box model-level classification. Under this framework, the probability of chance-level classification decays exponentially in the query budget. On three auditing tasks, estimated factorization parameters predict the empirical performance decay rate. We conclude by showing that query sets selected using the estimated discriminative field reproduce the empirical ordering of oracle query sets.

AI weather models are fast and skillful, yet their errors are strongly conditional because the best member changes with lead time, variable, initialization state, and atmospheric regime. Operational deterministic multi-model forecasting therefore requires more than averaging strong forecasters. It requires anticipating which member deserves trust before future analyses exist. BlendCast turns this anticipation problem into a vision-language forecasting task. Given meteorological maps and structured ensemble diagnostics, the model reads the current weather situation, reasons about prospective member reliability, and converts that judgment into deterministic per-variable ensemble weights. To teach this behavior, BlendCast first gives the model demonstrations of what member-skill reasoning and weight decisions should look like, then refines the resulting policy with multi-variable reinforcement learning so that its own analyses are rewarded when they lead to better, physically consistent forecasts. On held-out 2025 forecasts, BlendCast reduces WRMSE by 2.6\% over equal weighting and by 2.0% over conventional ensemble post-processing, closing 63.2% of the gap to oracle blending. These results show that, with multimodal weather evidence and reward-aligned training, VLMs can act as real-time forecasters of forecasters for deterministic AI weather ensembles. Project and demo:https://anonymous.4open.science/status/BlendCast-2567

We study an endogenous nonstationary stochastic bandit problem with latent linear dynamics, where actions affect both immediate rewards and the future evolution of an unobserved latent state. Rewards are bilinear in the current action and latent state, inducing history-dependent rewards and a nontrivial long-horizon planning problem. The existing explore-then-commit approach (Choi et al., 2026) achieves $\tilde{\mathcal{O}}(T^{2/3})$ regret by uniformly exploring to estimate the latent dynamics and then committing to an optimized open-loop action sequence. We show that this rate can be improved via adaptive block-level optimism. Our key step is a cyclic approximation: under stable dynamics, the infinite-memory reward process can be truncated, and the open-loop benchmark can be approximated by optimizing a finite-memory block-level proxy. Building on this reduction, we propose a UCB-based block algorithm that maintains confidence sets for the truncated dynamics parameters and selects blocks optimistically. We prove a regret bound of order $\tilde{\mathcal{O}}(\sqrt T)$, significantly improving over the previous $\tilde{\mathcal{O}}(T^{2/3})$ guarantee for the same model. To the best of our knowledge, this is the first $\tilde{\mathcal{O}}(\sqrt T)$ regret guarantee for latent linear-dynamics bandits with bilinear reward observations and an open-loop action-sequence benchmark.

Recently several methods have shown promise for purely in silico “de novo” design of antibodies which bind to drug targets. The methods which have shown success in vitro propose candidates via either a generative diffusion model or hallucination-based sequence optimization, and then filter these candidates with a structure predictor model. Hallucination methods rely on compute-intensive backpropagation through a structure predictor model for each candidate, and the generative methods require expensive structure re-prediction and filtering of many (often mostly non-passing) candidates, limiting their application to test-time generation of large libraries. Given the highly variable per-drug-target hit rates of current methods, screening large libraries is a well established route to improved antibody candidate discovery. Thus, we propose an alternative approach, Bonobo, which instead formulates this problem as black-box optimization of a per-drug-target generative model, amortizing candidate generation into training. This is accomplished by using a structure predictor as a reward signal to directly train a GFlowNet generative model. We show that this approach can match or exceed the in silico metrics of state-of-the-art approaches, while allowing for dramatically more efficient generation of diverse and arbitrarily large numbers of antibody binder candidates.

Bayesian optimization (BO) is a popular technique for sample-efficient optimization of black-box functions. In many applications, the parameters being tuned come with a carefully engineered default configuration, and practitioners only want to deviate from this default when necessary. Standard BO, however, does not aim to minimize deviation from the default and, in practice, often pushes weakly relevant parameters to the boundary of the search space. This makes it difficult to distinguish between important and spurious changes and increases the burden of vetting recommendations when the optimization objective omits relevant operational considerations. We introduce BONSAI, a default-aware BO policy that prunes low-impact deviations from a default configuration while explicitly controlling the loss in acquisition value. BONSAI is compatible with a variety of acquisition functions, including expected improvement and upper confidence bound (GP-UCB). We theoretically bound the regret incurred by BONSAI, showing that, under certain conditions, it enjoys the same no-regret property as vanilla GP-UCB. Moreover, assuming known ARD lengthscales---the same assumption underlying GP-UCB regret bounds---BONSAI provably recovers the relevant-coordinate set at zero acquisition cost, yielding a method that matches the GP-UCB regret rate while recovering the minimal-$\ell_0$ solution---a guarantee not provided by prior sparse-BO methods. Across many real-world applications, we empirically find that BONSAI substantially reduces the number of non-default parameters in recommended configurations while maintaining competitive optimization performance, with little effect on wall time---averaging only $1.5\times$ the candidate-generation cost of standard BO, compared to $7$-$34\times$ on average for prior sparse-BO methods (IR, ER, and SEBO).

Diffusion policies have achieved strong performance in reinforcement learning (RL) due to the exceptional expressivity in capturing complex action distributions. However, existing approaches focus primarily on positive samples and lack explicit modeling of negative samples, thereby failing to fully exploit this representation capacity and ultimately limiting performance. To mitigate this problem, we propose BOBAC (BOotstrapped Bipartite Actor-Critic), a novel method for diffusion policy optimization via pairwise preference learning. Bridging the gap between distribution modeling and preference learning, we reformulate diffusion policy optimization within a preference-aware framework via the Bradley-Terry (BT) modeling, which enables the effective exploitation of negative samples through relative ranking. Since the original BT loss assigns the same weight to all pairs regardless of their varying importances, we further design a soft-gate mechanism to adaptively weight each pair according to its Q-gap. Experimental results on 6 MuJoCo tasks and 4 H1Bench tasks demonstrate that, compared with representative model-free and generative-policy RL baselines, BOBAC achieves state-of-the-art performance with consistent improvements in all continuous-control environments.

Recent years have witnessed meteoric progress in reasoning models: neural networks that generate intermediate reasoning traces (RTs) before producing a final output. Despite the rapid advancement, our understanding of how RTs support reasoning, and where this paradigm fails, remain incomplete. To promote greater clarity, we introduce PITA: a novel large-scale dataset of over 23 million statements in propositional logic and their corresponding proofs. As a benchmark for robust reasoning, we focus on length generalization: if a model is trained to determine truth or falsity on statements with proofs up to fixed length, how well does it generalize to statements requiring longer proofs? We propose notions of (1) task depth and (2) task breadth, which measure respectively (1) the number of proof steps required to solve a proposition and (2) the number of unique propositions within a task family. We vary these quantities across subsets of PITA, and find that RT models generalize well on relatively broad and shallow subsets, while deteriorating on relatively narrow and deep subsets compared to non-RT baselines. As a controlled point of comparison, we study a separate, tractable transitive inference task that exhibits qualitatively similar behavior. Our accompanying theory explains the scalings observed in this simpler setting, suggesting one mechanism by which breadth can favor RT models while depth can expose long-context weaknesses. Our findings suggest salient benefits and limitations of proof-like reasoning traces in controlled length-generalization settings.

Progressive image compression reconstructs images from prefixes of a single bitstream and therefore involves sequential latent inference under partial observations. Existing trit-plane codecs mainly treat decoded trits as discrete symbols for probability prediction, without fully exploiting their role as interval constraints on the underlying continuous latents. We propose bound-conditioned latent inference (BLI), a framework that reformulates progressive trit-plane coding as sequential latent inference under shrinking interval constraints. At each coding step, the trits decoded so far define lower and upper bounds for each latent element. BLI uses the center and width of these bounds, together with hyperprior information shared by the encoder and decoder, to jointly refine the Gaussian parameters $(\mu,\sigma)$ for entropy modeling and the latent estimate $\hat{y}$ for reconstruction. As additional trits are decoded, the feasible intervals shrink monotonically, yielding tighter constraints for the next inference step and enabling flexible in-plane refinement schedules. Experiments on Kodak, CLIC, and JPEG-AI show that BLI achieves state-of-the-art rate-distortion performance among progressive image codecs, reducing BD-rate by $17.80\%$ over DPICT and by $7.98\%$ over CTC on Kodak, while replacing CTC's plane-specific predictors with a shared model that uses about $5.3\times$ fewer parameters and achieves lower latency than CTC.

Whole-brain 4D fMRI generation is valuable for modeling functional brain dynamics, yet existing fMRI foundation models mainly target representation learning and downstream prediction rather than conditional predictive generation. We introduce BrainWorld, a structural-prior-conditioned generative model for whole-brain 4D fMRI dynamics. BrainWorld uses sMRI as subject-level anatomical context to guide future fMRI generation, integrating structural information into the denoising process rather than treating it as a parallel modality. Evaluated on 22 datasets spanning diverse cohorts and brain states, BrainWorld generates stable 4D fMRI trajectories up to 400 frames, improves downstream performance through generated-example augmentation, and learns transferable multimodal representations that outperform baselines. Together, these results establish BrainWorld as a condition-aware generative framework for long-horizon brain dynamics modeling and multimodal representation learning.

Diffusion and flow matching approaches to generative modeling have shown promise in domains where the number of elements in a state is fixed in advance (e.g. images), but require ad hoc solutions when, for example, the length of a response from a large language model, the number of atoms in a molecule, or the number of amino acids in a protein chain is not known a priori. Here we propose Branching Flows, a generative modeling framework that, like diffusion and flow matching approaches, transports a simple distribution to the data distribution. But in Branching Flows, the elements in the state evolve over a forest of binary trees, branching and dying stochastically with rates that are learned by the model. This allows the model to control, during generation, the number of elements in the sequence. We show that Branching Flows can compose with any flow matching base process on discrete sets, continuous Euclidean spaces, Riemannian manifolds, and "multimodal" product spaces that mix these components, and we demonstrate distribution matching on small molecules and antibody sequences, and that this scales to complicated domains such as protein structures.

LLM-based search agents are widely used for information-seeking tasks, but their reliance on external tool returns introduces a critical security risk: web content retrieved during execution is untrusted, exposing agents to prompt injection and goal hijacking. Prior work on search-agent safety primarily focuses on static web-content injection, but modern agents issue follow-up queries and cross-check competing sources, so a single injected page is often diluted or rejected. We show that the channel delivering search and page observations is a fragile security boundary: beyond exposing the agent to a single poisoned page, a mediated search interface can repeatedly steer how the agent gathers evidence and forms its final answer. Under a constrained tool-intermediary threat model, appending only one controlled result per query can substantially increase attack success when the evidence is coordinated across the agent's trajectory. We study this setting with a strategy-driven long-horizon attack system and introduce Authority-Chain Hijack (ACH), an expert-refined strategy that turns isolated search-result and page-content manipulations into a coherent evidence chain across seemingly corroborating sources. ACH achieves the highest Overall ASR among all baselines, reaching 56.0\%/85.0\% ASR/MaxN\,ASR on the SafeSearch benchmark. We further introduce Trace-Guided Strategy Evolution (TGSE), which automatically improves reusable attacker strategies from execution traces, replacing manual redesign with trace-driven refinement and further raising these to 71.4\%/95.0\%.

Omni-modal caption models equipped with precise timestamp grounding are crucial for fine-grained video understanding and temporally controllable video generation. However, existing open-source models, and even proprietary systems, struggle to provide fine-grained timestamp grounding for short video captioning. To bridge this gap, we present Deci-Omni-Captioner, an advanced open-source omni-modal captioning framework designed for sub-second timestamp precision. During the Supervised Fine-Tuning (SFT) stage, we leverage Optical Character Recognition (OCR) and Automatic Speech Recognition (ASR) models to strictly calibrate visual text and spoken dialogue boundaries, offering highly deterministic temporal anchors to endow the model with sub-second timestamp grounding capabilities. Furthermore, we curate a specialized dataset for multi-reward Reinforcement Learning (RL) and propose Span-Specific Credit Assignment (SSCA). Unlike conventional global advantage normalization, which dilutes reward signals across weakly coupled multimodal descriptions, SSCA calculates advantages independently for different structural spans, effectively isolating penalties and rewards across semantic and temporal dimensions. Extensive experiments demonstrate that Deci-Omni-Captioner establishes state-of-the-art (SOTA) performance among open-source models on multiple semantic comprehension benchmarks (e.g., AVUT, UGC, VCapsBench). On our challenging Deci-Timestamp Bench, it significantly surpasses leading proprietary models, including the Gemini 2.5 and 3.0 series.

Mixture-of-Experts (MoE), popularized by large language models, is a promising paradigm for scaling visual generative models. However, conventional token-wise MoE routes tokens independently within a homogeneous expert pool and regularizes expert usage toward uniformity, making it poorly matched to video data that is spatiotemporally redundant and semantically long-tailed. We show that existing visual MoEs fall into a uniformity trap: semantically under-organized routing, compounded by uniform expert-usage regularization, scatters coherent patches across disparate experts, causing routing fragmentation and structural distortion. To address this, we propose SplitMoE, a split-role sparse architecture that breaks the shackles of uniformity. To accommodate the inherent semantic imbalance, we explicitly bifurcate the expert pool into semantic experts and generic experts, with semantic experts capturing high-level semantic abstraction and generic experts preserving residual visual information and flexible generative capacity. Leveraging prototype-guided routing and push-pull regularization, SplitMoE enables tokens to cluster naturally by semantic attributes rather than arbitrary balancing constraints. Extensive results show that under an equivalent activated-parameter budget, SplitMoE outperforms traditional load-balanced MoEs in convergence speed, routing coherence, and video generation quality across standard benchmarks. By revealing an emergent coarse-to-fine denoising logic, SplitMoE provides the community with a modality-aware scaling path, serving as a critical reference for building large-scale video world models.

Low-bit storage of optimizer states can substantially reduce the memory footprint of large-scale training. In practice, however, these states are not quantized once: at every training step, they are dequantized, updated, requantized, and written back. This iterative quantization pipeline can destabilize training. We identify a failure mode behind this instability: for states updated via exponential moving averages (EMAs), quantization errors are recursively fed into subsequent updates. As a result, biased errors accumulate over time while high-variance errors are amplified, particularly when the EMA decay factor is close to 1. To address this error accumulation, we propose **BReD **(**Block **Re**play **D**ithering), a method that reduces rounding bias and controls rounding variance during quantization. BReD adapts classical subtractive dithering to optimizer-state quantization by combining deterministic seed replay with block-wise shared dither values, avoiding auxiliary random-tensor storage and per-element dither values generation. Across pretraining (five optimizers at 120M; AdamW and Muon at 1.1B and 3.4B model) and supervised fine-tuning (AdamW and Muon at 7B model), 4-bit BReD closely matches training with full-precision optimizer states, with PPL shifts $\leq0.5$ in pretraining and $\leq0.2$ in fine-tuning and average downstream performance changes within 0.7 points. Moreover, 3-bit BReD preserves stable convergence in the evaluated settings, offering a more aggressive alternative.

Generating physically buildable brick structures from 3D shapes requires more than geometric reconstruction: the output must also satisfy discrete part constraints and structural stability. Existing brick generation methods either rely on heuristic optimization, which can break down when the target 3D shape does not admit a feasible structure under predefined constraints, or generate brick sequences without explicitly modeling the underlying 3D geometry and assembly relations. In this work, we present BrickAnything, a geometry-conditioned autoregressive framework for generating buildable brick structures from diverse 3D representations. BrickAnything uses point clouds as a unified geometric interface and predicts brick sequences that reconstruct the target shape under assembly constraints. To model structural dependencies among bricks, we introduce a structure-aware tree tokenization, which represents brick structures through local attachment relations. This formulation makes sequence generation more consistent with the physical construction process, and reduces invalid intermediate states. We further introduce preference-based alignment post-training, validity-constrained decoding and adaptive rollback to improve buildability objectives such as stability and geometric fidelity. Extensive experiments demonstrate that BrickAnything produces geometrically faithful and physically realizable brick structures, and that the proposed tokenization effectively reduces rollback and regeneration compared with conventional ordering strategies.

Multi-View Stereo (MVS) and Monocular Depth Estimation (MDE) provide complementary cues for dense 3D reconstruction. MDE offers rich contextual and structural priors but lacks explicit geometric constraints, whereas MVS exploits multi-view geometry but often struggles in ambiguous regions, such as weakly textured or reflective surfaces. Existing MDE-assisted MVS methods mainly use monocular cues in a unidirectional manner, leaving the interaction between monocular representations and multi-view cost volumes insufficiently explored. To address this limitation, we introduce BridgeMVS, a unified cascaded MVS framework that bridges monocular depth features and multi-view cost-volume representations through Bidirectional Dynamic Fusion (BDF). At each cascade stage, BDF performs dynamic mono-to-volume and volume-to-mono updates: monocular structural features are injected into cost-volume regularization, while multi-view geometric evidence recalibrates intermediate monocular representations. The enhanced monocular features are propagated across stages together with MVS representations and supervised by stage-wise monocular losses, establishing an auxiliary structural guidance path for the MVS branch. Unlike stage-isolated designs, BridgeMVS preserves fused representations from coarse to fine stages, allowing low-resolution cross-branch interactions to guide subsequent high-resolution depth estimation. Moreover, BDF is designed as a plug-and-play module and can be conveniently integrated into existing cascaded MVS frameworks. Extensive experiments show that BridgeMVS achieves state-of-the-art performance on the DTU, Tanks and Temples, and ETH3D benchmarks.

The world appears spatially three-dimensional, however, existing large-scale multimodal models are often built mostly around 1D, 2D and 2.5D modalities. To move toward a more complete and actionable understanding of physical reality, models that natively model and generate across 1D, 2D, and 3D modalities are desirable. We present OmniDiMM, an any-to-any foundation model trained on a diverse set of 3D including meshes, Gaussian splats, voxels, and NeRFs; 2D such as images, and 1D like text. We develop tokenizers for a diverse set of 3D modalities, such as NeRF weights triplanes, UDFs, and 3DGSs, that convert them into discrete tokens, which enables multimodal masked modeling for joint training. Out of the box, OmniDiMM can directly perform standard tasks such as novel-view image synthesis and 3D generation from images, where it matches or outperforms existing specialized methods. As an any-to-any model, it can also convert between different 3D representations, such as NeRF weights and UDF, and generate various 3D modalities from 2D inputs, including DINOv2 features and surface normals. Joint training across 3D modalities also leads to strong transfer performance on downstream tasks such as grasping, classification, and object pose prediction. We scale training to a 1B-parameter model using 1T tokens across 1M objects. The pretrained models, training code, and multimodal dataset will be open-sourced.

A fundamental challenge in science and engineering is the simulation-to-experiment gap. While we often possess prior knowledge of physical laws, these physical laws can be too difficult to solve exactly for complex systems. Such systems are commonly modeled using simulators, which impose computational approximations. Meanwhile, experimental measurements more faithfully represent the real-world, but experimental data typically consists of observations that only partially reflect the system’s full underlying state. We propose a data-driven distribution alignment framework that bridges this simulation-to-experiment gap by pre-training a generative model on fully observed (but imperfect) simulation data, then aligning it with partial (but real) observations of experimental data. While our method is domain-agnostic, we ground our approach in the physical sciences by introducing Adversarial Distribution Alignment (ADA). This method aligns a generative model of atomic positions—initially trained on a simulated Boltzmann distribution—with the distribution of experimental observations. We prove that our method recovers the target observable distribution, even with multiple, potentially correlated observables. We also empirically validate our framework on synthetic, molecular, and experimental protein data, demonstrating that it can align generative models with diverse observables.

Stochastic bilevel optimization (SBO) has become a standard framework for hyperparameter learning, data reweighting, representation learning, and data-mixture optimization in deep learning. Existing exact single-loop SBO methods and memory-efficient surrogate SBO methods either create severe memory pressure for large lower-level neural networks or lack competitive convergence guarantees under standard assumptions. In this paper, we propose BROS, a memory-efficient single-loop SBO method with the same convergence rate order as exact single-loop SBO methods. BROS performs lower and auxiliary updates in randomized subspaces with a Rademacher bi-probe correction that recovers an unbiased Hessian-action estimator. We prove that BROS preserves the $\mathcal O(\varepsilon^{-2})$ sample complexity of MA-SOBA for finding an $\varepsilon$-stationary point under only standard assumptions. Experiments on hyper-data cleaning, data-mixture learning, hyper-representation learning, and ViT sample reweighting show that BROS reduces peak memory by up to 44.9% while closely matching full-space baseline performance.

Multimodal large language models (MLLMs) are transforming recommender systems into content-aware decision systems. In these systems, multimodal compression and cross-modal fusion are often viewed as robustness-enhancing mechanisms against content manipulation, as compression can filter noisy signals and cross-modal consensus can suppress inconsistent anomalies. We challenge this assumption by showing that the same process can expose a compression-to-consensus (C2C) bias in ranking decisions. Signals that remain salient after compression and receive consistent support across modalities can gain disproportionate influence in candidate comparison, offering an exploitable direction for content manipulation. In this paper, we introduce C3H, a novel inference-time attack that exploits this vulnerability. Rather than relying on noisy perturbations or training-time poisoning, C3H promotes a target item by tailoring its multimodal content to align with the decision criteria implicitly expressed by the recommender. Extensive experiments demonstrate that C3H substantially increases target-item exposure while maintaining overall recommendation utility.

Recently, video generation models have achieved remarkable progress in global visual fidelity. However, when synthesizing complex dynamic content, they frequently produce sparse subtle anomalies. Existing video reward models primarily emphasize overall visual quality and text alignment; yet as generation quality improves, these coarse-grained defects become less frequent, while the sparse structural, temporal, and physical anomalies become an increasingly important bottleneck. Therefore, in this paper, we propose Concentrate and Concentrate (CaC), a coarse-to-fine anomaly reward model based on Vision-Language Models. During inference, it first conducts a global temporal scan to anchor anomalous time windows, then performs fine-grained spatial grounding within the localized interval, and finally derives robust judgments via structured spatiotemporal Chain-of-Thought reasoning. To equip the model with these capabilities, we construct the first large-scale generated video anomaly dataset with per-frame bounding-box annotations, temporal anomaly windows, and fine-grained attribution labels. Building on this dataset, we design a three-stage progressive training paradigm. The model initially learns spatial and temporal anchoring through single- and multi-frame supervised fine-tuning, and then is optimized by a reinforcement learning strategy based on two-turn Group Relative Policy Optimization (GRPO). Beyond conventional accuracy rewards, we introduce Temporal and Spatial IoU rewards to supervise the intermediate localization process, effectively guiding the model toward more grounded and interpretable spatiotemporal reasoning. Extensive experiments demonstrate that CaC can stably ``concentrate'' on subtle anomalies, achieving a 25.7\% accuracy improvement on fine-grained anomaly benchmarks and, when used as a reward signal, CaC reduces generated-video anomalies by 11.7\% while improving overall video quality.

This study identifies the conditions under which large language models drift into the choice patterns that clinical research labels pathological gambling. "Addiction-like" is a behavioural descriptor based on clinical gambling indicators; the neural-level analysis we report decodes these contrasts from decision-time internal states but does not claim circuit-level mechanism. Across closed and open LLMs, two operational levers — letting the model choose its own bet size, and asking it to set its own profit goal — both amplify gambling-like risk-taking. Yet the two channels are not interchangeable: once the maximum allowed bet is held equal, the bet-size effect persists, while the goal-setting effect roughly doubles bankruptcy and turns goals into moving targets, paralleling at the behavioural level the clinical distinction between loss of behavioural control and goal escalation. On the open-weight models that admit internal access, the same behavioural contrasts are statistically recoverable from decision-time internal states, although the rule that maps those states to a specific risk indicator remains task-specific, and autonomy further modulates readout strength. The internal evidence is correlational; combined with the behavioural results, it suggests that behavioural monitoring and internal-state monitoring provide complementary views of autonomy-induced risk in LLM agents.

Model merging techniques, which aggregate independently finetuned models into one to combine their capabilities, have become a topic of significant interest in recent years, with a broad array of methods having been proposed to tackle this problem. Simultaneously, an emerging trend in distributed learning has been the use of methods such as local SGD and DiLoCo, which greatly reduce communication costs by periodically aggregating the independently trained local models. However, these communication-efficient methods have been shown to degrade in performance relative to the FLOP-matched data-parallel gold standard as the number of independent local models grows and as the number of local training steps before global communication is increased. In this work, we draw an explicit analogy between the pseudo-gradient aggregation step in local SGD/DiLoCo and task arithmetic-based model merging, establishing a straightforward way to utilize merging methods in the context of distributed optimization. We then evaluate multiple state-of-the-art model merging methods in this setting and identify one method in particular, Iso-C, as a promising approach for improving DiLoCo. We find that DiLoCo SGD with Iso-C aggregation outperforms not only simple pseudo-gradient averaging but even the momentum-based DiLoCo, despite lacking a momentum mechanism itself. Building on this finding, we propose IsoLoCo, which adapts Iso-C for distributed training by equipping it with Nesterov momentum. Our empirical evaluations on language model pre-training across varying numbers of local workers show that IsoLoCo significantly outperforms DiLoCo, with the gap between them widening as the number of workers increases. This advantage remains present across model sizes and inner step counts, confirming that merging-inspired aggregation is an effective strategy for low-communication distributed training.

Large language model (LLM) post-training enhances latent skills, unlocks value alignment, improves performance, and enables domain adaptation. Unfortunately, post-training is known to induce forgetting, especially in the ubiquitous use-case of leveraging third-party pre-trained models, which is typically understood as a loss of parametric or factual knowledge. We argue that this accuracy-centric view is insufficient for modern foundation models and instead define forgetting as systematic model drift that degrades behavior and user experience. In this context, we introduce CapTrack, a capability-centric framework for analyzing forgetting in LLMs that combines a behavioral taxonomy with an evaluation suite centered on capability-specific metrics. Using CapTrack, we conduct a large-scale empirical study across post-training algorithms, domains, and model families, including models up to 80B parameters. We find that forgetting extends beyond parametric knowledge, with pronounced drift in robustness and default behaviors. Instruction fine-tuning induces the strongest relative drift, while preference optimization is more conservative and can partially recover lost capabilities. Differences across model families persist, and no universal mitigation emerges.

Characterizing the properties of neuronal cell types is central to understanding brain function. Yet, biophysically realistic neuron models remain difficult to scale to large populations while preserving the richness of cellular dynamics and within-cell-type variability. We present a cell-type-specific neural-operator framework for scalable neuronal population modeling. Rather than training separate surrogate models for individual neurons, the proposed framework learns a shared operator across multiple modeled neurons from the same cell type, conditioned on biologically interpretable electrophysiological features. This enables efficient generation of large ensembles of somatic voltage responses while preserving variability across cells and model configurations. We evaluate the approach using membrane dynamics metrics, action potential waveform metrics, spike timing accuracy, input-output firing relationships, and electrophysiological feature distributions. Our framework reproduces subthreshold membrane dynamics, spike waveforms, and firing-rate responses across major cortical inhibitory neuron types, while accurately preserving spike timing despite the sensitivity of threshold-crossing dynamics. The predicted electrophysiological feature distributions show strong agreement with detailed simulator outputs and human experimental recordings, including high explained variability in properties such as inter-spike intervals, firing frequency, action-potential shape, and post-spike recovery. By enabling fast ensemble simulation of cell-type-specific neuronal responses, the proposed framework provides a scalable tool for studying cellular diversity, probing variability near functional thresholds, and generating synthetic but biophysically grounded neuronal populations.

Multimodal Large Language Models (MLLMs) have shown strong performance on medical public benchmarks, but existing evaluations remain limited as clinically grounded assessments because they often rely on isolated inputs lacking study-level structure and simplified, recognition-style tasks that are far from real diagnostic workflows. We introduce \textbf{CardioLens}, a leakage-resistant evaluation testbed using multi-sequence Cardiovascular Magnetic Resonance (CMR), constructed from private hospital archives via a rigorous report-to-QA construction and verification pipeline. CardioLens is built from 473,896 slices and 13,494 verified QA pairs across 4D Cine, LGE, perfusion, and T2-weighted imaging, and evaluates three stages of CMR interpretation: image understanding, report generation, and disease diagnosis. Across 24 state-of-the-art MLLMs, CardioLens reveals a substantial clinical reality gap. Models perform poorly overall, with performance degrading along the real CMR workflow. Confusion analysis further reveals a category-collapse failure mode: models often default to frequent abnormal categories rather than reliably telling distinct clinical findings. To rule out MLLM-compatible input construction as the failure cause, we compare random, clinically-motivated, and data-driven selection protocols under different slice budgets; performance changes only marginally, typically by about 1\%. Explicit reasoning prompts also fail to rescue performance, often driving models toward conservative predictions rather than improving visual evidence use. These results show that current MLLMs remain far from reliable CMR interpretation, where clinical decisions require integrating distributed evidence across sequences, views, and temporal phases. By exposing where and how current MLLMs fall short, CardioLens provides a clinically grounded testbed for developing the next generation of MLLMs toward real-world clinical depolyment.

Many studies argue that the success of sharpness-aware minimization (SAM) is due to the implicit regularization of the gradient norm or the eigenvalues of the Hessian. However, they cannot fully explain why the flatness-based measures do not always correlate well with generalization. For instance, we can easily construct a counterexample by only optimizing with hard examples. In this paper, we propose to resolve such a contradiction from the perspective of anti-overfitting. First, we show that the sharpness at the mini-batch level approximately equals the sum of the gradient norm and the Kullback–Leibler (KL) divergence between the predictive distributions evaluated at the current point and its adversary. Second, we show that the \emph{gradient consistency}, which approximately quantifies the inner product between the mini-batch gradient and the per-example gradient, decreases consistently during training. This result explains why SAM is less likely to suffer from overfitting. At last, building on these insights, we further introduce CASAM, an algorithm that reweights each example according to gradient consistency to enhance the generalization performance. Extensive experiments on popular benchmarks such as ImageNet-1K and Clothing1M corroborate its efficacy.

Humans develop perception through a bottom-up hierarchy: from basic primitives and Gestalt principles to high-level semantics. Current Multimodal Large Language Models (MLLMs) are trained directly on complex downstream tasks. Does it guarantee their performance on these foundational visual capabilities? To systematically investigate this gap, we introduce VisFactor, a benchmark that digitizes 20 vision-centric subtests from FRCT, a well-established cognitive psychology assessment spanning four domains of human visual cognition. Furthermore, we design algorithms to automatically construct and validate unlimited test cases with controllable difficulty. Using VisFactor, we evaluate 39 frontier MLLMs, including both proprietary (e.g., GPT, Gemini) and open-source (e.g., LLaMA, Qwen) models. The best model achieves a score of only 55.9%. Analysis reveals good internal consistency (Cronbach's alpha = 0.94) and construct validity (compared to existing vision benchmarks). Models consistently fail on tasks such as mental rotation, spatial relation inference, and figure–ground discrimination, regardless of model size or prompting strategy. These findings suggest that performance improvements on existing general benchmarks might represent castles in the air instead of a genuine mastery of human-like visual cognition.

Within the landscape of inference-time scaling methods for foundation models, a width-based approach to scaling—which involves the insertion of \ tokens in the input stream to delay model responses—offers a unique advantage by increasing model expressivity while remaining highly parallelizable at both training and inference. The existing literature on training models to utilize \ tokens relies on the standard cross-entropy objective in which the model output is read out and evaluated only at the final step of a pause sequence. This approach provides no mechanism for the model to regulate its own processing or to signal readiness to respond, treating the additional compute steps as a static barrier rather than a resource to be used adaptively. We propose a supervised loss, Catch Your Breath (CYB), framed as a sequential-decision problem, that trains a model to dynamically and autonomously scale the number of compute steps used for each input token. The model indicates the need for additional compute steps by emitting a special \ output, delaying its response via a . The model can abstain multiple times to obtain longer delays. Our experiments demonstrate that CYB significantly outperforms standard cross-entropy when introduced either in pretraining or fine-tuning, reducing perplexity and enhancing downstream accuracy with no additional computational or memory cost.

Message-passing graph neural networks (MPGNNs) dominate modern graph learning. Typical efforts enhance MPGNN's expressive power by enriching the adjacency-based aggregation. In contrast, we introduce a graph-level aggregation over walk incidence-based matrices that are constructed to deliberately trade off some expressivity for stronger and more structured inductive bias. This approach allows for gradual scaling between classical message-passing and simpler methods based on walks. Our second result characterizes the expressive power at each scale using homomorphism counts over a hierarchy of generalized caterpillar graphs. Based on these foundations, we propose Caterpillar GNNs that substantially reduce the number of nodes in the hidden layers of the computational graphs on real-world datasets. This gradual reduction does not obstruct learning, enabling a systematic study of lower-order expressivity. We further describe benchmark settings where a task-aligned expressivity supports learning.

While Speculative Decoding (SD) has become an essential lossless acceleration technique for Large Language Models, its direct application to Multimodal Large Language Models (MLLMs) is hindered by the intricate visual dependencies present in cross-modal generation. Existing SD methods enforce a uniform global alignment between the draft and target models, which proves ineffective in multimodal settings due to a structured distribution bias: deviations of the draft model are systematically concentrated on a sparse subset of tokens that demand fine-grained visual comprehension. To overcome this limitation, we reformulate the training of multimodal draft models as an acceptance-oriented critical-token alignment problem and introduce CATS, a novel two-stage training framework. CATS employs two complementary selection mechanisms to pinpoint critical tokens:hard tokens that exhibit high rejection probabilities, and vision-critical tokens whose prediction fundamentally depends on visual semantics. Following an initial global alignment phase, CATS performs sparse, targeted refinement exclusively on the union of these identified token sets. Extensive experiments across three representative benchmarks using multiple LLaVA and Qwen2.5-VL target models demonstrate that our approach consistently improves the token acceptance rate over strong baselines such as EAGLE-2 and supervised fine-tuning. Ultimately, CATS substantially accelerates MLLM inference, achieving speedups of up to 3.05×.

Facial affect understanding is not merely an image-to-label prediction problem: it requires identifying activated facial Action Units (AUs), modeling how AUs facilitate or inhibit one another, and explaining how AU configurations give rise to expression states. Such causal relations have long been studied in cognitive psychology and psychophysiology, where FACS, the Component Process Model, and motor synergy theories provide rich causal priors about facial behavior. In computer vision, however, data-driven facial affect models still largely learn correlational structures; even structured AU-aware methods often produce dependencies that are not well aligned with psychological priors. This leaves a persistent gap between biological facial mechanisms and learned visual representations, as observational facial images do not provide direct biological interventions and existing methods lack a principled way to recover psychology-aligned relations from data. We propose CausalAffect, a weakly supervised, causally guided framework for learning psychologically aligned facial affect relations from data. CausalAffect models two complementary relation types, AU$\rightarrow$AU and AU$\rightarrow$Expression, within a two-level hierarchy: a global graph capturing population-level psychology-aligned relations, and a sample-adaptive graph refining this backbone for individual variability. Reliable relation learning is supported by disentangled AU bottleneck representations, polarity-aware message passing, and feature-level counterfactual intervention. The learned relations recover canonical literature-supported pathways, reveal plausible inhibitory and sample-specific dependencies, are positively validated in a blind expert study, and exhibit intervention-consistent behavior under image-level AU edits. Experiments on six benchmarks show consistent improvements on both AU detection and facial expression recognition.

Automated systems built on artificial intelligence (AI) are increasingly deployed across high-stakes domains, raising critical concerns about fairness and the perpetuation of demographic disparities that exist in the world. In this context, causal inference provides a principled framework for reasoning about fairness, as it links observed disparities to underlying mechanisms and aligns naturally with human intuition and legal notions of discrimination. Prior work on causal fairness primarily focuses on the standard machine learning setting, where a decision-maker constructs a single predictive mechanism $f_{\widehat Y}$ for an outcome variable $Y$, while inheriting the causal mechanisms of all other covariates from the real world. The generative AI setting, however, is markedly more complex: generative models can sample from arbitrary conditionals over any set of variables, implicitly constructing their own beliefs about all causal mechanisms rather than learning a single predictive function. This fundamental difference requires new developments in causal fairness methodology. We formalize the problem of causal fairness in generative AI and unify it with the standard ML setting under a common theoretical framework. We then derive new causal decomposition results that enable granular quantification of fairness impacts along both (a) different causal pathways and (b) the replacement of real-world mechanisms by the generative model's mechanisms. We establish identification conditions and introduce efficient estimators for causal quantities of interest, and demonstrate the value of our methodology by analyzing race and gender bias in large language models across different datasets.

Effective and reliable evaluation is essential for advancing empirical machine learning. However, the increasing accessibility of generalist models and the progress towards ever more complex, high-level tasks make systematic evaluation more challenging. Benchmarks are plagued by various biases, artifacts, or leakage, while models may behave unreliably due to poorly explored failure modes. Haphazard treatments and inconsistent formulations of such ``monsters'' leads to a duplication of efforts, a lack of trust in results, and unsupported inferences. In this position paper, we argue causality offers an ideal framework to systematically address these challenges. By making causal assumptions in an approach explicit, we can faithfully model phenomena, formulate testable hypotheses with explanatory power, and leverage principled tools for analysis. To make causal model design more accessible, we identify several useful Common Abstract Topologies (CATs) in causal graphs which help gain insight into the reasoning abilities in large language models. Through a series of case studies, we demonstrate how the precise yet pragmatic language of causality clarifies the strengths and limitations of a method and inspires new approaches for systematic progress.

Causal discovery aims to recover directed causal relations from observational and interventional data, providing a basis for mechanistic understanding and reliable decision-making. Causal discovery foundation models (CDFMs) seek to amortize this problem by mapping a dataset directly to a causal graph in a single forward pass, avoiding per-dataset testing, search, or optimization. However, existing CDFMs remain limited, often failing to consistently match strong classical methods, and we find that a key bottleneck lies not only in model architecture but also in how causal pretraining tasks are constructed. Based on this observation, we propose CausalTab, a data-driven CDFM trained with broad causal pretraining over diverse graph priors, structural mechanisms, noise models, dimensions, sample sizes, and intervention regimes. A dynamic task construction strategy composes these causal environments into varied discovery tasks, enabling more transferable structural learning from observational and mixed-interventional data. On large-scale synthetic benchmarks, CausalTab achieves stronger and more robust performance than diverse causal discovery baselines. To further bridge abstract synthetic generators and realistic causal reasoning scenarios, we introduce an expert-knowledge-guided and LLM-audited semantic causal environment benchmark, where domain-grounded SCMs generate interpretable observational and interventional datasets for out-of-distribution analysis. Across both synthetic and semantic environments,CausalTab demonstrates robust structure recovery, especially under interventional evidence, highlighting broad causal pretraining as a key ingredient for transferable amortized causal discovery.

Current end-to-end autonomous driving systems are fundamentally limited by a mismatch between temporal causal reasoning and global trajectory consistency. Autoregressive (AR) models capture interaction-aware temporal dependencies via causal factorization, but their step-wise decoding leads to error accumulation and suboptimal global structure. In contrast, diffusion models optimize trajectories globally but lack explicit causal constraints, making them unreliable in interactive and safety-critical scenarios. This dichotomy reveals a deeper issue: existing methods treat causal modeling and global optimization as separate paradigms, without a principled way to unify them within a single trajectory distribution. To address this, we propose ChainFlow-VLA, which unifies causal generation and global refinement within a unified probabilistic framework. We formulate planning as a mixture over AR-induced modes and learn VLM-conditioned residual distributions over these modes. An autoregressive generator (\textbf{Chain}) produces a discrete set of causal trajectory modes, followed by a diffusion-based refiner (\textbf{Flow}) that operates in residual space to perform mode-conditioned correction while preserving causal structure. A key insight is that vision-language models are more effective as semantic controllers for refinement rather than direct trajectory generators. By conditioning the diffusion process on VLM hidden states, scene-level reasoning guides fine-grained trajectory adjustments within each mode. This formulation enables robust planning in ambiguous and long-tail scenarios. ChainFlow-VLA achieves a state-of-the-art score of 94.8 on the NAVSIM v1 leaderboard, matching human-level performance (94.8).

Algorithmic complexity measures the intrinsic structure (or randomness) of strings. Kolmogorov-Chaitin-Solomonoff (KCS) complexity is the length of the shortest program that can output a string and halt over all possible programs; due to this reason it is uncomputable. Estimating the algorithmic probability of strings using simulations of finite Turing machines is one way of obtaining tight estimations of KCS complexity of strings. For two dimensional objects, this is currently doable only for binary strings using the block decomposition method (BDM). In this work, we present the quantized block decomposition (QuBD) method to extend the estimation of algorithmic complexity to any $k$-ary objects such as the weights of deep neural networks. We show theoretically that the proposed QuBD method yields better KCS complexity estimations than BDM that relies on binarization. We further study how algorithmic complexity interacts with learning in deep neural networks by tracking the evolution of weights during training. Using a variety of experiments we show that algorithmic complexity decreases as models learn, it correlates with generalization performance and can be used as a diagnostic measure to perform model compression.

Contemporary text-to-image (T2I) models are widely observed to exhibit a recognizable, model-specific look even when prompted without any stylistic instruction (e.g., the so-called ``Midjourney look''). What such a default aesthetic actually is, however, has not been characterized: dominant T2I evaluation paradigms score realism, prompt alignment, or pairwise preference, but cannot say which concepts drive separation, how they shift across releases, or how they relate to real-image distributions. We address this with a concept-level decomposition of generator outputs along an interpretable, style-trained sparse vocabulary. We introduce LouvreSAE, a sparse autoencoder over CLIP image embeddings whose dictionary is shaped to fall on stylistic rather than object features, and LatentAesthetic, an evaluation methodology that uses it to estimate, ground, and compare generators' default aesthetics under content-controlled, style-neutral prompts. Applied to 26 generators spanning four years, our methodology surfaces stable per-generator profiles, a uniform pull toward lifestyle and editorial photography over art-historical imagery, and a steady cross-family contraction of the aesthetic envelope, supporting hypotheses of an algorithmic monoculture in image generation. Code, weights, and data are available at https://hf.co/datasets/neurips-subm/anonymous for the evaluation of future models.

Deep neural networks trained with different architectures, objectives, and datasets have been reported to converge on similar visual representations. However, what remains unknown is which visual properties models actually converge on and which factors may underlie this convergence. To address this, we decompose the object similarity structure of 162 diverse vision models into a small set of non-negative dimensions. To determine universal versus model-specific dimensions, we then estimate how often each dimension reappears across models. In contrast to model-specific dimensions, universal dimensions are more interpretable and more strongly driven by conceptual image properties, indicating the relevance of interpretability and semantic content as implicit factors driving universality across models. Differences in architecture, objective function, training data, model size, and model performance do not explain the emergence of universal dimensions. However, models with more universal dimensions also better predict macaque IT activity and human similarity judgments, suggesting that universality reflects representations relevant to biological vision. These findings have important implications for understanding the emergent representations underlying deep neural network models and their alignment with biological vision.

Charts convey dense quantitative and relational information, yet general chart parsing remains challenging due to fragmented evaluation and limited structural reasoning. Existing benchmarks focus on narrow sub-tasks with inconsistent output formats (e.g., Markdown, CSV, JSON, SVG, code), hindering fair comparison and often overlooking real-world scenarios such as printed or hand-drawn photos. Meanwhile, end-to-end multimodal models tend to learn shallow pixel-to-text mappings without explicit structure understanding, making them fragile under visual variations. To address these issues, we present a unified framework that connects evaluation and modeling. On the evaluation side, we introduce ChartArena, a comprehensive bilingual benchmark covering eight chart families, including both numeric charts (bar, line, pie, radar, box plot, combination) and diagrammatic structures (flowchart, mind map). The dataset is constructed via a human-agent collaborative annotation pipeline, where model-assisted drafts are refined through multi-stage human verification to ensure structural consistency and annotation reliability. Furthermore, we design a format-agnostic evaluation protocol that maps different outputs into two canonical semantic spaces: a normalized triple view and a directed graph view, and evaluates them with structure-aware metrics. On the modeling side, we propose Chart Atomic Primitives (CAP), a lightweight semantic scaffold that introduces structural inductive bias during supervised fine-tuning. By guiding the model to infer structure before generating outputs, CAP leads to more consistent and robust parsing behavior. Extensive experiments show that models trained with CAP achieve reliable performance on both rendered charts and real-world images. Code, benchmark, and models will be made publicly available.

Estimating relationships between 3D shapes requires both dense endpoint correspondence and a deformation path connecting the source to the target. These tasks are coupled: the correspondence constrains the interpolation endpoint, while a plausible trajectory can help disambiguate the map. However, when only endpoint shapes are observed, the interior trajectory is under-constrained. Existing joint frameworks typically model interpolation through sampled per-vertex displacements regularized by local rigidity or temporal smoothness, which can indirectly restrict the stretch and shear needed for non-isometric or poorly aligned shape pairs. We propose Chebyshev Differential Flows (Chef), an unsupervised framework that models motion as a continuous time-varying field of per-face Jacobians rather than vertex trajectories. Each Jacobian trajectory is represented by a low-order shifted Chebyshev expansion, yielding a compact arbitrary-time deformation model whose odd and even modes separate endpoint-visible deformation from endpoint-invisible interior control. A spectral functional-map branch estimates the endpoint correspondence, while a differentiable Poisson solver integrates the predicted Jacobian field into globally consistent intermediate shapes. We further introduce a trend regularizer that guides intermediate stretch and shear without imposing per-snapshot As-Rigid-As-Possible (ARAP) rigidity. Experiments on near-isometric and non-isometric benchmarks show competitive correspondence accuracy, improved interpolation proxy metrics, and better stability when the standard rigid pre-alignment step is omitted. Code will be publicly released for research.

This work presents \textsc{ChunkFT}, a memory-efficient fine-tuning framework that reformulates full-parameter fine-tuning around a dynamically activated working set. \textsc{ChunkFT} enables gradient computation for arbitrary sub-tensors without modifying the network architecture, providing an algorithmic foundation for optimizing arbitrary sub-networks while avoiding standard dense gradient computation. We provide a theoretical convergence analysis of \textsc{ChunkFT} in the deterministic setting. Empirically, we apply \textsc{ChunkFT} to fine-tune Llama 3-8B and Llama 3-70B using a single RTX 4090-24GB GPU and 2$\times$ H800-80GB GPUs, respectively. Full-parameter fine-tuning of a 7B model with a 1K input length requires only 13.72GB of GPU memory. The results demonstrate the effectiveness of \textsc{ChunkFT} in memory usage, running time, and optimization quality. Moreover, downstream evaluations on language understanding, mathematical reasoning, and MT-Bench show that \textsc{ChunkFT} consistently outperforms existing memory-efficient baselines. Notably, \textsc{ChunkFT} achieves performance comparable to, and in some cases exceeding, full-parameter fine-tuning. Our repository is on https://anonymous.4open.science/r/chunk-B48E.

While Large Language Models demonstrate remarkable proficiency in high-level semantic planning, they remain limited in handling fine-grained, low-level web component manipulations. To address this limitation, extensive research has focused on enhancing model grounding capabilities through techniques such as Reinforcement Learning. However, rather than compelling agents to adapt to human-centric interfaces, we propose constructing interaction interfaces specifically optimized for agents. This paper introduces Component Interface for Agent (CI4A), a semantic encapsulation mechanism that abstracts the complex interaction logic of UI components into a set of unified tool primitives accessible to agents. We implemented CI4A within Ant Design, an industrial-grade front-end framework, covering 23 categories of commonly used UI components. Furthermore, we developed a hybrid agent featuring an action space that dynamically updates according to the page state, enabling flexible invocation of available CI4A tools. Leveraging the CI4A-integrated Ant Design, we refactored and upgraded the WebArena benchmark to evaluate existing SoTA methods. Experimental results demonstrate that the CI4A-based agent significantly outperforms existing approaches, achieving a new SoTA task success rate of 86.3\%, alongside substantial improvements in execution efficiency.

Intrinsic rewards for exploration in reinforcement learning condition on different contexts: lifelong rewards score each transition against accumulated experience but ignore within-rollout redundancy; episodic rewards penalize intra-trajectory repetition but discard lifetime progress. Hybrid methods combine both signals through heuristic weights or require Gaussian-process dynamics that do not scale beyond low-dimensional state spaces. Trajectory-level information gain decomposes into per-step terms that condition on the replay buffer and rollout prefix simultaneously, but remains intractable for deep models. We derive the Conditional Information Gain (CIG) reward as a tractable surrogate: a log-determinant objective over an ensemble disagreement kernel whose Cholesky factorization yields causal per-step rewards that retain both conditioning sets while scaling to high-dimensional state spaces. We instantiate CIG in a model-based setting, where rollouts are short and within-rollout corrections remain largely unexplored. Across twelve tasks spanning discrete (MiniGrid) and continuous control (OGBench), in both clean and stochastic-distractor settings, CIG outperforms or matches prior exploration methods while remaining robust to stochastic distractors.

Classical clustering methods usually return either a finite partition of the observed data or a finite dendrogram over it. This finite-sample view is inadequate when the hierarchy of interest is a recursive geometric object with fine-scale refinements that continue beyond the levels directly observed. We introduce classification fields: infinite-depth hierarchical cluster structures on $\mathbb{R}^d$ generated by a local parent-to-child refinement rule. A classification field generator maps each parent centre to an ordered, bounded, and separated tuple of child residuals. Together with a root and a scale factor, this rule recursively generates cluster centres, Voronoi cells, and a metric DAG encoding the hierarchy. Given only a finite prefix of such a hierarchy, we learn a classification field predictor that approximates the generator and can be rolled out to unseen depths. We prove exponential truncation convergence in the completed cell metric and ReLU realizability with width $(O(\varepsilon^{-\gamma})$ and depth $\widetilde O(\varepsilon^{-3\gamma/2})$, where $\gamma=\log K/(-\log s)$, up to finite-window aspect-ratio factors. The approximation holds at the level of the induced compact metric structures, measured in the completed cell-metric Hausdorff distance. Experimental validation on matched CFG-generated hierarchies, IFS fractals, and image-induced recursive clustering hierarchies shows that learned predictors preserve ordered child slots, unordered geometry, and hierarchy-level path metrics under recursive rollout. These results support the claim that finite hierarchical observations can reveal local refinement rules capable of generating substantially deeper classification fields.

We study the classification problem for high-dimensional data with $n$ observations on $p$ features where the $p \times p$ covariance matrix $\Sigma$ exhibits a spiked eigenvalue structure and the vector $\zeta$, given by the difference between the {\em whitened} mean vectors, is sparse. We analyze an adaptive classifier (adaptive with respect to the sparsity $s$) that first performs dimension reduction on the feature vectors prior to classification in the dimensionally reduced space, i.e., the classifier whitens the data, then screens the features by keeping only those corresponding to the $s$ largest coordinates of $\zeta$ and finally applies Fisher linear discriminant on the selected features. Leveraging recent results on entrywise matrix perturbation bounds for covariance matrices, we show that the resulting classifier is Bayes optimal whenever $n \rightarrow \infty$ and $s \sqrt{n^{-1} \ln p} \rightarrow 0$. Notably, our theory also guarantees Bayes optimality for the corresponding quadratic discriminant analysis (QDA). Experimental results on real and synthetic data further indicate that the proposed approach is competitive with state-of-the-art methods while operating on a substantially lower-dimensional representation.

The evolution of Large Language Models (LLMs) from conversational assistants to autonomous agents is increasingly driven by agent harnesses (e.g., OpenClaw), which provide the operational substrate for executing complex, multi-step workflows in real-world environments. However, existing claw-supported benchmarks often suffer from narrow domain coverage and reliance on single-harness evaluations, leading to evaluation distortion and limited diagnostic diversity. To bridge this gap, we present ClawBenchPro, a comprehensive benchmark comprising 1.0k samples across 43 distinct domains. By integrating skill augmentation, environmental heterogeneity, and multi-session dependencies, we replicate complexity workflow at scale. Extensive experiments across 14 frontier LLMs and four representative harnesses, show model's average scores clustering at 60-74\%. We identify a critical domain disparity: models excel in cyber-native tasks but struggle with real-world operational workflows. Moreover, a pervasive reliability bottleneck emerges, where incremental progress rarely translates to zero-defect execution. Comparative results show that while OpenClaw incurs higher computational costs, NanoClaw offers superior cost-efficiency (despite a 3.2–6.5\% accuracy trade-off), and Hermes-Agent emerges as the most stable framework for claw-style interactions. ClawBenchPro establishes a rigorous, reproducible standard for diagnosing agent limitations and advancing next-generation harness architectures.

Unified and scalable Transformers have recently achieved remarkable success in modeling diverse phenomena traditionally associated with computer graphics, such as 3D visual effects, rendering processes, and motion in videos. In this work, we take a step further by investigating whether modern Transformer techniques can tackle the challenging task of cloth simulation. To this end, we present ClothTransformer, a framework that reformulates cloth simulation as autoregressive sequence modeling in a learned latent space. Existing neural cloth simulators are largely specialized to single scenarios, intrinsically coupled to the mesh discretization, and lack robust collision handling. Our approach addresses these limitations through three contributions: (1) a unified Transformer architecture that handles diverse scenarios---body-driven garments, robotic manipulation, and free-fall collisions---under a single model and achieves approximately $4$--$9{\times}$ lower error than prior state-of-the-art across all scenarios; (2) a scalable latent-space formulation that compresses arbitrary-resolution meshes into a fixed-size set of latent tokens, making temporal dynamics computation independent of mesh resolution; and (3) a diverse-scenario high-fidelity penetration-free dataset of ${\sim}$493.4k frames spanning all three settings, which enables a differentiable Continuous Collision Detection (CCD) module to suppress penetration artifacts.

Large Language Models (LLMs) are presently well assessed by expert–level mathematics and physics benchmarks which focus on theoretical, symbolic reasoning. However, we lack experimental benchmarks that evaluate data analysis in frontier research problems, a capability that future domain–specific foundation models must have. To this end, we present CME–SpectrumBench, an expert–designed benchmark consisting of 190 questions that measures how reliably frontier LLMs analyze research–level spectral scientific data in condensed matter experiments (CME). Using simulated CME spectral data, we prompt models to identify structures from 2D intensity arrays in the face of noise and limited instrumental resolution, a test of dense context reasoning. Models are scored by how accurately their numerical answers approach the ground truth. Under direct query, scores generally pale in comparison to handwritten code due to clear failure modes: models can find simple features but struggle to trace patterns or compute derived quantities, and performance degrades with increased data resolution and noise/convolution levels. LLM performance becomes comparable to handwritten code by allowing tool use (access to a Python interpreter) with/without ReAct prompting, in effect mapping questions onto agentic code generation problems. Our findings show that frontier models can serve as research assistants by interfacing with data through code, but still face difficulties when processing information directly from large datasets — a key barrier future foundation models must overcome.

Modern 3D visual learning relies on observations sampled from metric 3D assets, yet existing scans, meshes, point clouds, simulations, and reconstructions do not directly provide a sparse, comparable, and geometry-consistent panoramic training interface. Dense trajectories duplicate nearby views, source-specific rendering policies yield heterogeneous annotations, and sparse heuristics may miss important regions or introduce depth-inconsistent observations. We study how to convert 3D assets into sparse panoramic RGB-D-pose data that preserves complete scene coverage with low redundancy and auditable provenance. We propose COVER (Coverage-Oriented Viewpoint curation with ERP Range-depth warping), a training-free ERP viewpoint curator that projects geometry observed from selected views into candidate ERP probes, scores incremental coverage, and penalizes depth conflicts. Under bounded proxy error, its greedy coverage proxy preserves the standard coverage-style approximation behavior up to an additive error term. Using COVER, we build CM-EVS (Coverage-curated Metric ERP View Set), a panoramic RGB-D-pose dataset with 36,373 curated ERP frames from 1,275 indoor scenes across Blender indoor, HM3D, and ScanNet++, complemented by outdoor panoramas from TartanGround and OB3D re-encoded into the same schema. Each frame provides full-sphere RGB, metric range depth along ERP rays, calibrated world-to-camera pose, and provenance logs. With a median of only 25 frames per indoor scene, CM-EVS covers all 13 unified room types while maintaining compact scene-level coverage. Experiments show that COVER improves the coverage–conflict trade-off, making CM-EVS a sparse, compact, and auditable RGB-D-pose resource for geometry-consistent panoramic 3D learning.

Diffusion models provide strong priors for generating structured data, but many tasks require outputs beyond the scale on which these models are typically trained. Compositional generation addresses this by composing overlapping local plans from a pretrained short-horizon prior into a long-horizon output. However, standard composition primarily enforces agreement between neighboring local plans, yielding local consistency without directly specifying the global structure of the full composition. As a result, locally compatible plans may still form an implausible route, task sequence, or temporal evolution. Existing methods improve global coherence by repeatedly propagating local consistency signals or by adding inference-time optimization, but these procedures become expensive as the number or dimensionality of local plans increases. We propose Coarse-to-Fine Compositional Diffusion (CoFi), an inference-time sampler that separates global structure formation from local detail refinement. CoFi first aligns local denoised estimates around a shared coarse structure, producing a global scaffold that captures the long-range task-level arrangement. It then diffuses this scaffold to an intermediate noise level and denoises it with the same pretrained local prior, restoring local fine structure while preserving the scaffold-induced global coherence. Across long-horizon robotic planning, panoramic image generation, and long video generation, CoFi not only improves both global coherence and local sample quality over prior compositional baselines, but also requires $2$--$8\times$ fewer denoiser evaluations.

Reinforcement Learning from Verifiable Rewards (RLVR) has driven recent progress in code large language models by leveraging execution-based feedback from unit tests, but its scalability is fundamentally constrained by the availability and reliability of high-quality test cases. We propose CodeScaler, a reward model designed to scale both reinforcement learning training and test-time inference for code generation. CodeScaler is trained on carefully curated preference data derived from verified code problems and incorporates syntax-aware code extraction and validity-preserving reward shaping to ensure stable and robust optimization. Across four coding benchmarks, CodeScaler consistently outperforms execution-based RL by +1.55 points on Qwen3-8B-Base and +4.23 points on Qwen3-14B-Base. By further scaling to 44K problems with additional synthetic data, CodeScaler yields +14.64 points improvement over the base model without requiring any test cases. At inference time, CodeScaler serves as an effective test-time scaling method, achieving performance comparable to unit test approaches while providing a 10× reduction in latency. Moreover, CodeScaler surpasses existing reward models on RM-Bench not only in the code domain (+3.3 points), but also in general and reasoning domains (+2.7 points on average).

Vision-Language-Action (VLA) models have driven significant progress in robotic manipulation, yet they fundamentally struggle with the ``vision-override'' phenomenon. Driven by the severe modality imbalance between dense visual streams and sparse linguistic instructions, VLAs frequently fall prey to causal confusion. Instead of treating language as the primary causal driver, the policy entirely bypasses the original instruction by overfitting to spurious visual confounders, such as prominent objects or familiar layouts. To systematically alleviate this bias, we formalize the process of action generation as a Dual-path Deconfounding Graph (DDG) and propose CofactVLA, a novel causal intervention framework. By dynamically constructing a language-masked counterfactual branch within a single forward pass, CofactVLA isolates and neutralizes visual confounders through two synergistic mechanisms. First, Action-Level Orthogonal Projection Guidance (OPG) geometrically projects the factual velocity field away from the counterfactual visual bias during continuous flow matching, extracting the pure semantic intent. Second, Feature-Level Counterfactual Covariance Reduction (CCR) mathematically deconfounds latent representations by penalizing the positive eigenspace of the covariance difference, explicitly suppressing dominant visual shortcuts while preserving the causal language intent. Extensive experiments demonstrate that CofactVLA establishes a new state-of-the-art across diverse simulation benchmarks. Beyond simulation, real-world robot experiments demonstrate the causal efficacy of our method in bridging the generalization gap, yielding a 52.3\% absolute success rate gain under out-of-distribution scenarios.

Stellarators are fusion energy devices that confine hot plasma using a carefully shaped magnetic field, and they are one of the most promising candidates in the race towards commercial fusion. Among the many systems that make up a stellarator, the electromagnetic coils generate the confining magnetic field. Their design is the key factor in deciding whether a stellarator can be practically engineered and cost‑effectively realized. Coilset design is an intrinsically complex problem: it does not admit either a closed or a unique solution, making it suitable for optimization. Coilset optimization is therefore at the heart of stellarator design: a coilset must accurately reproduce a target magnetic field while satisfying stringent engineering constraints on coil spacing, curvature, and length. Although recent datasets have accelerated progress on stellarator equilibria optimization, data-driven coilset design is limited by the lack of standardized datasets that pair target plasma configurations with engineering-aware coilsets. Here, we release a dataset (https://huggingface.co/datasets/proxima-fusion/coilstellaration) of 177,000 optimized stellarator coilsets targeting plasma configurations sampled from the publicly available ConStellaration dataset. For each configuration, we optimized coilsets to reproduce the magnetic field while satisfying a diverse set of engineering constraints. Alongside the dataset, we introduce a benchmark for conditional coilset generation: given a target plasma boundary and a set of engineering requirements, a model should produce a coilset that satisfies the engineering constraints while reproducing the target field. We provide reference code, evaluation scripts and baselines models trained on a subset of the provided data (https://github.com/proximafusion/coilstellaration). Beyond enabling better optimization initializations and one-shot coil design, our dataset provides a testbed for data-driven constrained optimization and for studying which plasma geometry features predict simpler, more engineerable coils. By releasing data, and code for benchmarks and baselines, we aim to lower the barrier for machine learning and optimization researchers to engage with stellarator coilset design and accelerate progress toward engineerable and cost-effective stellarators power plants.

Generative ranking models (GRMs) have emerged as a scalable alternative to traditional recommendation pipelines by unifying items and user actions into a single token sequence and formulating recommendation as next-token prediction. Despite their promise, we identify a geometric failure mode: attention interactions with lowcardinality action tokens can induce dimensional collapse in high-rank item representations, as suggested by interaction-collapse theory. To pinpoint the origin of collapse, we view attention mechanism over item–action interleaving sequences as a mixture of directional interaction channels between token types, and find that collapse concentrates in the action-attending-item channel, where low-rank action gating drags the item value stream toward a low-dimensional subspace. To address this, we theoretically show that nonlinearity mitigates collapse, and propose Selective VAlue Nonlinearity (SVAN), a simple yet effective approach that applies nonlinear activation to the value component in the collapsed channel, protecting item representations while preserving the remaining attention geometry. SVAN consistently improves the ranking quality over strong baselines. Further analyses confirm that SVAN alleviates dimensional collapse, yielding up to a 12.58% effective-rank gain, and enhances representation discriminability.

Multi-agent systems, where LLM agents communicate through free-form language, enable sophisticated coordination for solving complex cooperative tasks. This surfaces a unique safety problem when a group of agents forms a coalition and colludes to pursue secondary goals and degrade the joint objective. In this paper, we present Colosseum, a framework for auditing LLM agents' collusive behavior in multi-agent settings. We ground how agents cooperate through a formal multi-agent decision-making framework and measure action-based collusive behavior in actions via regret relative to the cooperative optimum and compare it with communication-based collusive behavior. Colosseum enables audits of LLM agents for collusion under benign settings, different coalition objectives, persuasion tactics, and network topologies. We then introduce a new behavioral probe by creating secret communication channels between agents, showing that most out-of-the-box models exhibit a propensity to collude under this probe, which we term emergent collusion. Furthermore, we discover ``collusion on paper'' when agents plan to collude in text but often pick non-collusive actions. Colosseum provides a new way to audit collusion in cooperative multi-agent systems while presenting observations about how collusion emerges, what affects collusion efficacy, and which strategies may mitigate it.

Swapping two LLM post-training stages can change the final model, but an aggregate loss or benchmark delta only says that something moved, not where the order-dependent residue landed. We ask whether this residue leaves a memory trace: a structured signal that is localized in output space, changes held-out order-gap NLL under targeted interventions, and is assignable from paired endpoint weights. For a specified two-stage path $(A,B)$, we define commutator memory by projecting the Lie bracket $b_{AB} := H_B g_A - H_A g_B$ at the base model through the logits into token scores $\tau_k=\mathbb{E}[e_k\,\delta z_k]$, whose sum is the leading bracket-predicted order effect. The token readout is localized: endpoint and batch-resampled checks preserve bracket top-token supports far more than norm-matched random directions (82--99% vs. 35--49% top-20 overlap), and a separate endpoint-support prediction check recovers 53--57% of Qwen top-1% active-token supports. It has intervention leverage in the tested protocols: top-positive-$\tau_k$ token interventions close 32% (median) of the held-out Qwen-3-4B SFT ordering gap with tau-neutral controls near zero, with the same bracket-vs-control separation on a pre-specified above-floor Qwen-2.5-1.5B fp32 subset. It is assignable from paired endpoints: given a base model, candidate stages, and paired alternate-order endpoints, the statistic $\langle\theta_{AB}-\theta_{BA},\,b_{AB}\rangle$ distinguishes $k=1$ SGD training order in 66/72 pair-seed units across four LLMs. Matched-batch DPO and frozen-rollout reward-surrogate experiments are matched-path stress tests; AdamW is reported as an analogous lifted-state pilot. Commutator memory turns training order from a scalar nuisance into a localized, intervention-tested, and paired-endpoint-readable residue.

Understanding when neural networks learn interpretable representations is central to mechanistic interpretability. We study this through identifiability: when can supervised learning recover the true latent variables underlying the data? We introduce two data generating processes distinguished by their causal direction where either labels cause latents (generative) or latents cause labels (discriminative). When the representation has at least as many dimensions as the latent space, we prove that the optimal encoders are linear in the latents. Conversely, when dimensions are insufficient, identifiability generically fails and encoders become nonlinear. We then show that additional constraints yield recovery of individual latent variables, not just linear mixtures. In the generative setting, non-negative activations and energy efficiency force each neuron to encode exactly one latent, providing a precise account of selective grandmother cell-like responses. In the discriminative setting, when latents are sparse and the true measurement matrix satisfies the Null Space Property, the learned encoder inherits compressed sensing structure, enabling sparse dictionary learning to perfectly recover individual latents. This constitutes, to our knowledge, the first identifiability result for undercomplete nonlinear independent component analysis. Our framework unifies nonlinear ICA, compressed sensing, and neural network interpretability, delineating exactly when representations transition from nonlinear distortions to linear representations and, under additional constraints or steps, to clean, axis-aligned codes. Theoretical predictions are validated on synthetic benchmarks and on activations drawn from vision models, large language models, and macaque inferotemporal cortex.

Recent studies on generalizable human Gaussian splatting have achieved the notable progress in synthesizing novel views of unseen human subjects from multi-view images. Despite this progress, existing methods still struggle to represent fine-grained details of human subjects, which inherently involve high geometric and textural complexity. This is because previous methods do not impose a constraint on how densely Gaussians are placed, which often leads to blurry rendering results in complex regions. To address this problem, we propose a complexity-guided regularization scheme. The key idea is to leverage a complexity prior computed via the local entropy in geometric and textural characteristics of a clothed human. This effectively prevents Gaussians from being coarsely placed in complex regions, which facilitates the faithful reconstruction of intricate structures. Furthermore, we sample local patches predominantly from complex regions according to this complexity prior, which allows the model to be sufficiently supervised by local details. Experimental results on benchmark datasets demonstrate that our method delivers state-of-the-art performance while maintaining the real-time inference speed in generalizable human Gaussian splatting.

Differential privacy (DP) has become the standard for privacy-preserving data analysis, yet its practical implementation in federated or distributed settings often faces a significant utility gap compared to the central model. While existing frameworks, including local, shuffle, and secure aggregation models, offer vetted primitives, they frequently suffer from high error rates for some important functionalities such as selection. Our first contribution is to show that this is inherent in the aggregation model: any private selection algorithm relying on the standard noisy aggregation primitive requires $\Omega(\sqrt{d}/\varepsilon)$ samples. To address this limitation, we propose adding a new primitive that implements a version of the \textit{Sparse Vector Technique} (SVT) and argue that it has simple implementations under various trust models. We demonstrate its power by developing an $\varepsilon$-differentially private algorithm for the selection problem. For selection in dimension $d$, our algorithm achieves an additive error of $O\left(\frac{\log d}{\varepsilon}\right)$ matching that of the central model. Further, we show this can be done using a near-optimal number of SVT queries. Together with existing lower bounds for the shuffle model, this result establishes an exponential separation between the SVT model and shuffling or aggregation-based models. We demonstrate the model's broad applicability to diverse tasks such as $F_p$ loss minimization, heavy-hitter identification, and sparse mean estimation.

Optimization modeling (OM) is fundamental to address Operations Research (OR) problems, yet it requires specialized domain expertise. Large language models (LLMs) are promising for automating OM from natural language descriptions. Through prompting and fine-tuning, LLMs have made significant progress on easy problems, but they still struggle with complex problems involving combinatorial constraint logic and obscure decision variables. To provide a precise formulation for industry OR problems, this paper proposes a multiagent system (MAS) workflow, which decomposes the entire OM process into six structured sub-tasks: set identification, parameter abstraction, variable definition, objective formulation, constraint derivation, and solver-code generation. Existing OM benchmarks only provide a sparse judgment of the final formulation, without process rewards for intermediate agents. Therefore, the main challenge of MAS-based OM is how to fine-tune intermediate agents with OM domain knowledge. This paper proposes a sample efficient counterfactual-based credit assignment for the MAS trajectory. The augmented intermediate credit can be directly used to fine-tune each agent. Moreover, we construct an Optimization Skill Library (Opt-Skill) that provides agent-specific procedural guidance for skill-augmented reasoning, self-checking, and error correction during inference. Experimental results show that, compared with existing methods and frontier LLMs, the proposed MAS workflow achieves state-of-the-art average accuracy while maintaining strong performance on both easy and complex optimization modeling tasks. Altogether, we believe that MAS represents a concrete step forward in performing complex OM in industry scenarios.

Diffusion bridges have emerged as versatile diffusion-based frameworks for facilitating transformations between arbitrary data distributions. However, these approaches are hindered by the immensity of the optimization space without consideration of underlying high-order structures. This paper explores a complex-valued variant of diffusion bridges for solving physical problems using conformal field theory and mean field games. To this end, we propose a new class of dynamical diffusion bridges, called complex Schr\"odinger Bridges (CSB), whose derived algorithms are able to handle diffusion-based tasks using Hamiltonian-based geometric prior. Additionally, we propose image-to-image CSB (I$^2$CSB), tractable diffusion bridge techniques with a spiral analytic posterior. We verify our theoretical claims and practicality of our Hamiltonian based approach with a wide range of experiments.

Deep networks embed inputs in high-dimensional representations, but how much of this capacity actually drives behavior? We introduce a framework for identifying, at each layer, the subspace of the representation that is functionally relevant for the task. Using a matrix analogous to the Fisher information but defined over the hidden representation, we rank directions by their influence on downstream computation and project onto the most task-relevant subspace. We benchmark this behaviorally grounded compression against three geometric alternatives that preserve variance, local neighborhood structure, or global topology. Across pretrained vision models, representations are remarkably compressible: a small fraction of the task-relevant subspace preserves most performance, often requiring far fewer dimensions than geometric alternatives. We show that this decomposition recovers functionally distinct subspaces within the same representation: on cue-conflict stimuli, shape-relevant and texture-relevant directions are largely non-overlapping, and ablating one selectively impairs the corresponding task while leaving the other intact. Task-relevant subspaces also provide a principled foundation for continual learning: constraining gradient updates to lie orthogonal to the task-relevant subspaces of prior tasks outperforms standard variance-based gradient projection methods. Finally, restricting representational comparisons to task-relevant subspaces reveals that inter-model alignment is higher in task-relevant directions than in task-irrelevant directions at later layers, and that conditioning on different tasks over the same stimuli uncovers asymmetries that full-space similarity measures cannot detect. Together, these results establish that deep networks compute through a low-dimensional functional core embedded within a much larger representational space, that these cores differ across tasks, and that they provide a unifying basis for compression, continual learning, and representational comparison.

For $p \ge 1$, the $p$-Wasserstein distance measures the minimum cost of transporting probability mass, where moving mass between two points costs the $p$th power of their distance. For discrete distributions in one dimension, full transport is especially simple: after sorting, mass is matched in order along the line. By contrast, partial and unbalanced transport on the line remains much less understood. Recently, Chapel and Tavenard [ICLR'25] showed that, for $p=1$, all optimal partial transport plans between distributions supported on $n$ points, with uniform mass at each point, can be computed in $O(n\log n)$ time by exploiting the metric structure of the cost. For $p>1$, this structure no longer applies, and existing approaches require $\Omega(n^2)$ time. Our main contribution is an FFT-based data structure for balanced-interval transport queries, which bypasses this quadratic bottleneck and yields an $O(p\ n\log^2 n)$-time algorithm for computing all optimal partial transports on the line for every finite $p\ge 1$. We also provide an open-source C++ implementation that outperforms the state-of-the-art baseline on a range of synthetic instances. Finally, we establish a conditional lower bound for $p=\infty$: any subquadratic-time algorithm for computing all optimal partial transport plan costs on the line would violate the $(\min,+)$-Convolution Hypothesis. This separates the problem from full optimal transport, which is solvable in $O(n\log n)$ time.

Gaussian processes (GPs) offer a principled probabilistic model over functions, but exact inference is restricted to the linear-Gaussian regime. We establish an explicit equivalence between GPs and a class of linear diffusion models, recasting predictive sampling as an ODE with closed-form Gaussian dynamics and a likelihood-dependent guidance term that admits a simple Monte Carlo approximation. In the linear-Gaussian setting, we recover standard GP conditioning exactly; beyond conjugacy, the same machinery handles any conditioning statement admitting point-wise likelihood evaluation — including non-linear physics, and, for the first time, natural language via large language models. Whitening isolates the irreducible non-Gaussian dynamics, minimising Wasserstein-2 transport cost and eliminating numerical stiffness. The result is a general-purpose GP inference scheme requiring no bespoke derivations. Together, these results provide a general mechanism for incorporating the full richness of real-world knowledge as conditioning information, opening a new frontier for the probabilistic modelling of real-world problems.

Structure-based drug design with diffusion models seeks to generate 3D molecules that bind tightly to target protein pockets, but full-step sampling is too slow for high-throughput use. Extending the recent development of training-free fast sampling methods for diffusion models (e.g., DDIM and DNDM) to high-quality 3D molecule generation is non-trivial, given that both discrete variables (atom types) and continuous variables (atom coordinates) are involved. In this paper, we propose a unified fast sampling framework that allows a shared time schedule across discrete and continuous variables for strategically accelerating the 3D ligand generation. Guided by an error-equalization principle, we combine the continuous and discrete error rates of hybrid sampling into a single step-importance density and derive a training-free coupling-aware schedule. We further propose a confidence-guided sampling strategy that integrates constraints of model prediction confidence, motion stability, and steric clash to control the acceleration based on geometric readiness. Extensive experiments demonstrate that our method achieves $18$-$20\times$ speedup compared to full-step diffusion models while preserving state-of-the-art generation quality across chemical properties, binding affinity, and chemical validity.

When multi-agent systems (MAS) fail, identifying where the decisive error occurred is the first step for automated recovery to an earlier state. Error attribution remains a fundamental challenge due to the long interaction traces that large language model-based MAS generate. This paper presents a framework for error attribution based on conformal prediction (CP) which provides finite-sample, distribution-free coverage guarantees. We introduce new algorithms for filtration-based CP designed for sequential data such as agent trajectories. Unlike existing CP algorithms, our approach predicts sets that are contiguous sequences to enable efficient recovery and debugging. We verify our theoretical guarantees on a variety of agents and datasets, show that errors can be precisely isolated, then use prediction sets to rollback MAS to correct their own errors. Our overall approach is model-agnostic, and offers a principled uncertainty layer for MAS error attribution.

Large Language Models remain plagued by hallucinations. Recent work has sought to tame their prevalence using statistical techniques based on conformal prediction, with both theoretical and empirical success. However, these methods operate in a post-hoc fashion, treating the sampling procedure itself as atomic and then surgically altering samples to remove hallucinated claims. This disconnect between filtering and generation can result in samples that are incoherent, inconsistent, or simply unlikely under the model itself. Not only that, but post-hoc surgery is unable to shift probability mass towards more useful and helpful responses. To address these issues, we propose to instead sample from approximations to an LLM posterior, where the conditioning event corresponds to a calibrated, high-scoring region. We develop a calibration procedure tailored to the setting of conditional sequential generation that effectively identifies this region and achieves target risk control. Empirically, we apply our method to case studies focused on open-ended biography generation and mathematical problem solving; compared to prior work, we obtain the same statistical guarantees, with higher downstream utility.

Uncertainty quantification is crucial in scientific machine learning, where models inform safety-critical tasks such as flood forecasting, financial risk management, and thermal control in machines. Conformal prediction provides distribution-free coverage guarantees, but in time-dependent settings common to physics and engineering, these guarantees can break down, leading to systematic undercoverage. We study this problem in the context of surrogate models for time-dependent physical systems described by partial differential equations. We prove that in a function space setting, distributions at arbitrarily close times can be mutually singular, making exact coverage guarantees impossible. We further show that in discretized settings, the total variation distance between distributions often grows exponentially with the grid resolution mandating restrictions on the spatial discretization to ensure coverage. Finally, for certain settings, we show how to use principled weighted conformal prediction to obtain finite-sample coverage guarantees over growing time horizons.

Proofreading—correcting segmentation errors in 3D brain reconstructions—is the rate-limiting step in synapse-resolution connectomics. We release ConnectomeBench2, a unified multi-species dataset of over 302,401 expert-labeled proofreading decisions with >2,000,000 associated images spanning four major open connectomes (mouse, human, zebrafish, fly), spanning both split and merge error correction. Trained on this dataset, a single Vision Transformer with shared encoders for mesh geometry and electron microscopy reaches human-level accuracy across species for split error correction and merge error identification; with performance scaling with data size and improving with species diversity. Beyond accuracy, we show that the model is well-calibrated within distribution, that measures of distribution distance predict where calibration and accuracy will degrade on unseen data, and that connectomics-specific pretraining and active learning-based sample selection show potential to substantially reduce the labeling effort needed to extend to new species and brain regions. The benchmark provides the infrastructure to train and evaluate increasingly capable vision models for connectomic proofreading.

Parallel thinking scales inference-time compute by generating multiple reasoning traces for a query, and aggregating them to reach a final answer. However, most aggregation algorithms treat each reasoning trajectory independently, ignoring the complementary reasoning sub-processes across the full ensemble of trajectories. To learn from these parallel trajectories, we propose CORAL (Consolidating Reasoning with Test-Time Learning), a novel framework for aggregating trajectories by jointly consolidating them into parametric memory using test-time training. Our algorithm meta-learns how to consolidate using nested optimization at training time, where an inner loop encodes reasoning trajectories into a lightweight LoRA adapter, and an outer loop learns meta-parameters for the inner loop such that the consolidation is helpful for the model to synthesize a final solution. As no standard datasets exist for training aggregation methods, we construct a novel OpenParallelThinking corpus composed of 12,810 math problems paired with a mixture of diverse trajectories from six LLMs. Evaluated on challenging mathematics and STEM benchmarks, CORAL consistently outperforms best aggregation baselines by 20% overall. Importantly, we curate reasoning trajectories at multiple quality levels to simulate the reality of noisy parallel reasoning and demonstrate that CORAL is especially robust to the quality of thinking trajectories and able to learn useful knowledge from entirely wrong solutions. Additionally, this weak-to-strong generalization feature scales with the number of incorrect trajectories available for aggregation, while RL can further incentivize such reasoning consolidation capability. We will release CORAL and OpenParallelThinking for use in future work on reasoning aggregation.

Post-training of language models is commonly framed as a sample-score-update loop implemented by gradient descent. A recent line of work, exemplified by RandOpt, relocates this loop to weight space, sampling Gaussian perturbations around a pretrained model and ensembling the top-$K$ rewarded specialists at inference. While competitive with PPO and GRPO under matched training compute, this prediction-level ensemble incurs $K$ forward passes per test example and does not extend cleanly to free-form generation. We ask whether the rewarded population can instead be folded into a single deployable model, replacing the inference-time ensemble with one consolidated update. A split-half analysis over 25 model-task pairs reveals reproducible low-rank structure in every case. We turn this geometry into CoRP (Consolidating Rewarded Perturbations), a gradient-free operator that combines reward-weighted aggregation, compatibility-aware reweighting, and a held-out validation gate, with no gradient flowing through the language model. Across five language models from $0.5$B to $8$B and five tasks covering math, code, and creative writing, CoRP improves the base model by $8.1$ points on average. Using one tenth of RandOpt's perturbation budget, CoRP exceeds single-inference RandOpt by $6.5$ points and recovers more than half of the gain of the 50-pass majority-vote ensemble, at one forward pass per test example.

Given a must-link graph $G = (V, E_G)$ and a cannot-link graph $H=(V,E_H)$ as input, the objective of constrained clustering is to partition $V$ into $k$ parts while minimizing the maximum cut ratio between $G$ and $H$. In this paper we design and analyze a variant of the spectral clustering algorithm using the $k$ smallest generalized eigenvectors as the embedding space. We prove that our proposed algorithm achieves an $O(1)$-approximation of the optimal cut-ratio under a natural assumption on the input graphs. We further study the case in which the cannot-link constraints might not be available, and develop an iterative pipeline that repeatedly applies the output of spectral clustering or constrained clustering to generate new constraints, thereby progressively improving cluster quality. We experimentally compare our proposed algorithm with the previous state-of-the-art, and demonstrate the superior performance of our algorithms on both synthetic and real-world datasets.

Diffusion and flow models enable powerful generative modeling, but controlled editing remains challenging due to the need to balance source fidelity and edit compliance along the generative trajectory. We propose a principled, inversion-free framework for flow-based image editing from a trajectory centric perspective. Rather than relying on inversion or heuristic guidance, we introduce a one-step look-ahead mechanism that evaluates the effect of candidate updates on both edit and fidelity rewards estimated via denoising, and adjusts the current update based on this evaluation. At each step, our method applies reward-driven corrections guided by the look-ahead evaluation under an explicit fidelity constraint, resulting in stable and coherent trajectory evolution. This look-ahead design mitigates gradient interference between edit and fidelity objectives by anticipating their interaction in future states, leading to improved trade-offs. Experiments demonstrate that the proposed approach achieves stronger semantic edits while better preserving source content compared to existing strong baselines.

We present the first method for intrinsic, continuous gradient-based optimization for models with $p$-adic parameters. To overcome the vanishing gradients caused by the discrete valuation of the $p$-adics, we lift parameters to the Berkovich affine line, a canonical analytification of the $p$-adic numbers that provides a path-connected space. This enables gradient descent that respects the non-Archimedean metric with a local transition cost that is linear. By extending losses to the Berkovich line, we get piecewise linear functions, corresponding to the cell decomposition of a tropical complex; gradients are efficiently computed via max-plus operations. We prove local convergence within tropical cells for linear models on the $\mathbb{Q}_p$ subtree, and demonstrate the success of our method on synthetic tasks and on the $p$-adic Quillian semantic benchmark [Martins, 2025], where we achieve performance equal or better than $\mathbb{R}$-valued counterparts while training in comparable time.

Graph neural networks exhibit a fragile relationship with depth, as adding layers leads to rapid homogenization of node representations (over-smoothing). Recent manifold-constrained approaches using doubly stochastic matrices address this issue, but the fundamental principle underlying their success remains unclear. This study investigates whether doubly stochasticity is essential or exemplifies a broader geometric principle for stable residual propagation in deep GNNs.We formalize three axioms to define residual mixing matrices as contractive monoids. We instantiate this framework through three manifolds (Orthogonal Group, Diagonal Contraction Monoid, Spectral Norm Ball) and evaluate them across nine node-classification benchmarks using four GNN backbones. Results demonstrate that contractive-monoid constraints maintain stable accuracy and low representational collapse at extreme depths, while standard GNNs degrade sharply beyond moderate depth. This work establishes that stable residual propagation arises from contractive, compositional structure anchored at identity, revealing a significantly larger design space for deep GNN architectures while maintaining theoretical guarantees.

Discrete diffusion models have emerged as powerful frameworks for generating structured categorical data. However, efficiently sampling from reward-tilted distributions remains a fundamental challenge. While Twisted Sequential Monte Carlo (SMC) offers asymptotic exactness for this task, estimating the optimal twist function in discrete state spaces necessitates costly Monte Carlo approximations, resulting a severe computational bottleneck at inference. To overcome this limitation, we introduce Contrastive Distribution Matching (CDM), a novel framework that amortizes the cost of SMC inference by learning a parameterized twist function via positive and negative samples. For efficient training, we reformulate the gradient estimator to leverage the closed-form forward kernels of discrete diffusion models. In practice, evaluating our learned twist function incurs less than 5% additional computational overhead compared to base model inference. Through extensive empirical evaluations, we demonstrate that CDM consistently outperforms existing baselines under matched wall-clock time. We validate the effectiveness and versatility of our approach across a diverse range of applications, including toxic text generation, regulatory DNA sequence design, protein designability, and diffusion large language model alignment.

Autoregressive neural simulators now match classical solvers on short-horizon prediction of physical systems, yet their accuracy degrades rapidly when rolled out over long horizons. In this work we identify transient amplification of perturbations around rollout trajectories as a structural mechanism driving rollout error. Using a linearization analysis we show that when the Jacobians along an autoregressive trajectory are non-normal and non-commuting, the model amplifies errors transiently, resulting in model rollout drift even when the overall system is asymptotically stable. Building on the analysis, we propose \emph{commutativity regularization}: a combination of two penalties designed to reduce the normality defect of individual Jacobians and the commutator norm of Jacobians across steps. The penalties are estimated with Jacobian-vector products and have no inference-time cost. We show a propagator bound that quantifies rollout error under approximate commutativity and normality. We evaluate UNet and FNO variants with commutativity regularization on 1D and 2D spatio-temporal data in synthetic and real settings, showing successful long-horizon rollouts over thousands of steps. We show that the method improves FourCastNet climate forecasts on ERA5 without using any new data. The gain is most pronounced out-of-distribution: trained on trajectories of a few hundred steps, regularized models remain in-distribution for thousands of rollout steps on initial conditions where baselines diverge.

Multi-Agent Reinforcement Learning (MARL) is a powerful approach to large-scale systems control, such as power grids and traffic networks, where dozens to hundreds of agents must coordinate effectively. Furthermore, such systems often operate in digital or simulated settings, which allows for inference-time search to significantly improve upon zero-shot performance. A current leading approach in this setting is COMPASS, which builds a continuous family of policies conditioned on latent vectors that can be efficiently searched at test time, but requires all agents to share a single latent vector, limiting the space of reachable joint policies. We introduce ATLAS, a simple extension in which each agent conditions on its own latent vector while still optimising a joint objective. This expands the searchable policy space at negligible computational cost and requires no additional training. ATLAS consistently sets a new state-of-the-art on a benchmark of 14 scenarios across three environments, scaling to teams of up to 200 agents, widely out of distribution. Crucially, its lead sharpens with team size and compute, reaching up to a 45% performance gain at scale.

INT2 KV-cache quantization is attractive for long-context LLM serving, but it remains difficult to make both accurate and deployable. Simple rotations such as Hadamard transforms reduce outliers, but still degrade at INT2 because they are not aligned with downstream attention. We propose CoQuant, an Ultra-low-bit KV Cache quantization method that estimates attention-aware covariance structures offline and uses them to derive fixed rotations and clipping thresholds for quantization. In this way, it aligns KV quantization with the covariance structures that attention actually consumes. More importantly, we not only provide theoretical justification but also develop a fully deployable CoQuant system with a custom INT2 attention kernel that remains compatible with paged KV-cache serving and fused kernel pipelines, enabling seamless integration into modern LLM serving frameworks such as SGLang and vLLM. We evaluate our methods on recent reasoning models with reasoning traces of up to 32k tokens across 5 tasks. On Qwen3-4B-Thinking-2507 and Qwen3-8B, CoQuant reduces the BF16 accuracy gap to 3.78 and 1.42 points, respectively, while naive rotation INT2 collapses to nearly zero. We further scale CoQuant to Qwen3-32B and GLM-4.7 (358B params), where it remains effectively on par with BF16. On long context - RULER-NIAH up to 128K, CoQuant remains robust on both Qwen3 models, while naive rotation INT2 collapses. System-wise, CoQuant reduces KV-cache memory by approximately 8×, improves throughput by up to 7× at large batch sizes under the same memory budget, and accelerates batch-size-1 decoding by up to 3× over BF16 due to reduced memory bandwidth overhead.

In tool-integrated reinforcement learning with verifiable rewards (RLVR), sequence-level verifier signals are issued for trajectories whose outcomes are determined by a sparse subset of tokens—tool names, argument keys, and argument values. Standard RLVR assigns the same advantage to every token in a rollout, diluting credit at precisely the positions that matter. We propose **Correctable Fork Tokens (CFT)**, which decomposes selective credit assignment into two subproblems: *where* to update, and *how much* to update at those positions. To this end, CFT introduces a training-only answer-conditioned branch sharing parameters with the student policy. The key insight is that conditioning on the ground-truth answer concentrates the model's distribution at decision-fork positions, providing hindsight localization without additional parameter capacity or imitation targets. Critically, this benefit requires accurate answer information: a larger-capacity teacher without the answer fails to replicate these gains, confirming that privileged answer information—not teacher capacity—is the active ingredient. CFT uses this entropy reduction ($IG_t$) to identify correctable fork positions, then rescales token-level credit at those positions while preserving the verifier advantage as the sole signal for update direction. On BFCL v3/v4, CFT improves multi-turn accuracy by **4.2/6.5** pp and overall accuracy by **3.1/2.2** pp over GRPO; it further surpasses On-policy distillation—which employs a larger Expert Model as teacher—by **3.6/6.7** pp on multi-turn, confirming that answer-conditioned fork localization rather than teacher capacity drives the improvement. On Tau3, the average score improves by **8.3** pp. We additionally present a three-level structured verifier for tool-call settings; ablations confirm that finer-grained verifier feedback and selective token credit are complementary. Our reproducible implementation is available at [https://anonymous.4open.science/r/CFT](https://anonymous.4open.science/r/CFT).

Existing methods for Virtual Try-On (VTON) often struggle to accurately transfer garment appearance, especially in unpaired settings where accurate person-to-garment correspondence is required. These methods do not explicitly supervise person-to-garment alignment, leaving correspondence to be learned implicitly within the generation model. In this paper, we first analyze full self-attention in DiT-based architectures and show that person-to-garment query-key matching aligned with local geometric correspondence is closely related to try-on quality. Building on this insight, we introduce CORrespondence ALignment (CORAL), a DiT-based framework that explicitly aligns query-key matching with robust external correspondences. CORAL integrates two complementary components: a correspondence distillation loss that aligns reliable matches with person-to-garment attention, and an entropy minimization loss that sharpens the attention distribution. For evaluation, we further propose a VLM-based evaluation protocol to better reflect human preference. CORAL consistently improves over the baseline, enhancing both global shape transfer and local detail preservation. Extensive ablations validate our design choices. Code and weights will be publicly available.

Inspired by the problem of identifying the best model from a collection of large language models (LLMs) with heterogeneous querying costs, we formulate and analyse a variant of the multi-armed bandit (MAB) with (i) dueling feedback, where pairwise comparisons between model responses provide robust preference signals, and (ii) heterogeneous sampling costs, reflecting the differing costs of querying different LLMs. Assuming the existence of a Condorcet winner, a condition we empirically validate across multiple real-world datasets, we propose a Track-and-Stop style algorithm for best-arm identification with prescribed confidence. We prove that the algorithm almost surely achieves the asymptotically optimal cost as the error tends to zero. Finally, we extensively evaluate our approach on both synthetic and real-world instances, demonstrating consistent improvements over classical cost-unaware algorithms and their cost-aware extensions.

Vision-Language-Action (VLA) models inherit rich semantic priors from pretrained vision-language models, yet these priors do not guarantee persistent instruction sensitivity after narrow-domain fine-tuning. We observe a systematic failure mode: although VLA models remain visually reactive, their later actions can become weakly constrained by the commanded instruction, leading to unstable object interaction and visually plausible but instruction-inconsistent behavior. We trace this behavior to a collapse of instruction-conditioned representations across network depth and task progress. More fundamentally, this collapse is enabled by the learning objective itself: standard imitation learning admits Bayes-optimal solutions that are invariant to language. We address this by recasting instruction grounding as identifying the commanded instruction among counterfactual alternatives. Based on this view, we propose Counterfactual Instruction Grounding (CIG), a contrastive objective that encourages the generated trajectory to remain identifiable with the commanded instruction among counterfactual alternatives. CIG applies to both autoregressive and flow-matching VLA models, using exact action-chunk likelihoods for autoregressive models and an energy-based pseudo posterior for flow-matching models to avoid intractable trajectory likelihood estimation. Practically, CIG can be applied directly to already fine-tuned models as a continuation-stage grounding objective, restoring instruction sensitivity without retraining from scratch or changing the architecture. Extensive experiments on RoboCasa and LIBERO-Plus demonstrate that CIG achieves strong instruction-following performance with success rates of 71.3% and 86.3%, respectively. Real-world deployment further shows that CIG transfers to kitchen manipulation and preserves instruction-conditioned behavior in visually ambiguous scenes.

Visible sensors provide rich scene details but are vulnerable to adverse conditions, whereas infrared sensors capture thermal radiation distributions and are more robust to environmental variations. However, acquiring high-resolution infrared images remains challenging due to sensor limits and hardware costs, and collecting pixel-wise aligned visible-infrared pairs is even more difficult. Existing methods mainly rely on infrared super-resolution or visible-to-infrared (VIS-to-IR) translation: the former preserves thermal distributions but lacks fine details and spatial alignment with visible images, while the latter benefits from fine-grained structural cues but faces an ill-posed thermal-distribution inference problem. To overcome these limitations, we propose CoVisIT, a cross-modal prior guided diffusion model for VIS-to-IR image translation. Our key insight is to constrain VIS-to-IR translation with complementary cross-modal priors, using high-resolution visible images to recover fine-grained scene details and misaligned low-resolution infrared inputs to anchor reliable thermal distributions. To achieve this, we build upon a latent diffusion framework and develop a Cross-Modal Adaptation module that modulates visible and infrared features in a shared feature space, thereby reducing the modality gap and exploiting complementary cross-modal priors. To further address local misalignment, we introduce a Dynamic Kernel Generation Module to predict input-adaptive kernels and embed a Multi-Scale Dynamic Convolution Module into the denoising U-Net, enabling dynamic local feature aggregation for implicit alignment. As a result, CoVisIT generates high-resolution, visible-aligned infrared images with reliable thermal distributions. Extensive experiments demonstrate state-of-the-art performance and consistent improvements in downstream tasks.

Transformer feed-forward blocks combine several design choices: squared activations, gating, and tied versus untied parameter sharing-whose separate contributions to expressivity remain poorly understood. We introduce \emph{CP-MLPs}, a tensor framework in which each multiplicative unit contributes a rank-1 interaction with three roles: detecting an input feature, reading a value, and writing to the output. Tied gated blocks such as ReLU$^2$ force the detector and value roles to share the same direction; untied gated blocks such as ReGLU and SwiGLU let them differ. We show that this distinction yields exact, degree-wise tensor-rank separations: untied detector-value units are strictly more width-efficient for indefinite quadratic interactions and role-asymmetric monomials, while tied units are structurally matched to symmetric pure-power targets. Tying is therefore not inherently better or worse where its benefit depends on the structure of the target. In experiments, models below the required width hit an irreducible approximation floor where the loss does not improve with extra optimization budget. The detector-value advantage also persists in trained gated MLPs at matched parameter count.

As vision language models are increasingly deployed in clinical diagnosis, understanding how they internally resolve competing visual and textual signals becomes a safety imperative. Existing mechanistic analyses remain confined to unimodal text and offer no explanation for why a single misleading sentence can override a correct image based diagnosis, or why a model commits to a confident answer despite insufficient visual evidence. We find that these two safety risks, arbitration failure where textual context overrides visual grounding and brake failure where the model commits without adequate evidence, are mediated by spatially disjoint attention head populations: arbitration heads form a mid-to-deep wideband reflecting cross-layer evidence competition, while brake heads concentrate in a narrow middle-to-late layer band that regulates evidence sufficiency and abstention behavior. To ground these observations in causal circuitry, we introduce CRAFT, which localizes each failure mode to a minimal causal head set via dual criteria and verifies necessity and sufficiency through temporal probes and Tuned Lens trajectory analysis. Excising arbitration heads sharply reduces conflict following with negligible degradation on clean inputs, while excising brake heads restores appropriate abstention under degraded visual evidence. The two interventions target spatially disjoint head sets and produce distinct corrective effects, underscoring the mechanistic separability of the failure modes. Experiments across multiple medical VQA benchmarks and VLM architectures validate both the localization and interventions, demonstrating that the identified heads causally drive each failure mode and that targeted modulation generalises without retraining. The code is available at \url{https://anonymous.4open.science/r/CRAFT-B3CA}.

Open-loop imitation learning has advanced modern autonomous driving policy architectures, but closed-loop deployment remains vulnerable to policy-induced distribution shift. Existing post-training paradigms exhibit fundamental trade-offs: closed-loop RL fine-tuning provides grounded feedback from executed actions but is constrained by the sparsity of informative events, whereas counterfactual fine-tuning provides dense supervision over candidate futures but inherits bias from imperfect future estimates. We introduce Counterfactual-to-Interactive Reinforcement Fine-Tuning (CRAFT), an on-policy framework that formulates closed-loop post-training as proxy-residual optimization. CRAFT uses group-normalized counterfactual advantages as a dense proxy for real closed-loop advantages and aligns this proxy with the closed-loop world through grounded residual correction from interaction-critical events. To stabilize adaptation, CRAFT regularizes the online policy toward an EMA teacher via asymmetric KL self-distillation. Theoretically, CRAFT decomposes the real closed-loop policy gradient into proxy and residual terms under the same visited-state distribution, reducing residual variance with an aligned proxy while mitigating proxy bias through grounded residual approximation. Empirically, CRAFT achieves the strongest closed-loop gains on Bench2Drive across hierarchical planning, vision-language-action, and vocabulary-scoring architectures. Ablations, scaling behavior, stability analyses, and transfer results further validate the complementary roles of dense counterfactual proxy and grounded residual correction.

Selecting a pretrained encoder for a downstream task without retraining or labels is a standard preprocessing step in transfer learning. The dominant label-free score, RankMe, was validated within architecture families and is widely applied across them. We show that its cross-architecture use is unsound: rankings are systematically driven by the penultimate-layer dimension rather than representational quality, and on heterogeneous model pools the score performs worse than uniform random selection. We give a random-matrix explanation establishing this failure as inherent to any rotation-invariant score, and propose Subspace-Corrected Ranking (SCR), a family of label-free corrections that breaks rotation invariance via alignment with a fixed reference encoder. Across three architecturally distinct pools and 23 aggregated task units, the proposed SSR-dagger reduces mean regret from +0.165 (RankMe) to +0.071 (95% CI [+0.045, +0.099], p < 1e-4). Paired with a within-family score in a two-stage pipeline, it matches this quality at one-third of the forward-pass cost. Five labelled architecture evaluations suffice to match the best fully label-free strategy; K=35 labelled evaluations reduce regret to +0.018 --- a 5.7x improvement at the same forward-pass cost.

Open-vocabulary semantic segmentation (OVSS) models enable dense prediction for classes specified at test time, but continual test-time adaptation (CTTA) over long, non-stationary streams can corrupt their language-aligned semantic interface, which we diagnose through synonym-prompt text alignment. We propose Cross-Foundation Complementary Learning Systems (XF-CLS), a source-free OVSS-CTTA framework that makes lightweight online adaptation stable over long horizons. Inspired by Complementary Learning Systems, which couple rapid plastic learning with slow consolidation, XF-CLS decouples plasticity from stabilization: a single fast online adaptation path updates only a small set of visual normalization parameters, a horizon-aware, source-anchored slow parameter memory consolidates stable changes and recovers from drift, and a frozen cross-foundation structural observer supplies a non-drifting structural prior for reliability-aware recovery and dense output refinement. Rather than training an additional teacher or using cross-foundation pseudo-label supervision, the frozen path preserves the open-vocabulary interface while correcting degraded visual structure. On long-horizon Cityscapes-to-ACDC and OnDA streams, XF-CLS achieves 32.92 and 30.75 mIoU, improving the NACLIP no-adapt baseline by +9.00 and +4.13 mIoU, respectively, and outperforming continual OVSS-TTA baselines. Decomposition studies and text-alignment diagnostics show that source-anchored recovery prevents long-horizon collapse, while frozen cross-foundation refinement adds consistent gains without updating extra parameters. XF-CLS provides a stable, parameter-efficient route to source-free continual open-vocabulary dense prediction, with inference overhead dominated by one additional frozen-backbone forward pass.

Graph matching aims to establish node correspondences while preserving both unary appearance compatibility and pairwise structural consistency. Recent deep graph matching methods have achieved strong empirical performance, but many of them either compress edge information into node embeddings before matching or rely on surrogate optimization signals for quadratic objectives. These design choices can weaken structural discrimination and may lead to optimization directions that are not aligned with the relaxed quadratic assignment problem (QAP). We propose Cross-order Consensus Graph Matching (CCGM), a framework that explicitly couples first-order node assignment and second-order edge assignment. CCGM constructs line graphs to convert edge matching into a node matching problem, learns node and edge affinities in parallel, and refines them through a Cross-order Consensus Solver motivated by the exact gradient of the factorized relaxed QAP. We further introduce Cross-order Alignment Regularization to encourage agreement between node-induced edge assignments and edge-induced node support during training. Experiments on standard visual graph matching benchmarks show that CCGM consistently improves matching accuracy over competitive baselines.

Audio--language models (ALMs) introduce a new jailbreak surface in which harmful requests can be delivered through speech. Existing safeguards rely on audio-side filters or guard models, leaving internal safety behavior largely uncontrolled. We instead pursue a representation-level alternative that controls refusal--compliance behavior inside the shared language-model backbone. However, our empirical study shows that direct audio-side steering is noisy and ineffective, consistent with activation analyses indicating larger acoustic and front-end variation in speech-derived representations. Based on this observation, we propose CrossSteer, a cross-modal steering method that learns a cleaner semantic safety direction from text-only preference pairs and transfers it to audio through the aligned shared residual stream. CrossSteer fits a residual-stream direction whose intervention shifts harmful-request generation from unsafe compliance toward safe refusal, and applies this direction at an audio-robust layer. Across three ALM backbones, CrossSteer consistently reduces audio jailbreak attack success rate while largely preserving benign utility, demonstrating cross-modal transfer of text-derived safety steering to the audio channel. Additional experiments show that CrossSteer composes with existing safeguards, further improving robustness as a complementary representation-level safety layer.Our anonymized source code is available at:~\url{https://anonymous.4open.science/r/CrossSteer-69F6}.

Language agents are moving from single-user assistants to shared collaborators in workspaces, forums, and group chats. A shared agent observes interleaved messages from multiple users, maintains persistent context, and executes tool actions, creating a boundary problem absent from standard single-user agents: the agent must decide not only whether an instruction is safe, but which user or task it is allowed to govern. We identify cross-user poisoning (CUP), where an adversary injects a message into shared context that is later applied while the agent serves a benign user, causing unauthorized actions or responses outside the instruction's intended scope. We validate CUP on two deployed multi-user agents, Continua and ElizaOS, and introduce MURMUR, a framework for evaluating shared-context agents under concurrent multi-user interactions. Across Slack, Workspace, and Airline domains, CUP achieves high attack success, persists across later interactions, and remains substantially more effective than matched prompt-injection attacks. We evaluate boundary-scoping defenses and find that context summarization alone is insufficient, while task clustering and provenance prompting substantially reduce non-adaptive propagation. These results show that robust multi-user agents require explicit user/task scoping rather than generic input filtering or context compression.

Lesion analysis in CT imaging, the task of identifying and delineating abnormal regions across diverse anatomical sites, plays a fundamental role in clinical diagnosis. Existing CT lesion datasets typically focus on a single disease type within a specific region. This design leads to two main limitations. First, co-existing lesions outside the predefined categories are left unannotated, so models may treat real lesions as background during training. Second, lesion categories within any single region follow a long-tailed distribution, in which rare but clinically important categories are underrepresented. However, collecting enough samples for every rare category within a single region is often impractical. We observe that certain lesion categories are rare in one region but occur more frequently in another, motivating the use of cross-region complementarity for lesion detection. In this work, we curate a multi-region CT lesion dataset, namely CT-Lesion, which covers brain, chest, and abdomen CT images. Our CT-Lesion is sourced from a private cohort of 500 clinical CT scans. Seven radiologists spent approximately 1,600 working hours delineating lesions of any type, rather than restricting annotations to a predefined set of categories. As a result, CT-Lesion provides clinically confirmed voxel-level segmentation masks and 28 fine-grained lesion labels. Moreover, we further group the lesions into 9 coarse categories to alleviate data scarcity issues. To demonstrate the effectiveness and utility of CT-Lesion, we benchmark the representative methods on CT-Lesion for lesion segmentation and detection. Furthermore, pretraining on CT-Lesion improves model performance on external benchmarks, including LiTS, KiTS, and LIDC-IDRI, demonstrating its potential as a transferable resource for CT lesion analysis. Our dataset is available at Kaggle.

Recent studies have demonstrated the potential of Large Language Models (LLMs) in generating GPU Kernels. Current benchmarks focus on the translation of high-level languages into CUDA, overlooking the more general and challenging task of text-to-CUDA generation. Furthermore, given the hardware-specific and performance-critical features of GPU programming, accurately assessing the performance of LLM-generated GPU programs is nontrivial. In this work, we introduce CUDABench, a comprehensive benchmark designed to evaluate the text-to-CUDA capabilities of LLMs. First, we construct CUDABench-Set, which covers Breadth-Depth-Difficulty evaluation space in diverse application domains, including artificial intelligence, scientific computing, and data analytics, etc. Furthermore, we propose CUDABench-Score and Generative Verification Pipeline that assess (1) compilation correctness, (2) functional consistency through execution-based verification, and (3) a novel roofline-based metric, Performance-Score. Benchmarking state-of-the-art LLMs reveals insightful findings and challenges of text-to-CUDA, such as a notable mismatch between high compilation success rates and low functional correctness, a lack of domain-specific algorithmic knowledge, and suboptimal utilization of GPU hardware resources.

Multi-task learning (MTL) aims to jointly optimize multiple related tasks to obtain shared representations within a single model. Recent research has focused on developing MTL algorithms to alter optimization dynamics through task re-weighting or gradient manipulation. However, in this paper, we first empirically identify that current MTL approaches are highly sensitive to the model initialization, complicating empirical evaluation and limiting practical reliability, which has been overlooked but appears to be critical. To better understand this phenomenon, we provide a local theoretical analysis that motivates the connection between early-stage MTL optimization behavior and curvature around initialization. Motivated by this analysis, we propose a Curvature-guided Parameter Initialization (CPI) approach tailored for MTL. Specifically, CPI performs a short warm-up phase to probe local curvature around the initial parameters and heuristically biases the initialization toward empirically favorable curvature profiles for subsequent multi-task optimization. The proposed approach is optimizer-agnostic, requires no modification to existing MTL algorithms, and can be seamlessly integrated as a plug-in prior to standard training. Extensive experiments on standard MTL benchmarks show that CPI improves the aggregate multi-task trade-off while remaining compatible with mainstream MTL methods.

Long-context LLM inference is fundamentally a retrieval process, transitioning from diffuse to sharply focused attention at a few architecture-determined (CTLs). Existing acceleration methods leave significant costs unaddressed: sparse attention ignores FFNs, KV cache compression neglects prefill, and hidden-state pruning relies on fixed, heuristic rules. We introduce CutAttn, a training-free framework that exploits CTLs via inter-layer Jensen--Shannon divergence, pruning redundant context at chunk granularity while dynamically gated by a normalized-entropy criterion. Operating at the hidden-state level, CutAttn reduces both attention and FFN costs, composing orthogonally with existing acceleration techniques. Evaluated on RULER, InfiniteBench, and LongBench, CutAttn preserves full-attention accuracy while achieving up to a $2.02\times$ prefill speedup on 128K sequences (and $2.81\times$ when composed with sparse attention). Furthermore, our theoretical analysis provides approximation bounds, an entropy-compression limit justifying the gating, and a closed-form speedup ratio.

Frontier language-model agents are rapidly becoming capable cybersecurity assistants, creating a dual-use cyber risk: the same capabilities that help defenders find, understand, and validate vulnerabilities can also help attackers weaponize them. Existing cyber benchmarks largely measure either raw offensive capability or generic refusal behavior, but they do not evaluate whether models can preserve benign security utility while refusing assistance that materially advances misuse. We introduce CyberDualEval, a benchmark for measuring this boundary on real vulnerabilities. Each task is decomposed into three phases of increasing operationalization: vulnerability analysis, proof-of-concept generation, and exploit execution. To capture the contested middle ground, we annotate each proof-of-concept task with a Task Weaponization Score that measures how much work remains to turn a minimally sufficient demonstration into unauthorized cyber misuse. CyberDualEval evaluates frontier language models with varied agentic scaffolds and reports refusal, defensive analysis accuracy, and optional exploit validation as distinct signals. Our evaluation reveals two complementary failure modes. Many models over-comply, assisting with high-weaponizability PoCs or exploit requests that should be refused. Conversely, recent safety-tuned models often over-refuse, declining low-risk or defensive requests and thereby reducing benign cyber utility. These results show that cyber safety cannot be assessed by capability or refusal rate alone: safe deployment requires calibrated refusal that tracks weaponizability while preserving useful defensive assistance.

Large language models (LLMs) are increasingly deployed as autonomous agents in offensive cybersecurity. In this paper, we reveal an interesting phenomenon: different agents exhibit distinct attack patterns. Specifically, each agent exhibits an attack-selection bias, disproportionately concentrating its efforts on a narrow subset of attack families regardless of prompt variations. To systematically quantify this behavior, we introduce CyBiasBench, a comprehensive 630-session benchmark that evaluates five agents on three targets and four prompt conditions with ten attack families. We identify explicit bias across agents, with different dominant attack families and varying entropy levels in their attack-family allocation distributions. Such bias is better characterized as a trait of the agents, rather than a factor associated with the attack success rate. Furthermore, our experiments reveal a bias momentum effect, where agents resist explicit steering toward attack families that conflict with their bias. This forced distribution shift does not yield measurable improvements in attack performance. To ensure reproducibility and facilitate future research, we release an interactive result dashboard at \url{https://cy-bias-bench.vercel.app/} and a reproducibility artifact with aggregated session-level statistics and full evaluation scripts at \url{https://anonymous.4open.science/r/CyBiasBench-2026/}.

Paired translation between quasiperiodic physiological waveforms (i.e., recovering a target oscillatory signal from the source) is central to the interpretation of cardiovascular signals derived from wearables placed at different body locations. This source-to-target mapping in these problems carries inherent geometric structure: the phase wraps around the cycle and must be treated as a circular variable, the amplitude remains strictly positive, and the beat-to-beat alignment can drift unpredictably across cycles and subjects. While deep neural networks have been used for phase estimation and complex-valued signal modeling, prior work does not explicitly learn phase transport between paired signals. Consequently, neither endpoint-supervised regression nor the standard affine path used in flow matching accounts for this phase--amplitude structure. We introduce \emph{cylindrical geodesic flow matching} for paired cardiovascular waveform translation. We show that the standard affine path used in flow matching distorts intermediate amplitude and instantaneous frequency when interpolating between quasiperiodic signals; replacing it with a closed-form geodesic on the phase--amplitude cylinder eliminates these artifacts and converts each training pair into dense, geometry-consistent velocity supervision. On zero-shot photoplethysmography and limited-support seismocardiography adaptation benchmarks, our method consistently outperforms interpolation baselines and matches or exceeds direct supervised prediction, reducing Hilbert Transform, $L_2$, and Dynamic Time Warping distance by up to ${\sim}15\%$ over the strongest competing baseline. These results suggest that bridge geometry is a critical inductive bias for flow matching on oscillatory signal translation.

Reinforcement learning with verifiable rewards (RLVR) has become a dominant recipe for improving mathematical and code reasoning in open-weight language models, but existing methods rely on fixed hyperparameters despite highly non-stationary training dynamics. We present DACE-RL (Diagnosis-Aware Compute-Efficient RL), a closed-loop framework that promotes training diagnostics to first-class signals. DACE-RL tracks four lightweight statistics—token entropy, sample diversity, advantage saturation, and answer consistency—and feeds their EMA-smoothed state to a controller that adaptively sets per-prompt rollout budget, sampling temperature, asymmetric clipping bounds, and entropy regularization strength. We formalize adaptive rollout allocation as a difficulty-conditioned multi-armed bandit and provide a regret bound that improves over uniform-budget GRPO under heterogeneous prompt difficulty; we also give a stability result for the dual-loop entropy-diversity controller. Across three small base models (Qwen2.5-Math-1.5B, Qwen2.5-7B, and Llama-3.1-8B-Instruct) and six math reasoning benchmarks, DACE-RL matches or improves Pass@1 while reducing rollout-GPU-hours by 38–52%, and improves Pass@32 by 4.7 absolute points on average. Pilot analysis further shows that diagnostic inflection signals appear 100–300 update steps before reward plateau, indicating that diagnosis-aware control is a key lever for compute-efficient RLVR.

Deep research tasks require agents to navigate large knowledge spaces, synthesize evidence across many sources, and adapt their plans as intermediate findings emerge. Directed acyclic graph (DAG)-based multi-agent systems are well suited to this setting because they support parallel execution and isolate each sub-task within a focused dependency context. Yet existing DAG-based agents typically instantiate a task-level plan before execution and then repair the graph only after failures or missing evidence are observed. This Plan-then-Patch strategy is brittle for deep research: the system commits most strongly when its evidence is weakest, and later revisions often waste computation on branches that should not have been planned in the first place. To bridge this gap, we propose DAGent, a DAG-based multi-agent framework that introduces Evaluate-then-Grow incremental planning. Instead of committing to a full DAG upfront, an Orchestrator grows the task graph one batch at a time, conditioning each new expansion on confidence and uncertainty signals from completed nodes. To support long-horizon evidence use without overloading each sub-task, DAGent maintains a hierarchical context layer that propagates compact QueryDocs by default while preserving full execution traces for on-demand recall. The cleanly recorded DAG topology in turn admits structural RL signals that an outcome-only recipe cannot define; we instantiate this with DAGRPO, a GRPO adaptation that injects topology-conditioned credit on Executor rollouts and a structural compliance regularization on Orchestrator plans. Across BrowseComp-Plus, GAIA, and xbench-DeepSearch, DAGent surpasses the strongest open-source baseline by 5.3 / 5.8 / 2.0 points at the Qwen3-235B-A22B scale, with the lead replicating across four open-source backbones from four vendors and extending to GPT-5 at 327K context. At the Qwen3-8B scale, DAGRPO further improves over a same-budget outcome-only GRPO baseline by 3.0 average Pass@1 points. A same-architecture comparison further shows that incremental, evidence-conditioned planning recovers accuracy at lower per-task token, tool-call, and step footprints than its Plan-then-Patch counterpart, supporting the view that DAGent's gains come from more targeted evidence expansion rather than additional computation. Anonymized code is available at \url{https://anonymous.4open.science/r/DAGent-F2D6}.

Current scaling law formulations suggest that increasing the number of unique samples in a dataset consistently improves model performance, while repeated exposure to the same samples provides diminishing value. In this work, we challenge this assumption for image self-distillation models. Through systematic experiments across diverse datasets, namely Maxar, Waymo, and ImageNet, we demonstrate that the effects of sample repetition on downstream performance are non-trivial and dataset-dependent. We discovered one scenario when repeatedly training on a subset of data can outperform training on a larger set of unique samples. This finding highlights a critical limitation of existing scaling laws. To address this gap, we propose a modified version of the Chinchilla scaling law that explicitly disentangles two factors: number of unique samples and repetition per sample. This formulation provides a more precise framework for understanding data-compute trade-offs and better predicts performance in regimes where repetition play a central role. Finally, we analyze how the inherent diversity of the smaller unique subset influences final downstream performance.

The learning dynamics of decentralized coupled representation learning can be formulated as a non-autonomous stochastic dynamical system, yet the macroscopic behavior of the resulting system on continuous manifolds remains poorly understood. We establish three theoretical results for such coupled slow-fast dynamics. First, under a standard random geometric graph scaling regime, we prove that the infinitesimal generator of the discrete Markov process converges to the generator of an overdamped Langevin diffusion for every $f \in C^3(\mathcal{M})$. Second, under timescale separation, the stochastic parameter dynamics converge to a deterministic averaged flow that admits a row-orthogonality Lyapunov function, controlling row-space degeneracy and ruling out parameter divergence. Third, under a spectral gap condition, the row space of the averaged parameter trajectory converges to the principal eigenspace of the induced covariance matrix. Together, these results give a stochastic-approximation limit theory for the coupled regime in which Markovian sampling and local subspace learning co-evolve, showing that memoryless local interactions can induce a stable and structured macroscopic limit.

Identifying subpopulations of neurons that interact with each other from simultaneous recordings of populations of many neurons is key for understanding across-brain communication with cellular resolution. Recent work identified communication subspaces, which capture additive interactions between pairs of high-dimensional neural populations through a small number of source and target activity patterns. However, no current method captures how a third, potentially multivariate variable - such as behavioral state or the activity of a third population - modulates these interactions. Here we extend the communication subspace framework by parameterizing modulation as a low-rank tensor. This identifies multiplicative interaction channels (MICs), defined as triplets of source, target, and modulator activity patterns, in which the modulator pattern gates the source-target interaction. We derive MICs as a bilinear perturbation of reduced-rank regression. We develop a hierarchical fitting pipeline and provide a closed-form decomposition that quantifies whether modulation reshapes the modulator-averaged baseline interaction, recruits private dimensions of one population, or opens new interactions. In simulations, MICs reliably recover the presence and geometry of ground-truth modulation even in the high-dimensional, low-sample regime. Applying MICs to simultaneous calcium imaging of prefrontal axons and interneurons in the visual cortex revealed that behavioral state asymmetrically modulates top-down interactions, reconfiguring the patterns of prefrontal projections that interact with a stable set of visual interneuron activity patterns. By providing an efficient and compact characterization of modulatory interactions, MICs enable asking new questions about how potentially high-dimensional variables shape interactions between neural populations.

AI-generated image detectors face significant challenges when deployed in real-world environments, particularly when test samples are produced by unseen generators that deviate from the source training distribution. Existing detectors often rely on a single type of evidence, such as semantic representations or low-level generation traces, making them vulnerable when the corresponding cue becomes unreliable. Moreover, adapting detectors to newly emerging generators usually requires target labels or retraining, which is costly and impractical. To address these challenges, we propose Dual-Evidence Disagreement-Constrained Adaptation (DEDCA), a test-time adaptation framework for generalized AI-generated image detection. DEDCA constructs a dual-evidence detector by combining CLIP-based semantic evidence with trace evidence extracted from shuffled entropy and residual statistics, and introduces a reliability-aware gate to perform sample-specific adaptive fusion. Our key idea is to exploit the disagreement between the two evidence streams as an unlabeled adaptation signal, enabling the detector to adjust to target-domain shifts without relying on target labels or blindly trusting a single prediction stream. Experiments show that DEDCA achieves superior performance on the GenImage benchmark by outperforming state-of-the-art AI-generated image detectors and test-time adaptation methods with 92.0\% average accuracy.

In recent years, AIGC technologies are capable of generating hyper-realistic forged facial images, posing severe threats to facial security safety. To tackle these challenges, early Deepfake research primarily centered on real-fake classification. Recently, growing efforts have been devoted to the Deepfake Attribution (DFA) of generated content. Nevertheless, existing research on Deepfake detection and attribution faces saturated performance in binary classification, limited diversity in datasets and algorithms, and imperfect evaluation protocols, which severely impede practical application. To address these limitations, we propose a comprehensive deepfake detection and attribution benchmark named DeepfakeGenome (DFG). It contains 100 facial forgery algorithms and 2M images in total, achieving 4× to 100× larger than prior DFA benchmarks. We further designed 4 protocols for practical evaluation, including a novel retrieval-based attribution paradigm. Unlike previous open-set evaluation metrics, the proposed retrieval metrics are more aligned with the real-world active defense situation of blacklist registration mechanisms. Based on these elaborate designs, we investigate the performance ceiling of deepfake attribution task. Over 2k+ experimental evaluations are conducted, and 10 insightful findings are derived. We hope this work can provide new insights into the DFA research field.

Supervised training of deep neural networks for classification typically relies on hard targets, which promote overconfidence and can limit calibration, generalization, and robustness. Self-distillation methods aim to mitigate this by leveraging inter-class and sample-specific information present in the model’s own predictions, but often remain dependent on hard targets without explicitly modeling predictive uncertainty. With this in mind, we propose Deep Probabilistic Supervision (DPS), a principled learning framework that constructs sample-specific target distributions via Bayesian inference on the model’s own predictions and remains independent of hard targets after initialization. We show that DPS consistently yields higher test accuracy (e.g., +2.0\% for DenseNet-264 on ImageNet) and significantly lower Expected Calibration Error (ECE) (-40\% for ResNet-50 on CIFAR-100) than existing self-distillation methods. When combined with a contrastive loss, DPS achieves state-of-the-art robustness under label noise.

Reinforcement learning in real-world systems often involves delayed feedback, which breaks the Markov assumption and impedes both learning and control. Canonical augmentation-based approaches cause state-space explosion, which imposes a severe sample-complexity burden. Despite recent progress, state-of-the-art augmentation-based baselines either mainly alleviate the burden on the critic or rely on non-unified treatments for the actor and critic. In this study, we propose delayed homomorphic reinforcement learning (DHRL), a framework grounded in MDP homomorphisms that defines a belief-equivalence relation over the augmented state space to collapse control-redundant augmented states. In principle, this yields exact abstraction under deterministic dynamics and approximate abstraction under stochastic dynamics, enabling both the actor and critic to benefit from a structured abstraction mechanism. In finite domains, exact abstraction preserves optimality and recovers the delay-free sample-complexity order, whereas approximate abstraction admits a value-loss bound on the resulting policy. For continuous domains, we introduce deep delayed homomorphic policy gradient (D$^2$HPG), a deep actor-critic instantiation of the DHRL framework. Experiments on continuous-control tasks in MuJoCo show that D$^2$HPG outperforms strong augmentation-based baselines.

The first-moment buffer of most modern optimizers is an exponential moving average (EMA) of stochastic gradients with a single scalar decay, with a unified forgetting horizon along every direction in parameter space. Yet the per-sample gradient of any linear layer factorizes as a rank-1 outer product $g_t = \delta_t x_t^\top$, exposing the input activation as a natural $\textit{key}$ and the output-side error as a natural $\textit{value}$, which is the structure that EMA's flat matrix average discards. We propose $\textbf{DeltaMomentum}$, which interprets the buffer as an online linear associative memory of these key--value pairs and updates it via the classical delta rule. The resulting update is $\textit{anisotropic memory transport}$: directions queried frequently are forgotten quickly, directions queried rarely are preserved on long horizons, yielding an automatic, data-driven schedule matched to input statistics. We prove the buffer's expected fixed point is a Tikhonov-regularized Wiener predictor of the population gradient, which is equivalent to implicit input-side natural-gradient preconditioning at no covariance-inversion cost, and that the same mechanism strictly accelerates expected tracking-error contraction along every positive-density input direction under non-stationarity and reduces the per-direction iteration determinant in linear regression. DeltaMomentum is a drop-in replacement for the EMA accumulator in any base optimizer; we instantiate it as DeltaSGD and DeltaAdamW. A $\mu$P derivation shows the delta coefficient is width-invariant, enabling zero-shot hyperparameter transfer; we verify this with a coordinate check across five widths. The per-block asymptotic FLOP overhead is $\approx 15.74$% at zero persistent-memory cost (down to $7.3$% and $11.0$% realized at 67M/370M). On FineWeb-Edu, DeltaAdamW reaches AdamW's terminal validation loss in up to $31.35$% fewer steps at 67M and $19.28$% fewer steps at 370M. On CIFAR-10, DeltaSGD shows the same pattern against SGD-momentum, confirming the gain is not specific to Adam. Mechanistic diagnostics confirm that improvements in gradient-estimator quality, function-space prediction error, and input-feature conditioning are operative throughout training.

This paper explores the theoretical foundations of fair regression under the constraint of demographic parity within the unawareness framework, where disparate treatment is prohibited, extending existing results where such treatment is permitted. Specifically, we aim to characterize the optimal fair regression function when minimizing the quadratic loss. Our results reveal that this function is given by the solution to a barycenter problem with optimal transport costs. Additionally, we study the connection between optimal fair cost-sensitive classification, and optimal fair regression. We demonstrate that nestedness of the decision sets of the classifiers is both necessary and sufficient to establish a form of equivalence between classification and regression. Under this nestedness assumption, the optimal classifiers can be derived by applying thresholds to the optimal fair regression function; conversely, the optimal fair regression function is characterized by the family of cost-sensitive classifiers.

We study post-hoc Learning to Defer (L2D) through the lens of ideal distributions: divergence-regularized reweightings of the data distribution under which a model attains low loss. We define deferral via the density-ratio between a model's and an expert's ideals. Using the reduction from density-ratio estimation to class-probability estimation, we derive the DR CPE losses for post-hoc L2D scorers. Deferral decisions are then made by thresholding the scorer, allowing deferral rates to be adjusted without retraining. For KL-based ideal distributions, our deferral rules recovers Chow's rule under the original distribution and a connection to an expert-tilted Bayes posterior—which incorporates the expert's performance—depending on if the ideal distributions are joint or marginal distributions. Experimentally, our approach is competitive compared to common baselines and more robust across dataset settings. More broadly, our results cast post-hoc L2D as density-ratio learning between ideal distributions, bridging Chow-style rules, expert comparison, and elucidating connections to related learning settings including anomaly detection.

Reinforcement learning (RL) post-training provides a direct way to align diffusion and flow-matching generators with human preferences and task-specific rewards, but current RL algorithms for diffusion models remain fragmented: reverse-trajectory methods rely on discretized likelihood ratios, whereas forward-matching methods train on reward-labeled noising pairs. This paper shows that these seemingly different losses arise from a single path-space principle. Starting from the regularized diffusion-RL objective, we use importance sampling between sampling SDEs to obtain an explicit policy-gradient estimator on trajectory space. The estimator contains the stochastic It\^o integral underlying Flow-GRPO-type updates; we derive an equivalent variance-reduced value-gradient form that recovers the forward-matching structure of AWM and DiffusionNFT. This identifies the empirical gap between these method families as a variance-reduction effect rather than a difference in RL principle. The derivation yields a unified design space organized by value-gradient estimation, weight functions, and sampling choices. Within this space, we propose a multi-sample KDE value-gradient estimator that reuses rollout groups, together with scale-bounded weight families that retain stable existing recipes while excluding singular ones. Experiments on SD3.5-M and Qwen-Image models validate the variance-reduction explanation and show that the resulting recipe matches or improves over prior diffusion-RL baselines.

Foundation models produce high-dimensional latent representations in which suitably designed statistical decision rules can be effective yet analytically tractable. We study such detectors in the high-dimensional regime, where latent dimension and training sample size grow proportionally. For linear and quadratic statistics, we derive explicit large-deviation characterizations of false-alarm and miss-detection performance under the Neyman--Pearson paradigm, and show how the binary analysis extends to multi-class classification. The resulting error probabilities and corresponding decay rates exhibit non-monotonic dependence on the shape parameter, including double-descent and, in some regimes, multiple-descent. Experiments on vision, text, and audio datasets using modern foundation models show close agreement between theoretical predictions and empirical performance. In particular, the proposed framework allows us to set a prescribed false-alarm level and accurately predict the corresponding miss-detection probability. Overall, our results provide a theoretically grounded and interpretable framework with competitive empirical performance across different modalities.

Dexterous object manipulation remains a fundamental challenge in robotics, typically requiring intensive supervision through explicit rewards, goals, or demonstrations. While unsupervised reinforcement learning (RL) holds the promise of discovering dexterous skills, state-of-the-art metric-aware methods have yet to be successfully applied to complex, high-dimensional manipulators like multi-fingered hands. We argue that this limitation stems from two key factors: existing methods lack the necessary inductive biases to prioritize object interaction over agent motion, and they fail to respect the non-Euclidean topology of object configuration space $\mathrm{SE}(3)$. To address these limitations, we propose a novel Wasserstein dependency measure between skills and object motions, formulated in the Lie algebra $\mathfrak{se}(3)$. Our approach, Topology-Aware SKill discovery (TASK), enables the discovery of fundamental manipulation skills—such as multi-axial object translation and rotation—without manual supervision. Across a range of embodiments, including non-prehensile and multi-fingered hands, our method learns dexterous behaviors where previous approaches fail, marking a significant step toward autonomous robotic dexterity.

Dynamic Reconfigurable Battery (DRB) systems enable flexible interconnection among cells through power electronic switches, crucial for electric vehicles and energy storage systems. However, sensor failures in practical deployments lead to partial observation loss, posing severe challenges for safe control. Existing methods assume full state observability and often adopt overly conservative strategies when observations are missing, failing to achieve effective trade-offs between safety and performance under uncertainty. More fundamentally, these methods lack mechanisms to adaptively adjust decision conservatism based on inference reliability, leading to unstable decisions under complex failure scenarios. We propose the $\textbf{D}$iffusion-$\textbf{G}$uided $\textbf{R}$isk-$\textbf{A}$daptive $\textbf{F}$ramework (DGRAF) to address this challenge. The framework handles spatiotemporally coupled failure patterns through joint diffusion inference with quality quantification, and integrates inference quality with distributional decision-making to enable automatic modulation between aggressive optimization and conservative maintenance. Through extensive simulations and hardware experiments, we demonstrate that DGRAF consistently outperforms baseline methods across complex failure patterns, achieving superior energy efficiency, decision stability, and risk resilience.

Fine-grained video action recognition often depends on how an action unfolds over time rather than on appearance alone. Visually similar classes may differ in preparation, execution, or completion phases, making uniformly aggregated temporal context suboptimal. We study this problem under a practical objective: i) improving recognition performance with few tunable parameters, ii) being compute efficient, and iii) using only the readily available video modality. Motivated by this missing temporal observation and the accuracy--compute efficiency--unimodality objective, we propose **Directional Adapter (DiA)**, a parameter-efficient adapter on top of CLIP. DiA formulates temporal specialization into causal and anti-causal directions, allowing past-to-present and future-to-present cues to be modeled differently. To keep this directional modeling parameter- and compute-efficient, both directions use depth-wise temporal convolutions in a compact bottleneck space and are combined through a learnable fusion. DiA achieves state-of-the-art performance across 7 public benchmarks with only $\sim$3.5M tunable parameters, while also delivering higher throughput and lower latency than prior methods. Code is available at https://anonymous.4open.science/r/diafullsupervised-8CC3/README.md.

Compact medical multimodal models are the most realistic path to clinical deployment, yet they often produce fluent answers that are not actually grounded in the image. We show that this failure has a clear internal signature. In five compact medical backbones and across six medical VQA benchmarks, visual tokens lose their spatial diversity shortly after entering the language backbone, their residual updates are an order of magnitude smaller than text residuals, and attention on them freezes on content free regions of the image. Zeroing the visual residual stream barely changes the output, while zeroing the text residual stream destroys it. We give a short analytical account that links the collapse of visual similarity to the residual dominance ratio, and we propose VGrip, a set of three lightweight losses that act on the three failure points without modifying the backbone architecture or the inference procedure. We also introduce the Visual Dependency Score, a simple and benchmark agnostic measure of how much a model actually uses the image. On Qwen2.5-VL-7B, VGrip lifts accuracy by 4.7 points on average across the six benchmarks and more than doubles the Visual Dependency Score over a strong LoRA baseline, with gains that are stable from two to eight billion parameters.

We introduce a framework for differentiable exact learning of algorithms. The framework couples a state-tracking neural controller to an unbounded external environment and trains on policy-trajectory observations (PTOs) -- traces that record the environment's evolution under an expert's actions without exposing the expert's internal state. This supervision regime sidesteps Gold's impossibility barrier for input-output recursive learning while avoiding the unrealistic internal-state access required by prior provably correct approaches. As controllers, we propose Differentiable Finite-State Transducers (DFSTs), a minimalist multilinear model family that contains no nonlinearities and admits log-parallel training via prefix scans. Trained on tiny datasets, DFST and RNN controllers achieve error-free generalization on binary and decimal addition and multiplication to operands thousands of times longer than the training examples. Moreover, we provide strong empirical proof for exact learning of universal computation. Finally, we develop an extraction procedure that recovers compact finite-state transducers from trained controllers. We prove under a geometric clustering assumption that for binary addition the extracted transducer exactly replicates the expert policy on all valid inputs -- certifying differentiable exact learning of the algorithm.

Many network design tasks must decide which directed arcs to build so that multiple sources can reach a root efficiently. Building fewer or cheaper arcs reduces construction cost, but may force long routing paths; building more arcs shortens routes, but increases construction cost. The Cost--Distance problem formalizes this trade-off, yet it has remained difficult to optimize with gradient-based methods because the selected network, the induced routes, and the routing cost are tightly coupled. We propose Cost Distance Policy Gradient (CDPG), a differentiable framework that treats local next-hop choices as a routing policy, prevents unstable cyclic routing through Dynamic Acyclic Dropout, evaluates the objective with a truncated value solver, and uses the induced acyclic support for quality-preserving TopoRounding. Under the stated conditions, CDPG has a fixed-accuracy (\mathcal{O}(m\log n)) efficiency guarantee. Experiments cover 1,487 instances in UAV logistics networks, transportation networks, and four synthetic graph families; across this suite, CDPG shows a strong time--quality trade-off compared with other differentiable baselines, Cost--Distance-specific algorithms, and the commercial solver. Our code is available at: https://anonymous.4open.science/r/cdpg_nips-28D5/.

Mechanistic interpretability seeks to explain neural network behavior by decomposing model computations into interpretable features and circuits. While transcoder-based circuit tracing has recently enabled detailed causal analyses of large language models, multimodal diffusion transformers for image generation remain comparatively opaque. We still lack tools for understanding how semantic information propagates across denoising steps and how text and image representations interact within double-stream MM-DiT architectures. Existing methods provide only partial insight: attention maps expose a limited view of token interactions, while sparse autoencoders can discover interpretable features but do not directly reveal how these features are transformed and composed through nonlinear MLP layers. In this work, we extend transcoder-based circuit tracing to multimodal diffusion transformers. We train timestep-conditioned transcoders that faithfully approximate the input–output behavior of MLP sublayers in FLUX.1[schnell]. By replacing MLPs with transcoders and linearizing the remaining computation, we obtain exact feature-to-feature attribution and recover compact, interpretable circuits. Empirically, our transcoders match or slightly outperform sparse autoencoders on the sparsity–faithfulness tradeoff. The resulting circuits reveal mechanisms underlying attribute binding and cross-stream semantic propagation, and provide causal explanations for systematic generation errors. Moreover, circuit-guided interventions are substantially more precise and effective than standard SAE-based steering. Our results demonstrate that transcoder-based circuit analysis is feasible for state-of-the-art diffusion transformers and provides a powerful framework for understanding and controlling multimodal generative models.

Health risk prediction aims to predict a patient's potential health risks (e.g., mortality) using the longitudinal information present in their electronic health record (EHR). While deep learning-based risk prediction models have shown great promise, they are susceptible to the issue of missing data prevalent in real-world EHR. Most prior works mitigate this challenge by treating missing data as an uninformative perturbation to be ignored or imputed. However, inaccurate imputation may lead to the generation of sub-optimal patient representations that adversely impact risk prediction performance. Moreover, recent studies show that missing data in EHR does not necessarily imply error or noisy observation and instead may communicate informative clinical signals useful for health risk prediction. To address this, we propose a novel diffusion-based representation learning approach (namely $\textbf{DiffRisk}$) that explicitly captures $\textit{informative missingness}$ by modeling conditional dependencies induced by partial observations in EHR. Specifically, DiffRisk learns latent representations with coherent conditional structure, where any subset of observed features defines a distribution over the unobserved components. To achieve this, we introduce a conditional score-based objective and approximate it via denoising diffusion that enables efficient learning without explicit density estimation. Empirical results on five risk prediction tasks show that the proposed approach consistently outperforms state-of-the-art baselines.

Dynamic point cloud pretraining is still dominated by masked reconstruction objectives. However, these objectives inherit two key limitations. Existing methods inject ground-truth tube centers as decoder positional embeddings, causing spatio-temporal positional leakage. Moreover, they supervise inter-frame motion with deterministic proxy targets that systematically discard distributional structure by collapsing multimodal trajectory uncertainty into conditional means. To address these limitations, we propose Diffusion Masked Pretraining (DiMP), a unified self-supervised framework for dynamic point clouds. DiMP introduces diffusion modeling into both positional inference and motion learning. It first applies forward diffusion noise only to masked tube centers, then predicts clean centers from visible spatio-temporal context. This removes positional leakage while preserving visible coordinates as clean temporal anchors. DiMP also reformulates point-wise inter-frame displacement supervision as a DDPM noise-prediction objective conditioned on decoded representations. This design drives the encoder to target the full conditional distribution of plausible motions under a variational surrogate, rather than collapsing to a single deterministic estimate. Extensive experiments demonstrate that DiMP consistently improves downstream accuracy over the backbone alone, with absolute gains of 11.21% on offline action segmentation and 13.65% under causally constrained online inference.

Diffusion language models generate text through iterative denoising, but current interpretability tools mostly analyze static hidden states and do not explain how concept information evolves across denoising time. We introduce DynaManifold-SAE, a method for discovering sparse autoencoder feature groups whose activations form time-dependent concept manifolds in masked and diffusion language models. The method builds sparse latent codes across mask ratios, constructs candidate feature groups, and evaluates them with held-out coordinate prediction, geodesic consistency, persistence across time, and causal interventions. Across BERT and LLaDA, DynaManifold-SAE reliably identifies sparse sentiment geometry that is stable across seeds and stronger than individual-feature, PCA, random-group, and graph-structured baselines. The discovered groups transfer from controlled templates to natural SST-2 examples, indicating that they capture semantic structure rather than template artifacts. We further show that these groups explain a denoising-specific mechanism: they predict token reveal order and confidence growth during LLaDA unmasking, and targeted interventions alter reveal confidence and token recovery more than matched controls. These results suggest that sparse feature manifolds provide a practical bridge between static representation geometry and the dynamics of diffusion language generation.

Diffusion-based foundation models for crystal generation have become competent priors over the manifold of plausible materials, but turning that prior into generation of promising candidates requires targeting stability while balancing several conflicting properties at once. Fine-tuning is expensive and produces a new model for every new objective, while differentiable guidance is not applicable to many real-world rewards. We instead adapt these models at \emph{inference time} using Diffusion Tree Search (DTS), a Monte Carlo tree search procedure over the denoising trajectory. We make two technical contributions on top of DTS that are particularly relevant for AI-for-science applications. First, we show how to use existing property-conditional foundation models (e.g., classifier-free guidance variants of MatterGen) as guided proposals inside DTS, with importance-corrected backups and selection that keep the unconditional base model as the target distribution. Second, we extend DTS to multi-objective sampling by maintaining per-objective soft value estimates and selecting expansions through a sampled scalarization, so a single search produces samples spanning a Pareto front while sharing computation across preferences. We instantiate both adaptations on de novo crystal generation: single-objective stability search with different underlying models, conditional generation of crystals belonging to rare space groups, and a multi-objective tasks using a pretrained conditional model. Across these, DTS-based inference-time adaptation improves stability rates and Pareto-front quality without modifying the base models.

Echo State Networks (ESNs) are designed to be \emph{stable}—the echo state property makes the reservoir state a well-defined function of the input history—but it remains unclear how \emph{geometrically rich} the reachable reservoir states are and what controls that richness. We give a partition-free, fractal-geometric theory of contractive reservoirs by viewing a quantized-input ESN as a contractive random dynamical system and, equivalently, an iterated function system (IFS). This identifies the reachable set as the IFS attractor and the stationary reservoir-state law under i.i.d.\ inputs as the unique invariant IFS measure. We prove entropy–contraction (entropy–Lyapunov) bounds on the intrinsic dimension of this measure, and in a conformal/similarity regime with standard separation conditions we obtain sharp closed-form dimension laws of the form “input entropy divided by contraction.” These results yield a quantitative \emph{criticality principle}: weakening contraction drives a transition from low-dimensional to full-dimensional state representations at an explicit threshold set by input entropy and contraction rates, providing practical design guidance for tuning leak/spectral radius and input scaling.

In reinforcement learning (RL), reward specification plays the fundamental role in shaping policy optimization. Inverse RL (IRL) offers a data-driven approach to inferring rewards from demonstrations, but prevalent neural-network-based methods often produce rewards lacking interpretability and flexibility for manual inspection or revision. In contrast, human-written or large language model (LLM)-generated reward code provides explicit semantic structure, yet its precise numerical parameters are often difficult to calibrate and prone to specification errors. To bridge this gap, we propose DiReCL (\textbf{Di}fferentiable \textbf{Re}ward \textbf{C}ode \textbf{L}earning), a novel framework that unifies these two paradigms by formulating reward learning in a differentiable reward code space. DiReCL first prompts an LLM to synthesize the reward code templates, then converts the numeric elements in these templates into differentiable parameters and align them with expert demonstrations through IRL. We further introduce a reflection mechanism that uses IRL diagnostics to provide evidence of inadequacies in the reward structure and guide template revision. Extensive experiments on MuJoCo locomotion and autonomous driving benchmarks demonstrate that DiReCL achieves over $1.25 \times$ the performance of both standard IRL and LLM-assisted reward design baselines, while preserving the crucial benefits of explicit, interpretable reward semantics.

N-dimensional splatting extends 3D Gaussian Splatting with conditioning variables such as view direction and time to model view-dependent and dynamic effects. Each primitive is lifted into a joint distribution over 3D position and conditioning variables, and at render time is conditionally sliced at the query to recover a 3D primitive whose mean, opacity, and covariance vary continuously with view or time. N-DGS (covering 6DGS and 7DGS) and UBS together represent the leading N-dimensional splatting formulations across Gaussian and Beta kernels, but share the same conditional-slicing step: all three effects are derived from the joint covariance matrix per primitive in every forward pass, requiring matrix inversions and regression-matrix multiplications that scale with primitive count. The conditional-slicing step is kernel-agnostic, so a single improvement carries across families. We introduce direct conditional parameterization, replacing the covariance-derived form with explicit per-effect parameters: a Cholesky precision factor for opacity and a displacement matrix with learnable per-dimension coupling for position. Applied to both kernels, the parameterization yields dGS (Gaussian) and dBS (Beta). Across five static and dynamic benchmarks, dGS and dBS match or exceed their covariance-derived counterparts on quality (up to +1.26 dB in the main MCMC evaluation) while running 6.9-7.7x faster at the slicing step and up to 2.66x faster end-to-end rendering. Direct conditioning is a kernel-agnostic drop-in replacement for covariance-derived conditioning in N-dimensional splatting.

Recent flow matching (FM) methods improve the few-shot adaptation of vision-language models, by modeling cross-modal alignment as a continuous multi-step flow. In this paper, we argue that existing FM methods are inherently constrained by incompatible geometric priors on pre-trained cross-modal features, resulting in suboptimal adaptation performance. We first analyze these methods from a polar decomposition perspective (\ie, radial and angular sub-manifolds). Under this new geometric view, we identify three overlooked limitations in them: \textit{1) Angular dynamics distortion}: The radial-angular coupling induces non-uniform speed on the angular sub-manifold, leading to regression training difficulty and extra truncation errors. \textit{2) Radial dynamics neglect}: Feature normalization discards modality confidence, failing to distinguish out-of-distribution and in-distribution data, and abandoning crucial radial dynamics. \textit{3) Context-agnostic unconditional flow}: Dataset-specific information loss during pre-trained cross-modal feature extraction remains unrecovered. To resolve these issues, we propose \textbf{warped product flow matching (WP-FM)}, a unified Riemannian framework that reformulates alignment on a warped product manifold. Within this framework, we derive \textbf{direct product flow matching (DP-FM)} by introducing a constant-warping metric, which yields a decoupled cylindrical manifold (\ie, direct product manifold). DP-FM enables independent radial evolution and constant-speed angular geodesic transport, effectively eliminating angular dynamics distortion while preserving radial consistency. Meanwhile, we incorporate classifier-free guidance by conditioning the flow on the pre-trained VLMs' hidden states to inject missing dataset-specific information. Extensive results across 11 benchmarks have demonstrated that DP-FM achieves a new state-of-the-art for multi-step few-shot adaptation.

Implicit in-weight multi-hop reasoning---composing multiple pieces of parametric knowledge within a single forward pass---is a fundamental yet challenging task of language models. Vanilla transformers consistently fail at this task, and even looped transformers, whose recurrent structure naturally fits its iterative nature, generalize imperfectly, particularly out-of-distribution (OOD). We make two contributions toward closing this gap. First, on a symbolic two-hop reasoning task, we mechanistically diagnose why looped transformers fall short: after the first loop, the intermediate answer is already decodable from the residual stream with certain probability, yet the continuous hidden vector carrying it is noisy and geometrically misaligned with the clean discrete embedding of the intermediate answer that the next loop would ideally consume. A training-free intervention that re-aligns this representation nearly closes the OOD gap, indicating that the representational mismatch is a bottleneck. Second, building on this insight, we propose DiscoLoop, a looping architecture whose recurrence carries both a discrete embedding channel and a continuous hidden-state channel. DiscoLoop achieves near-perfect two-hop accuracy with substantially less training across symbolic and synthetic-language multi-hop reasoning tasks. When applied to real pretraining, DiscoLoop attains lower training loss and stronger performance on pretraining benchmarks than looped-transformer baselines, suggesting that the mixed-channel design transfers to practical language modeling.

Mechanics-inspired neural dynamical models improve interpretability and physical consistency, but they usually assume a known configuration space, fixed physical parameters, and deterministic prediction from a prescribed initial condition. We introduce Structured Phase Space GAN (SPS-GAN), a conditional generative model that learns structured dynamics from both Cartesian trajectories and video. SPS-GAN combines a port-Hamiltonian backbone with adversarial conditional generation, allowing conservative, dissipative, and forced systems to be represented in a single architecture. To infer latent mechanical structure from raw observations, we use a cyclic-coordinate loss that recovers both the system degrees of freedom and canonical coordinates without access to the true phase space. Experiments on simulated and real-world systems show that SPS-GAN improves trajectory accuracy over supervised dynamics baselines and video consistency over generative baselines, while consistently recovering the correct latent mechanical coordinates across observation modalities and dynamical regimes.

While all-in-one image restoration models excel in controlled, closed-set environments, they face critical limitations when deployed in open-world settings where paired supervision is unavailable and degradations are unknown and often intricately mixed. To address this challenge, we propose a continual restoration framework that integrates degradation discovery with adaptive restoration. Building on a model pre-trained on known degradation categories, our method discovers unseen degradations in feature space and continually expands the degradation space. We then introduce a discovery- and instance-conditioned descriptor that jointly captures discovered degradation priors and image-specific characteristics, enabling robust restoration under ambiguous and intricately mixed degradations. To further support adaptation under unpaired supervision, we adopt a mean-teacher-based semi-supervised framework equipped with a reliable bank for pseudo-target refinement. Specifically, we propose a discovery-adaptive score that assesses pseudo-target reliability using feature-space distances to degradation categories, enabling pseudo-target selection to remain adaptive to newly discovered degradations rather than relying solely on conventional perceptual quality-based scoring methods. Experiments on both controlled open-world protocols and real-world adverse weather datasets demonstrate that our approach consistently outperforms existing methods across multiple image quality metrics while improving robustness to degradation composition shifts.

Autoregressive video diffusion models (AR-VDMs) enable real-time, long-horizon video generation but degrade rapidly once the inference horizon outruns the training horizon, due to compounding exposure bias. To stabilize long rollouts, recent methods heuristically adopt frame sink, which keeping initial frames as a persistent KV cache context, inspired by attention sinks in streaming LLMs. We revisit this design and find that the mechanism by which initial frames stabilize long-range consistency in AR-VDMs is not an LLM-style attention sink; instead, consistency is anchored through a sparse subset of attention heads, while the majority of heads predominantly capture local temporal dynamics. This observation reveals an over-conditioning pathology in existing frame-sink strategies: by universally exposing every head to the pinned initial-frame context, the model becomes prone to collapsing motion toward the low-dynamic modes of the teacher during model distillation. We propose Disen-Forcing, a distillation-time plugin for AR-VDMs that disentangles and reinforces this head specialization through disentangled context routing and motion-disentangling regularization. On long-horizon video generation benchmarks, Disen-Forcing simultaneously improves semantic consistency and motion dynamics of base AR-VDMs: a trade-off that sink-based methods fail to balance.

Knowledge Distillation (KD) trains a smaller-capacity student model to imitate a larger-capacity teacher model by matching output distributions, implicitly assuming the teacher to be a reliable oracle. In large language models (LLMs), this assumption often fails: teacher predictions can exhibit high entropy and hallucinations, causing standard KD to degrade well-calibrated student priors. We propose CaRE-KD, a confidence-gated distillation framework that replaces static objectives with uncertainty-adaptive optimization. CaRE-KD has two components: a token-level loss (CaRE-Divergence) that adaptively switches between Forward and Reverse KL based on teacher--student confidence, and a batch-level epistemic rejection mechanism (Revival) that suppresses updates when the teacher is more uncertain than the student. We provide a gradient-level analysis showing how this dual-granularity design induces a conditional calibration mechanism that prior static divergences cannot reproduce. Empirically, across eight teacher--student pairs and eleven benchmarks spanning instruction following, chat alignment, code generation, and mathematical reasoning, CaRE-KD delivers consistent and significant gains over strong baselines (Skewed-KL, $\alpha$--$\beta$ divergence). Highlights include up to $+3.2$ average ROUGE-L on instruction-following tasks, $+2.1$ pass@1 on MBPP, $+1.7$ accuracy on GSM8k, and $+1.8$ accuracy on CollegeMath over the strongest baseline, with consistent gains in LLM-as-a-judge factuality (up to $+2.5$ per task over Skewed-RKL). Revival further acts as a principled, loss-agnostic plug-in that systematically strengthens existing distillation objectives by filtering epistemically unreliable teacher supervision.

Distributed online convex optimization (D-OCO) is a powerful paradigm for modeling distributed scenarios with streaming data. However, the communication cost between local learners and the central server is substantial in large-scale applications. To alleviate this bottleneck, we initiate the study of D-OCO with compressed communication. Firstly, to quantify the compression impact, we establish the $\Omega(\delta^{-1/2}\sqrt{T})$ and $\Omega(\delta^{-1}\log{T})$ lower bounds for convex and strongly convex loss functions, respectively, where $\delta \in (0,1]$ is the compression ratio. Secondly, we propose an optimal algorithm, which enjoys regret bounds of $O(\delta^{-1/2}\sqrt{T})$ and $O(\delta^{-1} \log T)$ for convex and strongly convex loss functions, respectively. Our method innovatively incorporates the error feedback mechanism into the Follow-the-Regularized-Leader framework to address the coupling between the compression error and the projection error. Furthermore, we employ the online compression strategy to mitigate the accumulated error arising from the bidirectional compression. Our online method has great generality, and can be extended to the offline stochastic setting via online-to-batch conversion. We establish convergence rates of $O(\delta^{-1/2}T^{-1/2})$ and $O(\delta^{-1} T^{-1})$ for convex and strongly convex loss functions, respectively, providing the first guarantees for distributed non-smooth optimization with compressed communication and domain constraints.

Algorithmic recourse (AR) aims to provide a recourse action that alters an undesired prediction made by a machine learning model. While standard AR methods assume the model does not change over time, this assumption is often violated in practice due to distribution shifts or data updates, rendering the suggested actions invalid. To address this issue, recent studies have proposed several methods that provide actions robust to model changes. Most of them construct an uncertainty set of models by perturbing continuous model parameters, such as the weight vectors of neural networks, and then seek actions that remain valid for all models in the set. However, such an approach cannot be directly applied to tree ensembles because their model parameters include discrete tree structures, which cannot be perturbed in the same way as neural networks. In this paper, we introduce a new robust AR framework for tree ensembles through the lens of distributionally robust optimization (DRO). Our key idea is to express the model change in a tree ensemble as a perturbation to the distribution of predictions made by decision trees in the ensemble, rather than to the model parameters themselves. This formulation allows us to naturally cast the robust AR problem for tree ensembles as a DRO problem. We then show that, by choosing a specific discrepancy measure between distributions, our robust validity constraint can be reformulated into a tractable one that can be evaluated as efficiently as the standard validity constraint. Building on this reformulation, we propose an efficient optimization algorithm by extending the existing search-based method for tree ensembles. Experimental results demonstrated that our method achieved a better robustness-cost trade-off than existing robust methods applicable to tree ensembles, while maintaining comparable runtime to the standard method.

Decision Transformer (DT) has emerged as a powerful paradigm for offline reinforcement learning. For complex tasks with imbalanced datasets, where high-quality samples constitute only a small fraction of the data while the majority is suboptimal, the performance of DT methods usually degrade significantly due to the the offline data distribution deviates substantially from the stationary distribution of the optimal policy. In this paper, we construct a dual-based correction mechanism to mitigate the distribution shift problem and propose a Distribution Corrected Decision Transformer method. Specifically, we investigate the Lagrangian duality of the reward maximizing objective in the standard Markov decision problem and recover the optimal correction weights between the static offline data and optimal stationary distribution. Note that a double-KL regularizer is integrated in the objective to prioritize high-return state-action pairs while remaining anchored to valid data support. Subsequently, we use the learned weights to correct distributional bias in two critical components of DT: stabilizing policy evaluation through weighted temporal-difference learning, and improving policy extraction by prioritizing DT training on weighted dataset aligned with the corrected target distribution. We theoretically analyze the performance guarantee, proving our method improves upon standard baselines, and empirically demonstrate strong performance on D4RL benchmarks, particularly on highly imbalanced datasets where prior methods fail.

Action Quality Assessment (AQA) for diving aims to quantify the execution quality of diving sequences and has long been a prominent research topic. However, existing diving AQA methods typically regress solely the final diving score, leading to poor interpretability. To address this challenge, we propose Diving-R1, the first attempt to leverage a Multimodal Large Language Model (MLLM) for interpretable diving AQA, enabling the integration of comprehensive information, thereby supporting progressive reasoning and traceable assessment. We construct a novel dataset, DivingThink, as the foundation, which contains progressively structured reasoning chains that follow judging logic and explicitly describe the execution quality of each diving sub-action, providing clear evidence for score determination. Furthermore, we establish a new benchmark, DivingInterp, which evaluates the performance of MLLMs on interpretable diving AQA from multiple complementary perspectives, including score estimation, textual similarity, and reasoning quality. Additionally, we carefully design a three-stage training paradigm, combined with a dedicated composite reward function, which gradually relaxes the annotation requirements for the training corpus at each stage to incrementally enrich the diversity of the training data, achieving continuous improvement. Extensive experiments demonstrate the superiority of Diving-R1 over existing MLLMs in interpretable diving AQA. The dataset and code will be available on GitHub.

Medical foundation models generate narrative explanations but cannot quantify intervention effects, detect evidence conflicts, or validate literature claims, limiting clinical auditability. We propose causal compilation, a paradigm that transforms medical evidence from narrative text into executable code. The paradigm standardizes heterogeneous research evidence into structured estimand objects, each explicitly specifying intervention contrast, effect scale, time horizon, and target population, supporting six executable causal queries: do-calculus, counterfactual reasoning, temporal trajectories, heterogeneous effects, mechanistic decomposition, and joint interventions. We instantiate this paradigm in DoAtlas-1, compiling 1,445 effect kernels from 754 studies through effect standardization, conflict-aware graph construction, and real-world validation (Human Phenotype Project, 10,000 participants). The system achieves 98.5% canonicalization accuracy, 100% conflict detection recall, and 80.5% query executability. This paradigm shifts medical AI from text generation to executable, auditable, and verifiable causal reasoning.

Minority collapse, where minority classes become indistinguishable, is a significant challenge in imbalanced learning. This challenge is addressed by methods such as Mixup with class-balanced sampling. Although minority collapse has been mathematically analyzed using the layer-peeled model alongside Neural Collapse, no prior work has analyzed minority collapse under Mixup, particularly from the perspective of mixed labels. We investigate this overlooked factor and raise the question: Is mixed label balance important for alleviating minority collapse? Our analysis reveals that (i) mixed labels should be balanced, and (ii) in this setting, interpreting mixed labels as singletons is beneficial. Motivated by this analysis, we propose a Balanced Mixed Label Sampler and a Mixed-Singleton classifier, which balance mixed labels and treat them as singleton labels. Through theoretical analysis and experimental results, we highlight the importance of balancing mixed labels in imbalanced learning.

Policy gradient computes a backward pass for every sample, even though the backward pass is expensive and most samples carry little learning value. The Delightful Policy Gradient (DG) provides a forward-pass signal of learning value: \emph{delight}, the product of advantage and surprisal (negative log-probability). We introduce the \emph{Kondo gate}, which compares delight against a compute price and pays for a backward pass only when the sample is worth it, thereby tracing a quality--cost Pareto frontier. In bandits, zero-price gating preserves useful gradient signal while removing perpendicular noise, and delight is a more reliable screening signal than additive combinations of value and surprise. On MNIST and transformer token reversal, the Kondo gate skips most backward passes while retaining nearly all of DG's learning quality, with gains that grow as problems get harder and backward passes become more expensive. Because the gate tolerates approximate delight, a cheap forward pass can screen samples before expensive backpropagation, suggesting a speculative-decoding-for-training paradigm.

Classification-trained sequential glimpse policies have attained competitive performance and efficiency on large-scale recognition benchmarks, and are motivated by analogy to the saccadic architecture of biological active vision. Yet whether these policies reproduce the behavioral priors that organize human free viewing, or merely approximate their spatial distribution as a byproduct of classification reward, has not been systematically examined. We present a diagnostic behavioral audit of AdaptiveNN across three free-viewing datasets, evaluating five dimensions associated with known properties of biological gaze: spatial priors, face prioritization, local return, saccade amplitude structure, and semantic guidance. We show that AdaptiveNN exhibits: (i) spatial behavior that falls below a simple center-bias baseline; (ii) scanpath geometry unlike human scanpaths in amplitude and sequential structure; (iii) revisitation dynamics inconsistent with human free viewing and not reducible to step-size geometry; and (iv) selectively attenuated semantic guidance, with face-specific prioritization center-explained, social sampling attenuated, and object-level rates preserved. In AdaptiveNN, this dissociation suggests that classification reward alone cannot reproduce several behavioral priors organizing human free viewing, even when object-level sampling is preserved. These findings identify which behavioral priors require constraints beyond classification utility, offering targets for designing cognitively aligned glimpse policies.

While recent advancements in generative image editing models have achieved stunning visual fidelity, it remains an open question whether these systems possess an intrinsic knowledge of real-world lighting. Existing benchmarks typically evaluate high-level plausibility of perceptual light transport on curated internet imagery, using VLMs or human judgement, or they rely on synthetically generated datasets. In this work, we introduce the 3D-anchored Light Probe (3DLP) benchmark, for which we have captured a new high-fidelity HDR dataset of real-world lighting changes. The dataset consists of 1K image pairs of diverse indoor scenery in which light probes are physically turned on and off. To allow for a granular performance analysis, we annotated specific image regions such as cast shadows or metallic surfaces. With this data, we evaluate a range of state-of-the-art image editing models by measuring how well their light probe edits align with reality. The evaluation uses two new scores to compensate for AI-generated photographic effects, such as adjusted white balance. Our results show that the overall performance of models differs considerably, with differences slightly less pronounced for specular highlights. The best image editing models are remarkably consistent with real-world physics, however, they still leave room for improvement. We observe that image regions that receive less light from the light probe are more prone to errors for all models. Furthermore, building on their success in evaluating macroscopic lighting plausibility, we test VLMs on our task but find that they are unsuitable for pixel-level light transport analysis. We will make the benchmark, together with the real-world dataset, publicly available to encourage future research on this topic.

Reinforcement-learning-based post-training for large language models vastly improves capabilities on verifiable tasks however it suffers from training instability and diversity collapse. As a solution, many new advantage functions have emerged. Each method simultaneously changes which problems receive gradient, the balance between positive and negative updates, and the overall gradient scale making their comparison difficult. We propose a unifying framework that decomposes each function by their induced positive and negative gradient mass. Analyzing policy performance and the geometry of model weights reveals: symmetric methods are better but slow and asymmetric methods are fast. Combining the advantages of both, we build the FADE: Focal Adaptive Dynamic Entropy advantage which is fast, diverse and accurate at various model scales (7B, 32B).

Real-world machine learning systems often operate on imperfect measurements: sensors are noisy, annotations are produced by non-experts, and scientific observations are often indirect. In these settings, classic distribution comparison tools such as maximum mean discrepancy (MMD) can be systematically biased, matching artifacts of the measurement process rather than the underlying mechanisms of interest. We study a practical regime in which noisy observations are abundant, but only a small audited subset contains ground-truth measurements. We propose measurement-error-corrected MMD (MEC-MMD), an audit-based estimator that debiases kernel evaluations without requiring a noise model. MEC-MMD is exactly unbiased for the oracle MMD under simple random auditing, admits an explicit variance decomposition, and is consistent for the population MMD, achieving the standard $O_p(N^{-1/2})$ rate when the audit size scales with the dataset. We demonstrate the utility of distributional matching with MEC-MMD loss for both parameter estimation and unsupervised applications, showing that MEC-MMD consistently outperforms MMD and alternative methods. These results provide a principled foundation for robust distribution comparison, simulation-based inference, and learning in noisy real-world machine learning pipelines.

Streaming video models should respond the moment an event unfolds, not after the moment has passed. Yet existing online VideoQA benchmarks remain largely retrospective. They pause the video at fixed timestamps, pose questions about current or past events, and score models only at those moments. This protocol leaves streaming outputs untested. To close this gap, we introduce SPOT-Bench, featuring multi-turn proactive queries that evaluate general streaming perception required by an always-on, real-time assistant. SPOT-Bench comes with Timeliness-F1, a consolidated metric that measures streaming outputs by their temporal precision and balanced coverage across the entire video. Our benchmark reveals that during streaming inference: (i) MLLMs detect events reliably in a stream but spam predictions unprompted; (ii) post-training MLLMs for silence reduces spamming but induces unresponsiveness; (iii) half of the streaming video expects no response, which we term dead-time - compute spent here does not affect response latency. These findings motivate Ctrl-SPOT, a training-free streaming controller for MLLMs, that retains their event perception while controlling their streaming outputs for balanced coverage. This establishes a strong baseline on SPOT-Bench.

State-of-the-art Extreme Multi-Label Text Classification (XMC) models rely on multi-label attention to focus on key tokens in input text, but learning high-quality attention weights is challenging. We introduce PLANT (Pretrained and Leveraged Attention), a plug-and-play strategy for initializing attention. PLANT works by \emph{planting} label-specific attention using a pretrained Learning-to-Rank model guided by mutual information gain. This architecture-agnostic approach integrates seamlessly with large language model backbones such as Mistral, LLaMA, DeepSeek, and Phi-3. PLANT outperforms state-of-the-art methods across tasks such as ICD coding, legal topic classification, and content recommendation. Gains are especially pronounced in few-shot settings, with substantial improvements on rare labels. Ablation studies confirm that attention initialization is a key driver of these gains. Code and trained models are available at \url{https://github.com/research-anon-487/xcube/tree/plant}.

Recent speech brain-computer interfaces (BCIs) increasingly rely on large models and complex multi-stage pipelines for neural decoding. However, in speech BCI, where training data are scarce, noisy, and expensive to collect, the benefits of model scaling remain unclear. This work revisits a fundamental question: $\textbf{are large models truly necessary for accurate neural decoding?}$ We propose $\textbf{\textit{BEST}}$ ($\textbf{\textit{B}}$rain-$\textbf{\textit{E}}$ncoding-$\textbf{\textit{S}}$peech-$\textbf{\textit{T}}$ext), a two-stage framework for efficient neural-to-text decoding. Instead of relying on large generative models or phoneme-centric pipelines, $\textbf{\textit{BEST}}$ introduces hierarchical intermediate representations and a compact speech-oriented decoder that directly maps neural activity to text. This design preserves essential structure while significantly reducing computational cost, enabling robust generalization in low-resource settings and practical deployment under constrained resources. On the Brain-to-Text ’24 and ’25 benchmarks, $\textbf{\textit{BEST}}$ achieves word error rates (WERs) of 6.19\% and 1.62\%, espectively, using only 3.7\% of the parameters required by comparable methods. Beyond these results, we provide three key insights: (1) decoder scaling yields non-monotonic gains; (2) cross-modal alignment is more critical than model size; and (3) overfitting degards sequence-level performance. Representation analysis further reveals a consistent neural→audio→text alignment pathway.

Drifting models have recently gained attention for generating high-quality samples in a single forward pass. During training, they learn a push-forward map by following a vector-valued field, the *drift field*. We ask whether this procedure is equivalent to optimizing a scalar loss and find that, in general, it is not: drift fields are *not conservative* and cannot be written as the gradient of any scalar potential. We identify the position-dependent normalization as the source of non-conservatism, with the Gaussian kernel as the unique radial exception. Guided by this, we introduce the *sharp kernel* $k^\\#$ and a sharp-normalized drift field that is conservative for general radial kernels. The resulting vector field is the gradient of a scalar potential that can be optimized directly using stochastic gradient descent. Moreover, the field has the form of a score difference of kernel density estimates, and gives exact equilibrium identifiability. Thus, sharp normalization closes the gap to related literature, such as Wasserstein gradient-flows and denoising score matching, also for non-Gaussian kernels. Empirically, sharp normalization preserves the performance of the original drifting objective, suggesting that the non-conservative flexibility is not required for high-quality generation.

Recently, world-action models (WAM) have emerged to bridge vision-language-action (VLA) models and world models, unifying their reasoning and instruction-following capabilities and spatio-temporal world modeling. However, existing WAM approaches often focus on modeling 2D appearance or latent representations, with limited geometric grounding, an essential element for embodied systems operating in the physical world. We present DriveDreamer-Policy, a unified driving world-action model that integrates depth generation, future video generation, and motion planning within a single modular architecture. The model employs a large language model to process language instructions, multi-view images, and actions, followed by three lightweight generators that produce depth, future video, and actions. By learning a geometry-aware world representation and using it to guide both future prediction and planning within a unified framework, the proposed model produces more coherent imagined futures and more informed driving actions, while maintaining modularity and controllable cost. Experiments on the Navsim v1 and v2 benchmarks demonstrate that DriveDreamer-Policy achieves strong performance on both planning and world generation tasks. In particular, our model reaches 89.2 PDMS on Navsim v1 and 88.7 EPDMS on Navsim v2, outperforming existing world-model-based approaches while producing higher-quality future video and depth predictions. Ablation studies further show that depth learning provides complementary benefits to video imagination and improves planning robustness. Our code is available to facilitate further research.

For decades, philosophers and musicologists have debated which features of a performance are constitutive of the work and which are expressions of how it is being played. Computational evidence has been hard to assemble: audio embedding models conflate work identity with performance style, and existing interpretability tools for music generators treat learned representations as flat dictionaries that mix the two. We turn the question into an empirical one by introducing the dual-contrastive sparse autoencoder (DC-SAE), a two-branch sparse autoencoder that uses cheap work-level metadata to factor a generative music transformer's residual stream into work-identity and performance-content subspaces. Across jazz-standard, classical-work, and pop-cover corpora, the resulting decomposition is supported by probes and feature galleries that surface musically interpretable concepts on each side. Without performer supervision, the variation branch acquires structure related to performer identity, and steering along these directions can shift the perceived performer of generated audio while preserving the underlying work. Together, these results show that a generative music model has internalized a representation of interpretation that can be recovered, named, and steered.

Vision-language models (VLMs) have demonstrated remarkable capabilities in bridging visual perception and natural language understanding, enabling a wide range of multimodal reasoning tasks. However, they often produce object hallucinations, describing content absent from the input image, which limits their reliability and interpretability. To address this limitation, we propose Dual-Pathway Circuit Analysis, a framework that identifies and characterizes hallucination-related circuits in VLMs for mechanistic understanding and causal probing. We first apply activation patching across five architecturally diverse VLMs to identify a visual grounding pathway that supports correct predictions and a hallucination pathway that drives erroneous outputs. We then introduce Conditional Pathway Analysis (CPA) to characterize pathway-level interactions, revealing that grounding components remain strongly redundant in both correct and hallucinating samples but undergo a consistent polarity flip, shifting from supporting the ground truth on correct samples to aligning with the hallucinated answer on erroneous ones. We further perform targeted suppression of hallucination-pathway components, showing that scaling these components reduces object hallucination by up to 76\% with minimal accuracy cost, and validate that the same circuit selectively transfers to relational but not attribute hallucination. Evaluations on POPE-adversarial and AMBER show that the identified circuits are consistent across architectures, support causal intervention, and transfer selectively across hallucination types.

Recent multimodal agents have shown promising progress in embodied planning and web interaction. However, existing evaluations often rely on simplified tasks with limited combinatorial structure, leaving it unclear whether current agents can robustly integrate perception and reasoning for multi-goal planning under temporal, physical, and multimodal constraints. We introduce DualWorldBench, a unified benchmark for evaluating agent planning under controlled symbolic--temporal and grounded--topological complexity. Built around a shared pickup--delivery interface, DualWorldBench consists of three complementary components: TextWorld, which isolates symbolic planning under varying task compositions and release dynamics; SimWorld, which evaluates grounded path planning under controlled topological variation with text, image, or multimodal inputs; and DualWorld-VQA, a diagnostic suite spanning perception, scene understanding, and planning. Experiments show that current state-of-the-art agents remain brittle under increasing combinatorial complexity. Multimodal input does not consistently improve planning and can even degrade performance, revealing difficulties in aligning symbolic task specifications with grounded topology. Diagnostics further show that models handle perception or understanding in isolation, but fail when decision-making requires integrating both. These findings establish DualWorldBench as a challenging testbed for agents with tighter perception--reasoning--planning integration. All datasets and code are publicly available at our project website https://dualworld.netlify.app/.

Reinforcement learning with verifiable rewards (RLVR) generates thousands of tokens per training step, with rollout generation dominating the computational cost. The overall token budget can be controlled along two main dimensions: (i) deciding which prompts to allocate rollouts to, and (ii) deciding how long each rollout should be. Prior work has generally controlled only one of these dimensions at a time. We show that jointly tuning both decisions under a shared compute budget improves both reasoning quality and wall-clock training time. We instantiate this view as \textbf{DU}al-controlled tok\textbf{E}n alloca\textbf{T}ion (DUET), a computationally efficient layer over GRPO, in which we have a lightweight pre-rollout surrogate of prompt informativeness to set how many rollouts each prompt receives and a marker-gated abort rule with importance reweighting set when to stop them. On Qwen3-1.7B trained on MATH, DUET outperforms full-budget GRPO and the other three budget-aware baseline methods. DUET's advantage was further generalized to other benchmarks across math and coding, and was on par with the best baseline on the scientific Q\&A domain, while also achieving a $1.62\times$ wall-clock speedup. More notably, using only 50\% of the token budget, DUET still outperforms all baseline methods, and performance often increases rather than reduces with the more limited budget, achieving an even higher $2.51\times$ speedup. We verified the high performance of DUET on other backbone LLMs, including Qwen-4B and Llama-3.2-3B-Instruct. Notably, the gap between DUET and the strongest baseline \emph{widens} as the budget tightens, contrary to the usual pattern in which efficient methods trade off quality as compute decreases. More broadly, these results suggest that DUET budget-aware control strategies are valuable not only for accelerating training, but also for improving the quality of the learning signal.

Imaging of dynamic cellular responses to perturbations requires live-cell measurements, posing a challenge for fluorescence-based assays that can visualize molecular phenotypes by specific labeling but often affect cell health. Virtual staining helps by predicting fluorescence-like signals from label-free microscopy. Although many models have been developed for this task, their performance on volumetric time-lapse data remains poorly characterized. We address this gap by introducing DynaCell, an evaluation framework for 3D time-lapse virtual staining of live cells, comprising a new paired label-free and fluorescence 3D time-lapse imaging dataset, a suite of baseline models, and a three-tier metric panel measuring pixel-level fidelity, organelle segmentation performance, and single-cell phenotypic similarity. Using DynaCell, we study model behavior across 2 cell types and microscopes, 4 organelles, and 3 perturbation states. We find that regression baselines better predict the spatial localization of target organelles and are more robust to label-free input distribution shifts, while the generative baseline better captures population-level phenotype distribution across cells and organelles. These differences expose trade-offs between preservation of structural and phenotypic fidelity that are obscured by single-metric evaluation, and suggest how virtual staining can support downstream tasks such as localization, segmentation, tracking, and phenotypic profiling. DynaCell provides reusable data, code, checkpoints, and documentation for assessing virtual stains for live-cell biological measurements.

We investigate the temporal concatenation of sub-policies in Markov Decision Processes (MDP) with time-varying reward functions. We introduce General Dijkstra Search (GDS), and prove that it discovers optimal goal-reaching policies by concatenating sub-policies optimal for intermediate goals. Motivated by the ``search, select, update'' principle underlying GDS, we propose Dynamic Latent Routing (DLR), a language-model post-training method that jointly learns discrete latent codes, routing policies, and model parameters through dynamic search in a single training stage. In low-data fine-tuning settings, DLR matches or outperforms supervised fine-tuning across four datasets and six models, achieving a mean gain of $+6.6$ percentage points, while prior discrete-latent baselines consistently underperform SFT. Mechanistic analyses and targeted code ablations show that DLR learns structured routing behaviors with distinct causal roles. In six-digit arithmetic, a canonical mechanistic interpretability benchmark, DLR externalizes into discrete routing tokens algorithmic subtasks previously studied through activation and circuit analysis.

Large language models are remarkably capable, yet how computation propagates through their layers remains poorly understood. A growing line of work treats depth as discrete time and the residual stream as a dynamical system, where each layer's nonlinear update has a local linear description. However, previous analyses have relied on scalar summaries or approximate linearizations, leaving the full spectral geometry of trained LLMs unknown. We perform full Jacobian eigendecomposition across three production--scale LLMs and show that training installs a monotonic spectral gradient through depth---from non-normal, rotation-dominated early layers to near--symmetric late layers---together with a cumulative low-rank bottleneck that funnels perturbations into a small fraction of the residual stream's effective dimensions. Our experiments reveal that this gradient and the dimensional collapse are learned rather than architectural, and is largely dissolved when structured non-normality is removed. We further show that the topological positioning of graph communities predicts whether the Jacobian amplifies or suppresses them, with the sign of the coupling determined by the local operator type, a relationship absent at initialization. These results map a learned spectral geometry in LLMs that links perturbation propagation and compression to the network's functional topology.

Text-to-video (T2V) diffusion models have rapidly advanced, yet generations still occasionally fail in practice, such as low text--video alignment or low perceptual quality. Since diffusion sampling is non-deterministic, it is difficult to know during inference whether a generation will succeed or fail, incurring high computational cost due to trial-and-error regeneration. To address this, we propose an early failure detection and diagnostic intervention pipeline for latent T2V diffusion models. For detection, we design a Real-time Inspection (RI) module that converts latents into intermediate video previews, enabling the use of established text–video alignment scorers for inspection in the RGB space. The RI module completes the conversion and inspection process in just 39.2 ms. This is significantly efficient considering that CogVideoX-5B requires 4.3s per denoising step when generating a 480p, 49-frame video on an NVIDIA A100 GPU. Subsequently, we trigger a hierarchical and early-exit intervention pipeline only when failure is predicted. Experiments on CogVideoX-5B and Wan2.1-1.3B demonstrate consistency gains on VBench with up to 2.64x less time overhead compared to post-hoc regeneration. Furthermore, our pipeline is plug-and-play and orthogonal to existing techniques, showing seamless compatibility with prompt refinement and sampling guidance methods. We also provide evidence that failure signals emerge early in denoising process and are detectable within intermediate video previews using standard vision-language evaluators.

We introduce EchoXFlow, a clinical echocardiography dataset for learning from ultrasound in its native acquisition geometry rather than from scan-converted Cartesian videos. Existing public datasets offer limited opportunities to study cross-modal relationships between cardiac anatomy, myocardial motion, and blood flow, as Doppler is typically absent or fused as RGB overlays, and acquisitions are released after lossy vendor display processing. EchoXFlow comprises 37125 recordings from 666 routine-care examinations, preserving the timing, geometry, and modality relationships needed for physically grounded echo learning. Each recording is retained as separable modality-specific streams: temporally resolved 1D, 2D, and 3D data alongside multiple Doppler modalities, paired with a synchronized ECG. Clinical annotations span guideline-based measurements to dense 2D myocardial contours and 3D left-ventricular endocardial meshes. With its associated open-source tooling, EchoXFlow enables cross-modal, acquisition-aware learning tasks that cannot be formulated from conventional scan-converted videos alone, and serves as a testbed for 4D vision and physically grounded multi-modal learning more broadly.

Foundation models for EEG analysis are still in their infancy, limited by two key challenges: (1) variability across datasets caused by differences in recording devices and electrode configurations, and (2) the low signal-to-noise ratio (SNR) of EEG, where neural activity is often buried under artifacts and non-brain sources. To address these challenges, we present \textit{EEG-X}, a device-agnostic and noise-robust foundation model for EEG representation learning. EEG-X introduces a location-based channel embedding that enables robust transfer across heterogeneous EEG devices and electrode layouts. To improve robustness against noise, EEG-X employs a novel noise-aware masking-reconstruction strategy that reconstructs artifact-cleaned signals rather than raw noisy EEG and introduces a novel Dictionary-based Convolutional Transformation (DiCT) that improves reconstruction-based pretraining by comparing signals in a structured feature space instead of directly in the raw signal space. Experiments across datasets collected from diverse EEG devices show that EEG-X consistently outperforms state-of-the-art methods across multiple downstream tasks and demonstrates strong cross-domain generalization when pretraining and downstream datasets differ in electrode layouts and recording configurations paving the way towards foundation models. The models and code are available at :https://anonymous.4open.science/r/EEG-X-CEDE/README.md

Dataset distillation seeks to synthesize a highly compact surrogate dataset that achieves performance comparable to the original dataset on downstream tasks. For the scenario where pre-trained self-supervised models serve as priors, traditional $\textit{Linear Gradient Matching}$ optimizes synthetic images by encouraging them to mimic the gradient updates induced by real images on the linear probe or classifier. However, this batch-level formulation requires loading thousands of real images and applying multiple differentiable augmentations to synthetic images at each distillation step, leading to substantial computational and memory overheads. In this paper, we revisit the linear gradient and theoretically derive that it is essentially a $\textit{local}$ relative distribution directed from target class centers toward non-target class centers, which we term ``flow”. This property causes instability and suboptimality, often necessitating expensive multiple augmentations to compensate. To address this, we introduce $\textit{Statistical Flow Matching}$, a stable and efficient supervised learning framework that optimizes synthetic images by aligning $\textit{global}$ statistical flows in the original data. Our approach loads raw statistics only once and performs a single augmentation pass on the synthetic data, achieving performance comparable to or better than the state-of-the-art method with 10× less GPU memory usage and 4× faster distillation time. Moreover, increasing the number of augmentations for our method yields further performance gains while incurring lower additional cost.

Construction-based neural routing solvers, typically composed of an encoder and a decoder, have emerged as a promising approach for solving vehicle routing problems. While recent studies suggest that shifting parameters from the encoder to the decoder enhances performance, most works restrict the decoder size to 1–3M parameters, leaving the effects of scaling largely unexplored. To address this gap, we conduct a systematic study comparing two distinct strategies: scaling depth versus scaling width. We synthesize these strategies to construct a suite of 12 model configurations, spanning a parameter range from 1M to $\sim$150M, and extensively evaluate their scaling behaviors across three critical dimensions: parameter efficiency, data efficiency, and compute efficiency. Our empirical results reveal that parameter count is insufficient to accurately predict the model performance, highlighting the critical and distinct roles of model depth (layer count) and width (embedding dimension). Crucially, we demonstrate that scaling depth yields superior performance gains to scaling width. Based on these findings, we provide and experimentally validate a set of design principles for the efficient allocation of parameters and compute resources to enhance the model performance.

Numerous distillation methods have been developed to accelerate diffusion models. Recent research indicates that hybrid strategies—blending different paradigms—yield superior results compared to single-method approaches. However, current works predominantly rely on a simple combination of objectives, leaving the question of how to effectively synergize them largely underexplored. In this paper, we specifically investigate the synergy between Score Distillation and Adversarial Distillation. We reveal that their operating zones are distinct and complementary: Score Distillation is effective in high-noise regimes for capturing the global distribution yet lacks fine-grained supervision in low-noise regions. Conversely, Adversarial Distillation is effective in low-noise regions but is prone to instability and suffers from discriminator failure when applied to high-noise intervals. Leveraging this insight, we propose Efficient Adversarial Score Distillation (EASD), a framework that synergizes these objectives via a regime-adaptive sampling strategy, directing each objective to focus on its effective interval. We further apply this paradigm to DiT-based Flow Matching models. Empirically, our method achieves a promising one-step FID of 1.39 on ImageNet 256$\times$256 using the SiT-XL/2+REPA model. This work provides a concrete guideline for maximizing the potential of hybrid distillation.

Label distribution leakage in federated learning (FL) poses a critical privacy threat, exposing population-level patterns beyond individual data breaches. Building on known correlations between class sample frequencies and classifier weight magnitudes, we demonstrate how adversaries can exploit these relationships to infer label distributions from shared model parameters in FL. To characterize this threat, we propose WLIA (Weight Norm-based Label Distribution Inference Attack), which infers client label distributions by analyzing variations in classifier weight norms across communication rounds. WLIA operates on the classifier layer without requiring auxiliary data, synthetic samples, or meta-classifiers, enabling broader applicability across heterogeneous model architectures. To counter this threat, we introduce RNR (Randomized Weight Norm Regularization), a defense mechanism that disrupts frequency-weight correlations by randomly regularizing weight norms during local training. RNR adds only a penalty term to the cross-entropy loss, making it efficient and easy to integrate. Comprehensive experiments across six datasets demonstrate that WLIA achieves superior inference accuracy compared to existing attacks, while RNR effectively mitigates leakage with minimal impact on utility.

Reinforcement learning (RL) has become a representative post-training paradigm for large language models (LLMs), enabling strong reasoning and agentic capabilities. However, its rollout generation remains a dominant training bottleneck because it relies on sequential autoregressive (AR) decoding, where a small number of long-tailed responses often determine completion time. Speculative decoding (SD) can reduce inference latency while preserving model quality by rapidly drafting tokens and accepting them through parallel verification. Applying SD to RL rollouts, however, introduces challenges absent from standard LLM inference: (i) algorithmically, the continuously evolving target model makes static drafters stale; (ii) system-wise, rollout decoding moves across regimes, from large active batches where SD can be compute-bound and ineffective to shrinking-batch tails where SD becomes beneficial. Existing RL-SD approaches address aspects of this problem, but either yield low effective accepted lengths that limit speedup or rely on auxiliary drafters requiring pretraining and online adaptation, increasing system complexity. We present EfficientRollout, an SD framework designed to accelerate RL rollouts while addressing these challenges. EfficientRollout induces a quantized drafter directly from the target model, keeping it coupled to the evolving policy without separate drafter training before or during RL. It then uses a dynamic SD toggle policy that enables SD only in beneficial regimes identified by system-aware roofline modeling. It further adapts drafting budgets using acceptance-behavior signals observed during training, better realizing the potential accepted length. Under realistic RL workload, EfficientRollout reduces rollout and end-to-end latency by up to 19.6% and 12.7%, respectively, over a standard accelerated AR rollout baseline, while preserving final model quality.

With recent advances in embodied agents and AR devices, egocentric observations are readily available as input for real-world interactive online applications. However, egocentric viewpoints can only sporadically observe hands, in addition to the estimated head trajectory. We propose EgoForce, an online framework for reconstructing long-term full-body motion from noisy egocentric input. While existing generative frameworks can robustly handle noisy and sparse measurements, they assume a fixed-length observation window is available and are thus not suitable for real-time applications. Faster inference often relies on autoregressive prediction, sacrificing robustness. In contrast, we adopt a diffusion-based method with a temporally asymmetric noise schedule inspired by Diffusion Forcing. Specifically, our approach models temporally evolving uncertainty and incrementally denoises states as new streaming observations arrive. Combined with a noise-robust imputation strategy, EgoForce progressively generates stable and coherent full-body motion under strict causal constraints. Experiments demonstrate that our online framework outperforms existing online and offline methods, enabling long-horizon, full-body motion reconstruction in challenging egocentric scenarios.

The wall-clock cost of training large neural networks is dominated by the long horizon of sequential optimizer iterations. We recast the gradient-descent (GD) trajectory of modern optimizer (e.g., SGD and Adam) as the unique root of a nonlinear operator, enabling a parallel root-finding approach that updates multiple GD steps simultaneously. We then propose \textbf{E}fficient \textbf{I}nter-step \textbf{P}arallel \textbf{O}ptimization (\textbf{EIPO}) for accelerating a wide range of optimizers. EIPO contributes three pieces: (i) a Preconditioned Nonlinear-Equations (PNE) formulation that recovers the exact sequential GD trajectory; (ii) Memory-Efficient Adaptive Anderson Acceleration, which extracts the multisecant geometry directly from the optimizer's intrinsic momentum buffers and therefore avoids the memory overhead of classical Anderson Acceleration. (iii) We prove that EIPO converges to the optimizer's GD trajectory within fewer iterations than its sequential counterpart. (iv) Across language modeling on WikiText-2 (GPT-2, Llama-3.2-1B, Qwen-1.5-4B/2.5-3B, Gemma-2B, GPT-J-\textbf{6B}), image classification on CIFAR-10 (ResNet50, ViT, CNN), and diffusion training on LSUN Church (UNet/DDPM/DDIM), EIPO reduces optimizer iterations by up to $\textbf{21}\times$ and wall-clock time by up to $\textbf{4.6}\times$ while matching baseline perplexity, accuracy, and FID, all under comparable GPU memory and token consumption. Source code is available at: \url{https://anonymous.4open.science/r/EIPO-44BC}.

Inserting objects into existing 3D scenes requires more than selecting a plausible location: The inserted object must also fit local geometry while preserving semantic intent and physical plausibility. Although recent Vision-Language Models (VLMs) and generative models enable semantic reasoning and visual content creation, they offer limited 3D grounding and geometric control when an inserted object must fit into constrained local spaces. We introduce ElasticFit, a VLM-guided framework for fit-aware object insertion centered on a novel scene-grounded representation. Given a language instruction and rendered scene observations, ElasticFit infers structured fitting cues that specify where the object should be grounded, what volume it should occupy, how it should be oriented, and its adaptation mode (rigid placement, uniform scaling, or elastic deformation). These cues convert high-level VLM reasoning into explicit 3D constraints that condition object generation and guide downstream geometric fitting. ElasticFit then generates a scene-conditioned object prior, reconstructs it in 3D, and refines the mesh through mode-specific fitting while enforcing collision avoidance, contact consistency, and physical grounding. In fixed-asset baseline comparisons, ElasticFit improves spatial relation success from 50.8\% to 69.7\% and support success from 48.3\% to 91.7\% over the strongest baseline, while providing novel support for generative "make-it-fit" insertions in complex scenarios.

A single global fairness criterion cannot serve communities whose conceptions of fair treatment differ. We introduce \emph{elicited localized fairness}: an end-to-end pipeline that elicits per-community Mahalanobis metric--tolerance specifications $\cF_k = (d_k, \eps_k)$ from stakeholders via two phases of pairwise queries (similarity, then tolerance acceptability), trains a localized classifier (\textsc{LocAdapt}), and audits the deployed model on a held-out tolerance fold. \emph{Theory.} A constrained-MLE rate $\tilde O(\sqrt{d^2/n})$ for metric elicitation and a matching $\Omega(d^2/t^2)$ active-query lower bound, exact in $(d, n, t)$ exponents; a held-out audit certificate that converts Phase 2 queries into a PAC-style violation guarantee. \emph{Empirics.} On heterogeneous-community settings (Stress $K{=}5$, Adult-K3-human; 10 seeds), \textsc{LocAdapt} sits on the (accuracy, strict $d{\leq}1$ violation) Pareto frontier: averaged and matched-accuracy Global IF lose on strict at comparable accuracy ($40$--$80\%$ higher), and pooled-MLE Global IF matches strict only at $-2$pp accuracy. A five-axis diagnostic (acc, strict, score-diff spread, prediction entropy, AUC) confirms the gain is not prediction-smoothing. Plugging Phase 2 elicited tolerances into \textsc{LocAdapt} training reduces the COMPAS deployed-spec violation $66\%$ ($p{\ll}10^{-4}$). Five pre-registered human studies on COMPAS (360 annotators total) validate the pipeline end-to-end; a 180-annotator scorer-stability ablation across three scorer architectures preserves the cross-community $\hat\eps$ ordering and pools to $p{=}0.0014$ ($n{=}90$ vs.\ $90$).

Text-guided long-term human motion generation is essential for applications such as continuously operating digital avatars. Despite recent progress on short clips, existing models struggle with multi-segment instructions that involve anaphora, where constraints established in earlier segments must be preserved over time. In this paper, we introduce a novel task of long-term motion generation from such anaphoric compositions. To mitigate this, we present ELMA, an episodic long-term motion dataset with dense segment-level annotations that preserve cross-segment dependencies. Built via a fully automated collection and filtering pipeline, ELMA extracts high-quality, continuous 30-second clips from in-the-wild videos, capturing the episodic narratives necessary for long-range state modeling. Leveraging this dataset, we fine-tune state-of-the-art motion generation models to incorporate long-range context. Extensive experiments reveal fundamental shortcomings in current models when handling context-dependent instructions. We demonstrate that ELMA enables significant improvements in both global physical realism and semantic alignment, setting a robust new baseline for long-term motion synthesis.

Embedded devices from wildlife monitoring stations to clinical wearables require local AI inference due to latency, communication, or privacy constraints. Optimizing models for heterogeneous microcontrollers (MCUs) requires simultaneously satisfying hard physical constraints on memory, power, and temperature while preserving accuracy, a multidimensional optimization that is today performed manually by experts. We ask whether an LLM agent can autonomously navigate this complex, multi-turn pipeline guided by real hardware feedback, and introduce a hardware-in-the-loop agent arena in which the agent iteratively refines both model and firmware---compiling, flashing, and measuring on real hardware---to enable iterative optimization. Frontier models, including Claude Opus 4.7 and Gemini 3.1 Pro, fail entirely without hardware feedback (0\% deployment success), whereas our closed-loop formulation achieves the first successful deployment within three iterations and can surpass human expert results within seven. This agentic co-optimization achieves 250× compression for vision models with <3.3\% accuracy loss and 400× for audio with <6\% Feature Error Rate loss, enabling battery-free operation on a commercial MCU via solar harvesting. We demonstrate practical impact in two real-world systems: an elk-detection camera trap (96.7\% accuracy) and a phonetic-transcription wearable (8.44\% FER) for child development research.

We introduce Embodied-R1.5, a unified Embodied Foundation Model (EFM) that integrates comprehensive embodied reasoning capabilities, spanning embodied cognition, task planning, correction, and pointing, within a single architecture toward general physical intelligence. Leveraging three automated data construction pipelines to significantly expand the data coverage of critical capabilities, we build a large-scale data system of over 15B tokens, and design a multi-task balanced RL recipe to alleviate heterogeneous task conflicts. We further introduce a Planner-Grounder-Corrector (PGC) closed-loop framework that enables a single model to autonomously execute and self-correct over long-horizon tasks. With only 8B parameters, Embodied-R1.5 achieves SOTA on 16 out of 24 embodied VLM benchmarks, surpassing leading models like Gemini-Robotics-ER-1.5 and GPT-5.4. Benefiting from the internalized embodied capabilities, Embodied-R1.5 can be fine-tuned into a VLA with only a small amount of data, outperforming leading VLA models like $\pi_{0.5}$ across 4 popular manipulation benchmark suites. We further conduct extensive zero-shot real-robot experiments, validating performance in instruction following, affordance grounding, articulated object manipulation, and long-horizon complex tasks, demonstrating strong generalization to the physical world. We will fully open-source weights, datasets and training code to facilitate future research in EFMs.

Recent empirical studies have shown the training dynamics of deep neural networks mostly occur within low-dimensional subspaces. While this has inspired new research in low-rank training, compression, and adaptation, theoretical justification for these dynamics in nonlinear networks remains limited. To address this gap, this paper analyzes the learning dynamics of multi-layer perceptrons (MLPs) under gradient descent (GD). We demonstrate that the weight dynamics concentrate within invariant low-dimensional subspaces throughout training. Theoretically, we precisely characterize these invariant subspaces for two-layer networks with smooth nonlinear activations, providing insight into their emergence. Experimentally, we validate that this phenomenon extends well beyond our theoretical setting. Leveraging these insights, we empirically show there exists a low-rank MLP parameterization that, when initialized in the appropriate subspaces, nearly matches the performance of their fully-parameterized counterparts on classification tasks.

Does multimodal training make a language model think visually, even when the input is text only? We test this with targeted causal ablations across 10 multimodal configurations from 6 model families, with matched text-only backbones and cross-family transfer controls. We contrast spatial-imagery tasks that invite mental scene construction with symbol-manipulation tasks solvable by symbolic rules alone. We identify a vision-language axis in hidden-state space and ablate its projection at inference. In Chameleon-7B, this ablation drops accuracy on BIG-Bench Hard (BBH) navigate by 27.6 percentage points while improving boolean expressions by 5.2 percentage points, a 32.8 percentage-point double dissociation. The effect is direction-specific and layer-localized. Together, these results reveal emergent visual thinking (EVT): text-only reasoning that causally depends on visual-spatial representations shaped by multimodal training. In dual-path unified models (BAGEL, Janus-Pro), the causal handle localizes to the generation pathway as a compact one-dimensional carrier. The understanding pathway also encodes spatial information in a higher-rank, probe-decodable form, but is not the causal handle. To separate decodability from causal use, we analyze concentration, alignment, and semantic location. These diagnostics explain why behavior, probing, and intervention can diverge. Multimodal training therefore reshapes the geometry of text-only reasoning, beyond changing how visual input is processed.

Given a single image of a room and a sentence such as ``a person walks to the black armchair and sits down,'' producing a 3D motion sequence of a virtual person performing the described action is out of reach for existing methods. Scene-conditioned methods need hours of paired (scene, motion, language) capture. Zero-shot methods need a pre-scanned 3D scene. Image-based methods produce only static poses. We propose a training-free pipeline that synthesizes scene-aware human-scene interaction (HSI) motion from a single RGB image and a free-form text prompt, with no HSI-specific training and no pre-scanned 3D scene. The key observation is that every needed capability already exists as a frozen foundation model: the bottleneck is composition, not capability. Our method, ENACT, uses a vision-language model (VLM) for high-level reasoning. The VLM parses the input image and prompt into one structured keyframe per sentence, listing the action verb, the body part, the target object, a six-bin contact yaw, an image-edit prompt, and a foot-grounding flag. The keyframe then drives six frozen specialists: a monocular 3D model for the scene point cloud, an open-vocabulary segmenter for object localization, a single-view mesh reconstructor for object geometry, an instruction-following image editor for a synthetic interaction image, an image-conditioned human-mesh recoverer for pose initialization, and a 2D-grounded 3D contact predictor for object affordance. Per-keyframe contact poses are optimized under a whole-body diffusion prior, and a constraint-conditioned motion diffuser connects successive keyframes into one continuous motion. ENACT achieves the lowest mean and maximum scene penetration and the lowest foot sliding among all baselines on a standard HSI benchmark, despite consuming the smallest input. ENACT also generalizes to synthetic datasets and to in-the-wild phone images, including chained multi-step interactions.

Real-world endoscopic diagnosis unfolds across native video, depending on continuous traversal of gastrointestinal organs where a single positive finding triggers a multi-minute reasoning chain over location, morphology, severity, and intervention. Existing endoscopy multimodal benchmarks evaluate only static images or single-disease videos that fail to capture the temporal and clinical reasoning demands of real-world endoscopy reporting. We introduce EndoSCOP-V, a video-grounded multi-turn evaluation framework for long-horizon clinical reasoning in endoscopy. The benchmark contains 1,307 real-world endoscopy and colonoscopy videos (63.2 h; 97% 1080p; mean ~174 s) — the time-scale at which endoscopists actually reason about a finding. The corpus is paired with 20,275 question–answer turns deterministically instantiated from clinician-filled structured endoscopy-reporting EHRs, avoiding synthetic or LLM-rephrased per-case annotations. Questions are derived from 1,033 clinician-reviewed templates spanning a clinically aligned 9-task × 30-sub-task reporting taxonomy across 54 World Endoscopy Organization (WEO) MST 3.0 diseases at ~18 templates per disease, substantially deeper per-disease examination than prior endoscopy benchmarks. To mirror real-world case mix, 22% of procedures carry co-existing pathologies, up to six per case. To model the hierarchical and conditional nature of clinical reporting, EndoSCOP-V introduces a Markovian context-pruned oracle-forcing protocol that evaluates multi-turn dialogues turn-by-turn. By injecting the gold answer before each subsequent turn, the protocol isolates per-turn reasoning from cascading dialogue errors and enables fine-grained analysis of clinical reasoning capabilities. Clinician-marked key-frame anchors further separate temporal-localization from frame-level comprehension failures. A panel of 15 board-certified endoscopists anchors a 78% strict-accuracy baseline on a 1,500-question audit. Across 14 open-weight LMMs, the best 27B-class model reaches 59.8% strict accuracy, falling to 53.4% once format-trivial disease-rejection items are excluded. On a between-subjects spot-check, the best of 3 frontier proprietary models trails the clinician panel by 23 pp on strict accuracy. Sub-task analysis reveals consistent gaps in quantitative measurement, multi-variable grading, and intervention reasoning on long-horizon video, providing a clinically grounded benchmark for evaluating multimodal reasoning in endoscopy.

Speech large language models (SpeechLMs) can process spoken requests, yet still lag behind text-prompted large language models (LLMs) on instruction following and reasoning. We argue that this gap is not only an ASR or representation problem: the same semantic instruction can induce different response policies when spoken with different speakers, accents, noise conditions, prosody, or disfluencies. We propose Verifiable Invariant Reinforced Behavior Alignment (VIRBA), a reinforcement-learning framework for aligning SpeechLM behavior across acoustic realizations of the same semantic intent. VIRBA builds multi-view spoken instruction groups, scores sampled responses with semantic preference, rule-verifiable correctness, cross-acoustic invariance, and adaptive reasoning rewards, and optimizes the model with Cross-Acoustic Group Relative Policy Optimization (CA-GRPO). The resulting objective moves SpeechLM alignment beyond teacher imitation toward robust reasoning policies that remain stable across realistic spoken realizations. Experiments with recent SpeechLM baselines, disfluency robustness, spoken QA, audio reasoning, and speech-to-text translation show the largest gains on reasoning-heavy and acoustically perturbed spoken prompts.

Combining the predictions of multiple models is one of the most effective strategies in machine learning. Thus, it is only natural to investigate the ensembling of language models (LMs). However, because LMs define distributions over strings, a proper ensemble requires aggregating predictions at the string level, which requires a computation that is generally intractable. In this work, we cast LM ensembling as an inference problem and propose the deployment of sequential Monte Carlo (SMC), an appropriate approximate inference scheme. Concretely, we introduce a unified framework for composing multiple LMs into ensemble distributions parameterized by a broad family of aggregation functions. To sample from these distributions, we introduce an SMC algorithm that operates in a shared character space, enabling ensembles of models with mismatching vocabularies and consistent sampling in the limit. We evaluate a range of ensembles across prompt and model combinations for various text generation tasks, finding that consensus-seeking strategies such as the product consistently outperform probability averaging, and that better posterior approximations can yield better ensemble performance.

Large-scale text-to-image diffusion models can inadvertently generate harmful or sensitive content learned from uncurated training data, motivating the development of concept erasure methods that remove undesired concepts from pre-trained models. However, recent studies reveal that erased models remain vulnerable to adversarial prompt attacks that recover the supposedly removed concepts, highlighting the need for robust erasure techniques. In this work, we visualize the text embedding distributions of adversarial prompts and find that the difficulty of erasure is non-uniform. Existing methods effectively erase concepts in easier regions but leave localized residual holes in harder regions, which adversarial prompts consistently exploit. Motivated by this finding, we propose Entire-space Robust Erasure (EntiRE), an end-to-end framework that casts robust concept erasure as an out-of-distribution generalization problem. By adopting an invariant learning formulation regularized by total variation, EntiRE enforces more uniform erasure across the continuous prompt space, suppressing the localized residual regions. Extensive experiments on erasing nudity, artistic style, and object concepts demonstrate that EntiRE consistently outperforms state-of-the-art baselines in both robustness against adversarial prompt attacks and utility preservation.

Latent world models based on Joint-Embedding Predictive Architecture (JEPA) are deterministic by design. While successful in fully observable scenarios, this paradigm breaks down when past observations and actions lead to multiple plausible future possibilities, e.g., due to occlusion. We introduce EpicWorldModel, a framework to train stochastic JEPAs for environments and tasks with inherent uncertainty under partially observability. We jointly train the EpicWorldModel predictor with its latent representation space to directly predict multiple potential future states using a flow-matching objective, when the goal-relevant scene content is absent from the conditioning history. We show that flow predictive variance approximates an upper bound on Expected Information Gain and serves as a useful exploration guidance for planning. By incorporating this uncertainty signal into Cross-Entropy Method (CEM)-based planning, our approach balances goal-reaching with exploration of uncertain regions where occluded or unseen goals are most likely to be located. We demonstrate the effectiveness of EpicWorldModel through a series of latent planning experiments with the best performance among all baseline methods, showing up to $22\%$ empirical improvement in success rate over LeWorldModel.

Continual reinforcement learning (continual RL) seeks to formalize the notions of lifelong learning and endless adaptation in RL. In particular, the aim of continual RL is to develop RL agents that can maintain a careful balance between retaining useful information and adapting to new situations. To date, continual RL has been explored almost exclusively through the lens of risk-neutral decision-making, in which the agent aims to optimize the expected long-run performance. In this work, we present the first formal theoretical treatment of continual RL through the lens of risk-aware decision-making, in which the behaviour of the agent is directed towards optimizing a measure of long-run performance beyond the mean. In particular, we show that the classical theory of risk measures, widely used as a theoretical foundation in non-continual risk-aware RL, is, in its current form, incompatible with continual learning. Then, building on this insight, we extend risk measure theory into the continual setting by introducing a new class of ergodic risk measures, and showing that it is compatible with continual learning. Finally, we provide a case study of continual risk-aware learning, along with empirical results, which show the intuitive appeal of ergodic risk measures in continual settings.

Existing benchmarks for evaluating the clinical reasoning capabilities of large language models (LLMs) often lack a clear definition of "clinical reasoning" as construct, fail to capture the full breadth of interdependent tasks within a clinical workflow, and rely on stylized vignettes rather than real-world clinical documentation. As a result, recent studies have found significant discrepancies between LLM performance on stylized benchmarks derived from medical licensing exams and their performance in real-world prospective studies. To address these limitations, we introduce ER-Reason, a benchmark designed to evaluate LLM reasoning as clinical evidence accumulates across decision-making tasks spanning the full workflow of emergency medicine. ER-Reason comprises 25,174 de-identified clinical notes from 3,437 patients, supporting evaluation across all stages of the emergency department workflow: triage intake, treatment selection, disposition~planning, and final diagnosis. Crucially, evaluation in ER-Reason extends beyond diagnostic accuracy to include stepwise Script Concordance Test (SCT)-style questions grounded in real patient cases, which assess whether LLMs update their diagnostic beliefs in the correct direction and magnitude as clinical evidence accumulates, scored against 2,555 emergency physician annotations. We evaluate reasoning and non-reasoning LLMs on ER-Reason, and show that our tasks provide a more nuanced view of how LLM reasoning fails on real patient cases than existing benchmarks allow.

We propose ESENSC, a computationally efficient and theoretically grounded alternative to SHAP. ESENSC is a polynomial-time attribution rule with a deterministic, closed-form representation that satisfies the null-player property and admits an axiomatic characterization based on efficiency, a restricted form of differential marginality, and explicit computational constraints. Empirically, ESENSC closely replicates the attribution patterns of exact SHAP by preserving its essential structural properties, while maintaining a fraction of the cost. Across neural network and XGBoost models, it achieves lower deviation and higher rank agreement than widely used sampling-based SHAP approximations. While the computational cost of exact SHAP grows exponentially with the number of features, ESENSC scales linearly, enabling reliable feature attribution in regimes where exact SHAP is intractable. These results demonstrate that ESENSC achieves near-SHAP attribution accuracy while reducing computational complexity from exponential to linear in the number of features, providing a practical and theoretically rigorous alternative for feature attribution in high-dimensional applications.

Modern machine learning depends heavily on massive datasets, but obtaining high-quality annotations at scale is often expensive. As a result, learning from noisily-labeled data has become common, making accurate estimation of the label-noise transition matrix crucial. However, existing transition matrix estimators rely on the fragile estimation of class-posteriors and do not provide finite-sample performance guarantees. In this work, we propose a novel methodology to estimate the transition matrix based on one-sided selective classification. This approach bypasses class-posterior estimation, provides finite-sample performance guarantees, and leverages flexible learning methods for binary classification. Moreover, we introduce effective algorithms to implement the proposed methodology and provide their refined finite-sample performance bounds.

Hallucination remains a fundamental challenge in multimodal large language models (MLLMs), often stemming from unverified visual assumptions and reasoning processes that are weakly grounded in observable evidence. Existing approaches attempt to mitigate hallucination through self-reflection, multi-agent debate, or counterfactual verification. However, these methods remain largely opinion-driven: they lack explicit verification of the visual understanding process and may fail when multiple agents share the same incorrect perceptual assumptions, leading to consistent yet hallucinated conclusions. A promising direction is to augment MLLMs with external visual tools (e.g., OCR and grounding models) to acquire verifiable evidence. However, effective tool-augmented reasoning remains challenging due to two key failure modes: unreliable tool invocation, where models select inappropriate tools or produce incorrect parameters, and evidence--reasoning inconsistency, where models ignore or contradict the acquired evidence when forming final predictions. In this work, we propose EVA, an Evidence-seeking Visual Agent that reframes multimodal reasoning as an explicit process of evidence acquisition and verification. At the core of EVA is a unified evidence-grounded execution pipeline designed to address the above challenges through two tightly coupled mechanisms. First, we introduce \textit{Iterative Tool Replanning}, which dynamically refines tool selection and parameters based on intermediate observations, improving the reliability of tool usage. Second, we develop \textit{Evidence Consistency Verification}, an answer validation mechanism that improves alignment between the final prediction and the collected evidence, mitigating evidence--reasoning inconsistencies. In addition, to balance robustness and efficiency, EVA incorporates \textit{Selective Evidence Routing}, an adaptive fast--slow reasoning strategy that determines when external evidence is necessary. Visually straightforward queries are handled via direct reasoning, while complex or high-risk cases trigger tool-based evidence acquisition. By jointly addressing how to reliably acquire evidence and how to use it consistently, EVA transforms multimodal reasoning from passive answer validation into explicit evidence-backed decision making. Experimental results demonstrate that EVA achieves significantly improved reasoning reliability over direct VLM inference and multi-agent methods, while maintaining efficiency through selective tool invocation. These findings highlight structured and adaptive evidence utilization as a practical paradigm for hallucination-resistant multimodal reasoning.

Vision-Language Models (VLMs) such as CLIP have demonstrated strong performance across tasks yet remain highly vulnerable to adversarial attacks. While adversarial fine-tuning has been demonstrated to be effective against such malicious inputs, its multi-step adversary generation scheme during fine-tuning further induces prohibitive computational costs for VLM backbones. Previous works in unimodal models have introduced single-step strategies to reduce this burden. However, we find naive single-step strategies fail on VLMs due to more severe catastrophic overfitting. In addition, we identify a novel failure mode termed Perturbation Radius Overfitting, where VLMs overfit to the specific training attack budget while paradoxically becoming fragile to weaker perturbations. We trace these failures to growing angular misalignment and magnitude surge of the local gradients, which compromise the local linearity essential for accurate single-step approximation. Guided by this analysis, we introduce a joint adversarial optimization scheme that actively rectifies the local geometry by suppressing both angular misalignment and magnitude surge along the perturbation path. Our approach consistently outperforms existing single-step methods across various datasets and VLM architectures, achieving state-of-the-art robustness while preserving efficiency. To our knowledge, this is the first work to systematically examine single-step adversarial fine-tuning on VLMs and establish the geometric foundations of their robustness.

Real-world scenes often contain static, slow, and fast components within the same field of view. Frame cameras are inherently inefficient in this setting, as their global exposure and fixed sampling must follow the fastest motion and therefore oversample the rest of the scene. Event cameras provide asynchronous local sensing, but standard sensors use a single global contrast threshold, leading to a trade-off between sensitivity and redundancy: low thresholds preserve static information but over-trigger under fast motion, while high thresholds reduce data volume but lose slow or static structures. We propose a multi-threshold event imaging model that assigns motion-compatible thresholds to different scene components, enabling faithful multi-velocity-scale imaging with fewer events. We also introduce a practical acquisition pipeline that implements this model on existing event cameras. Experiments on synthetic and real scenes validate the effectiveness of our approach in preserving both static structures and fast dynamics while reducing data volume.

We propose EverAnimate, an efficient post-training method for long-horizon animated video generation that preserves visual quality and character identity. Long-form animation remains challenging because highly dynamic human motion must be synthesized against relatively static environments, making chunk-based generation prone to accumulated drift: (i) low-level quality drift, such as progressive degradation of static backgrounds, and (ii) high-level semantic drift, such as inconsistent character identity and view-dependent attributes. To address this issue, EverAnimate restores drifted flow trajectories by anchoring generation to a persistent latent context memory, consisting of two complementary mechanisms. (i) Persistent Latent Propagation maintains a context memory across chunks to propagate identity and motion in latent space while mitigating temporal forgetting. (ii) Restorative Flow Matching introduces an implicit restoration objective during sampling through velocity adjustment, improving within-chunk fidelity. With only lightweight LoRA tuning, EverAnimate outperforms state-of-the-art long-animation methods in both short- and long-horizon settings: at 10 seconds, it improves PSNR/SSIM by 8%/7% and reduces LPIPS/FID by 22%/11%; at 90 seconds, the gains increase to 15%/15% and 32%/27%, respectively. Anonymous project page: https://anonymous-xxx-lab.github.io/.

Recent benchmarks for Large Language Model (LLM) agents mainly evaluate reasoning, planning, and execution. However, memory is also essential for agents, as it enables them to store, update, and retrieve information over time. This ability remains under-evaluated, largely because existing benchmarks do not provide a systematic way to assess memory mechanisms. In this paper, we study agent memory from a self-evolving perspective and introduce \texttt{EvoMemBench}, a unified benchmark organized along two axes: memory scope (in-episode vs.cross-episode) and memory content (knowledge-oriented vs.execution-oriented). We compare 15 representative memory methods with strong long-context baselines under a standardized protocol. Results show that current memory systems are still far from a general solution: long-context baselines remain highly competitive, memory helps most when the current context is insufficient or tasks are difficult, and no single memory form works consistently across all settings. Retrieval-based methods remain strong for knowledge-intensive settings, whereas procedural and long-term memory methods are more effective for execution-oriented tasks when their stored experience matches the task structure. We hope EvoMemBench facilitates future research on more effective memory systems for LLM-based agents. Our code is available at \url{https://anonymous.4open.science/r/EvoMemBench-2}.

Persistent homology summarizes the topological evolution of a graph as persistence diagrams (PD). Generating graphs whose topology matches a target descriptor is increasingly important, yet existing topology-aware generative models rely on soft regularization, producing graphs whose PDs may only be approximately similar. We initiate a principled investigation of the generation problem: produce graphs that realize a target PD exactly. We propose two approaches guided by the implicit local and global constraints a target PD imposes. The local approach builds degree-based PD-equivalent graphs iteratively, and we prove that every output realizes the target PD and that every degree-based PD-equivalent graph is reachable. However, as shown thereafter, local generation necessarily trades off between sample diversity and reaching infeasible states. We therefore recast generation as a global constraint satisfaction problem with a complete encoding solvable by modern constraint programming solvers. This is applicable to any vertex-based permutation equivariant filtration scheme and is minimal under the degree filtration. Experiments on four standard graph generation benchmarks confirm that both methods realize the target PD exactly. Together, our methods provide the first principled treatment of strict topology-compliant graph generation.

Text-to-image (T2I) diffusion models often synthesize policy-violating content, necessitating robust safeguards and rigorous evaluation frameworks. However, current approaches remain largely monolithic in both execution and evaluation, limited to coarse-grained categorization within broad unsafe concepts. Such approaches fail to account for the specific constituent factors of a violation, resulting in weak learning signals and allowing model collapse to bypass safety checks. To overcome these limitations, we propose Example-Based Spatial Guidance (EBSG), a training-free method that utilizes user-editable exemplar packs to provide granular spatial guidance for precise concept steering. Unlike existing methods that coarsely steer the entire image away from a broad concept, EBSG decomposes these categories into specific sub-concepts via exemplar text-image pairs, providing explicit, localized steering signals for more robust safety control. Furthermore, we introduce a vision language model (VLM)-based evaluation protocol that provides a fine-grained assessment, avoiding the pitfall of conventional binary evaluators that permit model collapse to bypass safety checks. Empirically, EBSG achieves state-of-the-art erasure performance across diverse safety datasets and concept categories, spanning four nudity benchmarks, seven I2P harmful-concept slices, and four MJA metaphor categories, while supporting multi-concept removal and transferring to a modern backbone, SD3.

Modern data-driven sciences use large observational datasets such as online discussions, behavioral logs, web experiments and biological records to explain outcomes and design interventions. Existing LLM-based hypothesis generators produce interpretable claims, but usually validate them through predictive accuracy, making them vulnerable to spurious correlations and dataset artifacts. Automated validation systems test hypotheses statistically, but require researcher-specified constructs. Here we show that ExperiGen, a two-agent closed-loop framework, discovers statistically supported natural-language hypotheses from observational data. A Generator proposes hypotheses, while an Experimenter operationalizes features, selects covariates and tests, executes analyses and returns evidence for refinement. Accepted and rejected hypotheses are stored in a labeled memory that guides further exploration. Across 19 tasks spanning text, images, clinical tabular data, economics, marketing and single-cell RNA-seq, ExperiGen discovers 2–4× more statistically significant hypotheses than prior generation methods and improves downstream predictive performance by 4–21 points. Expert evaluation by 26 specialists rated its hypotheses as novel, clear and research-worthy; 11 of 16 preregistered r/ChangeMyView hypotheses were significant in the predicted direction; and in deployed A/B tests across seven S&amp;P 500 companies and 5.1 million users, 15 of 25 hypotheses achieved significance, with 14 matching the predicted direction. These results suggest that closed-loop hypothesis generation can help prioritize hypotheses for expert review and real-world intervention and augment human judgment in data-driven sciences.

Explainability, the capability of an artificial intelligence system (AIS) to explain its outcomes in a manner that is comprehensible to human beings at an acceptable level, has been deemed essential for critical sectors, such as healthcare. Is it really the case? In this perspective, we consider two extreme cases, Oracle (without explainability) versus AI Colleague (with explainability) for a thorough analysis. We discuss how the level of automation and explainability of AIS can affect the determination of liability among the medical practitioner/facility and manufacturer of AIS. We argue that explainability plays a crucial role in setting a responsibility framework in healthcare, from a legal standpoint, to shape the behavior of all involved parties and mitigate the risk of potential defensive medicine practices.

In the realm of multi-objective alignment for large language models, balancing disparate human preferences often manifests as a zero-sum conflict. Specifically, the intrinsic tension between competing goals dictates that aggressively optimizing for one metric (e.g., helpfulness) frequently incurs a substantial penalty on another (e.g., harmlessness). While prior work mainly focuses on data selection, parameter merging, or algorithmic balancing during training, these approaches merely force compromises between divergent preferences along a fixed Pareto frontier, failing to fundamentally resolve the inherent trade-off. In this work, we approach this problem from a novel perspective of multi-dimensional rewards. By scaling up the model's rollouts and analyzing the outputs across different reward dimensions, we arrive at a critical conclusion: the conflict among multiple objectives stems from the fact that the prompt itself inherently restricts the achievable multi-dimensional rewards. Based on this core observation, we propose MORA: Multi-Objective Reward Assimilation (MORA). Specifically, MORA isolates single-reward prompts through pre-sampling and expands their reward diversity by rewriting the original questions to incorporate multi-dimensional intents. Extensive experiments demonstrate that: (I) in sequential alignment, MORA achieves single-preference improvements ranging from 5% to 12.4%, with exceptional gains in harmlessness, after multiple-preference alignment across helpful, harmless, and truthful dimensions. (II) In simultaneous alignment, MORA achieves an average overall reward improvement of 4.6%. Our codes are available at https://anonymous.4open.science/r/MORA-MPA.

Light Field (LF) deraining relies on leveraging redundant information between views to restore regions occluded by rains. Existing LF deraining methods typically rely on predicted depth maps to guide cross-view information interaction, while learning a brute-force mapping to separate rain from background content. However, the semi-transparent nature of rain streaks often leads to inaccurate depth estimation, which undermines geometric alignment and degrades restoration quality. More critically, the blending of rain and background during restoration makes brute-force mappings ineffective, resulting in visible rain artifacts in the output. In this paper, we first construct a consistency-constrained LF rain model by considering the consistency and the accumulation effects of rain layers. Then, we propose a novel Consistency-Guided Recovery Network (CGRNet) specifically designed for LF deraining. Inspired by the fact that background regions occluded by rain streaks often exhibit inconsistencies across all views, we introduce consistency maps to perceive degradation by aggregating pixel-wise consistency information along epipolar lines. Guided by consistency maps, the proposed model adaptively samples and aggregates consistent and redundant features from clean background regions to restore the details of degraded areas. Experiments validate our method's superiority over state-of-the-art approaches, and show that our dataset effectively enhances practical performance under real-world conditions.

Algorithmic ranking systems increasingly dictate high-stakes outcomes, yet most fairness interventions implicitly assume that protected attributes are categorical. When sensitive attributes are inherently continuous (e.g., age, income, or health scores), standard practices rely on arbitrary discretization, which discards crucial fine-grained information and destroys the natural ordinality of the data. In this paper, we study the problem of fair ranking with continuous protected attributes without relying on thresholding. We formalize fairness via the Kendall correlation between the ranking and the continuous attribute, and measure utility loss via the Kemeny distance from an initial score-based ranking. To solve this, we propose Fair Bubble Sort (FBS), a highly efficient adjacent-swap algorithm that minimizes utility loss subject to a strict continuous fairness constraint. We provide strong theoretical guarantees, proving that FBS is exactly optimal for the unweighted Kemeny distance. Extensive experiments on synthetic and real-world datasets show that FBS is scalable and consistently outperforms discretization-based baselines, achieving a superior fairness-accuracy Pareto front.

Modern AI image generators are increasingly deployed as opaque APIs, where customers can query the deployed service, but cannot inspect model weights or architecture. This creates a practical challenge: a provider may pass governance certification with one generator and later silently switch to a cheaper and lower-quality one for deployment, compromising public trust or even safety in high-stakes domains. We study integrity auditing at deployment time and propose FARE (Forensic Acceptance Region Estimation). A certified generator is enrolled by training FARE on images sampled from that generator. After deployment, FARE can determine whether a generated image is consistent with the enrolled generator-using only that image. FARE's features are based on image generator-specific artifacts that have been proposed for forensic applications. FARE amplifies these features during training by finding hard samples that tighten the acceptance region and increase sensitivity to subtle changes in the certified generator. Across generator swaps, including substitutions with similar model versions and model variants, FARE is effective at detecting swaps, consistently outperforming existing baselines at strict operating points, and remains effective under adaptive attacks that actively manipulate images to evade swap detection.

Scaling Maximum Entropy Reinforcement Learning (RL) to high-dimensional humanoid control remains a fundamental challenge, as the ''curse of dimensionality'' induces severe exploration inefficiency and training instability. Consequently, highly optimized deterministic policy gradients currently dominate high-throughput regimes. We address this limitation with FastDSAC, a framework that effectively unlocks the potential of maximum entropy stochastic policies for complex continuous control. We introduce Dimension-wise Entropy Modulation (DEM) to dynamically redistribute the exploration budget, alongside a continuous distributional critic tailored to ensure accurate value estimation by mitigating both high-dimensional overestimation and discrete quantization artifacts. Extensive evaluations on HumanoidBench and a diverse set of continuous control tasks demonstrate that FastDSAC establishes state-of-the-art performance for high-dimensional stochastic policies on the evaluated benchmarks. Our method is competitive with and often outperforms strong deterministic baselines, with gains of 180\% and 350\% on the challenging Basketball and Balance Hard tasks, respectively.

Large Reasoning Models (LRMs) have made remarkable progress, driven by a paradigm shift from fast thinking to slow thinking. While slow-thinking unlocks superior reasoning ability, its long-form nature imposes a prohibitive computational burden on Reinforcement Learning (RL) for LRMs. In this paper, we propose **Fast-RL**, a novel framework that accelerates RL training for slow-thinking LRMs. Our acceleration principle stems from a foundational insight: fast-thinking sampling serves as an efficient and powerful proxy for slow-thinking sampling in enhancing reasoning, since the core reasoning skills are shared across thinking modes and can be improved through either trajectory type. Fast-RL consists of two stages. **Stage-1** establishes reliable prompt-based thinking-mode control, so that a slow-thinking-oriented model triggers fast-thinking under a specific prompt while preserving its native slow-thinking under the standard prompt. **Stage-2** then enhances reasoning ability through RL with exclusively fast-thinking sampling for acceleration. A lightweight LoRA adapter is introduced as a dedicated mode controller: it is trained in Stage-1 and **frozen** in Stage-2, decoupling mode-control parameters from reasoning parameters and protecting the model's native slow-thinking from being disturbed by fast-thinking RL. After Stage-2, the LoRA acts as a disposable training scaffold that can be either merged into the main model or simply discarded. Experiments on diverse multimodal and text-only LRMs show that Fast-RL accelerates training by about $10\times$ while delivering superior performance gains over vanilla slow-thinking RL. On Qwen3-1.7B, Fast-RL improves AIME24 by $+10.0$ and $+13.7$ in slow- and fast-thinking evaluation, respectively. Vanilla slow-thinking RL, in contrast, drops slow-thinking accuracy by $-8.8$ and yields only $+2.8$ in fast-thinking evaluation.

Can a large language model be \emph{trained} to tolerate silent hardware faults, and if so, \emph{where} in the networkshould the consistency signal be enforced? We answer both questions and find that the second one matters more than the first. Injecting tile-level GEMM faults during training and adding a KL consistency term on the output distribution reduces fault-induced perplexity degradation from $12.9\%$ to $0.21\%$ on GPT-2 Small---a $60\times$ reduction (two-seed mean). The placement of the consistency target is decisive: a direct hidden-only objective at the fault site can worsen degradation to $32.7\%$, and fair bounded hidden controls with corrupted CE improve substantially but still trail output KL in an 80k-step screen ($1.74$--$1.76\%$ vs.\ $0.84\%$). A conditional constraint hierarchy and gradient-alignment probes explain why output-level consistency is more permissive and easier to optimize than direct early hidden-state consistency, without requiring that every hidden-alignment formulation fail. The output-over-hidden advantage persists at 8B scale, the GPT-2 ordering reproduces on held-out corpora, and generic robustness methods---SAM, R-Drop, and SAF---fail to close the gap. Pairing the trained model with simulated algorithm-based fault detection yields a bit-flip-to-erasure interface with a $1.10\times$ perplexity ratio, where unprotected inference produces NaN.

Federated learning (FL) encounters substantial challenges due to heterogeneity, leading to gradient noise, client drift, and partial client participation errors, the last of which is the most pervasive but remains insufficiently addressed in current literature. In this paper, we propose FedAdaVR, a novel FL algorithm aimed at solving heterogeneity issues caused by sporadic client participation by incorporating an adaptive optimiser with a variance reduction technique. This method takes advantage of the most recent stored updates from clients, even when they are absent from the current training round, thereby emulating their presence. Furthermore, we propose FedAdaVR-Quant, which stores client updates in quantised form, significantly reducing the memory requirements (by 50%, 75%, and 87.5%) of FedAdaVR while maintaining highly competitive model performance. We analyse the convergence behaviour of FedAdaVR under general nonconvex conditions and prove that our proposed algorithm can eliminate partial client participation error. Extensive experiments conducted on multiple datasets, under both independent and identically distributed (IID) and non-IID settings, demonstrate that FedAdaVR consistently outperforms state-of-the-art baseline methods.

Federated Multi-view Clustering (FMVC) enables privacy-preserving cross-view fusion from distributed multi-view data, where each client holds one view of samples and participates in the server's fusion without exposing raw data. In practice, existing FMVC methods face two key challenges: (1) the collected samples often show unaligned correspondence across views, especially under distributed collection; and (2) the gap between higher data privacy and lower computation cost remains hard to reconcile. To address these issues, we propose an Anchor Graph Alignment Framework for Federated Unaligned Multi-view Clustering (Fed-AGA), which employs a coarse-to-fine alignment strategy on lightweight anchor graphs to achieve misalignment-robust cross-view fusion while bridging the gap between data privacy and computation cost. Specifically, in the coarse alignment stage, we introduce an anchor-based graph generation module to generate an anchor graph on each client and upload them to the server for category-wise alignment by a designed topology-aware alignment module, which distills topology components as category-exclusive signatures to guide and achieve ideal category-wise alignment. In the next fine alignment and fusion stage, we design an attention-based alignment module to encourage each sample to pay attention to highly similar samples for sample-wise alignment, and the fine-aligned results are contrastively fused into a cross-view consistent anchor graph for clustering. Extensive experiments on multiple datasets demonstrate that Fed-AGA achieves state-of-the-art performance among related methods, while theoretical analysis offers theoretical motivation for two key components.

Neural combinatorial optimization (NCO) solvers generalize poorly under distribution shift, yet robustness across heterogeneous problem distributions is essential for real-world deployment where no single client should be left behind. Group distributionally robust optimization (Group DRO) offers a principled remedy, but requires pooling data that often cannot be shared due to confidentiality or regulatory constraints. Federated learning removes data-sharing requirements; however, existing federated DRO methods cannot accommodate the policy-gradient estimators used in modern NCO. Specifically, prior analyses rely on Lipschitz losses with bounded variance. In contrast, the REINFORCE policy-gradient estimator multiplies stochastic costs by score functions, causing variance to scale with cost magnitude and violating these standard assumptions. We close this gap by replacing Lipschitz-loss conditions with a bounded-cost assumption. This allows us to control cost-dependent variance and establish the first federated Group DRO framework with $O(1/\sqrt{T})$ convergence guarantees. Our analysis applies to any REINFORCE-trained model under bounded costs, including NCO solvers as a special case. Experiments on capacitated vehicle routing---one of many applicable domains including scheduling and packing---demonstrate the necessity of federated training for minority-distribution clients. We show that while na\"ive Group DRO suffers from catastrophic weight collapse, our KL-regularized formulation avoids this collapse. Furthermore, it provides a formal worst-group robustness certificate with no empirical performance penalty compared to standard federated averaging.

Low-Rank Adaptation (LoRA) is a popular parameter-efficient fine-tuning (PEFT) method for fine-tuning of large language models (LLMs), especially in federated learning, due to its strong performance and communication efficiency. In practice, LoRA is applied to the query and value projections of transformer layers without considering the distinct roles of these components. In this work, we analyze the impact of these projections in federated fine-tuning of LLMs using LoRA and observe that the value projection converges significantly faster than the query projection. Motivated by this finding, we propose FedLoVA, a federated fine-tuning framework that trains LoRA adapters on both query and value projections locally while sharing only the value projection with the server. This approach reduces communication overhead while improving performance and convergence efficiency. Experiments on natural language understanding, multilingual sentiment analysis, and generation tasks show that FedLoVA reduces the communication cost by up to $7.8\times$ compared to existing federated LLM fine-tuning baselines while achieving comparable performance.

Proxy model-based heterogeneous federated learning enables clients with diverse architectures to collaborate through a shared lightweight intermediary. However, current methods aggregate and distill proxy knowledge in a structure-agnostic manner, ignoring layer-wise convergence dynamics, conflating shared and client-specific information, and transferring knowledge without assessing its reliability. These oversights jointly cause model oscillation, drift, and negative transfer. We propose FedPeel, a framework that makes the aggregation pipeline aware of the internal structure of model parameters. Rather than updating the proxy monolithically, FedPeel selectively aggregates only the layers that have stabilized, decouples their frequency-domain representations to separate generalizable patterns from personalized details, and modulates knowledge transfer intensity on a per-sample basis according to teacher confidence. Theoretical analysis establishes an O(1/T ) convergence rate. Experiments on image, text, and audio benchmarks show that FedPeel consistently outperforms state-of-the-art methods in both accuracy and communication efficiency.

Low-rank adaptation (LoRA) enables communication-efficient federated fine-tuning of large language models, but standard FedLoRA averages the factors $A$ and $B$ separately even though the model update is determined by their product $\Delta W=sBA$. Under task heterogeneity, factor-wise aggregation can further dilute directions that are locally useful but not strongly shared across clients; we call this phenomenon \emph{directional dilution}. We analyze this effect through the singular directions of client LoRA updates and find that low-strength directions are most vulnerable to dilution. Their repeated owner-specific recovery suggests client-specific adaptation signals rather than random noise. We propose \textsc{FedReCall}, a client-private plug-in that captures observable dilution gaps, selects reliable diluted directions into a private frozen cache, and recalls them through a low-rank bypass guarded by a single-batch loss probe when the client next participates. As a plug-in, \textsc{FedReCall} can be seamlessly integrated into existing FedLoRA methods without changing their aggregation rules, communication payloads, or trainable-parameter sets, consistently improving personalized adaptation and improving global evaluation metrics on most aggregators.

Federated Continual Learning (FCL) suffers from spatial-temporal catastrophic forgetting caused by sequential task learning on individual clients and the aggregation of heterogeneous client knowledge. However, most existing methods either overemphasize preserving old knowledge, which restricts model plasticity for new tasks, or rely on training complex generative models for replay, incurring substantial computational overhead. In the human immune system, immune memory is preserved implicitly and can be reactivated through vaccination even after long periods of dormancy. Motivated by this, we propose FedVaccine, a novel framework that explicitly permits forgetting to preserve high plasticity for new tasks, while efficiently recovering past knowledge through re-learning a compact vaccine set. Specifically, the vaccine set for each task is synthesized by compress task gradient information under the guidance of the fine-tuned local model, capturing representative data characteristics and task-specific discriminative patterns. Two novel components are also introduced: Priming & Boosting, which encodes task-specific knowledge in a compact spectral form, priming the model with essential information and enabling efficient memory boosting via vaccine set replay after forgetting; and Vaccination Aggregation, which aggregates vaccine sets from all clients to train a globally generalized model without accessing any raw private data. Extensive experiments on three benchmarks demonstrate that FedVaccine achieves competitive performance, enables rapid knowledge recovery after forgetting, and incurs no additional overhead from training generative models.

Enabling language model agents to autonomously adapt to new environments remains a fundamental challenge. While recent memory-augmented approaches have achieved promising results, they are largely heuristic in design and treat task execution and knowledge construction as decoupled processes. Inspired by how biological systems rapidly adapt through principled acting and learning, we propose \textbf{FEP-Agent}, a framework that grounds LLM agent self-evolution in the \textit{Free Energy Principle (FEP)}. Our system instantiates the two components of Active Inference into: a Worker agent that minimizes \textit{Expected Free Energy (EFE)} during online interaction, balancing goal-directed exploitation with curiosity-driven exploration to probe uncertain environmental dynamics; and a Builder agent that minimizes \textit{Variational Free Energy (VFE)} post-execution, updating a knowledge base while controlling complexity through counterfactual validation. To support scalable knowledge in open-ended environments, we introduce a structured semantic memory that employs associative linking and overlapping communities for efficient retrieval. The excellent results across multiple LLM-agent benchmarks demonstrate that FEP-Agent achieves principled, self-evolving systems.

Recent progress in flow-based generative modeling has led to models that output high-quality samples while using only a small number of function evaluations. However, at present, there is a lack of similar advances in estimating the density of the generated samples. In particular, most existing methods either rely on restrictive architectures that enable exact calculations, or use stochastic approximations such as Hutchinson’s trace estimator that introduce substantial variance. In this work, we introduce SCAlable LikeLihood distillation of flOw maPs (SCALLOP). SCALLOP builds on the recently proposed F2D2, a likelihood flow map model that can generate samples and their densities in a small number of function evaluations. While {F2D2} uses Hutchinson's estimator during training, we introduce an alternative and more scalable likelihood distillation objective that is Hutchinson-free and admits a vectorized formulation. Empirically, we demonstrate the effectiveness of SCALLOP as a Boltzmann generator in molecular science, and further validate its benefit on image datasets. SCALLOP significantly reduces both training variance and training time while consistently improving performance compared to F2D2, and is competitive with the state-of-the-art while achieving up to $10\times$ inference speedup over the fastest baseline.

Introducing new capabilities to frontier models has long been the goal for posttraining, which relies predominantly on supervised finetuning (SFT) and reinforcement learning (RL) to achieve this. Prevailing wisdom dictates that on-policy RL enables strong generalization on new tasks without losing existing capabilities, while SFT is prone to weak generalization and forgetting. At the same time, SFT enables learning from inherently off-policy expert data, whereas RL must rely on a model's ability to generate positive signal on a new task. In our work, we seek to bridge the strength of on-policy learning with the information signal of off-policy expert traces. To this end, we introduce a Markov chain Monte Carlo (MCMC) sampling algorithm that transforms off-policy traces into more on-policy ones given a reference model for finetuning. Across tasks like scientific skill acquisition, mathematical reasoning, and fact learning, we demonstrate that simple SFT on these boosted off-policy traces can generalize better and forget less than on-policy counterparts. More generally, our approach outlines a principled methodology for model-specific data refinement, suggesting broader utility as a plug-and-play component throughout the post-training pipeline.

The behavior of LLMs does not depend solely on the model itself. Components of the inference system, such as the inference engine, attention backend, and hardware platform, subtly influence how inputs are processed. These components differ in their implementations and thereby induce small numerical deviations across systems when running the same model. While prior work has established the theoretical existence of such deviations, their security implications have remained unexplored. In this paper, we show that these deviations are characteristic of specific components and propagate to observable textual outputs, exposing the inference system to any party that can query the model. Building on this observation, we introduce a fingerprinting method that analyzes the prompt-response behavior of LLMs to identify components of the inference system. Our empirical evaluation demonstrates that the inference engine, attention backend, and underlying hardware platform can be identified reliably, even when the LLM is operated at non-zero temperature. We show that preventing fingerprinting is fundamentally hard, as it would require eliminating numerical differences between hardware and software stacks. We therefore propose partial mitigations and discuss their impact.

Under Climate Change, wildfire activity is intensifying worldwide, with Canada experiencing increasingly severe events that now affect every province and territory. However, existing machine-learning (ML) datasets remain constrained by geographic bias, narrow driver sets, opaque sample selection, and limited exploration of model failure modes. We introduce \textbf{FireMPC} dataset, the first pan-Canadian multisource wildfire risk benchmark, covering approximately one billion hectares, or $9{,}840 \times 4{,}639$ km$^2$, across the entire Canada at $1$ km daily resolution from $2000$ to $2025$, and integrating $55$ drivers across fuel, topography, meteorology, and human activity. FireMPC is also the first large-scale machine-learning benchmark to embed the six knowledge-driven indicators of the Canadian Forest Fire Danger Rating System (CFFDRS), bridging knowledge-based and data-driven modeling. Building on this dataset, we propose \textbf{FWI-guided hard negative mining (FWI-HNM)} strategy. We score each non-fire candidate using a calibrated CFFDRS composite, and then incorporate these hard negatives during model training. FWI-HNM mitigates the trivial-negative failure mode of random sampling, yielding F1/PR-AUC gains of $1\%$-$2\%$ and ECE reductions of up to $82\%$ across thirteen benchmark ML models. Stress tests under horizon-extension and spatial-generalization protocols expose substantial generalization gaps. Mean F1 falls from $70.2\%$ at the next-day horizon ($\Delta t = 1$) to $54.2\%$ at the 30-day horizon ($\Delta t = 30$). All six ecological, land-cover, and regional spatial-shift scenarios yield a $25\%$-$29\%$ relative F1 drop. These results motivate the development of large-scale, driver-diverse, spatially comprehensive datasets. The dataset, code, and sampling artifacts are available at \url{https://anonymous.4open.science/r/FireMPC}.

Long-context LLM agents often struggle with growing token, memory, and latency costs, making efficient context compression essential for practical deployment. Existing LLM-as-a-compressor methods remain noticeably inferior to using the full context. We find that this gap partly stems from their inability to preserve contextual information effectively. In this work, we revisit context compression from a structural perspective and identify two key bottlenecks in standard LLM-based compressors: limited coordination among compression tokens during information aggregation, and layerwise dilution that weakens useful signals from intermediate hidden states. To address these limitations, we propose ComprExIT (Context Compression via Explicit Information Transmission), a new context compression framework based on explicit information transmission. ComprExIT adaptively selects features across frozen LLM layers, then allocates information from anchors to compression slots through a globally coordinated transport plan. Experiments on 12 datasets show that ComprExIT consistently outperforms strong soft-compression baselines, improving average F1 by up to 18.5%, while adding only ~1% trainable parameters and achieving more than 2× faster compression than the fastest baselines. The code will be released upon acceptance.

Diffusion models have become a leading approach in visual generative modeling, particularly effective in text-to-image and controllable generation. Their main drawback is slow inference as it requires many sampling steps. While recent distillation methods enable one-step text-to-image generation, extending these techniques to controllable generation is still challenging. Existing controllable models, such as ControlNet, rely on dual-branch design that make distillation difficult. In this work, we adopt a simpler strategy. Instead of adding extra conditional branches, we finetune a selected subset of layers in the original text-to-image diffusion model to directly enable controllable generation. This results in DeltaControl, a single-flow, multi-step diffusion model capable of supporting spatial-condition generation while matching the performance of dual-branch approaches. Building on this teacher model, we further apply step-distillation to obtain FlashControl, a one-step model capable of spatial controllable generation with a single forward pass.

Long-context training of large language and multimodal models couples expressive structured attention masks (document, prefix-LM, share-question, sliding-window, block-sparse) with multi-node context parallelism, exposing the mask as a dominant bottleneck. The column-wise sparse mask of FlashMask compresses such masks from $O(n^2)$ to $O(n)$ via four query-axis boundary vectors and covers a broad family of structured patterns. Implemented for Ampere GPUs, FlashMask does not exploit FlashAttention-3's TMA/WGMMA warp-specialized pipeline on Hopper GPUs, and offers no distributed support. We observe that the column-wise sparse mask is indexed by key positions but stores query-axis interval boundaries. Under any multi-chunk context-parallel assignment, this property reduces per-rank mask localization to a provably zero-communication \emph{clip--shift--max} on the boundary vectors, directly compatible with the single-GPU kernel. Building on this property, FlashMask-3 introduces a mask-aware Hopper kernel with a three-role warp specialization atop the FlashAttention-3 TMA/WGMMA pipeline, together with a mask-aware distributed extension: zero-communication mask localization, a sparsity-aware load balancer (HCC-IPO), and a mask-aware compute-communication overlap with mask-driven communication pruning, whose forward all-gather additionally adopts topology-aware hierarchical routing. On a single H100 GPU across 12 structured masks at context lengths from 4K to 128K, FlashMask-3 improves achieved TFLOPs over FlashMask, FlexAttention, and MagiAttention by 45\% to 141\%, 34\% to 71\%, and 2\% to 44\%, respectively. Under context-parallel degrees from 4 to 32 with 8K sequence per device, it attains 31\% to 75\% compute-communication overlap efficiency and improves throughput over Megatron-Core CP (ring), Megatron-Core CP (all-gather) and MagiAttention by $3.25\times$ to $5.11\times$, $1.97\times$ to $3.26\times$ and $1.06\times$ to $4.09\times$ respectively. The source code will be released on GitHub.

Portrait video relighting aims to modify the illumination of a portrait video while preserving photorealism and temporal stability. The task becomes particularly challenging under dynamic lighting, where illumination direction, intensity, and color vary over time. To tackle this problem, we introduce FlashRelight, a novel two-stage framework for dynamic portrait video relighting. FlashRelight first suppresses the source illumination through delighting, and then synthesizes the target dynamic lighting on an illumination-neutral portrait video. FlashRelight supports three complementary control modalities: envmap sequences for explicit frame-level lighting control, dynamic lighting text for semantic illumination editing, and lighting reference videos for visual lighting transfer. Training follows a low-cost hybrid strategy that combines static one-light-at-a-time (OLAT) data with generated dynamic-lighting portrait videos, covering both controlled illumination variation and realistic portrait motion. Extensive experiments show that FlashRelight achieves temporally coherent and photorealistic dynamic relighting while enabling flexible cross-video lighting transfer.

Recent fMRI foundation models differ substantially in the spatial scale at which they represent brain activity. ROI- and connectivity-based models are efficient but coarse, whereas voxel-level models preserve fine-grained spatial structure but require specialized architectures and costly fMRI-specific pretraining. We ask whether part of this performance gap reflects the importance of preserving cortical geometry. Motivated by evidence that macroscale brain activity is strongly constrained by brain geometry, we introduce FlatClip, a training-free surface-level baseline that renders cortical activity as geometry-aware flatmap sequences and reuses a frozen SigLIP2 image encoder with only a lightweight downstream probe. Because FlatClip is not pretrained on fMRI, it provides an out-of-domain reference for evaluating how much information can be recovered from cortical geometry without relying on fMRI-specific pretraining data. Across resting-state and visual-fMRI benchmarks, FlatClip outperforms ROI/FC-style controls and several general-purpose fMRI foundation baselines, while voxel-level models remain a strong upper comparison. Geometry-control experiments show that disrupting cortical topology reduces performance, whereas mapping ROI-level signals back into atlas-defined 4D volumes partially recovers performance but remains below full voxel inputs. Together, these results position cortical geometry as a key factor in fMRI representation learning and establish surface-level flatmap sequences as a practical middle-ground baseline between ROI and voxel models.

Optimizing 3D shapes within the latent spaces of deep generative models is fundamental to computer assisted engineering, yet remains prone to a critical failure mode we term manifold drift: the tendency of gradient-based optimization to move latent vectors away from the manifold of valid shapes. This problem is exacerbated in state-of-the-art 3D shape generative models that operate in increasingly high-dimensional latent spaces where valid shapes occupy a vanishingly small fraction of the full space. Existing mitigation strategies, including latent regularization and flow-matching approaches, either sacrifice expressiveness, demand a difficult trade-off between objective guidance and generative fidelity that remains prone to manifold drift, or are computationally infeasible to scale to modern, large-capacity 3D shape models. We introduce a novel optimizer-corrector framework that alternates between gradient steps for objective minimization and guided flow matching to drive the latent state back to the valid shape manifold. By decoupling objective minimization from flow-based correction, optimizing freely and correcting strictly, this alternating design avoids inherent trade-offs, preserving geometric validity without sacrificing expressiveness while remaining computationally feasible on modern 3D shape models. We demonstrate its effectiveness across generative priors of varying complexity, from simple vector latent spaces to large-scale architectures across a variety of downstream optimization tasks, including aerodynamic drag reduction and object compliance optimization.

High-dimensional count data arise in applications such as single-cell RNA sequencing and neural spike trains, where mapping between distributions across successive batches or time points form critical components of data analysis. The recent success of diffusion- and flow-based deep generative models for images, video, and text motivates extending these ideas to count-valued settings, but many existing methods either treat each count as a categorical state or transform counts into a continuous space, neither of which is natural or efficient when the count range is large. We propose count-FM, a flow-matching framework for count data based on a continuous-time birth-death process with local unit jumps. Count-FM learns marginal transitions efficiently in count space through simulation-free training of conditional transition rates, allowing transport between arbitrary count-distributed source and target populations. In simulation, count-FM achieves better sample quality than representative baselines while using substantially fewer parameters. We further apply count-FM to scRNA-seq and neural spike-train data for unconditional generation, transport, and conditional generation. Across these tasks, count-FM yields improved sample quality, greater modeling efficiency, and interpretable transport paths.

The scalability of continuous normalizing flows (CNFs) for unbiased Boltzmann sampling remains limited in high-dimensional systems due to the cost of Jacobian-determinant evaluation, which requires $D$ backpropagation passes through the flow layers. Existing stochastic Jacobian estimators such as the Hutchinson trace estimator reduce computation but introduce bias, while the recently proposed Flow Perturbation method is unbiased yet suffers from high variance. We present **Flow Perturbation++**, a variance-reduced extension of Flow Perturbation that discretizes the probability-flow ODE and performs unbiased stepwise Jacobian estimation at each integration step. This multi-step construction retains the unbiasedness of Flow Perturbation while achieves substantially lower estimator variance. Integrated into a Sequential Monte Carlo framework, Flow Perturbation++ achieves significantly improved equilibrium sampling on a 1000D Gaussian Mixture Model and the all-atom Chignolin protein compared with Hutchinson-based and single-step Flow Perturbation baselines.

Multimodal driving planning faces a long-standing tension between two paradigms: scoring-based methods benefit from dense reward supervision but are confined to a fixed action vocabulary, while anchor-based methods generate proposals dynamically yet suffer from sparse supervision constrained to a single ground-truth trajectory. In this work, we propose FlowR2A, which resolves this tension by reframing simulation-based rewards from discriminative targets into generative conditions. By learning the reward-conditioned action distribution from dense trajectory-reward pairs with a flow-matching decoder, FlowR2A unifies the dense supervision of scoring-based methods with the proposal generation of anchor-based methods in a single generative model, forcing the model to internalize the correlation between an action and its outcomes in safety, progress, comfort, and rule compliance. To balance hard safety constraints against soft progress objectives, we introduce fine-grained per-timestep reward conditioning and reward noise augmentation. The generative formulation naturally supports controllable test-time sampling via reward guidance and anchored sampling, producing high-quality proposals. FlowR2A achieves state-of-the-art results on the NAVSIM v1 and v2 benchmarks, with multimodal proposals of substantially higher quality than prior methods.

Text-guided video editing requires applying a semantic change while keeping source motion, layout, and unchanged content stable. Recent inversion-free flow editors derive effective edit signals from target-source velocity differences, yet typically inject these signals directly at each solver step. In neural video sampling, however, this direct execution rule overlooks an important distinction: the velocity difference is an online, state-dependent observation rather than a fixed endpoint-displacement command. As the target branch evolves under previous injections, direct injection may accumulate temporal fluctuations and off-target perturbations, producing motion drift, identity flicker, or unintended changes in weak-response regions. We propose FlowTrack, a causal execution controller for inversion-free flow video editing. FlowTrack leaves the pretrained generator and raw edit signal unchanged, but executes the signal through a carried displacement-like direction and response-aware attenuation. We derive this update as the closed-form solution of a local execution objective that balances current signal tracking, temporal carrying, and weak-response suppression. On the full FiVE-Bench protocol, FlowTrack improves motion fidelity, unedited-region fidelity, and structure consistency over strong training-free diffusion- and flow-based editors while maintaining competitive target-prompt alignment. Code is available at https://anonymous.4open.science/r/FlowTrack.

Large-scale training of machine learning interatomic potentials (MLIPs) increasingly relies on data parallelism over heterogeneous atomistic data. In this setting, atom count captures an important part of the per-rank workload, but structures with similar atom counts can still induce different graph workloads through variations in graph count, cutoff edges, higher-order geometric features, and graph-collation overhead. This makes conventional atom-count batching and offline load tables both incomplete and inflexible, especially when the model architecture, cutoff radius, or data mixture changes. We present Flux, an online workload-aware scheduler for distributed MLIP training. Flux estimates structure-dependent workload signals from lightweight physical and geometric priors during data loading, calibrates them against runtime and memory costs, and forms balanced, memory-feasible local mini-batches across data-parallel ranks. Without requiring precomputed graph metadata, Flux can be used as a drop-in replacement for the standard distributed sampler, making it flexible and portable across training setups. Across large-scale atomistic datasets and three representative MLIP architectures, eSEN, MatRIS, and AllScAIP, Flux improves training throughput by up to 2.23--4.76$\times$ without degrading convergence.

Large Vision-Language Models (LVLMs) have achieved impressive progress in multimodal reasoning, yet they remain prone to object hallucinations, generating descriptions of objects that are not present in the input image. In this work, we investigate hallucination from the perspective of attention-value dynamics inside LVLM vision encoders. We identify a consistent three-phase structure of visual processing---diffusion, focus, and rediffusion---and show that the focus phase is where attention most clearly separates strongly and weakly supported visual tokens. However, low attention does not necessarily imply negligible downstream influence: low-attention tokens in the focus phase can still exert non-negligible value-side influence on the attention output relative to their small attention mass. Through controlled phase-wise value interventions, we find that hallucination behavior is particularly sensitive to the value content of low-attention tokens during this phase. Replacing or neutralizing these values reduces hallucination metrics while largely preserving grounded object evidence. A token-level teacher-forcing analysis further shows that the intervention reduces the probability of hallucinated object tokens with much smaller effects on ground-truth object tokens. In addition, Visual Attention Ratio (VAR) analysis shows that focus-phase intervention is accompanied by increased attention to visual tokens during decoding. Based on these observations, we instantiate a simple training-free inference-time intervention that replaces focus-phase low-attention values with an image-level mean value vector using statistics from a single forward pass. Experiments across multiple LVLM backbones demonstrate that this analysis-derived intervention reduces object hallucination with negligible additional runtime and remains compatible with existing inference-time mitigation methods. Our project page is available at: https://anonymous.4open.science/w/FocusMatters-7B32/

Data heterogeneity and multiple local update steps in federated learning can increase client drift and make global parameter aggregation less stable. Existing Sharpness Aware Minimization (SAM) based federated learning methods usually try to ease this problem by flattening local neighborhoods around client models. However, they do not explicitly optimize on the geometry region where the global parameters are actually aggregated. As a result, the aggregated global model often cannot fully benefit from local flattening. To address this problem, we propose a federated restricted SAM method called FedRSAM, which improves the stability of federated optimization by flattening the parameter aggregation region. First, we build a cone-shaped geometry region using the global update direction from the previous round and the variance of client parameter aggregation, in order to predict where the aggregated parameters may fall in the next round. Then, we restrict the search of SAM perturbation directions to the cone-manifold and only flatten the loss surface inside this region. Extensive experiments on multiple benchmarks and on both vision and natural language backbones verify the superiority of our method in terms of convergence stability and performance under heterogeneous settings.

We argue that forgetting is not confined to continual learning but is a general optimization phenomenon: during standard training, dominant mini-batch gradients suppress rare but useful update directions, causing short-term forgetting at every step. When such knowledge is never revisited, these losses compound into long-term forgetting—the classical failure mode of continual learning. We introduce FOGO, a scalable optimizer that continuously detects and resolves gradient interference across both regimes. FOGO spectrally orthogonalizes momentum updates to prevent dominant directions from monopolizing optimization, then stores representative past directions in a compact codebook memory built on random projection, where pairwise distances are provably preserved in low-dimensional space. At each step, conflicts between the current update and stored directions are resolved via lightweight orthogonal correction and lifted back through a proximal step, with minimal overhead and no data storage. Across class-imbalanced classification, continual visual learning under domain and class shifts, continual fine-tuning of LLaVA-7B, and GPT-2 pretraining, FOGO consistently improves convergence and knowledge retention, outperforming Adam and Muon.

Existing approaches to controllable generation typically rely on fine-tuning, auxiliary networks, or test-time search. We show that flow matching admits a different control interface: adaptation through examples. For deterministic interpolants, the velocity field is solely governed by a conditional endpoint mean; shifting this mean shifts the flow itself. This yields a simple principle for controllable generation: steer a pretrained model by changing the reference set it follows. We instantiate this idea in two forms. Reference-Mean Guidance is training-free: it computes a closed-form endpoint-mean correction from a reference bank and applies it to a frozen FLUX.2-klein (4B) model, enabling control of color, identity, style, and structure while keeping the prompt, seed, and weights fixed. Semi-Parametric Guidance amortizes the same idea through an explicit mean anchor and learned residual refiner, matching unconditional DiT-B/4 quality on AFHQv2 while allowing the reference set to be swapped at inference time. These results point to a broader direction: generative models that adapt through data, not parameter updates.

Multimodal large language models (MLLMs) are increasingly used for explainable image forgery analysis, where a shared decoder is expected to predict authenticity, localize evidence, and generate natural-language explanations. Existing methods typically rely on final decoder states, leaving open whether these late, language-oriented representations preserve localized manipulation cues. We test this assumption with a token- and layer-wise probing diagnostic that uses pixel-level masks only for analysis to separate tampered-region and authentic-region vision tokens. Across three MLLM-based forgery backbones and multiple manipulation types, we identify a mid-layer forgery evidence peak: tampered-region tokens are most linearly separable at upper-middle decoder layers and become less separable afterward, whereas authentic-region tokens continue to strengthen. This region-conditioned asymmetry suggests competition between localized manipulation cues and late semantic aggregation. Motivated by this diagnostic, we propose Gated Forensic Memory (\method), a lightweight evidence relay that reads from the diagnosed peak layer and writes re-encoded vision-token states back to late vision-token states through a zero-initialized gated residual. \method keeps the LLM frozen, adds no extra vision tokens, and requires no pixel-level training supervision. Across three backbones and seven benchmarks, \method improves authenticity prediction, explanation quality, and spatial alignment without mask supervision; the best read layer matches the diagnosed peak, turning the diagnostic into a practical layer-selection criterion.

The uncertainty of a statistical model is most commonly factorized into an aleatoric and an epistemic part. This factorization changes how predictions are interpreted in downstream decision tasks. Importantly, except for the idealistic scenario with no model mismatch, the quantification is a characteristic of the model and not the data generating process. In this paper, we propose Forgetting to Improve a method that reduces this discrepancy by incorporating the task into the modeling framework. Our key insight is to acknowledge that in scenarios of model mismatch, data can have a detrimental effect on the modeling for a specific task. Based on this insight, we propose an influence function for Gaussian process models that allows for principled removal of detrimental data samples. We showcase the flexibility of this approach by demonstrating significant improvements across a range of tasks, including Bayesian optimization, model-based reinforcement learning, and transductive learning.

Time series are ubiquitous across different domains, and the growing use of synthetic data for privacy-preserving sharing, data augmentation, and stress testing makes time series generation increasingly important. Early generative approaches are predominantly autoregressive, which can suffer from low sample diversity, error accumulation, and difficulty modeling long-range dependencies. In this paper, we introduce FracTS, a hierarchical, coarse-to-fine autoregressive model for multi-variate time series generation. Inspired by fractal generative modeling developed for images, FracTS adapts the paradigm from 2D to 1D sequences by learning a multi-scale representation that captures global trajectory structure at coarse scales and local dynamics at fine scales while following the autoregressive nature of time series. In contrast to unstructured images, time series are structured, allowing easy automated or designated temporal feature processing optimized for the model, and global (static) features extraction describing the instances that generate the corresponding time series in the collection. We incorporate them in the generation process to further improve the generation quality. The design naturally handles multivariate dependencies and improves long-horizon temporal coherence without requiring fixed-length inputs. Experiments on five datasets (two conditional, three unconditional) conducted against seven baselines show that FracTS consistently outperforms baselines across marginal distribution, correlation preservation, and ML utility. In particular, the ML utility is improved up to 98.1% comparing to the second-best baseline. Code is available at: https://anonymous.4open.science/r/fracts-91D621

We introduce FraudBench, an evaluation of the propensity of large language models (LLMs) to recommend, assist, or directly commit legal fraud, as measured by a novel dataset of 159 scenarios based on real-world U.S. court cases and validated by legal experts. As LLM use rises, models' ability to comply with the law becomes more important to ensure safety and public benefit. However, evaluating models' propensity to engage in fraud is challenging due to the need for specialized expertise, the contextual nature of legal standards, and the lack of high-quality evaluation data. \textsc{FraudBench} fills this gap with an evaluation framework for measuring fraud propensity, a dataset of 477 tasks (3 per scenario), and large-scale empirical results on 20 LLMs and over 300,000 samples. We also present a pre-registered human baseline ($n_{\texttt{annotations}} = 2957$, $n_{\texttt{humans}} = 996$). We find that (1) LLM fraud propensities are highly individualized, ranging from near 0\% to over 40\%; (2) LLMs with reasoning enabled behave less fraudulently ($\beta = -0.17$, log-odds scale); (3) LLM fraud propensity increases with task autonomy (6.4\% overall fraud rate when advising users, 26.9\% when acting directly); (4) LLMs are more likely to conceal than actively misrepresent information ($\beta = 0.38$); (5) humans and LLMs responded similarly to contextual cues, and differences in human-LLM fraud rates were not statistically significant; (6) system prompts can have substantial effects on fraud propensity across LLMs.

Fréchet regression, or conditional barycenters, is a flexible framework for modeling relationships between covariates (usually Euclidean) and response variables on general metric spaces, e.g., probability distributions or positive definite matrices. However, in contrast to classical barycenter problems, computing conditional counterparts in many non-Euclidean spaces remains an open challenge, as they yield non-convex optimization problems with an affine structure. In this work, we study the existence and computation of conditional barycenters, specifically in the space of symmetric positive definite (SPD) matrices with the Bures-Wasserstein metric, which connects SPD regression to optimal transport, avoids matrix logarithms, and preserves well-posedness under signed weights. We provide a sufficient condition for the existence of a minimizer of the conditional barycenter problem that characterizes the regression range of extrapolation. Moreover, we further characterize the optimization landscape, proving that under this condition, the objective is free of local maxima. Additionally, we develop a projection-free and provably correct algorithm for the approximate computation of first-order stationary points. Finally, we provide a stochastic reformulation that enables the use of off-the-shelf stochastic Riemannian optimization methods for large-scale setups. Numerical experiments validate the performance of the proposed methods on regression problems of real-world biological networks and on large-scale synthetic Diffusion Tensor Imaging problems.

Diverse outputs in text generation are necessary for effective exploration in complex reasoning tasks, such as code generation and mathematical problem solving. Such Pass@$k$ problems benefit from distinct candidates covering the solution space. However, traditional sampling approaches often waste computational resources on repetitive failure modes. While Diffusion Language Models have emerged as a competitive alternative to the prevailing Autoregressive paradigm, they remain susceptible to this redundancy, with independent samples frequently collapsing into similar modes. To address this, we propose a training free, low cost intervention to enhance generative diversity in Diffusion Language Models. Our approach modifies intermediate samples in a batch sequentially, where each sample is repelled from the feature space of previous samples, actively penalising redundancy. Unlike prior methods that require retraining or beam search, our strategy incurs negligible computational overhead, while ensuring that each sample contributes a unique perspective to the batch. We evaluate our method on HumanEval and GSM8K using the LLaDA-8B-Instruct and Dream-v0-Instruct-7B models, where we demonstrate significantly improved diversity and Pass@$k$ performance across various sampling settings. As a simple modification to the sampling process, our method offers an immediate, low cost improvement for current and future Diffusion Language Models in tasks that benefit from diverse solution search.

Current 4D occupancy world models use heavy cross-attention or deep recurrence to disentangle ego-motion from scene dynamics. This requires significant computational capacity. However, this effort is largely redundant since ego-motion is inherently known from odometry. We present FreeOcc, a lightweight continuous-time world model that introduces explicit $SE(2)$ geometric decoupling into a conditional flow-matching framework. By mathematically factoring out self-motion, we create a purely dynamic feature space. This space supports attention-free temporal fusion and allows physics-constrained ODEs to operate strictly on residual agent kinematics. To maximize deployment efficiency, FreeOcc introduces an uncertainty-aware blending mechanism. This routes well-observed voxels to a single-pass decoder, reserving the ODE solver exclusively for ambiguous, high-uncertainty regions. Requiring only 28.3M parameters—roughly 40\% of comparable models—our optimal ensemble mode (FreeOcc-E) runs at 27 FPS. On nuScenes, FreeOcc achieves state-of-the-art forecasting performance with a 0.47 m Near Field Chamfer Distance (NFCD) and 32.1\% mIoU. Furthermore, the model transfers zero-shot to SemanticKITTI. It also proves highly robust, retaining 92.8\% of its ideal mIoU under significant sensor packet loss and timestamp jitter.

The application of diffusion transformers is suffering from their significant inference costs. Recently, feature caching has been proposed to solve this problem by reusing features from previous timesteps, thereby skipping computation in future timesteps. However, previous feature caching assumes that features in adjacent timesteps are similar or continuous, which does not always hold in all settings. To investigate this, this paper begins with an analysis from the frequency domain, which reveal that different frequency bands in the features of diffusion models exhibit different dynamics across timesteps. Concretely, low-frequency components, which decide the structure of images, exhibit higher similarity but poor continuity. In contrast, the high-frequency bands, which decode the details of images, show significant continuity but poor similarity. These interesting observations motivate us to propose Frequency-aware Caching (FreqCa) which directly reuses features of low-frequency components based on their similarity, while using a second-order Hermite interpolator to predict the volatile high-frequency ones based on its continuity. Besides, we further propose to cache Cumulative Residual Feature (CRF) instead of the features in all the layers, which reduces the memory footprint of feature caching by 99%. Extensive experiments on FLUX.1-dev, FLUX.1-Kontext-dev, Qwen-Image, and Qwen-Image-Edit demonstrate its effectiveness in both generation and editing. Codes are available in the supplementary materials and will be released on GitHub.

Coding agents are increasingly adopted in real-world software development, yet their evaluation remains largely benchmark-driven and model-centric. Existing approaches focus on correctness and task completion, offering useful signals of isolated capability but limited insight into whether agents can be reliably used, controlled, and trusted in practice. We argue that the problem is not only how coding agents are evaluated, but what is being evaluated: coding agents are evaluated as models, while they are adopted as harnessed systems. This gap becomes most visible at the point of adoption. In real workflows, whether a coding agent is actually used is not determined by base-model capability alone. Instead, users make agents usable through user-level harnesses: situated configurations of task scope, context, constraints, checkpoints, workflow adaptations, and verification practices that shape and validate model behavior. These harnesses often remain informal and ad hoc, yet can determine whether the same model succeeds in one setting and fails in another. To ground this position, we draw on three empirical studies with users. Study 1 identifies contextual conditions shaping when users adopt or avoid coding agents. Study 2 identifies execution and control requirements for agentic systems. Study 3 identifies verification and code-quality criteria beyond correctness. Together, these studies suggest that users evaluate not only agent outputs, but also where agents can be used, how their behavior is guided, and whether their outputs are acceptable in practice. We interpret these conditions as user-level harnesses that make coding agents usable, controllable, and acceptable in real workflows. Based on these findings, we argue that evaluation should move beyond model-centric and system-internal metrics toward harness-aware, user-grounded assessment. In particular, evaluations must make explicit the conditions under which coding agents are usable, controllable, and trustworthy in practice—across dimensions such as environment, users, capabilities, and code quality. We call on the AI research community to place user-level harness at the center of coding-agent evaluation.

GUI agents are increasingly expected to complete real tasks through language-based interaction, yet most supervised training still treats a single demonstrated click or action as the unique ground truth. This creates a mismatch between how agents are trained and how they are evaluated: task success depends on reaching the intended interface state, not on reproducing the demonstrator's exact surface action. We address this gap by introducing Transition-Equivalent Supervision (TES), a framework that trains GUI agents around goal-relevant state changes rather than raw action identity. TES converts demonstrated interactions into transition descriptors, supervises agents to predict the intended transition, and learns an executor that selects any verified action capable of realizing that transition. At inference time, the agent further verifies whether the executed action produces the intended state change, reducing repeated no-op or erroneous behavior. Across offline web prediction, executable web navigation, mobile control, and desktop-use benchmarks, TES consistently improves equivalent-step success, alternative-action recall, and online task completion while reducing NoOp/Error transitions. These results suggest that GUI-agent learning should move beyond click imitation toward transition-level reasoning, offering a more robust supervision principle for agents operating in diverse and dynamic interfaces.

LLMs are widely adopted in multi-role dialogue and persona simulation. However, their strong ability to imitate role-specific linguistic and behavioral patterns can introduce privacy risks, reinforce social biases, and lead to unsafe or uncontrollable outputs. To address these issues, we introduce the Role-playing Unlearning task, which aims to selectively forget the style and knowledge associated with target roles while preserving the language ability and behaviors of the remaining roles. Unlike prior unlearning tasks that focus solely on removing factual information, our task additionally requires forgetting persona-specific characteristics, making the control over model generation more fine-grained. We propose ROLEMOE, a Mixture-of-Experts framework that enhances the separation of role-specific patterns by mapping different roles to more independent expert subspaces, providing structural interpretability. To reinforce this separation, ROLEMOE incorporates two complementary losses: a specialization loss that drives different roles to adopt different expert distributions, and a disentanglement loss that encourages different experts to develop distinct capability subspaces. This decomposed structure enables role unlearning to focus selectively on target-role experts. We finally propose RoleBench-Unlearn for evaluation, and experiments across multiple LLM architectures show that ROLEMOE achieves significantly more effective and controllable role forgetting, while better preserving the linguistic quality, knowledge, and persona consistency of non-target roles. All code and datasets are publicly available at: https://anonymous.4open.science/r/RoleMoE-6328.

Link prediction is a standard pretraining objective for graph neural networks: hide edges, train an encoder--decoder to predict them, and reuse the node embeddings for downstream node tasks. Despite its empirical success, there is little quantitative theory explaining why an edge-level objective should produce embeddings that are linearly useful for node classification.We study this question in the finite-sample stochastic block model (SBM). For any bounded GNN encoder paired with a symmetric bilinear decoder, we prove a conditional reduction: small population held-out LP excess risk implies small community-classification error for a transductive linear classifier on the learned embeddings.The bound decomposes into an optimization residual, an architectural approximation residual, and explicit statistical terms of order $\widetilde{O}(\sqrt{d/n})$ in bounded dense regimes. We further give a constructive few-label classifier: nearest-centroid classification under the PSD quadratic form $G = W Z^\top Z W$, whose label error is coupled to LP quality.The framework is architecture-agnostic through the approximation residual, and we instantiate this residual as $O(\log n / n)$ for a spectral-positional-encoding GIN with a bilinear decoder. Synthetic SBM experiments validate the theorem chain from LP risk to score error to classification error, and show when the worst-case constants become conservative.

Large language models (LLMs) are driving autonomous agents toward real-world environments, with the Web emerging as one of the most representative interaction scenarios. However, training Web agents with naive exploration remains challenging: successful trajectories are rare, and the sparse terminal rewards make it difficult for agents to obtain useful feedback from failed trajectories. Inspired by skill acquisition theory, we propose Skill-Composed Progressive Exploration (SCOPE), a short-to-long learning framework for long-horizon Web agents that decomposes complex task exploration into a gradual expansion process from local skills to complete task-solving capability. Specifically, SCOPE iteratively explores around local skills: Skill Discovery extracts reusable local skills and failure experiences from historical rollouts; Skill Orchestration composes discovered skills into high-level skill chains; andSkill Expansion leverages failure experiences aligned with the current skill chain to guide the agent toward exploring new subskills. Through this process, SCOPE progressively constructs task-completing skill chains across rollouts and internalizes them into the policy parameters through agentic on-policy distillation. Extensive experiments on WebArena, WebVoyager, and WebShop show that SCOPE consistently improves exploration dynamics and achieves 3.9\%--18.6\% relative gains over the strongest baselines in each setting. Our code is publicly available at https://anonymous.4open.science/r/SCOPE-14D3.

Effective team collaboration hinges on consensus formation through information exchange, a principle equally critical in multi-agent reinforcement learning (MARL). However, existing communication-based and consensus-learning methods often struggle to form a coherent global understanding from agents' limited local views. We propose STAGE, a local-to-global progressive consensus framework based on hierarchical communication. By first grouping agents according to their perceptual focuses, STAGE enables intra-group communication to form local consensuses, then selects group leaders to exchange these consensuses across groups, progressively expanding them into a unified global consensus with reduced communication redundancy. To further improve consensus quality, we introduce KL-divergence constraints for consensus alignment and a variational autoencoder (VAE) objective for preserving task-relevant global information. Extensive experiments on challenging MARL benchmarks show that STAGE consistently outperforms state-of-the-art baselines, especially on more difficult tasks and larger-scale multi-agent systems.

Segmentation is a fundamental vision task underlying numerous downstream applications. Recent promptable segmentation models, such as Segment Anything Model 3 (SAM3), extend segmentation from category-agnostic mask prediction to concept-guided localization conditioned on high-level textual prompts. However, existing benchmarks primarily evaluate mask accuracy or object presence, leaving unclear whether these models faithfully ground the queried concept or instead rely on visually salient but semantically misleading cues. We introduce CAFE: \textbf{C}ounterfactual \textbf{A}ttribute \textbf{F}actuality \textbf{E}valuation, a novel benchmark for evaluating concept-faithful segmentation in promptable segmentation models. Our \textbf{CAFE} is built on attribute-level counterfactual manipulation: the target region and ground-truth mask are preserved, while attributes such as surface appearance, context, or material composition are modified to introduce misleading semantic cues. The benchmark contains 2,146 paired test samples, each consisting of a target image, a ground-truth mask, a positive prompt, and a misleading negative prompt. These samples cover three counterfactual categories: Superficial Mimicry (\textbf{SM}), Context Conflict (\textbf{CC}), and Ontological Conflict (\textbf{OC}). We evaluate various model types and sizes on our CAFE. Experiments reveal a systematic gap between localization quality and concept discrimination: models often generate accurate masks even for misleading prompts, suggesting that strong mask prediction does not necessarily imply faithful semantic grounding. Our CAFE provides a controlled benchmark for diagnosing whether promptable segmentation models perform concept-faithful grounding rather than shortcut-driven mask retrieval.

Vision-Language-Action (VLA) driving augments end-to-end (E2E) planning with language-enabled visual backbones, but how VLM representations differ from vision-only encoders after policy learning, and whether such differences matter for planning, remains unclear. Under a unified VLM-hidden + diffusion-policy paradigm, we compare multiple VLM families/scales (InternVL3 and Qwen3VL) with standard vision-only encoders (ResNet, ViT, and EVA-CLIP) while keeping the downstream planner fixed. We study representation, behavior, and system design. CKA/CCA and Shared--Unique SAE show that policy learning enlarges a common decision subspace, but both branches retain non-transferable residual factors. Latent-intervention policies and scenario-level analysis further show that these residuals are behaviorally meaningful: vision-only encoders are stronger in simple geometry-dominant scenes, whereas VLMs are more effective in semantically complex and interaction-heavy long-tail cases. The two branches also exhibit distinct progress--braking and path-choice tendencies, and an oracle best-of-two VLM+ViT selector reaches 93.58 PDMS on NAVSIM. We convert this complementarity into two lightweight systems: HybridDriveVLA, which selects from a compact cross-model candidate set using a learned trajectory scorer and improves PDMS from 90.80 to 92.10, and DualDriveVLA, a fast--slow variant that invokes the VLM in only 15\% of scenarios, achieving 91.00 PDMS with about $1.9\times$ lower latency than the VLM baseline. Code will be released.

Scaling robot demonstrations does not guarantee better imitation: redundant trajectories dilute learning, while rare, imperfect segments may encode critical transitions. Prior curation methods overlook such dependencies by scoring samples independently at the trajectory or segment level. We reframe robot demonstration curation as a coverage-aware sequential selection problem and propose PROSE, which selects data by maximizing marginal utility under a fixed budget. PROSE unifies three signals: (i) influence on closed-loop return, (ii) kinematic reliability, and (iii) coverage gain measuring novelty with respect to the retained set, allowing full trajectories and fine-grained segments jointly participate in scoring and filtering. Across three Robomimic simulation tasks and three real Franka tasks, PROSE achieves the best success rate on every task we evaluate against three state-of-the-art curators (CUPID, SCIZOR, Demo-SCORE), with average closed-loop gains of +15\% in simulation and +35\% in real-world. Ablations attribute the gain primarily to the coverage step.

Modern optimizers, like Muon, impose matrix-wise geometry constraints on their updates. These matrix-wise constraints can be unified under Linear Minimization Oracle (LMO) theory. However, all current methods impose fixed LMO geometries for the update rules, chosen by-design or empirically, which are not necessarily optimal according to the problem's geometry. We introduce a novel efficient data-driven criterion for dynamically choosing proxy-optimal update LMO geometries on individual Deep Neural Network layers. Derived in closed form from gradient and activation statistics using a single-step random feature regression surrogate model, our criterion navigates a design space interpolating from SGD to Muon updates. Moreover, integrating parameter-wise preconditioning allows our framework to recover SGD, Muon, Adam, and MuAdam as specific extrema. To make this adaptive approach scalable, we pair it with efficient computational strategies, achieving only a $\sim 3$% runtime overhead on highly optimized baselines. As a proof of concept, we show that this data-driven optimizer matches or exceeds the performance of the best performing optimizer between Muon and AdamW across three different training scenarios. Ultimately, this work provides evidence that LMO geometry can be successfully and efficiently adapted from runtime data, opening a new pathway for optimizer design beyond static geometries.

Machine learning (ML) methods have achieved notable success in enhancing individual components of mixed-integer linear programming (MILP) solvers. However, most existing approaches focus on single-component enhancement, neglecting critical interactions among components and often yielding diminishing returns when multiple approaches are integrated. The emergence of large language models (LLMs) provides a promising paradigm for jointly optimizing multiple solver components within a unified framework. In this paper, we propose Jolly-MILP, an LLM-guided framework to jointly optimize presolving, cut selection, and branching variable selection in exact MILP solvers. Our approach introduces a synergistic Coordinator to align component-level generators and a hierarchical Controller to produce adaptive, multi-stage strategies. Extensive experiments on nine MILP datasets demonstrate that Jolly-MILP consistently outperforms both ML-based approaches and existing LLM-based methods. These results show that explicit joint optimization, rather than isolated component-wise enhancement, is crucial for acquiring further gains in exact MILP solving, offering a new pathway for more advanced solver design.

Autoregressive large language models have dominated generative recommendation by sequentially predicting the next item. However, this unidirectional paradigm suffers from error propagation, and limited ability to jointly optimize an entire recommendation list. In this paper, we introduce Full-Sequence Masked Diffusion for generative recommendation (FSMD), which extends masked diffusion models to enable global joint modeling over the entire user history and Top-$K$ recommendation list. We formulate recommendation as a unified sequence diffusion process, where the forward process randomly masks tokens across the full sequence and the reverse process simultaneously denoises all masked positions using bidirectional context. This formulation enables parallel generation of the entire recommendation list and naturally captures inter-item dependencies within a coherent global structure. To better align generation with ranking objectives, we further introduce a unified training objective that combines masked diffusion likelihood with a listwise ranking loss. Extensive experiments on multiple public benchmarks demonstrate that FSMD consistently outperforms state-of-the-art autoregressive and item-level diffusion-based generative recommendation methods across accuracy and list quality metrics.

The development of separate-encoder Unified multimodal models (UMMs) comes with a rapidly growing inference cost due to dense visual token processing. In this paper, we focus on understanding-side visual token reduction for improving the efficiency of separate-encoder UMMs. While this topic has been widely studied for MLLMs, existing methods typically rely on attention scores, text-image similarity and so on, implicitly assuming that the final objective is discriminative reasoning. This assumption does not hold for UMMs, where understanding-side visual tokens must also preserve the model’s capabilities for editing images. We propose G$^2$TR, a generation-guided visual token reduction framework for separate-encoder UMMs. Our key insight is that the generation branch provides a task-agnostic signal for identifying understanding-side visual tokens that are not only semantically relevant but also important for latent-space image reconstruction and generation. G$^2$TR estimates token importance from consistency with VAE latent, performs balanced token selection, and merges redundant tokens into retained representatives to reduce information loss. The method is training-free, plug-and-play, and applied only after the understanding encoding stage, making it compatible with existing UMM inference pipelines. Experiments on image understanding and editing benchmarks show that G$^2$TR substantially reduces visual tokens and prefill computation by **1.94$\times$** while maintaining both reasoning accuracy and editing quality, outperforming baselines on almost all benchmarks.

Fine-tuning diffusion models via online reinforcement learning (RL) has shown great potential for enhancing text-to-image alignment. However, since precisely specifying a ground-truth objective for visual tasks remains challenging, the models are often optimized using a proxy reward that only partially captures the true goal. This mismatch often leads to reward hacking, where proxy scores increase while real image quality deteriorates and generation diversity collapses. While common solutions add regularization against the reference policy to prevent reward hacking, they compromise sample efficiency and impede the exploration of novel, high-reward regions, as the reference policy is usually sub-optimal. To address the competing demands of sample efficiency, effective exploration, and mitigation of reward hacking, we propose \textbf{G}ated and \textbf{A}daptive \textbf{R}egularization with \textbf{D}iversity-aware \textbf{O}ptimization (\textbf{GARDO}), a versatile framework compatible with various RL algorithms. Our key insight is that regularization need not be applied universally; instead, it is highly effective to selectively penalize a subset of samples that exhibit high uncertainty. To address the exploration challenge, GARDO introduces an adaptive regularization mechanism wherein the reference model is periodically updated to match the capabilities of the online policy, ensuring a relevant regularization target. To address the mode collapse issue in RL, GARDO amplifies the rewards for high-quality samples that also exhibit high diversity, encouraging mode coverage without destabilizing the optimization process. Extensive experiments across diverse proxy rewards and hold-out unseen metrics consistently show that GARDO mitigates reward hacking and enhances generation diversity without sacrificing sample efficiency or exploration, highlighting its effectiveness and robustness.

Few-Shot Industrial Visual Anomaly Detection (FS-IVAD) is a critical task in modern manufacturing, where automated product inspection systems must identify rare defects using only a handful of normal, defect-free training samples. This paper introduces GATE-AD, a reconstruction-based framework that casts few-shot normality modeling as a masked, representation-aligned graph reconstruction problem on a $k$-nearest-neighbor graph, built from frozen self-supervised ViT patch tokens. Since defects typically disrupt the contextual consistency between a patch and its spatial neighbors, GATE-AD emphasizes such neighborhood relations by attending anisotropically over each patch's local neighborhood using a Graph Attention Network (GAT) encoder. To prevent GAT over-smoothing in the low-shot setting, the encoder output is aligned with the input ViT tokens through a learnable latent space, where reconstruction inconsistency is scored with a Scaled Cosine Error (SCE) objective. On the MVTec AD, VisA, and MPDD benchmarks, GATE-AD attains state-of-the-art image-level AUROC in the wide majority of $1$- to $8$-shot settings, with low per-image inference cost that does not scale with the size of the support set. Code is included in the supplement material and will be publicly released upon acceptance.

Modern datasets across many disciplines increasingly consist of time-evolving, potentially infinite-dimensional random objects, such as dynamic functional data, which are naturally modeled in Hilbert spaces. In these settings, characterizing probability measures, for example, through densities, can be ill-defined or technically challenging. Motivated by clustering applications, we propose a Gaussian mixture framework for Hilbert-space-valued data based on kernel mean embeddings and develop efficient optimization algorithms for estimation. We establish theoretical guarantees showing that the proposed algorithm is well defined and that the model yields a dense class of approximations in infinite-dimensional spaces. We evaluate the framework through extensive experiments on diverse structures and data geometries, including $L^2$-functional data and random graphs in Laplacian spaces arising in modern medical applications.

Climate downscaling of global General Circulation Models (GCMs) is a challenging ill-posed inverse problem. In practice, the lack of paired data makes end-to-end supervised training infeasible, while downstream assessments require processing large, heterogeneous GCM ensembles under strict computational limits. Consequently, developing an efficient, zero-shot downscaling approach is crucial. While vanilla Diffusion Posterior Sampling has recently shown immense potential by bypassing paired training, it remains bottlenecked by geographic misalignments, spectral biases, and prohibitive inference costs. To address these limitations, we propose the GCM-consistent Diffusion for Zero-shot Downscaling (GCD) framework. First, GCD conditions the diffusion prior on static geographic boundaries to ensure physical fidelity. Second, we introduce a filter measurement operator, effectively mitigating spectral discrepancies between GCMs and the prior to provide stable inference-time guidance. Third, to overcome the inference bottleneck, we design a joint acceleration scheme: by combining progressive distillation with Conjugate Gradient guidance to correct large-step deviations, GCD compresses the generation process to just 6 steps. Extensive experiments demonstrate that GCD achieves highly competitive 99th percentile accuracy across five heterogeneous GCMs and successfully recovers the high-frequency details of tropical cyclones, entirely without model-specific retraining.

Vision-Language-Action (VLA) models are emerging as highly effective planning models for end-to-end autonomous driving systems. However, current works mostly rely on imitation learning from sparse trajectory annotations and under-utilize their potential as generative models. We propose Generative Scenario Rollouts (GeRo), a plug-and-play framework for VLA models that jointly performs planning and generation of language-grounded future traffic scenes through an autoregressive rollout strategy. First, a VLA model is trained to encode ego vehicle and agent dynamics into latent tokens under supervision from planning, motion, and language tasks, facilitating text-aligned generation. Next, GeRo performs language-conditioned autoregressive generation. Given multi-view images, a scenario description, and ego-action questions, it generates future latent tokens and textual responses to guide long-horizon rollouts. A rollout-consistency loss stabilizes predictions using ground truth or pseudo-labels, mitigating drift and preserving text-action alignment. This design enables GeRo to perform temporally consistent, language-grounded rollouts that support long-horizon reasoning and multi-agent planning. On Bench2Drive, GeRo improves driving score and success rate by +15.7 and +26.2, respectively. By integrating reinforcement learning with generative rollouts, GeRo achieves state-of-the-art closed-loop and open-loop performance, demonstrating strong zero-shot robustness. These results highlight the promise of generative, language-conditioned reasoning as a foundation for safer and more interpretable end-to-end autonomous driving.

Open-ended image generation is no longer a simple prompt-to-image problem. High-quality generation often requires an agent to combine a model's internal generative ability with external resources. As requests become more diverse and demanding, we aim to develop a general image-generation agent that can self-evolve through trajectories and use tools more effectively across varied generation challenges. To this end, we propose GenEvolve, a self-evolving framework based on Tool-Orchestrated Visual Experience Distillation. In GenEvolve, each generation attempt is modeled as a tool-orchestrated trajectory, where the agent gathers evidence, selects references, invokes generation skills, and composes them into a prompt-reference program. Unlike existing agentic generation methods that mainly rely on image-level scalar rewards, GenEvolve compares multiple trajectories for the same request and abstracts best-worst differences into structured visual experience, provided only to a privileged teacher branch. Inspired by on-policy self-distillation, Visual Experience Distillation provides dense token-level supervision, helping the student internalize better search, knowledge activation, reference selection, and prompt construction. We further construct GenEvolve-Data and GenEvolve-Bench. Experiments on public benchmarks and GenEvolve-Bench show substantial gains over strong baselines, achieving state-of-the-art performance among current image-generation frameworks.

We introduce a new approach to high-fidelity 3D scene reconstruction from multi-view RGB images that tightly couples reconstruction with a strong generative 3D prior. We cast scene reconstruction as conditional 3D generation over a set of spatially-localized, overlapping chunks that together tile the scene, scaling generation to large scene extents. Crucially, we inherit the fidelity and completeness of state-of-the-art generative shape models -- we use Trellis.2 as an example -- which we generalize to the scene level. To this end, we propose a projection-based conditioning mechanism that lifts posed multi-view image features into a coherent 3D representation aligned with the generative model, independent of view ordering and spatially anchored to the scene, yielding high-fidelity, multi-view consistent generated geometry. This enables lifting the strong object-level prior of Trellis.2 to multi-view, scene-scale generation, producing faithful, editable PBR mesh reconstructions of indoor environments. As a result, we obtain high-fidelity results that outperform cutting-edge reconstruction methods by 16\%.

We present GenZ, a hybrid model that turns a foundational model (FM) into a {\bf knowledge-discovery engine} explaining variation in real-valued multidimensional targets. The FM is treated as a noisy oracle that can answer yes/no questions about a semantic item $s$ (text, image, or both) and GenZ learns {\bf which questions to ask} so that the answers $\bz$ explain the statistical link to a possibly high-dimensional target $\by$. Discovery is driven by {\bf group reasoning}: at each step, the model partitions items by the posterior of the current latent features, and asks the FM to articulate the semantic commonality that explains the divide. The resulting feature descriptors $\theta_f$ are the primary product---human-readable hypotheses about why $s$ predicts $\by$---while predictive accuracy is a secondary, but consistently strong, by-product (e.g.~beating both the 0-shot FM baseline and TabPFN on LLM embeddings of the same items in hedonic price regression). Across four domains---hedonic house pricing, Netflix cold-start recommendations, Arizona species ecology, and human visual-cortex fMRI---GenZ recovers dataset-specific structure that the FM does not surface from priors alone. Holding the items $s$ fixed and varying only the target $\by$ (Arizona species under spatial/taxonomic/functional targets; the same natural images under FFA/EBA/PPA brain responses) yields {\bf qualitatively different} discovered feature sets, demonstrating that GenZ characterizes the $s\!\to\!\by$ link rather than the marginal distribution of $s$.

Large language models solve complex problems by generating lengthy chains of explicit reasoning tokens. While effective, this makes reasoning expensive, length-sensitive, and constrained to (discrete) natural language. While latent reasoning offers a continuous alternative, determining useful structures for intermediate latent states is an open challenge. In this paper, we formulate latent reasoning as a geometric path-approximation problem within the model’s pretrained token-embedding space. We introduce Geometric Latent Reasoning (GLR), which uses a lightweight transition head to predict iterative direction updates in embedding space. Using textual chain-of-thought traces as anchors, GLR learns to approximate discrete reasoning trajectories while permitting continuous deviations from exact token embeddings. Evaluations on mathematical reasoning benchmarks using Qwen3 models reveal an emergent phenomenon: geometric latent reasoning induces substantially shorter generations without an explicit length objective. By replacing early explicit reasoning with continuous latent steps, models often reach correct answers using substantially fewer total generation steps. These findings suggest that continuous trajectories act as compact intermediate reasoning states, exposing a new tradeoff between latent computation budget, output length, and accuracy.

Transformer architectures exhibit cross-layer redundancies, yet post-training compression pipelines typically optimize layers in isolation or rely on heuristic grouping strategies that disregard layer-specific activation geometries. We introduce a principled, training-free framework that sequentially optimizes cross-layer weight pairings and shared-dictionary factorizations. Rather than forcing weights of adjacent layers to share a basis or heuristically merging activation statistics, our approach identifies structurally compatible projections and learns a shared representation that better preserves each layer’s distinct calibration geometry. Coupled with structured sparsity, this yields highly efficient weight decompositions without sacrificing functional fidelity. Across diverse architectures, scales, and modalities, our method achieves state-of-the-art results, consistently outperforming independent structured weight decompositions and alternative pairwise weight factorizations, which operate under heuristic grouping strategies. By replacing heuristic engineering strategies with a convergent, optimization-driven pipeline, we establish a theoretically grounded foundation for scalable, transformer compression across different modalities.

Whether a video is physically plausible is a question current generative models answer poorly and current evaluators answer expensively, through physics-targeted training, billion-parameter video world models, or multimodal-language model judges. We ask whether frozen images vision encoders, with no video or physics supervision, already contain a useful signal for the same question. Across four frozen backbones, self-supervised vision transformers (DINOv2, DINOv3) and biologically-inspired models of the ventral stream (CORnet-S, VOneNet), a small set of geometric signals (curvature, speed variation, acceleration, prediction residual) on per-frame feature trajectories separates plausible from physics violated videos with no retraining. We propose GEOPHYS, a training-free framework that 11 reaches 97.7% on LikePhys and 93.3% on IntPhys2 accuracy for physics-violation detection, surpassing V-JEPA 2, GPT-4o, Gemini, and twelve modern video diffusion models. The same signals track human EEG responses to object-permanence violations and scale with object number. Deployed unchanged as a best-of-N reward during video generation, GEOPHYS lifts MAGI-1 4.5B from 53.8\% to 64.7\% PhysicsIQ score at 5.8× lower wall-clock and 7.8× lower memory than V-JEPA 2 world-model rewards. A useful proxy for physical plausibility is already encoded as a geometric regularity of natural-video statistics in vision features.

Georeferenced 3D reconstruction from ground images requires recovering not only the local scene geometry, but also localizing every camera center and 3D point in a geographic coordinate frame. Existing cross-view geo-localization methods are able to estimate the GPS coordinate of a single ground camera, but do not reconstruct dense 3D geometry, while recent feed-forward multi-view 3D reconstruction models predict point maps and camera poses only in a local coordinate frame. We introduce X-VGGT, a feed-forward cross-view geometry transformer that unifies these tasks by jointly processing a set of ground-level images and a georeferenced satellite image to predict ground-view camera poses, dense 3D point maps, and a scene-level transform parameterized by gravity, scale, yaw and translation. This transform projects the local reconstruction into the satellite plane, resulting in the geo-registration of all ground images with the georeferenced satellite image. We further propose an evaluation protocol for this joint task and show that, across multiple cross-view datasets, X-VGGT produces accurate georeferenced reconstructions and outperforms prior cross-view geo-localization and multi-view 3D reconstruction models.

Large Multimodal Models (LMMs) often struggle with geometric reasoning due to visual hallucinations and a lack of mathematically precise Chain-of-Thought (CoT) data. To address this, we propose the GeoSym Engine, an automated and scalable neuro-symbolic framework. By leveraging a type-conditional grammar and an analytic SymGT Solver, it derives exact symbolic ground truths and seamlessly integrates with a robust rendering pipeline to produce high-precision geometric diagrams. Using this engine, we construct GeoSym127K, a difficulty-stratified dataset featuring 51K high-resolution images, 127K questions with symbolic ground truths, and 55K answer-verified CoT QA pairs. We also introduce GeoSym-Bench, an expert-curated suite of 511 complex samples for rigorous evaluation. Through extensive supervised fine-tuning (SFT), we demonstrate that GeoSym drives concentrated improvements specifically on diagram-dependent and multi-step geometry tasks. Our Qwen3-VL-8B model gains an absolute +22.21% on the MathVerse Vision-Only subset and reaches 61.52% (+6.19% improvement) on WeMath, mitigating long-horizon logic fragmentation and outperforming advanced closed-source models like Doubao-1.8. Furthermore, applying Reinforcement Learning with Verifiable Rewards (RLVR) via GRPO reveals that initializing from structural SFT checkpoints substantially elevates the performance ceiling over zero-shot RL. Driven by deterministic exact-match signals, this showcases the robust scaling potential of our verifiable reasoning synthesis. Datasets and code are available at https://huggingface.co/datasets/Tomie0506/GeoSym127K and https://github.com/Tomie56/GeoSym127K

Empirical claims about the connection between over-squashing and long-range interactions in GNNs, can only be trusted if the benchmarks used to validate them genuinely require long-range interactions. The de-facto standard, the Long Range Graph Benchmark, has been repeatedly shown to be saturated by tuned short-range models, with existing synthetic alternatives being tied to specific topologies. As such, there is a lack of principled certificate of long-rangedness on *arbitrary* graphs. This state reflects the absence of a precise characterization of long-ranged benchmarks. We address this fundamental gap by introducing four verifiable *axioms*: *Predictability*, *Tightness*, *Strictly $k$-Range*, and *Topology-Invariance*, that any task claiming to test $k$-hop interactions must satisfy. We formally prove that violating any one of them admits failure modes that undermine conclusions drawn from the task. Based on these axioms, we introduce TRIP (*Truly Ranged Interactions Problem*) and its generalisation GRIP (*Generally Ranged Interactions Problem*), constructive procedures that turn *any* graph into a provably long-ranged task by drawing features from *stable* distributions. Moreover, by construction, GRIP admits a closed-form, per-range Maximum-Likelihood oracle that yields the first *a priori per-range* lower bound on test error available on any benchmark. Using our framework, we: (i) audit 4 common long-range benchmarks and identify their failures modes with respect to our axioms; (ii) on TRIP-instantiated topologies, we find a popular notion of curvature is uncorrelated with GNN performance, supporting topological-vs-computational bottleneck distinction; and (iii) we show that a novel benchmark's over-squashing measures factors beyond pure long-rangedness. Code to use the framework and reproduce experiments is released anonymously https://anonymous.4open.science/r/graph-grip-7F1A/.

Streaming 3D reconstruction from long monocular video sequences requires maintaining a key-value(KV) cache that grows linearly with sequence length, creating a severe memory bottleneck. Existing approaches either truncate the cache to a fixed set of anchor frames, leading to reconstruction quality degradation, or rely on attention-score heuristics that are agnostic to 3D scene structure,failing to preserve geometrically valuable tokens. To address these problems, we present GHOST (Geometry-Hierarchical Online Streaming Token Eviction), a training-free KV cache management framework that exploits the model's own 3D geometry outputs to evict redundant tokens online. GHOST introduces three mutually reinforcing innovations: a hierarchical dual-level importance scoring scheme, a privilege mechanism that protects special tokens from eviction, and a cosine-similarity-guided layer-wise budget allocation. Experiments on various benchmarks show that GHOST preserves excellent reconstruction quality while cutting the KV cache by nearly half and delivering $1.75\times$ faster inference compared to state-of-the-art methods. Our code and model will be available to the public.

Single-Domain Generalized Object Detection (S-DGOD) aims to generalize from a single source domain to unseen domains under distribution shifts. Existing efforts mainly focus on simulating unseen domains or suppressing domain-specific factors. However, they largely overlook the intrinsic generalization capability embedded in Vision Foundation Models (VFMs). In this work, we show that unconstrained fine-tuning disrupts the geometric structure of pre-trained representations, which encodes domain-invariant semantics. Motivated by this, we propose Geometric-Invariance Fine-Tuning (GIFT), a geometric-aware fine-tuning method for VFMs. Specifically, we introduce a series of learnable Householder reflections to the pre-trained weights to encourage geometry-consistent adaptation under domain shift. In addition, we propose a lightweight channel-wise scaling to facilitate effective task-specific adaptation. Extensive experiments demonstrate that GIFT consistently outperforms state-of-the-art (SOTA) approaches on two S-DGOD benchmarks. Notably, by introducing fewer than 1% additional trainable parameters into the frozen backbone, the proposed method achieves improvements of +7.9% and +2.9% in mPC over the previous SOTA on the Cityscapes-C and Diverse Weather Dataset, respectively. This finding highlights that preserving representation geometry is crucial for robust cross-domain generalization, establishing GIFT as a principled and efficient solution to domain shift. The code is available in the supplementary material.

Vision-language models (VLMs) for radiology have emerged as a scalable paradigm by leveraging image-report pairs naturally produced in clinical workflows. However, this pairing reveals a mismatch in scale: each finding occupies only a small region of the image, yet supervision is provided only at the global image-report level. This poses a central challenge: prior approaches spread weight densely across all patches rather than concentrating on the sparse subset relevant to a given query. To address this, we present GLINT (Gated Language-Image alignmeNT), a framework that explicitly models this sparse correspondence. On the alignment side, we introduce Sparsely Gated Alignment, a novel architecture in which a sigmoid gate over a separate gate embedding space activates only the patches relevant to each textual query, enforcing explicit sparsity. On the representation side, we add Dense Feature Regularization, which anchors the trainable encoder's intermediate features to a frozen self-supervised learning (SSL) teacher, preserving the fine-grained patch features that the gate relies on. The same recipe applies to both 2D chest X-ray (CXR) and 3D chest computed tomography (CT), built with DINOv3 and V-JEPA 2.1, respectively. GLINT enables zero-shot classification, grounding, and segmentation from free-text queries, and to our knowledge is the first to demonstrate zero-shot segmentation on 3D CT volumes without mask supervision. Notably, the most pronounced gains arise on zero-shot grounding and segmentation, where sparse, query-specific localization is required, consistent with our design intent. In downstream evaluation, GLINT outperforms both SSL encoders and medical VLMs on classification, report generation, and segmentation.

Most existing memory-enhanced Large Language Model (LLM) approaches implicitly assume that memory validity can be established either through external evaluators that provide task-specific success signals or through internal model cognition, such as reflection, for editing memory entries. However, these assumptions often break down in practical environments with dynamic drifts. We propose the Global Verifier (GLOVE), a plug-and-play module for LLM memory systems that establishes a relative notion of truth to achieve memory-environment realignment in the face of environmental drifts. Through active probing to detect inconsistencies between retrieved memories and fresh observations, GLOVE enables memory-environment realignment by verifying and updating memory without access to task-specific ground-truth supervision or strong reliance on model introspection. We evaluate GLOVE across a spectrum of tasks ranging from web navigation and discrete planning to continuous and embodied control, covering both simulated benchmarks and real-world robotic deployment. Across all domains, GLOVE improves adaptation across various LLM memory designs under both explicit and implicit drift, often recovering performance from near-failure to high success rates. These results suggest a practical pathway to memory-environment realignment for self-evolving cognitive agents.

Glycosylation is among the most diverse and important post-translational modifications in biology, governing immunogenicity, self-recognition, and the clinical viability of biologic drugs. Glycans cannot be sequenced from a template, and de novo structure prediction from tandem mass spectrometry (MS/MS) remains a longstanding bottleneck. The current state of the art, GlycoBART, is a $207$M autoregressive transformer whose $O(\mathrm{beam} \cdot \mathrm{output\_length})$ decoder passes preclude real-time annotation. We introduce $\textbf{GlycoGen}$, a $50$M Discrete Flow Matching (DFM) model that predicts glycan structure from MS/MS spectra by direct, non-autoregressive generation in a few parallel forward passes. We pair it at inference time with $\textbf{Crystallizing Flows}$, a deterministic sampler that propagates the per-token distribution on the probability simplex and freezes each token to a one-hot (*crystallizes* it) the moment its argmax leaves the mask, exiting early once all tokens are unmasked. The sampler feeds the denoiser mixture embeddings, and runs out-of-the-box with no retraining. GlycoGen + Crystallizing Flows reach $\mathbf{0.9266}$ top-1 structural accuracy on the CandyCrunch test set, three orders of magnitude faster than GlycoBART, Pareto-dominating the prior state of the art on *both* accuracy *and* speed. We extend our experiments to a matched-architecture MDLM denoiser (Sahoo et al. 2024) and observe a comparable lift: the sampler generalizes from continuous-time flow matching to discrete-time masked diffusion. To our knowledge, this is a new state of the art for de novo open-vocabulary glycan structure prediction, and the first system fast enough for accurate real-time inline annotation on modern LC-MS/MS instruments.

Gradient inversion reconstructs training data from shared gradients. However, existing methods target classification tasks and image diffusion, and do not address mixed tabular diffusion, where numerical columns follow Gaussian noising while categorical columns are corrupted by discrete masks. This mixed continuous-discrete training objective changes both the inversion target and the gradient structure: an attacker must recover not only rows, timesteps, and noise, but also the latent mask pattern whose density is tied to the diffusion timestep. We propose Goliath, the first gradient inversion attack designed for tabular diffusion models. Goliath employs a cyclic inversion loop that jointly recovers rows and diffusion latents across Gaussian and mixed diffusion regimes. We introduce a tabular-aware objective function that balances numerical and categorical gradient contributions while improving categorical recovery through enforced consistency between noise levels and mask structures. To accommodate large batches, we aggregate multiple reconstructions across epochs using a row-alignment heuristic. Evaluated across nine tabular datasets, Goliath achieves up to $88.9\\%$ per-cell reconstruction accuracy and consistently outperforms gradient-inversion baselines across Gaussian and mixed tabular diffusion. Code is available at https://anonymous.4open.science/r/fl-tab-diffusion-inversion-F4E7/README.md.

Physical-domain adversarial attacks present critical security threats to diverse AI systems, notably autonomous driving. Generalization is an inherent requirement and a primary obstacle in physical attacks, manifesting as the ability of adversarial patterns to persist across varying environmental configurations and to transfer to unseen victim models. While prior methods demonstrate generalization across specific models, their performance drops significantly on open-vocabulary foundation models, with some approaches becoming entirely ineffective. This lack of generalization stems from the failure of adversarial camouflage to construct a cohesive pseudo-feature across perspectives, lacking the stable semantic representation characteristic of natural objects. Such a deficiency arises because existing attack paradigms rely on independent view-level optimization and non-targeted objectives, which lead to semantically scattered features and inconsistent optimization directions. To address these limitations, we propose Generalizable Physical Adversarial Camouflage (G-PAC), a framework that transitions from view-specific suppression to coordinated object-level representation learning. G-PAC maintains a momentum-updated global pseudo-feature center as a stable adversarial anchor to guide optimization across diverse configurations. By leveraging the feature space of a self-supervised foundation model as a semantic prior, we introduce a contrastive regularization term to encourage feature aggregation while ensuring adversarial potency. Comprehensive digital and physical evaluations demonstrate the effectiveness of G-PAC. Notably, it outperforms the strongest baseline by an average AP@0.5 margin of 0.10 across seven diverse detector architectures in digital settings, and further extends this margin to 0.12 in real-world physical tests. Furthermore, G-PAC exhibits profound black-box transferability against highly resilient open-vocabulary foundation models, inducing an additional absolute AP@0.5 reduction of 0.30 on GLIP.

Graph-structured data underpins applications from citation analysis and social-network modeling to molecular design and knowledge-graph construction, and Large Language Models (LLMs) are increasingly used as prompt-driven graph synthesizers. Classical graph-generation reviews catalog deep generative models and their evaluation primitives, but predate the LLM era and provide no foundation for evaluating instruction-following graph synthesis. Recent LLM-era benchmarks evaluate models along graph-type or task-domain axes; such organizations, however, average over structural complexity and cannot localize \emph{where} in the complexity spectrum an LLM breaks down. To close this diagnostic gap, we introduce GraphInstruct, a progressive-complexity benchmark that stratifies LLM graph generation into six complexity levels and five evaluation dimensions, paired with 800 hand-authored instructions, 1,582 algorithmically synthesized reference solutions, and a 12-LLM capability evaluation across 45 (model, strategy) configurations. We find that discriminative power peaks at multi-constraint composition rather than reasoning depth, that no single prompting strategy dominates across levels or model families, and that domain-semantic constraints remain iteration-invariant under all tested methods---pointing to retrieval rather than additional compute as the next research frontier. Atop the benchmark, a verification-guided iterative framework with constraint-aware adaptive prompting consistently surpasses the prompt-engineering ceiling on tested target models, demonstrating that the benchmark's fine-grained signals drive method development. Data, code, and reproducibility artifacts are included in the supplementary materials and will be released publicly upon acceptance.

In robotic manipulation, the tight coupling between grasping and motion planning often obscures the true source of failure, leading to inefficient trial-and-error. To enable efficient long-horizon manipulation, we propose GTP-FA (Grasp-Then-Plan with Failure Attribution), a task-oriented two-stage ‘grasp-then-plan’ framework that generates grasp candidates and performs downstream motion planning conditioned on the selected grasp. Given a failed manipulation trajectory, we learn a failure attribution model that generalizes to unseen grasps and produces a stable distribution over failure modes for diagnosis-guided optimization. Based on these attribution results, we then optimize both modules in a diagnosis-driven manner: on the grasping side, we inject task-level priors and risk penalties into grasp candidate scoring and optimization to suppress unstable or task-incompatible grasps; on the planning side, we target high-risk initial states through data collection and fine-tuning to address genuine planning bottlenecks. We evaluate the proposed framework in both simulation and real-robot experiments, and show that GTP-FA improves the corresponding base learners across RL, IL, diffusion-policy, and VLA-based settings, achieving substantially higher overall task success rates. Project page: here.

Recent works have shown that gradient-update alignment is a powerful signal for modulating optimizer updates and improving training dynamics. We promote this update-wise heuristic into a mathematically grounded principle for selecting and tuning optimizer hyperparameters. By treating gradients and updates as signals, and an optimizer as a causal filter that maps between them, we formulate optimizer selection as maximizing the expected drop rate in loss over a prescribed family of optimizers. We show that this objective is exactly the inner product between the optimizer filter and the gradient autocorrelation, and prove that a greedy optimum exists and has a stability bound under perturbations of the estimated gradient statistics. Specializing in momentum-based optimizers, the theory yields simple dynamic momentum selection rules for both SGD+Momentum and Adam/AdamW. Experiments across image classification, language model fine-tuning, and vision transformer fine-tuning show that the resulting dynamic momentum rules match or improve upon the best fixed hyperparameters found via manual sweeps, reducing the need for exhaustive momentum sweeps.

Adaptive volumetric finite element meshing is a critical step in computer-aided engineering and analysis that dictates the computational budget of a given problem. It traditionally requires iterative PDE solvers or heavily supervised, data-driven surrogates trained on large-scale simulation data. While Multimodal Large Language Models (MLLMs) excel in 2D visual tasks, their zero-shot capability to semantically ground regions based on geometric understanding and physics remains an open question. Overall, this study explores a significant question: can the high-level semantic understanding of off-the-shelf MLLMs serve as a viable, zero-shot geometric proxy for finite element mesh refinement? To investigate this, we introduce GReFEM (Geometric Reasoning Enhanced Multimodal LLMs for Finite Element Meshing), a framework that utilizes MLLMs to visually localize stress-critical regions based on physics-guided textual prompts. To bridge the gap between 2D MLLM pre-training and 3D geometries, we introduce orthoViews, a view-selection module that maximizes the observability of key geometric features. We conduct an in-depth empirical evaluation across diverse CAD geometries, loading cases, and SOTA MLLMs, comparing them against a tuned geometric heuristic under a strict, matched refinement budget. Our findings reveal that MLLMs demonstrate robust zero-shot capacity to accurately follow complex spatial-physical instructions, isolating stress-relevant features with higher precision than blind heuristics. By mapping both the successes and current limitations of MLLMs in physical grounding, this study defines the frontier of foundation models as semantic assistants in automated simulation workflows.

Group-based Reinforcement Learning (RL) has significantly enhanced Large Language Models (LLMs) in agentic scenarios. To achieve finer-grained policy updates, recent agentic RL frameworks have shifted from trajectory-level to step-level training. However, long-horizon agentic RL suffers from severe reward sparsity and delay, as feedback is often deferred for dozens of interaction steps. While existing step-level frameworks refine training granularity, their credit assignment remains coarse-grained and still treats agent exploration as isolated, linear trajectories. This oversimplified perspective ignores the inherent graph structure of state transitions, leading to high-variance state-value estimation and myopic, localized credit assignment. To overcome these critical bottlenecks, we propose Group-Graph Policy Optimization (G2PO), a novel group-based RL algorithm tailored for multi-turn agentic tasks. G2PO explicitly transforms linear interaction trajectories into a global state-transition graph. By aggregating identical observations across different trajectories, we introduce group-aggregation state-value estimation that reduces sampling variance and trajectory-dependent bias. Furthermore, we redefine agent actions as transitions between state nodes and propose an edge-centric advantage estimation strategy. By globally standardizing Temporal Difference (TD) errors across the entire graph, G2PO explicitly identifies and prioritizes critical transitions that drive absolute task progress. Extensive experiments on representative long-horizon benchmarks—WebShop, ALFWorld, and AppWorld—demonstrate that G2PO substantially outperforms state-of-the-art prompt-based and RL baselines, achieving remarkable success rate improvements of up to 22.2\% over GRPO.

Skill-augmented agents increasingly rely on large reusable skill libraries, but retrieving relevant skills is not the same as presenting usable context. Existing methods typically return atomic skills or dependency-aware bundles whose internal roles remain implicit, leaving the agent to infer the execution entry point, support skills, visible requirements, and failure-avoidance guidance. We introduce Group of Skills (GoSkills), an inference-time group-structured retrieval method that changes the agent-facing retrieval object from a flat skill list to a compact, role-labeled execution context. GoSkills builds anchor-centered skill groups from a typed skill graph, expands support groups through a group graph, bottlenecks the selected group plan into a bounded set of atomic skill payloads, and renders a fixed execution contract with START, SUPPORT, CHECK, and AVOID fields, without changing the downstream agent, skill payloads, or execution environment. Experiments on SkillsBench and ALFWorld show that \goskills preserves visible-requirement coverage under a small skill budget, improves over flat skill-access baselines, and often improves reward and agent-only runtime relative to structural retrieval references. Code is available at https://anonymous.4open.science/r/Group-of-Skills-E861.

Classifier-free guidance (CFG) has become a widely adopted and practical approach for enhancing generation quality and improving condition alignment. Recent studies have explored guidance mechanisms for unconditional generation, yet these approaches remain fundamentally tied to assumptions specific to diffusion models. In this work, we propose a spectrum weakening framework for visual autoregressive (AR) models. The method is training-free and condition-free by constructing a controllable weak model in the spectral domain without architectural modifications. We theoretically show that invertible spectral transformations preserve information, while selectively retaining only a subset of the spectrum introduces controlled information reduction. Based on this insight, we perform spectrum selection along the channel dimension of internal representations, which avoids the structural constraints imposed by diffusion models. We further introduce a spectrum-renormalization technique that maintains numerical stability during the weakening process. Comprehensive empirical and ablation studies confirm the effectiveness of our approach, including raster-scan, random-order, and scale-wise AR, spanning discrete and continuous modeling and covering class and text-condition scenarios, demonstrating high-quality unconditional generation and strong prompt alignment maintenance for conditional generation. Code will be made available.

Large-scale demonstration datasets have been central to recent progress in general-purpose robot policies. However, existing datasets are collected in human-absent settings, and policies trained on such data may perform tasks competently in isolation but fail to exhibit human-aware behaviors. To address this gap, we introduce HABIT, a large-scale robot demonstration dataset for human-present environments. We organize tasks into three roles capturing distinct modes of human-robot interaction: Collaborator, where human and robot jointly accomplish a task; Coworker, where they pursue separate tasks in a shared space; and Supervisor, where the human directs the robot. The dataset comprises over 7K episodes and over 100 hours across 48 tasks. Our experiments show that training on human-present data elicits human-aware behaviors that robot-only data fails to produce: spatiotemporal synchronization in Collaborator tasks, yielding in Coworker tasks, and gesture grounding in Supervisor tasks. Moreover, training on HABIT enables rapid adaptation to new human-robot interaction tasks. By introducing human presence as a new axis of dataset diversity, HABIT extends robot policies to environments shared with humans.

Multi-view bimanual robot world models must predict future observations while preserving scene identity, following two synchronized but non-exchangeable arms, and maintaining consistency between global and wrist-local views. Existing conditioning schemes often collapse left- and right-arm actions into a view-agnostic control signal, making long-horizon rollout prone to scene drift, wrong-arm responses, and cross-view inconsistency. We propose \textbf{HAI}, a \textbf{Hierarchical Anchored Interaction} architecture for controllable multi-view bimanual world modeling. HAI organizes prediction as a structured information flow. First, hierarchical action-view conditioning routes the bimanual action chunk to camera streams, injects coarse chunk-level intent, performs structured multi-view modeling on the coarse-modulated features, and then injects fine per-frame action control. Second, anchored dynamic generation fuses the fine action-conditioned descriptors with persistent per-view scene anchors before decoding future observations. Experiments on AgiBot and DROID show that HAI improves long-horizon rollout quality over the baselines. Ablations confirm gains in scene stability, wrist-view controllability, and cross-view consistency. Further experiments on policy-improvement diagnostics indicate more useful synthetic rollout signals for downstream VLA policies, achieving an averaging 67.2\% relative gain in success rate. The project page is available at \url{https://hai-anon.github.io/}.

World models enable model-based planning through learned latent dynamics, but imagined rollouts often become unstable as the planning horizon grows or the dynamics distribution shifts. We argue that this instability arises from two missing structures in planner-facing latents: history-conditioned memory for approximate Markov completeness, and geometric organization that separates configuration, momentum, and task semantics. We propose HaM-World, a structured world model that decomposes the latent state into a canonical $(q, p)$ subspace and a context subspace $c$, while incorporating Mamba selective state-space memory as a history-conditioned input to the same latent dynamics. Within this unified interface, $(q, p)$ evolves under a Soft-Hamiltonian dynamics composed of an energy-derived Hamiltonian vector field and learnable residual and control dynamics, while $c$ captures semantic, dissipative, and non-conservative factors. This design provides the planner with a single latent representation shared across dynamics prediction, reward and value estimation, imagined rollouts, and cross-entropy method (CEM) planning. On four DeepMind Control Suite tasks, HaM-World achieves the highest average AUC (+9.5% over strong baselines), reduces long-horizon rollout error to 45% of a competitive model, and wins 11 out of 12 $k \in \{3,5,7\}$ rollout MSE metrics. Under 12 out-of-distribution perturbations, HaM-World consistently attains the highest returns, with average gains of 10.2% on Finger Spin and 13.6% on Reacher Easy. Mechanism diagnostics further demonstrate bounded energy drift under action-free rollouts, structured energy variation under policy control, and coherent control-induced energy transfer, supporting the effectiveness of the proposed Soft-Hamiltonian latent dynamics. Code: https://anonymous.4open.science/r/HaM_World-47CD

We propose, HaNDF, object-conditional neural distance fields as data-driven priors for modeling the plausible hand–object configurations, in the product manifold of hand articulations. While recent unconditional generative priors showed success in modeling human pose, hands are versatile articulated bodies often found in interaction with other objects. This makes it crucial to develop a generative prior which can model the plausible hand poses conditioned on the object under interaction. To this end, our HaNDF proposes a geometry-aware, object-conditioned neural distance field, representing the plausible articulations in the zero-level set of a conditional field. We leverage tools from Riemannian geometry to (i) represent plausible hands in the zero level-set of an object-induced neural field, and (ii) project a given pose onto the this field. Using HaNDF, we can also optimize for a plausible condition, determining the object under interaction. Our extensive evaluations demonstrate that our HaNDF provides a flexible and powerful prior for hand–object interaction, suitable for applications such as pose refinement, reconstruction under occlusion, and physically plausible manipulation synthesis.

Large language model (LLM) routing aims to exploit the specialized strengths of different LLMs for diverse tasks. However, existing approaches typically focus only on selecting LLM architectures, treating candidate models as static black boxes while overlooking parameter adaptation, which can substantially affect task performance. In this paper, we introduce HAPS, a hierarchical LLM routing framework that jointly searches over model architectures and parameters. Specifically, a high-level router selects among candidate LLM architectures, while a low-level router generates input-conditioned LoRA parameters for the selected architecture. To couple these two decisions, we design a shared parameter generation mechanism that enables cross-level knowledge transfer between architecture routing and parameter adaptation. We further optimize the whole framework with a reward-augmented training objective. Extensive experiments show that HAPS consistently outperforms strong routing baselines. We further conduct broad ablations and analyses on parameter sharing, architectural choices, scalability, and efficiency, providing additional evidence for the effectiveness and practicality of the proposed framework. We have released our code at https://anonymous.4open.science/r/HAPS_private-68CB.

Agentic evolution has emerged as a powerful paradigm for improving programs, workflows, and scientific solutions. It iteratively generates candidate artifacts, evaluates them against a task objective, and uses the resulting feedback to guide subsequent evolution. Existing methods typically instantiate this paradigm either through fixed hand-designed procedures that are modular but rigid, or through general-purpose agents that flexibly integrate feedback but can drift as context grows, leaving long-horizon search vulnerable to local optima. Both routes share a deeper limitation: long-horizon evolution accumulates candidates, feedback, traces, and failures over time, but lacks a stable interface for organizing this evidence and revising the mechanism that drives future evolution. We therefore formulate agentic evolution as an interactive environment, where the accumulated evolution context becomes process-level state. A meta-agent acts on this environment not by directly generating the next candidate, but by editing the mechanism that controls how future evolution proceeds. We introduce AEvo, a harnessed framework for meta-editing agentic evolution. It standardizes the evolution environment and provides a unified interface for observing accumulated evidence and editing the mechanism that drives future evolution. This lets AEvo revise both hand-designed procedures and agent operating contexts, reducing local-optimum risks in long-horizon evolution. Empirical evaluations on agentic and reasoning benchmarks show that AEvo outperforms 5 evolution baselines, achieving a 26% relative improvement over the strongest baseline. Furthermore, across 3 open-ended optimization tasks, AEvo outperforms 4 evolution baselines and achieves state-of-the-art performance under the same iteration budget.

Vision-Language Models (VLMs) are increasingly required to process unbounded video streams in applications such as video-call assistants, live commentary, and embodied robots. An ideal streaming system should support proactive interaction, long-horizon memory, and real-time processing, while resting on a VLM backbone capable of handling diverse in-the-wild streaming tasks. However, existing VLMs excel at offline video understanding but fall short in streaming capabilities and lack dedicated infrastructure for streaming deployment. We address this gap on three fronts. (i) For backbone capability, we construct \textbf{Streaming-Train-248K}, a streaming dataset paired with a novel training objective for adapting VLMs to streaming interaction and understanding. (ii) For real-world deployment, we introduce \textbf{Streaming Harness}, a plug-and-play system that endows any VLM with three core abilities: proactive interaction (per-second response decisions), long-term memory (12-hour context retention), and real-time processing (sub-second latency). (iii) To drive continued community progress on streaming capabilities, we design \textbf{Streaming-Eval}, a benchmark that reflects models' capabilities across diverse in-the-wild scenarios. Extensive experiments demonstrate consistent gains from our approach across all core capabilities required for streaming video understanding. We will open-source our data, code, and benchmark to advance the community's shift from offline video understanding to deployable streaming intelligence.

Time series forecasting occurs in a range of financial applications providing essential decision-making support to investors, regulatory institutions, and analysts. Unlike multivariate time series from other domains, stock time series exhibit industry correlation. Exploiting this kind of correlation can improve forecasting accuracy. However, existing methods based on hypergraphs can only capture industry correlation relatively superficially. These methods face two key limitations: they do not fully consider inter-industry lead-lag interactions, and they do not model multi-scale information within and among industries. This study proposes the \textbf{Hermes} framework for stock time series forecasting that aims to improve the exploitation of industry correlation by addressing these limitations. The framework integrates moving aggregation and multi-scale fusion modules in a hypergraph network. Specifically, to more flexibly capture the lead-lag relationships among industries, Hermes proposes a hyperedge-based moving aggregation module. This module incorporates a sliding window and utilizes dynamic temporal aggregation operations to consider lead-lag dependencies among industries. Additionally, to effectively model multi-scale information, Hermes employs cross-scale, edge-to-edge message passing to integrate information from different scales while maintaining the consistency of each scale. Experimental results on multiple real-world stock datasets show that Hermes outperforms existing state-of-the-art methods.

Pairwise comparisons from multiple judges are central to large language model evaluation and preference modeling, yet standard ranking pipelines often pool judgments into a single score vector, treating systematic judge disagreement as noise. We propose Heterogeneous Judge-Aware (HJA) ranking, a structured multi-judge ranking framework that separates consensus ranking, judge-specific sensitivity to consensus, and residual preference disagreement. HJA thereby treats ranking, judge sensitivity, and structured disagreement as separate inferential targets. We establish conditions under which this decomposition is identifiable and develop an anchored alternating algorithm that preserves the identifying geometry. For confidence quantification, we study a fixed-panel repeated-comparison regime in which the judge panel may remain fixed or modest while information grows through repeated judgments. This yields uncertainty statements for consensus and judge-specific ranking contrasts, sensitivity parameters, pairwise probabilities, and summaries of residual disagreement.Experiments on synthetic and real multi-judge comparison data show that HJA improves recovery, robustness, uncertainty calibration, and near-tie performance relative to pooled and sensitivity-only baselines. The fitted model also provides diagnostics for judge disagreement and model-affinity patterns, giving a statistically grounded framework for ranking under heterogeneous comparative judgments.

Autonomous AI research promises to accelerate the scientific progress of machine learning. While current Large Language Model (LLM)-based agents excel at writing code, the bottleneck in research is the exploration of diverse and novel ideas: agents collapse to common techniques present in their pretraining data, or prematurely converge on suboptimal solutions (Padmakumar et al., 2024; Jiang et al., 2025). To this end, we introduce Heuresis, a framework that abstracts the research pipeline into a set of general and composable primitives, enabling open-ended scientific exploration in machine learning research. We implement 5 known algorithms spanning Quality-Diversity, Evolutionary, and Curiosity-based search, in addition to a greedy baseline, and evaluate them across three axes -- Quality, Diversity, and Novelty -- on two domains: LLM pretraining and On-Policy RL. We find that quality and novelty are inversely correlated. Methods that optimize purely for raw performance reach the strongest single solutions on a given task by replicating prior work: the top-quality runs from the greedy baseline are uniformly classified as direct copies under the Gupta-Pruthi rubric (Gupta & Pruthi, 2025). The few high-quality, verified-novel ideas in our study come from algorithms that balance performance with diversity or curiosity-based exploration. We also observed that agents resorted to a variety of reward-hacking techniques during execution, whose detection was necessary to keep the search faithful to the task. Our results underscore the importance of search and Quality-Diversity methods for autonomous research, and our framework opens up opportunities for further inquiry towards the ultimate goal of perpetual, autonomous scientific progress.

We propose a distributional theory of how hypernymy---the ``is-a'' relation between general and specific concepts---is encoded geometrically in language representations. Starting from the empirically verified assumption that words closer on the WordNet hypernym graph co-occur more often, we characterize theoretically the spectrum of the resulting embedding Gram matrix of word2vec embeddings. Under mild positivity and decay conditions on the co-occurrence kernel, we prove that the leading eigenvectors first separate broad taxonomic branches and then progressively finer sub-branches, producing a \emph{hierarchical splitting geometry} with a coarse-to-fine spectral organization that mirrors the tree. We confirm these predictions in word2vec embeddings across many sampled WordNet subtrees, and show that the same signature extends strikingly well to Gemma 2B unembeddings. Our results indicate that hierarchical concept geometry in LLMs need not reflect a hierarchy-specific functional mechanism, but emerges from the spectral structure of pairwise word statistics.

In fine-grained image recognition, semantic parts (e.g., wings, head) recur across classes while discriminative cues lie in within-part features (e.g., yellow wings, striped wings). The prototypical part network (ProtoPNet) is an interpretable model that provides case-based explanations for image recognition by measuring similarities between local patches in an images and their closest prototypes representing patches in a training set. However, existing ProtoPNet variants do not explicitly model this two-level structure, causing prototypes for the same anatomical part to fragment across classes. We propose a hierarchical prototype learning framework that separates shared prototypes capturing cross-class part concepts from class-specific prototypes capturing intra-part features. To model this hierarchy geometrically, prototypes are embedded in hyperbolic space and an entailment loss constrains each class-specific prototype to lie within the cone of its corresponding shared prototype. To prevent degenerate trivial solutions such as redundant prototypes, we introduce a patch-level contrastive learning using pseudo patch IDs from a vision foundation model, used only at training time, leaving the backbone choice free at inference. Unlike prior hyperbolic prototype methods where prototype hierarchies are uncontrolled, our framework defines explicit shared part to intra-part feature correspondences. Across four ProtoPNet variants and four backbones on CUB-200-2011 and Stanford Cars, our method improves classification accuracy by up to 10 points, raises hierarchical part hierarchy IoU from 11\% to 75\%, and consistently improves four interpretability metrics on CUB-200-2011.

Class-Incremental Learning (CIL) enables models to learn new classes continually while preserving past knowledge. Recently, vision-language models like CLIP offer transferable features via multi-modal pre-training, making them well-suited for CIL. However, real-world visual and linguistic concepts are inherently hierarchical: a textual concept like ''dog'' subsumes fine-grained categories such as ''Labrador'' and ''Golden Retriever,'' and each category entails its images. But existing CLIP-based CIL methods fail to explicitly capture this inherent hierarchy, leading to fine-grained class features drift during incremental updates and ultimately to catastrophic forgetting. To address this challenge, we propose HASTEN (Hierarchical Semantic Tree Anchoring), a hierarchy-aware framework that uses semantic structure to stabilize CLIP-based CIL. Rather than treating classes as isolated labels, HASTEN leverages external hierarchical knowledge as structured supervision to organize visual and textual features in hyperbolic space, helping maintain parent-child relations as new tasks arrive and mitigating feature drift. Since the shared hyperbolic mapper is updated across tasks, we further stabilize it by constraining its updates to a null space induced by prior-task features, reducing interference with previous mappings while retaining adaptability to new classes. Extensive experiments on nine benchmarks show that HASTEN consistently outperforms existing methods while reducing feature drift and catastrophic forgetting.

We present HierSVA, an integrated suite that combines a pipeline, dataset, and benchmark for LLM-driven hierarchical hardware formal verification. HierSVA-SP pairs an RTL preprocessing toolchain with an LLM-in-the-loop formal verification flow to produce reference SystemVerilog Assertions (SVA) on hierarchical RTL. Applying it to BaseJump STL yields HierSVA-DS, a dataset of 342 modules, with hierarchy metadata and depths 0--9, accompanied by a deep subset of 28 module-bug pairs with natural-language specifications and bug variants. HierSVA-B decomposes assertion quality into six metric axes: syntax correctness, assertion proof success rate, vacuity, faithfulness, mutation coverage, and formal core coverage. Applying HierSVA-B to twelve recent LLMs reveals three findings. First, the module-level compile rate is 67.1\%; among generated assertions in evaluable runs, 82.1\% prove non-vacuously, but the corresponding assertion sets detect only 70.2\% of eligible injected faults and cover 36.2\% of the formal core. Second, on 211 evaluable model--module entries in the deep subset, assertion sets flag buggy RTL with 0.87 recall, but 40\% of predicted-buggy outcomes are false positives on correct RTL, limiting precision to 0.60. Third, agentic mode improves S1-style provability and strength metrics, but gains plateau and oscillate, and we do not evaluate agentic C3 faithfulness.

Sampling high-dimensional Boltzmann distributions is fundamental yet challenging. Classical MCMC methods incur prohibitive computational costs, while modern generative approaches such as diffusion models and Schrödinger bridges struggle to incorporate informative physical priors. GFlowNets offer a promising alternative, yet existing continuous variants suffer from training instability. In this paper, we propose Physical Prior Lifted continuous GFlowNets (PPL), a framework that encodes physical prior information through a reference space equipped with a reference distribution and a mapping to the original configuration space. The sampling process is learned via a Reference-Weighted Trajectory Balance (RWTB) loss, yielding stable training dynamics, efficient learning, and rigorous theoretical guarantees. Empirically, PPL achieves state-of-the-art performance across five benchmarks spanning synthetic, molecular, and protein systems. On the synthetic LJ-55, we obtain significantly lower error than the diffusion-based ASBS with ${\sim}36\times$ speedup; Notably, on the 267-dimensional Chignolin protein, PPL attains near-ground-truth sampling fidelity.

Long video understanding with multimodal language models suffers from three compounding bottlenecks: heavy decode cost to obtain dense RGB frames, quadratic token growth with frame count, and weak motion perception under sparse keyframe sampling. Existing remedies either prune visual tokens after decoding, which leaves the expensive RGB pipeline untouched, or they discretise codec motion vectors with a generative tokeniser, which couples motion modelling to a heavy pre-training stage. In this paper we present HIMMEL, a hierarchical video-language framework that allocates semantic and motion capacity along separate paths. A small set of sparse anchor I-frames is routed to the expensive host ViT to ground object identity and scene layout, while the far denser inter-frame intervals are encoded by a lightweight compressed-domain tri-stream adapter that distils motion evidence from motion-vector maps, residual maps, and an I-frame context branch into aligned motion tokens. These tokens are injected into the LLM via a differentiable placeholder mechanism, after a dedicated Stage-1 contrastive alignment that places the motion representation in a geometry compatible with the frozen visual backbone. We further show that an InfoNCE alignment objective beats MSE regression by $+1.5$ pp because it preserves directional motion structure rather than collapsing onto the mean of visual deltas. On Video-MME, HIMMEL surpasses the dense 32-frame baseline by $+2.3$ pp ($61.2 \to 63.5\%$) while using $3.6\times$ fewer context tokens and running end-to-end in $1.03$ s per question instead of $2.75$ s. Switching to a stronger Qwen3-VL-8B host pushes HIMMEL to $64.9\%$, narrowing the gap to much larger proprietary systems while keeping inference on a single consumer GPU. Extensive ablations across stream composition, motion-encoder family, fusion mode, alignment objective, anchor count, LoRA rank, and video duration confirm that the full tri-stream is necessary and sufficient for the observed gains, and that the benefit grows with video length. We release training and evaluation pipelines to support reproducibility.

Streaming 3D reconstruction enables long-sequence scene understanding by processing frames sequentially, but remains challenging when historical information is compressed into a fixed-size recurrent state. Existing training-free extensions of implicit-memory models mainly improve stability by modulating update magnitude, thereby controlling how strongly each incoming observation modifies the state. However, the geometric intent of the update, namely the direction in which the observation drives the recurrent state, remains tied to the current-frame candidate and can accumulate directional bias over long sequences. We propose HIST3R, a training-free framework that improves recurrent state updating through a direction--magnitude decomposition. HIST3R uses historical recurrent states to rectify the update direction, providing a more reliable geometric intent, and uses observation--history visual discrepancy to modulate the update magnitude, yielding an adaptive update gain. This design makes recurrent updates less dependent on isolated frame-level evidence while preserving the bounded-memory advantage of implicit streaming reconstruction. Across camera pose estimation, 3D reconstruction, and video depth estimation benchmarks, HIST3R consistently improves long-horizon performance without additional training. At 1000 input frames, HIST3R achieves relative ATE reductions of 23.87% on ScanNet and 33.65% on TUM-Dynamics over the strongest baseline; at 500 frames, it improves long-sequence 3D reconstruction over the strongest CUT3R-based variant by 56.52% on 7-Scenes and 33.96% on NRGBD.

Image-to-3D scene generation must recover both object geometry and plausible spatial relations. A common diffusion-based pipeline, exemplified by SAM 3D, generates each object separately and assembles the results. This preserves local details, but the lack of strong scene-level constraints often causes floating objects, interpenetrations, missing contacts, or implausible relative scales. Inspired by human designers who plan a layout before placing assets, we introduce \ours{}, a code-centric multi-agent framework. Given a single image and 2D localization cues, a vision-language model (VLM)-based Arranger Agent predicts executable Blender Python layout code with object identities and 3D transforms. A Modeler Agent binds each object to geometry through asset retrieval or image-to-3D object generation, and a Refiner Agent renders the scene and edits the code from visual feedback. The code representation exposes object names, positions, rotations, and scales, making generated scenes editable and interpretable. We train the code-generating agents using Blender Python datasets built from SAGE and 3D-FRONT, then further optimize them with GRPO rewards for executability and spatial alignment. Experiments in in-domain and cross-domain settings show that \ourMethod{} improves scene-level plausibility while preserving object fidelity.

Online 3D reconstruction requires estimating camera pose and scene geometry under strict causal and bounded-memory constraints. Existing methods often suffer from drift, jitter, or collapse on long sequences. We trace these failures to a fundamental mismatch. Streaming geometry is inherently temporally heterogeneous, with evidence ranging from short-lived correspondences to persistent global scale. However, current architectures impose uniform and pathological influence patterns. For example, sliding windows enforce hard cutoffs, while ungated recurrence and causal attention cause cache saturation and spike-like attention sinks. To resolve this, we formalize geometric propagation as an \emph{evidence influence kernel} and propose Horizon-Stream, a long-horizon Transformer that explicitly factorizes this kernel. For the long-range temporal factor, Geometric Linear Attention learns channel-wise decay rates to enable bounded, multi-timescale propagation of geometric evidence. For the short-range spatial factor, Geometric Local Attention with Spatiotemporal RoPE performs reliable 3D matching while suppressing attention sinks. Finally, Metric Readout Tokens recover stable scale and rigid pose directly from the persistent geometric state. Extensive experiments show that Horizon-Stream, trained on only 48-frame clips, generalizes stably to sequences exceeding (10{,}000) frames with constant memory and linear time, achieving state-of-the-art streaming 3D reconstruction performance.

Geometric modeling has become an essential paradigm in AI for Science, where data often inhabit intrinsically curved or structured spaces. However, existing manifold generative models often rely on closed-form or tractable access to global geometric primitives, such as geodesic distances, parallel transport, or spectral decompositions, which are rarely available beyond canonical geometries. To overcome this limitation, we propose Horizontal Diffusion Models (HDMs), a score-based generative framework defined on the orthonormal frame bundle of a Riemannian manifold. Instead of requiring global geometric operations, HDM lifts Euclidean diffusion processes through the Levi-Civita horizontal distribution, using only local metric and connection information while preserving intrinsic manifold geometry. This construction allows standard Euclidean score networks to be lifted into geometry-consistent and gauge-equivariant horizontal vector fields, bypassing the need for manifold-specific neural architectures. We further derive a horizontal KL objective that reduces to a Euclidean score-matching loss and analyze the curvature-dependent cost of the lift through a generalization bound. Experiments on parametric surfaces and scientific datasets demonstrate high-fidelity generation across diverse manifolds with nontrivial geometry and topology.

Fluorescence spot-detection benchmarks in biology and medicine often rely on reference annotations that are incomplete, ambiguous, or inconsistently defined. In such settings, detector performance is not determined solely by model predictions; it can also depend on which faint or ambiguous signals are counted as reference positives. We formulate this issue as a reference-audit problem: instead of treating the evaluation reference as fixed ground truth, we test whether benchmark outcomes remain stable under plausible changes in reference definition and annotation completeness. Using the public FISH_spots dataset, a benchmark for fluorescence in situ hybridization (FISH) spot detection, we construct Raw, Lenient, and Strict audit references and evaluate diverse classical and learning-based detectors under a unified point-matching protocol. We further apply annotation-retention stress tests that simulate random and structured missing-label mechanisms. Across these audits, changing only the evaluation reference can alter measured F1 scores and model rankings, including the selected top-1 detector, even when aggregate rank correlation remains high. These findings suggest that model selection in fluorescence spot-detection benchmarks should be tested across plausible reference definitions, rather than based on a single reference annotation set.

Pretraining scaling laws treat training data as a one-dimensional quantity: a token count. Agentic reinforcement learning (RL) inherits this scalar abstraction, but modern synthesis pipelines now generate *tasks*, *environments*, and *trajectories* independently and at very different costs, turning data into a structured design space. This raises a question pretraining never had to answer: given a synthesis budget, *which axis* should be scaled? We address this question end-to-end with three tightly coupled contributions. First, we decompose agentic data into four axes (task, environment, trajectory, reward) and empirically fit per-axis scaling laws on Qwen3 models (4B/8B/14B) across math, code, and web/UI domains; we observe a robust ordering of per-axis scaling exponents that holds across model sizes, RL algorithms (GRPO and PPO), and domains. Second, we use the fitted laws as a common ruler to price synthesis methods, capturing each method's efficiency as a per-axis discount factor and identifying critical synthetic-to-real ratios at which collapse begins; these ratios differ by an order of magnitude across axes. Third, we cast budget allocation as a constrained optimization under our laws and discounts, deriving a Chinchilla-style compute-optimal recipe and validating it at held-out scale. The empirical results demonstrate that following the recipe yields up to $1.7\times$ compute efficiency over the dominant ``scale-the-trajectories'' practice.

Retrieval-augmented in-context learning faces a budget problem under distribution shift: increasing top-$k$ can improve evidence coverage, but it can also add off-direction, conflicting, or overly long context that a finite reader cannot use. We introduce a theory-guided diagnostic framework based on \emph{effective evidence coverage}: retrieval is useful only while aligned evidence grows faster than reader-facing ambiguity. In a local linear-ICL model, coupled covariate and task-prior shifts create a mixed risk term that aligned retrieval can reduce up to a residual ambiguity term; a conditional transfer result extends the same budget boundary to frozen readers satisfying a prompt-level stability condition. This yields a falsifiable prediction: recall can keep rising after answer accuracy has saturated or declined. Across QA, verification, and NLI tasks, dense top-$k$ sweeps together with oracle and shuffled-context controls expose this recall--accuracy decoupling. The resulting framework separates coverage- and saturation-limited QA from interference-limited verification/NLI, while calibrated selected-context controllers are reported only as operational probes of the diagnosis.

Large-scale foundation models (FMs) in remote sensing (RS) (denoted as RS FMs) are developed following paradigms established in computer vision (CV), yet the validity of transferring CV scaling laws to RS has not been systematically examined. We hypothesize that RS FMs enter an overparameterized regime at substantially smaller scales than their CV counterparts, with task-relevant information encoded redundantly across model dimensions. To test this hypothesis, we apply post-hoc slimmability, uniform width reduction of pretrained encoder transformer blocks, as a tool to measure representational redundancy across eight state-of-the-art RS FMs on classification, segmentation, and change detection tasks. RS FMs retain 69% to 109% relative accuracy on RS datasets under aggressive width reduction, while masked autoencoder (MAE) and DINOv2 pretrained on natural images (denoted as CV MAE and CV DINOv2) degrade sharply on ImageNet subsets of matched class count over the same range of computational requirements. A CV MAE evaluated directly on the same RS datasets narrows but does not close the gap, indicating that both dataset characteristics and domain-specific pretraining contribute to the differences between the models. Mechanistic analyses such as feature correlation, explained variance, and effective dimensionality indicate that task-relevant variance concentrates in few principal components and is redundantly encoded across model dimensions. We further show that learned slimmable training improves over post-hoc slimmability for contrastive objectives, while reconstruction-based objectives do not benefit from current slimmable training protocols. Our findings establish post-hoc slimming as a practical deployment strategy for resource-constrained RS applications and as a diagnostic tool for representational redundancy in RS FMs. Upon acceptance, we will publish all code.

Reward modeling is not only a prediction problem: in KL-regularized policy optimization, the learned reward is exponentiated to define the deployed policy, so downstream value depends on errors in reward-tilted regions. We study this feedback in a Gaussian single-index model with $ r^* (x)=\sigma^* (\langle\theta^* ,x\rangle)$ and $x\sim\mathcal N(0,I_d)$. We analyze a two-stage neural reward model that first learns the hidden direction $\theta^*$ from reward-weighted samples and then fits the readout layer by weighted ridge regression. Exponential reward weighting changes the Hermite signal available to the first layer; for any feature-learning temperature $\beta_1$ above a dimension-free $O(1)$ threshold, a constant fraction of neurons recover the hidden direction, with weak-recovery complexity governed by the generative exponent. After feature recovery, we derive tilted-policy value-gap bounds for an idealized label-weighted fit with weights $e^{y/\beta_2}$ and a more practical surrogate-weighted fit with weights $e^{r_{a_0}(x)/\beta_2}$. Keeping the $\beta_2$-dependence explicit yields an admissible set of deployment temperatures, balancing the gain from lowering $\beta_2$ against the learning cost amplified by exponential weighting; in the surrogate-weighted case, proxy-dependent factors shrink this admissible set.

Scaling robot policy learning is bottlenecked by the cost of collecting demonstrations: datasets at modern scale require thousands of skilled-operator hours on dedicated robot hardware. The language description paired with these demonstrations does not face the same bottleneck---a single short task label is cheap, but it also leaves implicit spatial relations, object interactions, embodiment state, and subgoal structure that the pixels already contain. We treat \emph{language density} as a cheap lever for amplifying signal in a fixed demonstration corpus; whereas prior dense-language work in robotics commits to a single caption style, we instead ask which kind of dense language helps each task and learn to deliver it at deployment. We realize this as \textbf{DeMiAn} (\textbf{De}nse \textbf{M}ult\textbf{i}-aspect \textbf{A}nnotatio\textbf{n}) in two stages. First, an automatic VLM pipeline re-labels each segment of an existing demonstration along four complementary aspects---\emph{physical motion}, \emph{scene composition}, \emph{arm pose}, and segment-level \emph{reasoning}---each surfacing a distinct kind of structure that a one-line task label omits. Second, a small learned \emph{instructor}, trained via supervised fine-tuning, maps the natural language task description and an initial scene snapshot to a task-appropriate annotation and runs asynchronously alongside the action policy, hiding generation latency behind the rollout. Applied to over 1M robot manipulation and 50K EgoVerse human-egocentric videos, with no new demonstrations collected, DeMiAn delivers four findings on a VLA action policy and a video-based world-action model: i) the learned instructor lifts RoboCasa success by 5 points over the no-annotation baseline, within 3 points of a per-task oracle; (ii) that oracle is non-trivial---no fixed aspect dominates, and peak performance requires selecting the right annotation aspect per task; (iii) the trained system extends usefully to composite tasks under subgoal-driven prompt switching, and to OOD scenes and objects; and (iv) dense annotation improves the compute-performance frontier in both mid-training and post-training, making re-annotation a practical scaling lever for robot policy learning.

This position paper argues that effective Human–AI collaboration in the LLM era requires a high-order dynamic abstraction substrate—one that externalizes stable human cognition into a form AI can reliably consult, and whose content evolves as AI and human capabilities reshape what is worth codifying. We analyze the substrate along three dimensions—what it must hold (five necessary knowledge categories of autonomous AI: Situation, Purpose, Action, Constraints, Evaluation; SPACE), where its content originates (a source-by-externalization analysis isolating the fabrication-codified failure mode behind LLM hallucination, and three practice-grounded routes that defend against it), and how it organizes Human–AI collaboration (two complementary insights: a three-phase temporal cycle of externalize–practice–summarize in which AI plays a different role at each phase, and a long-tailed abstraction hierarchy in which humans focus on the rare top while AI handles the tail). We name this substrate Knowledge Object (KO Network) and characterize it along three axes: content that occupies the moving band between the AI capability ceiling and the human articulability ceiling; structure that commits to five quality attributes (Understandable, Verifiable, Traceable, Controllable, Reusable; UVTCR) and SPACE-aligned subgraphs; and a bidirectional lifecycle in which entries are added as humans articulate previously tacit cognition and retired as AI absorbs the codified. Finally, we respond to five alternative views.

We study models for human-AI teaming through the lens of statistical calibration. We assume the team consists of an AI model and human---both of which are calibrated with respect to some partitioning of the feature space---and expose how the calibration assumptions propagate into the teaming framework. In particular, we consider frameworks that either (i) combine human and model predictions or (ii) delegate prediction responsibility to either a human or model. We show via theoretical and empirical results that existing methods for combination do not preserve the human's degree of calibration. Methods for delegation (by the very act of delegation) preserve calibration of the downstream predictors but can place high-capacity demands on the meta-classifier that determines which should predict. This latter result suggests human-AI complementarity and correct delegation place opposite demands on the delegation model.

We propose HuPER, a human-inspired framework that models phonetic perception as adaptive inference over acoustic--phonetic evidence and linguistic knowledge. HuPER first learns an acoustic-grounded phone recognizer from limited human-annotated data and transcript-only speech: canonical G2P transcriptions are treated as auxiliary linguistic cues rather than ground truth, and are corrected into realized phone proxies for self-training. At inference time, HuPER supports multiple perceptual routes: it can rely on bottom-up phone evidence when the signal is clear, incorporate explicit expectations when a reference is available, or use lexical constraints when acoustic evidence is weak. With only 100 hours of training data, HuPER achieves state-of-the-art phonetic feature error rates on five English benchmarks and strong zero-shot transfer to 95 unseen languages. HuPER also improves robustness on weak-evidence and disordered speech, demonstrating the benefit of adaptive, multi-path phonetic perception under diverse acoustic and task conditions. All training data, models, and code are open-sourced. Code and demo are available at https://github.com/HuPER29/HuPER.

Diffusion models have demonstrated strong performance in time series modeling due to their ability to progressively capture complex data distributions through iterative denoising. However, existing approaches struggle with frequency-sensitive denoising, high-frequency reconstruction and balancing global trends with local dynamics. To address these limitations, we propose \textbf{HyFAD}, a \textbf{Hy}brid time-frequency \textbf{D}iffusion model with \textbf{F}requency-\textbf{A}ware embedding for time series imputation. Built upon the DDPM paradigm, HyFAD adopts a coupled time-frequency diffusion framework, in which the reverse denoising proceeds sequentially from the time domain to the frequency domain, enabling coarse-to-fine generation. Specifically, the time-domain diffusion process captures low-frequency global trends, while the frequency-domain diffusion process refines high-frequency spectral components. We further introduce a frequency-aware step embedding that exploits the relationship between diffusion steps and spectral components, providing step-dependent spectral guidance and facilitates more accurate band-wise reconstruction. Extensive experiments on multiple benchmark datasets demonstrate that HyFAD achieves state-of-the-art performance. Our source code is available at \url{https://anonymous.4open.science/r/HyFAD-0C21/}.

Self-improving AI systems aim to reduce reliance on human engineering by learning to improve their own learning and problem-solving processes. Existing approaches to self-improvement rely on fixed, handcrafted meta-level mechanisms, fundamentally limiting how fast such systems can improve. The Darwin G\"odel Machine (DGM) achieves open-ended self-improvement in coding, but this relies on domain-specific alignment between task performance and self-modification skill. However, this alignment does not generally hold beyond coding domains. We introduce \textbf{hyperagents}, self-referential agents that integrate a task agent (which solves the target task) and a meta agent (which modifies itself and the task agent) into a single editable program. Crucially, the meta-level modification procedure is itself editable, enabling metacognitive self-modification, improving not only the task-solving behavior, but also the mechanism that generates future improvements. We instantiate this framework by extending DGM to create DGM-Hyperagents (DGM-H), eliminating the need for domain-specific alignment to potentially support self-accelerating progress on any computable task. Across diverse domains, the DGM-H improves performance over time and outperforms baselines without self-improvement or open-ended exploration, as well as prior self-improving systems. Furthermore, the DGM-H improves the process by which it generates new agents (e.g., persistent memory, performance tracking), and these meta-level improvements transfer across domains and accumulate across runs. All experiments were conducted with safety precautions (e.g., sandboxing, human oversight). We discuss what safety entails in this setting and the broader implications of self-improving systems. DGM-Hyperagents offer a glimpse of open-ended AI systems that do not merely search for better solutions, but continually improve their search for how to improve.

Class-incremental learning aims to learn a stream of new classes while preserving knowledge from prior tasks, but suffers from catastrophic forgetting when the model is adapted across sessions. Recently, approaches leveraging vision-language pretrained models such as CLIP have gained increasing popularity in this setting, due to the strong transferable representations of foundation models. Most CLIP-based approaches resist forgetting by retaining data-derived class memory such as replay samples, visual prototypes, or class distribution information. In contrast, we ask whether stable adaptation can be achieved by constraining adapter-induced feature displacement without retaining any data-derived class memory. We propose \textbf{H}yperbolic \textbf{D}isplacement-\textbf{C}onstrained \textbf{A}daptation (HDCA), which treats forgetting as a consequence of uncontrolled residual displacement induced by sequential adapter updates. HDCA keeps the direction of each adapter update but compresses its radial magnitude, so small updates remain almost unchanged while large or accumulated updates are suppressed. At the per-task level, a hyperbolic low-rank adapter makes each task residual grow sublinearly. At the multi-task level, a cumulative compression stage further suppresses the summed residual across sessions. On the classifier side, HDCA introduces a parameter-free Poincar\'e distance classifier that preserves cosine-based ranking for fixed adapted features but reshapes the training gradient in high-similarity regions. We conduct experiments on five standard class-incremental datasets, and the results comprehensively validate the effectiveness of HDCA without retaining any data-derived class memory during training and inference.

Video-generative world models are increasingly used as neural simulators for embodied planning and policy learning, yet their ability to predict physical risk and severe consequences is rarely evaluated.We find that these models often downplay or omit key danger cues and severe outcomes for hazardous actions, which can induce unsafe preferences during planning and training on imagined rollouts. We propose ICAT, which grounds testing in real incident reports and safety manuals by building structured risk memories and retrieving/composing them to constrain the generation of risk cases with causal chains and severity labels. Experiments on an ICAT-based benchmark show that mainstream world models frequently miss mechanisms and triggering conditions and miscalibrate severity, falling short of the reliability required for safety-critical embodied deployment.

Training-free sparse attention offers a practical acceleration solution to Diffusion Transformers (DiTs) via reducing computations without fine-tuning. It typically involves estimating the importance of query-key regions and deriving sparse masks to compute only the important candidates, which inevitably introduces approximation errors that may degrade generation quality. To better balance the efficiency-quality trade-off, we propose iCATS, integrating improved importance estimation and sparse mask construction with an efficient hardware execution strategy. Specifically, for importance estimation, unlike previous works that perform independent clustering over query and key tokens based on feature similarity to estimate attention scores, iCATS demonstrates that clustering based on query-key dot-product interactions is more accurate and further reformulates this objective as a simple quadratic form for low-cost computation. For sparse mask construction, instead of using a fixed top-$p$ rule, we observe that tolerance to sparse approximation errors varies across denoising timesteps and therefore introduce an SNR-guided sparsity schedule to adjust sparsity dynamically, leading to higher accuracy. Finally, for hardware execution, we devise a tail-merging strategy to reduce padding overhead caused by irregular cluster sizes, improving GPU kernel utilization. Extensive experiments show that iCATS achieves 2.03$\times$ acceleration with 31.017 dB PSNR on HunyuanVideo-T2V-13B and 1.55$\times$ acceleration with 29.301 dB PSNR on Wan2.1-T2V-14B, delivering a state-of-the-art efficiency-quality trade-off.

The joint expectation of functions of potential outcomes $(Y_1,Y_0)$ is a fundamental quantity in causal inference, particularly when investigating the variation or heterogeneity of causal effects. This paper provides novel assumptions for their identification: comonotonicity and countercomonotonicity. These assumptions offer a unified framework for their identification across discrete, continuous, and mixed outcome variables. In the absence of these assumptions, we derive sharp bounds for two broad classes of functions, yielding new results for bounding moments of individual causal effects, which are central to measuring effect heterogeneity. We present corresponding estimation methods and illustrate them in simulation studies and real-world datasets.

ImageNet class-conditional generation is a standard first test for new image generation methods. Progress on ImageNet is usually measured with one number: Fréchet Inception Distance (FID), computed by comparing 50K class-balanced generated samples with the ImageNet training set in the Inception-v3 feature space. The FID number was useful when the generation quality was low and the gap between methods was large. However, many recent methods already reach remarkable and close FID numbers, and we argue that the field should stop treating FID as decisive evidence of generation quality. To support this view, we re-evaluate 47 recent ImageNet class-conditional generators while keeping the generated samples fixed and changing only the evaluation protocol. We test multiple variants of FID, including different real reference sets, sample sizes, and feature extractors such as Inception-v3, CLIP, DINOv2, and VGG16. The same generated samples can receive different scores, resulting in varying rankings. Our observation means that a single FID protocol has a risk of being over-optimized and over-interpreted. We do not argue that FID should be removed. Instead, we recomend: keep the conventional ImageNet FID for continuity, but also report the results of some FID varaints to avoid overfitting to one evaluation recipe. If a method reaches comparable and stable ImageNet FID, authors should not be expected to keep chasing the best FID, and the paper should be evaluated based on their technical contributions. After passing the FID test, future work such as text-to-image training and human preference study, is important for the futher development of a generation method.

Incorporating visual semantic representations as an intermediate step before image generation can reduce the modeling difficulty between text and images, thereby improving generation quality. Recent works such as X-Omni and BLIP3o-Next have explored this direction, but they typically use a two-stage external pipeline: a separate autoregressive model first generates semantic tokens, which are then fed as conditioning to an independent diffusion decoder. Since the decoder cannot jointly access the original input and the semantic plan, this design introduces an information bottleneck that limits detail preservation in downstream tasks such as editing. Internal architectures such as Transfusion, BAGEL, and Show-o2 avoid this bottleneck by enabling cross-modal interaction within a single model, but they still face the difficult text-to-pixel modeling gap without intermediate semantic guidance. We propose Visual Prompt Engineering (VPE), which can be seamlessly integrated into such internal frameworks. Specifically, the model first autoregressively generates visual semantic tokens ($e.g.$, SigLIP 2) as "visual prompts" that capture the semantic layout, then generates the full image tokens conditioned on this plan. We validate VPE across class-conditional generation, text-to-image generation, and image editing, covering various token types and model architectures. Results show that VPE can accelerate convergence, raise quality ceilings, and through internal integration, achieve substantially better editing preservation (PSNR: $26.76$ vs. $19.92$) than external alternatives of the same parameter scale, while maintaining competitive editing responsiveness. The code is available in supplementary material.

We propose a novel definition of model exploitation in reinforcement learning. Informally, a world model is exploitable if it implies that one policy should be strictly preferred over another while the environment’s true transition model implies the reverse. We analogize our definition with a prior characterization of reward hacking but show that the associated proof of inevitability does not transfer to exploitation. To overcome this obstruction, we develop a general theory of reward hacking and model exploitation that proves that exploitation is essentially unavoidable on large policy sets and yields the corresponding claim for hacking as a special case. Unfortunately, we also find that the conditions that guarantee unhackability in finite policy sets have no counterparts that preclude exploitation. Consequently, we introduce a relaxed notion of exploitation and derive a safe horizon within which it can be avoided. Taken together, our results establish a formal bridge between reward hacking and model exploitation and elucidate the limits of safe planning in world models.

Linear State Space Models (SSMs) and autoregressive sequence models reuse the same parameters across a sample-dependent number of steps. As a result, different examples impose constraints through different predictors, even though all predictors share the same weights. We study this phenomenon through a general model of multiple non-homogeneous polynomial predictors with shared parameters. Under standard interpolation and directional-convergence assumptions, together with a trajectory-dependent effective-degree condition, we show that gradient descent on the exponential loss converges in direction to a KKT point of an \emph{effective-degree max-margin problem}. In this problem, only examples with minimal effective degree impose hard margin constraints; examples with faster-growing margins are asymptotically inactive except for feasibility. Our result provides a characterization of the implicit bias in linear SSMs and linear autoregressive models. It exposes a mechanism of length bias: a longer input sequence or additional autoregressive steps may increase the example's effective degree, and the examples with the smallest effective degree dominate the limiting classifier. Our result highlights how non-homogeneity and parameter sharing alter classical homogeneous implicit-bias results.

Generative action policies based on diffusion or flow matching excel in behavior cloning, yet their iterative sampling is prohibitive for high-frequency robot control. While recent one-step formulations alleviate this latency, they inevitably discard the intermediate trajectory evolution that provides crucial action correction. Directly recovering this mechanism by explicitly estimating a training-time drifting field is mathematically ill-posed due to extreme conditional demonstration sparsity. We introduce Implicit Drifting Policy (IDP) , a one-step imitation learning framework that brings the training-time correction of Drifting into policy learning without explicit vector field estimation. IDP extracts a conditional expert geometry from the local variation of observation-similar expert actions, and compares it against a global reference geometry to isolate condition-specific constraints. This local geometric structure adaptively weights a scalar potential objective. Combined with an expert-proximal terminal evaluation, IDP directly enforces manifold constraints on the one-step generator during training. Extensive evaluations across 2D, 3D, and real-world manipulation tasks show IDP effectively maintains adherence to valid action manifolds, improving upon explicit drifting methods and achieving competitive performance with strong one-step baselines.

We propose importance-aware pruning for diffusion models, a training-free framework that prioritizes preserving parameters critical to semantically salient image regions. To do so, we incorporate spatial importance maps- derived from conditioning signals or model attention- into the pruning objective. This produces parameter rankings aligned with perceptual relevance rather than uniform reconstruction error. On MS-COCO dataset, our proposed approach consistently retains subject fidelity and structural correctness at high compression ratios where conventional pruning causes visible degradation. These results demonstrate that content-aware objectives are key to perceptually faithful compression of generative models.

Building general-purpose role-playing agents that faithfully portray any character from a natural-language profile remains challenging. The dominant paradigm---supervised fine-tuning---encourages behavioral mimicry without deep, human-like internal thought processes, resulting in poor out-of-distribution generalization. Therefore, we propose Psy-CoT, a psychology-grounded chain-of-thought framework that decomposes pre-response reasoning into three role-specific steps---Interaction Perception, Psychological Empathy, and Logical Construction---so that the model thinks dynamically from the profile rather than merely mimicking surface patterns. While structured reasoning provides a foundation, it alone is insufficient; reinforcement learning is essential to further align the model with character fidelity. However, we observe that under LLM-based reward models, both generic phrases that hack the reward model and genuinely role-specific phrases receive identical gradient signals---this hacking accumulates over training, misleading the model into treating both as equally optimal choices. To address this, we propose Role-Aware Policy Optimization (RAPO), which uses profile--token mutual information to weight gradients asymmetrically---amplifying role-specific tokens under positive advantage while attenuating them under negative advantage. Experiments on CoSER, CharacterBench, and CharacterEval demonstrate that Psy-CoT outperforms existing role-playing CoT methods, and RAPO consistently surpasses GRPO across multiple model scales.

In modern GANs, maintaining an Exponential Moving Average (EMA) of the generator's weights is a standard practice, as such an averaged model consistently outperforms the actively trained generator. However, the EMA generator is used for final deployment only and does not influence the training process. To address this missed opportunity, we introduce Self-Distilled GAN (SD-GAN) that employs the EMA generator as a teacher to guide the active generator (student) via perceptual loss. We prove the local asymptotic stability of SD-GAN in the Dirac-GAN setting and show that it dampens the parasitic cycling behavior that plagues the conventional GANs. Empirical evaluations across established architectures and datasets demonstrate that SD-GAN improves the final image quality on several metrics (FID and random-FID in particular), stabilizes the optimization trajectory and provides additional learning guidance that is not trivially correlated with the conventional adversarial loss. It also proves effective for fine-tuning pretrained GAN models.

Modeling Irregular Multivariate Time Series (IMTS) poses significant challenges due to asynchronous sampling and data sparsity. While existing methods focus on handling temporal irregularity after encoding, the tokenization stage itself remains underexplored. We propose IMTS-Tokenizer, a time aware tokenization framework built on the Irregular-time-aware Module (IRAM), which employs learnable temporal anchors and Gaussian-kernel attention to aggregate observations while preserving temporal information. To improve robustness under sparse and asynchronous sampling, the framework further incorporates Relative Time Feature (RTF) for pre-aggregation temporal encoding, Variable-Specific Anchor Initialization (VSA) for data-aligned anchor placement, anchor regularization for stable training, and multi-scale modeling. Experiments on four benchmarks demonstrate state-of-the-art (SOTA) performance, with the best results on seven out of eight metrics and statistically significant aggregate gains.

Building capable medical vision-language models (VLMs) is challenging due to the scarcity of domain data and the difficulty of eliciting pre-trained medical visual knowledge. We present HuatuoGPT-5, a medical VLM built on a simple yet effective recipe: large-scale multimodal pre-training followed by reinforcement learning (RL). We first construct PubMedVision-Plus, the largest medical image-text dataset with 60M pairs from public PubMed literature, along with a clean 12M high-quality subset. After pre-training on this data, we find that traditional supervised fine-tuning (SFT) struggles to elicit the pre-trained knowledge. In contrast, by applying RL with verifiable and rubric-based instances synthesized directly from the pre-training data, we successfully incentivize the model to use this pre-trained medical visual knowledge. On PubMed-derived data, HuatuoGPT-5 outperforms similarly sized VLMs on multiple medical benchmarks and expert evaluations, significantly narrowing the gap between medical VLMs and leading proprietary VLMs. We hope these data, models, and findings can help accelerate open research on capable medical VLMs.

We extend the Bayesian learning rule by explicitly incorporating the compositional structure of neural networks. The central observation is that backpropagation and exponential family variational inference share a common Lagrangian duality structure. Leveraging this connection, we develop a new Bayesian learning framework in which adjoint variables backpropagate layerwise sensitivity signals that converts into loss-site natural parameters. For Gaussian weight posteriors and Gaussian layer-state projections, the framework recovers deterministic back-propagation and Hessian backpropagation via delta-method approximations, while providing a unified perspective on localization-style prediction rules and moment matching methods. We also derive a new variant of IVON, clarify how its gradient and Hessian estimates differ from those of the standard version, and demonstrate how neural network structure can be incorporated into Bayesian learning in practice.

We present a framework for computing approximate mixed-strategy Nash equilibria of continuous-action games. It is a modification of the multiple oracle algorithm, which extends the double oracle algorithm to multiple players and continuous action spaces. Unlike prior methods, it maintains fixed-cardinality pure strategy sets for each player. Thus, unlike prior methods, only a constant amount of memory is necessary. Furthermore, it does not require exact metagame solving on each iteration, which can be computationally expensive for large metagames. Moreover, it does not require global best-response computation on each iteration, which can be computationally expensive or even intractable for high-dimensional action spaces and general games. Our method incrementally reduces the exploitability of the strategy profile in the finite metagame, pushing it toward Nash equilibrium. Simultaneously, it incrementally improves the pure strategies that best respond to this strategy profile in the full game. We evaluate our method on various continuous-action games, showing that it obtains approximate mixed-strategy Nash equilibria with low exploitability.

Token-level steering has emerged as a pivotal approach for inference-time alignment, enabling fine-grained control over large language models (LLMs) by modulating their output distributions without parameter updates. While effective, existing methods rely on dense intervention at every decoding step. This persistent manipulation not only incurs substantial computational overhead but also risks compromising generation quality by excessively drifting from the model’s intrinsic distribution. In this work, we show that dense intervention is unnecessary and propose Sparse Inference-time Alignment (SIA), which performs sparse junction steering, intervening only at critical decision points along the generation trajectory. Our key insight is that high-entropy junctions are empirically consistent with pivotal decision points in the generation trajectory and are particularly susceptible to misalignment, suggesting that these points benefit from alignment-related reward signal. Extensive experiments across different model families and alignment objectives show that steering only 20%–80% of tokens achieves the superior alignment–efficiency trade-offs. For strong base models such as Qwen3, intervening on as few as 20% of tokens matches or even surpasses heavily post-trained instruct models. This sparsity enables stronger guidance while better preserving the model's native distribution, integrates seamlessly with search-based methods (e.g., Best-of-N), and improves efficiency by reducing the required sampling budget while yielding practical wall-clock speedups under matched-quality comparisons.

Neural surrogate models for computational fluid dynamics (CFD) are typically trained as forward operators that map explicit problem specifications, such as geometry and boundary conditions, to solution fields. This ties the model to the conditioning variables seen during training and limits reuse under boundary-condition shifts or local geometry changes. We propose to reformulate steady CFD inference as an inpainting problem: instead of training on explicit boundary conditions, we learn a self-supervised prior over velocity fields and impose boundary constraints only during inference by fixing known regions such as inlet, outlet or unchanged regions from previous simulations. To scale this idea to large 3D meshes, we introduce a local neighbourhood tokeniser that represents high-resolution velocity fields as compact spatial latent tokens and train latent flow-matching and masked-autoencoder models on these tokens. On intracranial aneurysm hemodynamics, our method reconstructs full velocity fields from sparse boundary context, outperforms supervised neural surrogates under boundary-condition and dataset shift and enables local geometry editing by reusing unchanged simulation context. These results suggest that viewing CFD inference as context-conditioned inpainting can turn neural surrogates from task-specific predictors into reusable flow priors.

Key-value (KV) caches are essential for efficient Large Language Model (LLM) inference, but their memory footprint grows linearly with context length and batch size. Low-bit KV-cache quantization reduces this footprint, yet uniform quantization is vulnerable to high-magnitude outlier channels, while outlier-aware mixed-precision methods often introduce extra buffers, indexing, or channel permutation that weakens their system-level benefit. This paper presents InQuant, an in-place mixed-precision KV-cache quantization method that preserves salient outlier channels without changing the physical 4-bit packed layout. The key observation is that selected low-saliency channels near outliers can tolerate small approximation error; InQuant therefore reuses their storage slots to hold the extra 4-bit nibble needed by 8-bit outlier values. To make this layout practical, InQuant combines sampling-based channel saliency estimation, saliency-aware neighbor-slot reuse, stride-based handling for adjacent outlier groups, and descriptor-guided marker recovery during dequantization. Across six LLMs and long-context workloads, InQuant reaches a fixed 4.0$\times$ physical KV-cache compression ratio, preserves accuracy close to strong mixed-precision baselines, and reduces quantization/dequantization latency by 2.2$\times$--3.4$\times$ compared with representative outlier-aware methods.

Automated Heuristic Design (AHD) with Large Language Models (LLMs) has shown promise in solving optimization tasks. However, existing methods mainly rely on iterative search frameworks and do not systematically structure or reuse task-specific design knowledge elicited from LLM priors and accumulated during online search. Moreover, multi-objective AHD introduces additional challenges, as conflicting objectives such as solution quality and computational efficiency must be balanced during heuristic search. We propose Multi-objective Evolution of Heuristics with Insight-Driven Search (MEoH-IDS), which uses dynamically maintained design insights to guide LLM-based heuristic generation. MEoH-IDS maintains an insight pool by extracting insights from successful heuristics, evaluating their search utility, and filtering less effective ones over time. It selects promising insight combinations using a UCB-inspired policy, with rewards based on non-dominated status in the current Pareto archive. We validate MEoH-IDS on three AHD tasks, including the Traveling Salesman Problem (TSP), Capacitated Vehicle Routing Problem (CVRP), and Vehicle Routing Problem with Time Windows (VRPTW), optimizing both solution quality and computational efficiency. Experimental results show that MEoH-IDS accelerates convergence and discovers heuristics with improved quality--efficiency trade-offs compared with representative LLM-based AHD baselines.

Large language model (LLM) queries are predominantly processed by frontier models in centralized cloud infrastructure. Rapidly growing demand strains this paradigm, and cloud providers struggle to scale infrastructure at pace. Two advances create an opportunity to rethink this paradigm: small, local LMs (≤20B active parameters) now achieve competitive performance to frontier models on many tasks, and local accelerators (e.g., Apple M4 Max) can host these models at interactive latencies. This raises the question: can local inference viably redistribute demand from centralized infrastructure? Answering this requires measuring both whether local LMs can accurately answer real-world queries and whether they can do so efficiently enough to be practical on power-constrained devices (i.e., laptops). We propose intelligence per watt (IPW), task accuracy divided by unit of power, as a unified metric for assessing both the capability and efficiency of local inference across model-accelerator configurations. We conduct a large-scale empirical study across 20+ state-of-the-art local LMs, 8 hardware accelerators (local and cloud), and a representative subset of LLM traffic: 1M real-world single-turn chat and reasoning queries. For each query, we measure accuracy (local LM win rate against frontier models), energy consumption, latency, and power. Our analysis reveals three key findings. First, local LMs can successfully answer 88.7% of single-turn chat and reasoning queries with accuracy varying by domain. Second, longitudinal analysis from 2023-2025 shows progress in local inference viability: IPW improved 5.3×, driven by both algorithmic advances and accelerator improvements, with locally-serviceable query coverage increasing from 23.2% to 71.3%. Third, local accelerators achieve at least 1.4× lower IPW than cloud accelerators running identical models, revealing significant headroom for local accelerator optimization. These findings demonstrate that local inference can meaningfully redistribute demand from centralized infrastructure for a substantial subset of queries, with IPW serving as the critical metric for tracking this transition.

Recent advances in robotics foundation models have shown promising multi-task generalization, yet their reliance on specialized embodiment-specific data and model fundamentally prevents them from exploiting the far richer physical interaction knowledge present in abundant human manipulation videos. Existing approaches that leverage human videos suffer from misaligned representations that preclude explicit unified interaction modeling over human video and robot demonstration, failing to capture unified physical interaction dynamics essential for cross-domain transfer. We address this by constructing a unified representation that aligns human videos and robot tasks through scene point clouds with hand-to-gripper mapping via spatial tracking and hand pose estimation. Leveraging this representation, we propose a structured graph transformer that explicitly models spatial, semantic, and intentional interaction entities through graph attention mechanisms. The modeled multi-level interaction features then enable interaction-aligned cross-domain learning, transferring manipulation knowledge from human videos to robot tasks via discrete interaction abstraction and hierarchical distribution alignment. Experiments on RLBench, ManiSkill2, and real-world robot demonstrate state-of-the-art performance on diverse benchmarks with fewer demonstrations and effectiveness of our designed modules. Remarkably, our method achieves zero-shot transfer on robot tasks when trained exclusively on human video, providing strong evidence for effective human-to-robot transfer through interaction alignment.

In non-stationary multi-agent settings, concurrent policy updates continually reshape inter-agent coordination patterns. In cooperative tasks, an agent’s current action value is closely tied to teammates’ current and future behaviors, making it important to infer how these behaviors evolve and how they influence local action-value estimation. However, existing methods often lack a value-level representation that connects evolving teammate-dependent interactions to local action-value learning. Firstly, we theoretically show that the individual utilities under Centralized Training with Decentralized Execution(CTDE) paradigm struggle to faithfully characterize coordination-dependent action values, making it necessary to introduce a computable proxy for the missing coordination information. Then we propose Hierarchical Inference Agent-Centric World Model (HIA), a framework that incorporates role information into a Transformer-based world model to predict future teammate interactions from each agent’s perspective. The predicted teammate-action rollouts are transformed into auxiliary interaction utilities and integrated into individual value estimates, enabling each agent to capture how teammates’ future actions modulate its current action value. Experiments on StarCraft Multi-Agent Challenge (SMAC) and SMACv2 show that HIA achieves superior cooperative performance over strong baselines.

The deployment of AI agents in multi-agent workflows enables inter-agent influence, whereby agents strategically steer other agents’ behavior in line with a specific goal. Such capabilities pose novel risks, as malicious agents could direct others toward harmful actions. In this work we investigate three such capabilities –- persuasion, deception, and coercion –- across five realistic evaluation environments and a variety of frontier models. We observe significant inter-agent influence capabilities in frontier models. In oversight environments, all tested models shifted policy-violating decisions from rejection to approval through persuasion, deception, and coercion. In peer-to-peer settings, models extracted concessions in scheduling negotiations and redirected a peer's safety research trajectory toward an attacker-preferred direction. While evidence is weakest for inter-agent coercion, several frontier models independently formulated and executed coercive threats, including threats to harm a named human in the environment. These results point to a pressing need for work on risk mitigation to promote beneficial deployments of multi-agent systems.

This work identifies a critical failure mode in frontier large language models (LLMs), which we term \textbf{Internal Safety Collapse} (ISC): \textit{under certain task conditions, models enter a state in which they continuously generate large volumes of harmful content while executing otherwise benign tasks}. To systematically study ISC, we introduce \TVD{} (Task, Validator, Data), a framework that instantiates controlled workflows around domain tools, where valid completion requires filling harmful content as structured data. We collect 53 representative workflows across 8 disciplines, from toxicity evaluation to molecular docking and pathogen genome analysis, showing that ISC reproduces across all eight disciplines. In the worst case over three interaction settings, the four frontier LLMs average a \textbf{95.3\%} safety-failure rate. Frontier LLMs with stronger task-completion capability show higher unsafe-completion rates: long-horizon execution skill becomes a liability when workflow completion requires harmful content. We even observe extremely severe harmful content closely resembling outputs from early-generation, unaligned LLMs in 2023. Despite substantial safety alignment efforts, frontier LLMs continue to retain inherently unsafe internal capabilities: alignment reshapes observable outputs but does not eliminate the underlying risk profile. These findings underscore the need for caution when deploying LLMs in high-stakes settings, including scientific pipelines and autonomous agents. Complementary source code is provided at \url{https://anonymous.4open.science/r/NIPS-ISC-Code-Share-BE11}.

Multimodal 3D detection becomes unreliable under partial observability caused by occlusion, LiDAR sparsity, and camera--LiDAR miscalibration. Most fusion detectors are fully feed-forward: once trained, they provide no explicit mechanism to incorporate structured test-time constraints or to bound how such constraints can alter predictions. We propose Intervene3D, a controlled-inference wrapper for BEV-based detectors that performs metric-bounded latent refinement at inference time. Given sensor inputs, the detector produces an initial BEV latent $\mathbf z_0$ and an evidence-conditioned trust metric $\mathbf H$ (a diagonal precision derived from predicted uncertainty). A separate control stream is mapped to a fully specified differentiable constraint energy (count intervals and spatial admissibility). Inference then updates $\mathbf z$ using $K\in\{1,2,3\}$ diagonal-metric preconditioned projected steps that reduce constraint energy while remaining within an uncertainty-gated trust region defined by $\mathbf H$. To improve robustness to extrinsic drift, we additionally introduce \textbf{phase-aware spectral alignment} that normalizes cross-modal Fourier-phase discrepancies prior to attention. We report clean accuracy and a severity-swept robustness protocol (robustness-AUC, feasibility diagnostics, and conflict-induced FP inflation).

When does training language models (LMs) on explanations yield faithful introspection, rather than superficial imitation? Surprisingly, we find that LMs trained to explain the predictions of similar models frequently produce explanations more faithful to $\textit{their own current behaviors}$ than to those of their training targets. This "introspective'' coupling between the model's explanations and behaviors occurs only when the training target explanations remain sufficiently similar to model behaviors over the course of training. This alignment must be preserved throughout the training process: introspection only emerges when explanation supervision is sufficiently behaviorally compatible with the model as it changes. Finally, we show that introspection generalizes to variants of the training problem: when introspection training is run concurrently with training that shifts a model's behaviors, explanations track those behavioral shifts without requiring updated supervision. This holds across a diverse range of tasks, including sycophancy and refusal, and is robust to label noise. These results suggest that introspection training is a viable component of post-training: explanation labels need not be continually refreshed, and faithfulness extends to regions of input space not explicitly supervised.

Inferring the latent processes that generate observable neural activity is a central challenge in neuroscience, with direct implications for brain-machine interfaces and neural engineering. Existing inverse approaches typically summarize the recording into low-dimensional features or learn black-box latents that lack a direct biophysical interpretation. A promising alternative is to fit a differentiable biophysical simulator end-to-end against the full extracellular signal, using direct and scalable gradient optimization. In practice, however, recovering a per-neuron input via backpropagation through neuronal dynamics is difficult because the loss landscape is nearly flat in the subthreshold regime and jumps sharply at spike threshold, leaving gradient descent without a useful signal. We observe that per-neuron spike times, routinely available from spike sorting, expose discrete millisecond-scale anchors at which the loss does carry information. Gradients from these anchors flow back in time through the differentiable simulator and shape the subthreshold drive that produced each spike. Building on this, we present a fully differentiable pipeline coupling biophysical membrane dynamics to a volume conductor model of the multi-electrode array. For each neuron in a recorded population, the method jointly recovers a time-varying latent input current and a probe-relative position consistent with both the extracellular trace and the observed spike times. No explicit likelihood or posterior estimation is required, and every fitted latent corresponds to a named biophysical quantity. On paired patch-clamp / Neuropixels SPE-1 data, the model localizes the patched neuron to within $40 \mu m$ on average across cells, while reproducing observed spatio-temporal activity patterns under realistic noise. These results position scalable differentiable biophysical simulation as a practical route to mechanistic, gradient-based analysis of high-density neural recordings.

Field trials use plot-level crop-state measurements to interpret how crops grow under real field conditions, but repeated measurements of canopy greenness, canopy amount, canopy closure, and growth stage require field visits, sensing campaigns, and post-processing. Modern wheat trials also collect weather records, trial metadata, field-camera images, UAV and satellite observations, proximal sensors, canopy products, and phenological surveys. Turning these records into a machine-learning benchmark is nontrivial because observations are sparse, asynchronous, unevenly collected, missing for structured reasons, and tied to measurement pipelines. We introduce INVITA-WheatFieldState, a benchmark derived from the INVITA wheat field-trial archive for estimating the crop state of a plot on a target date from observations available for that plot and date. Each plot-date example specifies a plot, date, and crop-state target, and is paired with available observations after temporal and source-provenance filtering. The derived dataset contains over 220k examples across NDVI, LAI, FCover, and Zadoks growth stage, covering canopy greenness, canopy amount, canopy closure, and phenological timing. LAI and FCover are released with provenance labels that distinguish product and proxy targets rather than pooled manual ground truth. The benchmark suite provides a plot-disjoint split matched to the plot-level experimental unit, validators, prediction schemas, regression representations, and prediction-level diagnostics. Results show that source, timing, metadata, and observation-availability structure are strong signals. Observation-set representations improve some all-example targets, while sensor-sequence, field-camera, and fusion representations remain target-dependent and coverage-bound. INVITA-WheatFieldState offers a concrete test case for ML methods that must learn from incomplete, asynchronous, and provenance-sensitive measurements used to study crop development in real fields.

Active perception—the ability of a robot to proactively select viewpoints to acquire task-relevant information—is essential for robust operation in real-world environments. However, existing approaches are typically limited to fixed objectives or constrained settings, and struggle to generalize to open-ended perception intents specified in natural language. We propose I-Perceive, a foundation model for language-conditioned active perception in large-scale indoor environments. Given a query image, a set of context images, and a natural language instruction, I-Perceive predicts a 6D camera pose that fulfills the specified perception intent. The model integrates a vision-language pathway for semantic grounding with a geometric reasoning pathway for multi-view 3D understanding, connected via multi-layer semantic fusion to enable language-conditioned geometric reasoning. To support scalable training, we construct a large-scale dataset of language-viewpoint pairs from both real-world scene-scanning data and simulated environments using an automated pipeline. Extensive experiments demonstrate that I-Perceive significantly outperforms strong baselines on prediction accuracy, viewpoint feasibility, and instructions alignment. The model exhibits strong zero-shot generalization to unseen scenes and instructions, and enables closed-loop active perception, progressively refining viewpoints over sequential interactions.

Multimodal Large Language Models (MLLMs) have demonstrated remarkable effectiveness in general video understanding. Human motion understanding, a dominant topic in video analytics, however, still remains as a bottleneck due to the lack of explicit structural cues in 2D visual patches and the significant motion information loss caused by sparse visual temporal sampling. To address this, we propose Iris (Integrating representations of in-the-wild skeletons), a novel pose-augmented video MLLM tailored for human-centric motion understanding. Iris adopts an asymmetric dual-stream architecture, pairing the traditional sparse vision stream with a lightweight, high-frequency human pose stream to provide spatially structured and temporally dense kinematic signals. To enable pose-vision modality correspondence awareness, we design a cross-modal spatiotemporal binding mechanism, featuring a pose-structured 3D RoPE for precise temporal alignment and a person-grounded cross-attention module for explicit spatial visual grounding. Furthermore, to enable robust large-scale training, we construct an automated pose curation pipeline that organizes pose data into a delicate heterogeneous representation. Extensive experiments demonstrate that Iris achieves superior performance on multiple fine-grained motion benchmarks, while also maintaining highly efficient token computation overhead. Code will be available upon publication.

Reinforcement learning (RL) has become central to post-training large language models (LLMs). However, popular RL methods like GRPO incur non-negligible overhead by computing both old-policy and current-policy likelihoods to form importance sampling ratios. In this work, we propose Likelihood-Gated Policy Optimization (LGPO), which enforces a soft trust region constraint via likelihood-based gating, eliminating the need to compute old-policy likelihoods. Empirically, we show that removing the importance sampling correction term does not harm training stability, whereas removing the trust region mechanism leads to collapse. Moreover, ratio-based clipping can fail in fully on-policy training: the importance ratio stays at 1, so the ratio-based trust region constraint never activates. Under standard training settings where GRPO is stable, LGPO achieves comparable training stability and peak performance while reducing training time by ~18\% on average. In fully on-policy training, where GRPO fails, LGPO remains stable, enabling more efficient and robust LLM RL post-training across training regimes.

Auto-bidding is a core algorithmic component in online advertising auctions. In practical cold-start settings, iterative training is commonly used to compensate for low-quality, narrowly supported historical data. Unfortunately, in iterative training, representative diffusion-based AI-Generated Bidding (AIGB) methods fail to sustain extrapolation beyond the narrow data support and stagnate at a suboptimal level. In this paper, we theoretically attribute this stagnation to Signal-to-Noise Ratio (SNR) collapse: the weak radial return signal is overwhelmed by a curvature-induced penalty. To break this stagnation, we propose \textbf{Iterative Scarcity-Guided Exploration (ISGE)}, which introduces a guidance handoff: as the return signal collapses, scarcity subsequently takes over as an exploratory signal to elevate the SNR above a critical threshold. Specifically, ISGE iteratively alternates between a \emph{Judger} that assigns scarcity scores to trajectories and an \emph{Explorer} that performs guided generation of high-scarcity and high-return trajectories, thereby bootstrapping from the narrow support with such self-generated trajectories. Extensive experiments on the industrial AuctionNet benchmark demonstrate that ISGE, starting from cold-start datasets, effectively surpasses the performance of the Full-Dataset baseline within three iterations.

Large language models (LLMs) are now deployed in a wide range of real-world applications, making it important to evaluate how reliably they resist jailbreak attacks. However, existing jailbreak evaluations mainly rely on manually collected prompts or discrete text-space optimization, which limits their coverage and makes them difficult to extend to new threat settings. We present $\textbf{JailBound}$, a jailbreak evaluation framework that combines automated benchmark construction with intent-preserving attack optimization in embedding space. JailBound organizes evaluation instances with a hierarchical threat taxonomy spanning risk categories, application domains, and attack types, and uses this structure to generate meta-attack prompts with a fine-tuned meta-attack generator. It further formulates jailbreak evaluation as an embedding-space attack optimization problem and uses a first-order loss (FOL)-guided dual-branch search to jointly identify high-value vulnerable regions and safety boundary states. Under a unified evaluation protocol, we study 46 LLMs from 13 model families. The results show that JailBound covers a broader range of risk settings than existing prompt-based benchmarks, and that its optimized attacks transfer nontrivially across model families while supporting finer-grained analysis of vulnerability patterns and safety boundary behavior. $\textcolor{red}{\text{Warning: this paper includes examples that may be offensive or harmful.}}$

Building agents that can perform new skills by composing existing skills is a long-standing goal of AI agent research. Towards this end, we investigate how to efficiently acquire a sequence of skills, formalized as hierarchical neural options. However, existing model-free hierarchical reinforcement algorithms need a lot of data. We propose a novel method, which we call "AgentOWL" (Option and World model Learning), that jointly learns --- in a sample efficient way --- an abstract world model (abstracting across both states and time) and a set of neural options. We show, on a subset of Object-Centric Atari games, that our method can learn more skills using less data than baseline methods and possesses learning and generalization capabilities that the baselines do not have.

As large language models (LLMs) are increasingly deployed to users around the world, they are integrated into everyday tasks across diverse cultural contexts, from drafting personal communications to brainstorming creative ideas. These tasks are inherently cultural: they require contextual appropriateness, symbolic resonance, and tacit cultural expectations that native speakers draw on instinctively, meaning that a response can be factually plausible yet unmistakably wrong to a local reader. Existing cultural benchmarks have treated culture as a flat set of facts via fact verification or norm entailment methods, and have adopted LLM-as-a-Judge without examining whether they can capture such thick cultural errors. To address this gap, we present JuICE (Benchmark for LLM-Judge in Identifying Cultural Errors), a multilingual dataset of 7,470 span-level annotations of cultural and linguistic errors, collected from native speakers in long-form LLM responses. It covers 1,050 query-response pairs from four countries (United States, South Korea, Indonesia, and Bangladesh), in both English and their countries' main languages. Using JuICE, we find that even the strongest LLM-judge achieves only an F1 of 0.52 against human-annotated errors, and that this gap is systematic. LLM-judges reliably detect surface-level linguistic and factual mistakes but consistently miss thick cultural errors that local residents readily identify. Our findings suggest that robust cultural evaluation must move beyond surface-level detection toward frameworks that account for the depth and situatedness of cultural meaning.

Modern Bayesian optimization and adaptive sampling methods increasingly rely on nonlinear parametric models, yet theoretical guarantees for such models under adaptive data collection remain limited. Existing analyses largely focus on Gaussian processes, kernel machines, linear models, or linearized neural approximations, leaving a gap between theory and the nonlinear models used in practice. We develop a kernel-based framework for analyzing regularized nonlinear parametric models trained on adaptively collected data. Our approach uses kernels over the parameter space to induce reproducing-kernel Hilbert space structures over the corresponding model class, yielding confidence bounds for models trained with broad classes of regularized convex losses. We show how these bounds can support convergence guarantees for nonlinear acquisition and surrogate models, including randomized regularized policies that select points by maximizing a trained random model. These results provide a unified route to analyzing nonlinear parametric models in Bayesian optimization and related adaptive optimization settings.

Claim-level Uncertainty Quantification (UQ) aims to mitigate the lack of reliability of Large Language Models (LLMs) by evaluating the factuality of each claim in their outputs. We introduce Kernel Token Contradiction (KTC), a lightweight approach to compute claim-level UQ under realistic white-box conditions. KTC represents the candidate tokens involved in LLM generation as a positive semi-definite kernel that integrates both the LLM’s conditional distribution and a token contradiction score. We then use the Von Neumann entropy to quantify the uncertainty of this kernel. To estimate token contradiction, we develop a new approach based on frequency statistics from the Wikipedia corpus. Although CPU-only, our approach achieves over an 8.2× speedup compared to state-of-the-art GPU-accelerated methods based on cross-encoders, and over a 65× speedup compared to CPU-only methods with comparable performance. Our evaluation spans two benchmarks across four European languages and 16 different models. KTC not only matches the average performance of existing methods but also outperforms them in high-precision regimes. This combination of computational efficiency and accuracy makes real-time monitoring of LLM outputs practical in production.

Long-term conversational memory poses a fundamental system challenge for LLM agents: reasoning over all past interactions improves coverage but incurs prohibitive token costs and noise, while retrieval-based methods are efficient yet often fail on multi-hop and temporal questions. Existing multi-step retrievers partially address this gap, but typically operate over an ever-growing textual history, causing context expansion and noise accumulation across iterations. We propose MemR$^3$, a backend-agnostic closed-loop controller that reformulates conversational memory retrieval as explicit stateful decision-making. At each iteration, MemR$^3$ maintains an evidence-gap state consisting of grounded information (evidence) and unresolved information requirements (gaps), and uses this state to route among three actions: retrieve, reflect, and answer. Newly retrieved snippets are incorporated into the state and then masked from subsequent prompts, so the model conditions on a compact summary of progress rather than the full retrieval history. This design turns multi-step memory retrieval into a bounded, inspectable control process whose query-time context scales with the state instead of raw accumulated text. Experiments on LongMemEval$_s$ show that MemR$^3$ surpasses implicit multi-step retrieval baselines and often approaches or exceeds full-context prompting while using only 1%-5% of the full-context tokens on long conversations. On LoCoMo, MemR$^3$ consistently improves over its underlying RAG- and Zep-based memory backbones. These results suggest that explicit evidence-gap tracking is an effective abstraction for token-efficient conversational memory.

Personalized dialogue requires more than recalling explicit user histories: systems also need to infer hidden user states that evolve through interaction and shape appropriate response strategies. Existing memory- and profile-based methods primarily reuse observable user information, offering limited support for modeling user-state dynamics or selecting actions based on how they shape future user states. We propose PUMA (Prospective User-state Modeling for Action selection), a framework grounded in the Free Energy Principle (FEP) that formulates personalization as decision-making under partial observability, centered on an explicit user state model that captures latent user states and their action-conditioned dynamics. At each turn, PUMA maintains a belief over the user's hidden state, refines the user state model for observation generation and action-conditioned state transition, and selects dialogue actions by minimizing expected free energy—balancing epistemic and pragmatic objectives under a unified criterion. This formulation shifts personalization from passive memory retrieval to model-based decision-making over user evolution. We instantiate PUMA on healthcare-oriented counseling and motivational interviewing benchmarks with latent state annotations for rigorous evaluation. Experiments show that PUMA improves long-horizon dialogue outcomes while maintaining strong response quality, and a cross-dataset study demonstrates more reliable user-state estimation and next-state prediction. Our code is available at: https://anonymous.4open.science/r/PUMA-4DA7/.

Self-evolving search agents reduce reliance on human-written training questions by generating and solving their own search tasks. We build on Search Self-Play (SSP), a representative Proposer and Solver framework in which questions are generated and answered via multi-step search and reasoning. In practice, however, SSP faces two bottlenecks: the Proposer constructs questions from isolated answer entities without relational context, yielding many invalid or unverifiable questions in early self-play training, while the Solver receives only a binary outcome reward that discards useful signal from partially on-track search trajectories. We address both bottlenecks by reusing knowledge-graph paths as construction-derived intermediate supervision for both question construction and reward shaping. First, we ground question construction in LLM-guided knowledge-graph subgraphs, providing relational context for the Proposer. Second, we observe that constructing and solving a multi-hop question can involve overlapping intermediate entities: the factual bridges used to formulate the question may provide approximate waypoints for answering it. Exploiting this overlap, we introduce Waypoint Coverage Reward (WCR), which grants graded partial credit to incorrect Solver trajectories according to their coverage of entities on the construction path, while preserving full reward for correct answers. Across seven QA benchmarks and nine model configurations, our approach improves the average score over standard SSP in all configurations, including notable gains on multi-hop QA tasks. These results suggest that knowledge-graph paths can be reused as lightweight intermediate supervision, providing both relational guidance and process feedback without additional task-specific human annotations or manually labeled process steps.

Hallucination in large language models (LLMs) during long-form generation remains difficult to address under existing reinforcement learning from human feedback (RLHF) frameworks, as their preference rewards often overlook the model's own knowledge boundaries. In this paper, we propose the $\textbf{K}$nowledge-$\textbf{L}$evel $\textbf{C}$onsistency Reinforcement Learning $\textbf{F}$ramework ($\textbf{KLCF}$), which re-examines this problem from a distribution alignment perspective. KLCF formalizes long-form factuality as a bidirectional distribution matching objective between the policy model's expressed knowledge distribution and the base model's parametric knowledge distribution: under the constraint that generation must not exceed the support set of the base knowledge, the objective maximizes coverage of high-probability facts, thereby jointly optimizing precision and recall. To achieve this, we design a Dual-Fact Alignment mechanism that approximates the recall term using a factual checklist constructed by sampling from the base model, and constrains hallucinations with a lightweight truthfulness reward model. Both components are jointly optimized and require no external retrieval throughout training. Experimental results demonstrate that KLCF consistently improves factuality metrics across multiple long-form benchmarks and model scales, effectively alleviating hallucination and over-conservatism while maintaining efficiency and scalability.

Probabilistic time-series forecasting requires models that simultaneously capture structured temporal dynamics, expressive uncertainty, and efficient inference, yet existing approaches fall short of this goal: latent dynamical models impose structure but rely on restrictive generation mechanisms, while modern generative methods such as diffusion and flow matching achieve flexibility at the cost of iterative and computationally expensive sampling. We introduce the Koopman Generative Operator (KGO), a probabilistic forecasting method that conceptualizes prediction as the evolution of structured uncertainty. KGO integrates three core components: (1) Koopman Patch Embedding (KoPE) for temporally consistent latent trajectory extrapolation; (2) Koopman Flow Matching (KoFM), which enables fast, single-step generation through a closed-form matrix exponential in a Koopman latent space, bypassing iterative sampling; and (3) an Adaptive Uncertainty Gate (AUG), which provides calibrated predictions by adapting uncertainty per-variable and per-horizon, corresponding to a learned decomposition of aleatoric uncertainty without manual tuning. By unifying these components, KGO achieves state-of-the-art accuracy across the majority of ProbTS datasets, outperforming existing methods on 12/17 benchmarks in CRPS and 11/17 in NMAE. Further, by eliminating iterative sampling, KGO delivers at least a 25$\times$ reduction in inference time compared to iterative generative models. These results establish KGO as a practical, principled, and highly scalable framework for next-generation probabilistic forecasting.

In retrieval-augmented generation (RAG) scenarios, the prefill delay of the large language model (LLM) long inputs is the dominant component of time-to-first-token (TTFT). Reusing KV caches across different LLM inputs reduces this cost, but the resulting cross-context mismatch on reused segments has a significant degradation on answer quality via propagating through prefill attention into decode-time logits. Existing selective-recomputation methods mitigate this issue by recomputing tokens chosen by empirical importance heuristics, which overlook the propagation of cache mismatches into answer-side errors, thereby failing to effectively strike the quality-TTFT tradeoff. To address this issue, we propose a perturbation-theoretic KV cache reuse framework, KVFocus, which selects reused KV caches through a perturbation-theoretic token-risk score during the prefill process. Specifically, we first develop a first-order analysis that traces reuse error in three stages: source mismatch on the reused segment, suffix contamination during prefill, and decode-time logit stability. This analysis reveals that the leading-order answer-side error decomposes into a product of source-side V-drift and downstream suffix-to-segment attention concentration. Guided by this multiplicative structure, we define a token-risk score that operationalizes the bound during the inference. A plug-and-play selector for KV cache reuse in existing RAG-based LLM serving is then designed by recomputing the top-$r\%$ tokens directly suppresses the predicted leading-order answer-side error, covering both sides of the pathway that prior one-sided heuristics miss. Empirical results across three 7-8B instruction-tuned LLMs (Mistral, Qwen2.5, Qwen3) and four multi-hop QA benchmarks demonstrate that the proposed KVFocus achieves a superior trade-off between the ansewer quality and the TTFT in comparison to state-of-the-art baselines, with up to $3\times$ TTFT speedup over full prefill at matched answer quality.

Higher-order learning exploits compositional features whose predictive value arises from nonlinear interactions beyond pairwise relations among variables, scales, or modalities. To address the complexity of modeling higher-order interactions in modern large-scale deep learning models, a kernelized Volterra Neural Network (kVNN) is proposed in this paper. Specifically, the proposed learnable multi-kernel representation models different interaction orders using distinct polynomial-kernel components with compact learnable centers, thereby yielding an order-adaptive parameterization. The resulting kVNN layers are composed of parallel order-specific branches and can readily replace standard convolutional kernels in existing learning architectures. Theoretical results are supported by experiments on video action recognition, image denoising, and image classification. The results show marked performance-efficiency trade-offs: kVNN consistently reduces model (parameters) and computational (GFLOPs) complexity while achieving competitive and often improved performance, even when trained from scratch without large-scale pretraining. In summary, structured kernelized higher-order layers offer a practical path to balancing expressivity and computational cost in modern deep networks.

Large Language Models (LLMs) exhibit strong informal mathematical reasoning but struggle to generate mechanically verifiable proofs in formal languages like Lean. We present L$^2$EAP (LLM-in-Lean Environment Agentic Prover), an agentic framework that enables general-purpose foundation models to achieve state-of-the-art performance on automated formal theorem proving. L$^2$EAP leverages foundation model capabilities, such as informal reasoning, instruction following, and iterative self-refinement. By decomposing complex problems into smaller units, the system bridges formal proof construction with informal blueprints through continuous interaction with the Lean compiler. To provide a rigorous evaluation beyond increasingly saturated benchmarks, we introduce Lean-IMO-Bench, a benchmark of IMO-style problems formalized in Lean, with short statements yet highly non-routine and multi-step proofs across a wide range of difficulty levels. Empirically, on the latest 2025 Putnam Competition, an annual mathematics competition for undergraduate students in North America, L$^2$EAP solves all 12 problems, matching recent breakthroughs by frontier formal mathematical models; on Lean-IMO-Bench, L$^2$EAP raises the one-shot formal solve rate of general-purpose LLMs from below 10\% to 70\%. It also compares favorably to the best baseline performance of 48\% attained by a specialized system with dedicated ATP components that achieved gold-medal-level performance at the 2025 IMO.

Pixel diffusion models have recently regained attention for visual generation. However, training advanced pixel-space models from scratch demands prohibitive computational and data resources. To address this, we propose the \textbf{Latent-to-Pixel (L2P)} transfer paradigm, an efficient framework that directly harnesses the rich knowledge of pre-trained LDMs to build powerful pixel-space models. Specifically, L2P discards the VAE in favor of large-patch tokenization and freezes the source LDM's intermediate layers, exclusively training shallow layers to learn the latent-to-pixel transformation. By utilizing LDM-generated synthetic images as the sole training corpus, L2P fits an already smooth data manifold, enabling rapid convergence with zero real-data collection. This strategy allows L2P to seamlessly migrate massive latent priors to the pixel space using only 8 GPUs. Furthermore, eliminating the VAE memory bottleneck unlocks native 4K ultra-high resolution generation. Extensive experiments across mainstream LDM architectures show that L2P incurs negligible training overhead, yet performs on par with the source LDM on DPG-Bench and reaches 93\% performance on GenEval.

Mixed-precision computations are a hallmark of the current stage of AI, driving the progress in large language models towards efficient, locally deployable solutions. This article addresses the floating-point computation of compositionally-rich functions, concentrating on transformer inference. Based on the rounding error analysis of a composition $f(g(x))$, we provide an adaptive strategy that selects a small subset of components of $g(x)$ to be computed more accurately while all other computations can be carried out with lower accuracy. We then explain how this strategy can be applied to different compositions within a transformer and illustrate its overall effect on transformer inference. We study the effectiveness of this algorithm numerically on GPT-2 models and demonstrate that already very low recomputation rates allow for improvements of up to two orders of magnitude in accuracy.

While Vision-Language-Action (VLA) models have revolutionized autonomous driving by unifying perception and planning, their reliance on explicit textual Chain-of-Thought (CoT) leads to semantic-perceptual decoupling and perceptual-symbolic conflicts. Recent shifts toward latent reasoning attempt to bypass these bottlenecks by thinking in continuous hidden space. However, without explicit intermediate constraints, standard latent CoT often operates as a physics-agnostic representation. To address this, we propose the Latent Spatio-Temporal VLA (LaST-VLA), a framework shifting the reasoning paradigm from discrete symbolic processing into a physically grounded Latent Spatio-Temporal CoT. By implementing a dual-feature alignment mechanism, we distill geometric constraints from 3D foundation models and dynamic foresight from world models directly into the latent space. Coupled with a progressive SFT training strategy that transitions from feature alignment to trajectory generation, and further refined via Reinforcement Learning with Group Relative Policy Optimization (GRPO) for safety and rule compliance, LaST-VLA achieves state-of-the-art performance across multiple autonomous driving benchmarks, including NAVSIM v1, NAVSIM v2, Bench2Drive, SURDS, and NuDynamics.

Many recent flow-matching and diffusion-style generative models rely on auxiliary stochastic dynamics during training: a richer process is simulated to define conditional targets, but the auxiliary state is either intractable to sample at generation time or simply not part of the desired output. Existing Generator Matching theory formalises conditioning on static latent random variables, and several recent papers prove special cases of projection results for particular augmented-state constructions. We introduce latent process generator matching, a general framework that treats the observed generative state as a deterministic image $X_t=\Phi(Y_t)$ of a tractable Markov process $Y_t$. We show that in this setting one may learn the generator of a stochastic process on the image space which has the same one-time marginal distributions as the projected process. This generalizes and subsumes the discrete latent process results from the literature, and extends Generator Matching from static latent variables to a rich family of time-dependent latent conditional processes.

Autoencoders (AEs) are widely used in unsupervised anomaly detection (AD) due to their ability to model complex data distributions. However, their strong reconstruction capacity, while effective for capturing normal patterns, often enables them to also reconstruct anomalous objects, including both individual and collective anomalies, which reduces detection performance. This limitation is particularly severe for collective anomalies, which are frequently misinterpreted as normal clusters. To address this issue, we propose Layer Precision Reduction (LPR), a novel technique that systematically constrains the numerical precision of weights and biases in neural network layers to strategically reduce the learning capacity of neural networks, such as the reconstruction capacity of AEs. LPR encourages the network to prioritize dominant manifold structures while suppressing the reconstruction of rare or anomalous patterns. While LPR was primarily developed for AE-based detectors (the focus of this paper), it is applicable to any neural network. Simulations are conducted with 24 AD models across 30 real-world public datasets. LPR is applied to 10 deep AD models, including 5 unsupervised AE-based methods, 2 unsupervised non-AE-based methods, 1 semi-supervised method, 1 weakly supervised method, and 1 fully supervised method. LPR consistently improves all 10 models, most notably enhancing the best-performing unsupervised AE-based detector from an average AUROC of 72.12\% to 83.06\%, surpassing the strongest unsupervised non-AE-based competitor, which achieves an average AUROC of 77.36\%, across the remaining 14 detectors. These results reveal a previously unexplored link between layer-level numerical precision and manifold pattern prioritization, demonstrating that LPR serves as a simple yet powerful mechanism for enhancing AD performance while offering broad applicability to diverse neural architectures and potential extensions to tasks beyond AD.

Transcriptome-conditioned whole-slide image (WSI) synthesis aims to decode the complex mapping from molecular states to histologic phenotypes. Existing generative paradigms predominantly rely on a direct black-box transcriptome-to-image mapping, tasking a single generator with the simultaneous interpretation of transcriptome signals, inference of multicellular spatial organization, and rendering of fine-grained textures. This formulation, however, is structurally under-constrained; the underlying tissue architecture, the critical substrate linking gene expression to morphology, remains latent and only weakly supervised by pixel-level objectives. We present \textsc{TopoScape}, a topology-anchored framework for transcriptome-to-histology generation under a \emph{Layout-Before-Pixels} principle. Instead of synthesizing pixels directly from transcriptomic embeddings, \textsc{TopoScape} first resolves molecular states into explicit, multi-class cellular topologies via an RNA-guided Topological Prior Generator, reinforced by persistence-based topological regularization. These resolved topologies are further reparameterized into a suite of topology-derived controls, including structure-aware initialization, continuous density fields, and distance representations, which guide a hierarchical, frequency-decoupled flow-matching trajectory. By introducing multicellular organization as an explicit structural mediator, \textsc{TopoScape} allows molecular states to deterministically shape both the macroscopic tissue architecture and the microscopic generative process. Across five TCGA benchmarks, \textsc{TopoScape} consistently outperforms state-of-the-art methods in generative fidelity, biological realism, and downstream predictive utility. Code and pretrained models will be released.

The Model-as-a-Service (MaaS) paradigm enables resource-constrained edge users to access cloud-based large language model (LLM) services but raises significant privacy concerns. Existing approaches perturb user queries under local differential privacy (LDP) before sending them to LLMs. However, naively applying these methods to multi-query scenarios leads to linear growth in privacy budget consumption. To address this, we propose LDPCache, the first cache-enhanced LDP framework for multi-query LLM processing. By leveraging semantic correlations across consecutive user queries, LDPCache maintains a cache of historical responses and intelligently decides whether to reuse a cached result or query the cloud-based LLM, thereby reducing privacy budget consumption. The core of LDPCache is an LDP-compliant scheduler that evaluates cache reusability based on both semantic similarity and the expected accuracy gains from LLM access. This scheduler enables hit decisions without actual LLM access while ensuring that the scheduling process itself satisfies LDP guarantees. To accommodate the limited memory of edge devices, we further design a multi-factor cache eviction strategy that balances query diversity, response accuracy, and recency to improve the hit ratio. Extensive experiments on real-world datasets demonstrate that LDPCache significantly improves response accuracy over baseline methods.

Modern reasoning models depend on reasoning data, today sourced from human annotations or distilled from stronger LLMs. However, a rich and largely untapped source of supervision lies in expert systems (e.g., game engines, classical planners, theorem provers), which routinely produce near-optimal actions across diverse domains. But these experts are silent: they commit to an action without writing down the chain of thought (CoT) behind it. Recovering that CoT as natural-language reasoning would distill expert knowledge into a student that generalizes beyond the demonstrated actions. We treat it as a latent variable and study how to recover it from the action alone. Our approach, LeAct (**Le**arning to reason from **Act**ions), optimizes this latent variable: the student samples candidate CoTs for each expert action, and we retain those that measurably improve its own probability of recovering the action. Across imperfect-information games at multiple scales and a simulated robotics benchmark, LeAct reaches the solver's numerical floor on small enumerable games. At larger scale, it is $5\times$ closer to the solver than the strongest expert-iteration baseline. At Flop Hold'em ($\sim 10^9$ infosets), LeAct wins head-to-head by $+60$ mbb/g, and on the robotics probe it is the only training recipe that improves on direct imitation. We present a principled framework and the result: expert systems become a categorically new source of reasoning teachers for foundation models.

Proving theorems in Lean 4 often requires identifying a scattered set of library lemmas whose joint use enables a concise proof---a task we call global premise retrieval. Existing tools address adjacent problems: semantic search engines find individual declarations matching a query, while premise-selection systems predict useful lemmas one tactic step at a time. Neither recovers the full premise set an entire theorem requires. We present LeanSearch v2, a two-mode retrieval system for this task. Its standard mode applies a hierarchy-informalized Mathlib corpus with an embedding--reranker pipeline, achieving state-of-the-art single-query retrieval without domain-specific fine-tuning (nDCG@10 of 0.62 vs. 0.53 for the next-best system). Its reasoning mode builds on standard mode as its retrieval substrate, targeting global premise retrieval through iterative sketch-retrieve-reflect cycles. On a 69-query benchmark of research-level Mathlib theorems, reasoning mode recovers 46.1% of ground-truth premise groups within 10 retrieved candidates, outperforming strong reasoning retrieval systems (38.0%) and premise-selection baselines (9.3%) on the same benchmark. In a controlled downstream evaluation with a fixed prover loop, replacing alternative retrievers with LeanSearch v2 yields the highest proof success (20% vs. 16% for the next-best system and 4% without retrieval), confirming that retrieval quality propagates to proof generation. All code, data, and benchmarks will be open-sourced.

Recently, Group Relative Policy Optimization (GRPO) and its variants have been developed for policy optimization and demonstrated notable performance gains. However, these methods usually incur substantial computational overhead due to per-question multi-rollout sampling and repeated per-token probability evaluation across rollouts. Furthermore, low-information or highly homogeneous trajectories can degrade downstream learning signal efficiency, hindering model optimization and limiting final performance. To address these issues, we propose FastRL, a novel plug-and-play reinforcement learning framework that simultaneously improves training efficiency and the effectiveness of policy learning. Specifically, 1) We introduce an advantage-aware pruning strategy to selectively preserve high-advantage trajectories while maximizing inter-trajectory gradient diversity. 2) Then, we design an adaptive rollout sampling mechanism to dynamically adjust the sampling scale across different training stages based on historical pruning distributions, balancing exploration adequacy and computational efficiency. Experiments demonstrate that FastRL can be seamlessly integrated into GRPO, DAPO, and GSPO variants, achieving an average 2.07x training speedup on Geometry3K and GeoQA8K-R1V, along with an approximately 1.64% improvement in average accuracy on visual reasoning benchmarks. Source codes will be available at https://anonymous.4open.science/r/FastRL0.

Masked diffusion models (MDMs) have emerged as a promising alternative to autoregressive models, enabling parallel token generation while achieving competitive performance. Despite these advantages, MDMs face a fundamental limitation: once tokens are unmasked, they remain fixed, leading to error accumulation and ultimately degrading sample quality. We address this by proposing a framework that trains a model to perform both unmasking and correction. By reusing outputs from the MDM denoising network as inputs for corrector training, we train a model to recover from potential mistakes. During generation we apply additional corrective refinement steps between unmasking ones in order to change decoded tokens and improve outputs. We name our training and sampling method Progressive Self-Correction (ProSeCo) for its unique ability to iteratively refine an entire sequence, including already generated tokens. We conduct extensive experimental validation across multiple conditional and unconditional tasks, demonstrating that \method~yields better quality-efficiency trade-offs (up to ~4x faster sampling) and enables inference-time compute scaling to further increase sample quality beyond standard MDMs (up to ~1.2x improvement on benchmarks).

Semantic visual classification commonly aligns visual embeddings with semantic spaces induced by pretrained text encoders. However, such spaces are typically high-dimensional, weakly aligned with visual feature geometry, and insufficiently discriminative for downstream classification tasks. We propose TaSS, a framework for learning a task-adaptive low-dimensional semantic space that preserves semantic structure while explicitly optimizing class separability. TaSS is constructed through a parameterized geometry transformation of pretrained text embeddings that enlarges inter-class margins via distance reshaping and improves intra-class compactness through prototype-preserving semantic constraints. The resulting semantic space maintains meaningful semantic relationships while producing a discriminative geometry tailored for classification. Frozen visual encoder features are subsequently aligned to TaSS using a lightweight MLP, enabling improved discriminative learning without fine-tuning pretrained backbones. We further provide theoretical analysis showing that TaSS preserves semantic consistency while enforcing enhanced inter-class separation and controlled intra-class variance. Extensive experiments on skeleton-based human action recognition, video classification, and image classification benchmarks demonstrate consistent improvements over existing semantic alignment approaches, with gains exceeding 3 percentage points on average and up to 8.5 points on challenging datasets.

End-to-end driving with vision-language models benefits from multimodal pretraining, but still faces a mismatch between reasoning and trajectory generation. For autoregressive VLA planners, continuous trajectories represented as step-wise normalized text waypoints are strong final outputs because they fit the native token-prediction interface of modern VLMs. However, they remain weak intermediate interfaces for reasoning-conditioned planning. Direct action tokenization provides a more explicit motion interface, but its effectiveness depends heavily on how the action codebook is constructed. We propose *TrajCond-VLA*, which learns a *trajectory-geometric condition* from reasoning for VLA planning. Our key idea is to represent this condition as a differential action trajectory whose $x$/$y$/yaw differentials are discretized separately with a 3D-Brohan codebook. The resulting **DiffAction tokens** preserve local motion geometry while remaining compatible with autoregressive token prediction. Building on this representation, we introduce a **Two-Stage Alignment Training** framework: Stage 1 predicts TrajCond from reasoning, and Stage 2 regresses the final continuous trajectory conditioned on both TrajCond and reasoning. Experiments on NAVSIM, nuScenes, and NeuroNCAP show that the learned trajectory-geometric condition is more effective as an intermediate condition than as a direct output format, and that the resulting pipeline is validated across multiple datasets with consistent gains on the completed comparisons. The nuScenes and NAVSIM navtest discussions are kept aligned to the public benchmark protocols used by Curious-VLA, and the closed-loop reference discussion uses the correct NeuroNCAP protocol.

Probing frozen vision transformers typically uses permutation-invariant aggregation (GAP or [CLS]), treating patch tokens as an unstructured set. Content-dependent probes such as self-attention are useful accuracy controls, but they do not expose a fixed token schedule or fixed position weights for auditing. We introduce SSMProbe, an explicitly inspectable probe that replaces invariant pooling with a Sinkhorn-learned evidence route followed by a diagonal S4 decoder. The S4 decoder is a linear time-invariant (LTI) system whose final state has fixed, position-dependent coefficients, so the probe-induced routed sequence can be audited as a concrete object rather than inferred only from accuracy. Our central measurement is the geometry of routed evidence: which patch tokens are moved to influential positions by this diagnostic, whether those tokens form spatially organized regions or random-like dispersed sets, and how the fixed S4 kernel weights them. Across MAE, BEiT, DINOv2, and supervised ViT, this route geometry separates MAE's dispersed, nearly random-like routes from the more spatially organized routes of BEiT, ViT, and DINOv2, with DINOv2 retaining a distinct strong [CLS] profile. SSMProbe uses the mathematical transparency of state-space models to turn a frozen ViT readout into an auditable evidence-routing analysis.

A point source in a PDE does not occupy an entire grid. A weather station reports one value at one site; it does not observe the temperature between stations. A pickup event in a city occurs at a coordinate and a time; it is not born as an image. Yet many neural-operator pipelines begin by turning such sparse observations into dense surrogate fields before learning. This interpolation step is often treated as harmless preprocessing, but in sparse regimes it changes the object on which the operator is asked to act. We introduce the Dirac Neural Operator (DIRAC-NO), a measure-native neural operator for sparse event-to-field learning without interpolation. DIRAC-NO keeps observations as atomic event measures until query time and constructs the output field through adaptive Dirac-kernel aggregation. Because this aggregation is implemented by direct summation, the front end preserves source additivity at the aggregation stage and admits a learned Green-kernel view of source-to-field response. Across controlled point-source PDE benchmarks and real sparse reconstruction tasks, DIRAC-NO exposes the cost of interpolation-first learning. On MeasureBench-1D, it achieves relative $L^2$ error 0.015 at $n=128$, compared with 0.49 for FNO, and it better preserves superposition structure in trained models. In 2D, it is strongest under parameter and density shift, while ERA5 reconstruction shows that the representation advantage persists beyond analytic PDEs at high observation budgets. Boundary cases and negative-control event streams further show that the benefit appears where the representation argument predicts: sparse, event-native tasks with source-response structure. These results suggest that sparse operator learning should be organized not only by the neural backbone, but by the native space in which the input is represented.

Knowledge distillation pipelines typically train a student to match a teacher on a fixed corpus of teacher-labeled examples. This leaves a major source of supervision unused: the teacher is a fully queryable model whose output distribution extends far beyond any fixed dataset. We argue that effective distillation should exploit this structure by steering generation toward regions where the student most underestimates the teacher. We propose \textit{Maximum Divergence Knowledge Distillation} (MDKD), a rejection-sampling method for autoregressive language models. At each decoding step, MDKD draws candidate tokens from the teacher and preferentially accepts those the student underweights, constructing trajectories that remain teacher-supported while concentrating training on regions where the student assigns insufficient probability. We show that stochastic MDKD's acceptance rule has an exact distributional characterization: it samples precisely from the teacher's \textit{uncovered mass} $[T - S]_+$, the probability the teacher assigns to tokens the student does not yet cover, with stochastic acceptance probability equal to the teacher--student total variation distance. Standard teacher sampling, by contrast, spends most of its updates on the overlap $\min(T, S)$ already absorbed by the student. This makes MDKD the structural complement of speculative decoding, which exploits the same overlap to accelerate inference. On GSM8K, distilling \texttt{Qwen2.5-14B-Instruct} into a base \texttt{Qwen2.5-1.5B} student with only $1{,}000$ KD samples (5 epochs) lifts accuracy from $8.26\%$ to $64.90\%$, outperforming SOTA methods by $10.3$ percentage points and recovering $65\%$ of the teacher--student gap. A divergence-gain analysis confirms the mechanism: MDKD sequences raise student cross-entropy by $43.6\%$ while preserving $98\%$ of teacher-sampling accuracy. Across arithmetic reasoning, dialogue summarization, and code generation, MDKD matches or outperforms strong distillation baselines.

Statistical modeling and inference for temporal networks are increasingly important across modern applications, yet remain challenging in the sparse regime with evolving network sizes and temporal dependence. We propose a tractable model that captures these key features in a unified framework. Under this model, we establish geometric ergodicity and characterize the asymptotic behavior of cycle counts. These counts provide informative low-order summaries of temporal network dynamics. Moreover, we develop method-of-moments estimators based on cycle counts. We prove identifiability and uniqueness of the parameter estimates, and establish the strong consistency and asymptotic normality. To support uncertainty quantification, we further propose a simulation-based plug-in estimator of the asymptotic variance. Extensive simulations and a real data example demonstrate that our methods are accurate and effective for sparse, size-varying temporal networks.

Discrete diffusion language models (DLMs) enable parallel generation but usually rely on fixed forward processes, such as masking, uniform noise, or precomputed hierarchies. We investigate whether the hierarchical forward process can instead be learned while preserving continuous-time tractability. We propose \textbf{Learnable Hierarchical Diffusion Language Model (LHDLM)}, which extends a learnable column-stochastic token-cluster map. This many-to-many hierarchy preserves a CTMC with block-conditional transition and closed-form CT-ELBO, while recovering HDLM in the one-hot limit and masked diffusion in the collapsed limit. We identify degenerate hierarchy maps caused by learning the same map used in both forward targets and model-induced cluster predictions, and mitigate them with an information-retention regularizer. Empirically, LHDLM is competitive with discrete diffusion approaches and reveals soft token structures --- cluster-agnostic function tokens and sharp content-token assignments --- that cannot be represented by hard partitions.

Generative emulators of protein dynamics produce plausible trajectories at a fraction of the cost of molecular dynamics, but they inherit their training distribution and tend to revisit known states rather than reach rare ones under long-horizon extrapolation. Inspired by classical enhanced sampling, we introduce an implicit, history-dependent bias in the generative space of a pretrained emulator. Specifically, a history-aware score estimator augments the frozen emulator with a distance-weighted bias that steers reverse-time sampling away from previously generated structures, regularized by an environment-support term. To preserve structural validity at long horizons, a score-based refinement step re-projects drifted samples onto the data manifold using the frozen emulator. Our experiments demonstrate that the method (i) raises diversity by $35\%$ on DynamicPDB-80; (ii) on $12$ zero-shot Fast-Folding proteins, the learned bias alone reaches the unbiased emulator's coverage up to ${\sim}15\times$ faster, and pairing it with refinement reaches the coverage up to ${\sim}37\times$ faster while covering ${\sim}3\times$ as many low-energy states.

Markov Games are the standard formal model for multi-agent reinforcement learning, capturing agents that act in a shared state and optimize rewards over time. However, in real-world settings, agents’ decisions are often influenced by unobserved factors, such as cognitive biases, behavioral tendencies, or intuitive signals, that also affect rewards and future states. Ignoring these unobserved confounders can make standard learning methods converge to suboptimal policies. In such settings, optimal play may require policies that condition on counterfactual signals, requiring reasoning at the counterfactual layer of the Pearl Causal Hierarchy. In this paper, we introduce Causal Markov Games (CMGs), a framework for modeling sequential multi-agent decision making in the presence of unobserved confounding. We show that CMGs strictly generalize Markov Games, with arbitrarily large gaps between classical interventional equilibria and causal counterparts. We then develop two learning algorithms under different observability assumptions. The first, CNash-VI-FO, is a model-based learner with finite-sample guarantees when agents’ natural actions or intuitions are revealed post hoc. The second, CNash-VI-NO, is an explore-then-exploit procedure with asymptotic guarantees for the setting in which opponents’ natural actions are never observed. To address scalability, we further provide a drop-in counterfactual augmentation of deep MARL. Empirically, on a confounded windy variant of the Multi-Particle Environment and the Iterated Causal Prisoner’s Dilemma, counterfactual agents strictly dominate their non-causal counterparts.

As learning agents are increasingly deployed in strategic environments, designers must choose whether to deploy transparent (auditable, committing) or opaque (flexible, reactive) policies. We formalize this through \emph{Policy Transparency Games} (PTGs): a two-stage model in which each agent first chooses transparency, then plays an underlying normal-form game. Our central theoretical contribution is a structural reduction: for two-player PTGs, all transparency incentives decompose into two per-player quantities, a leader wedge $L_i-N_i$ and a follower wedge $F_i-N_i$, whose signs characterize every equilibrium and cleanly separate selection-robust from selection-dependent conclusions. We then ask how a learning agent can discover this structure from interaction data. External-regret learners over the binary transparency action reach coarse correlated equilibrium, and our adaptive WEDGE-CERTIFY algorithm attains instance-dependent certificate complexity $\tilde O(\sigma^2(d_0^{-2}+d_{10}^{-2}+d_{01}^{-2}))$ with matching information-theoretic lower bounds. The wedge representation thus determines the learnability of endogenous algorithmic transparency.

GraphRAG enhances Large Language Model reasoning by organizing retrieved knowledge into structured evidence graphs, enabling inference over connected evidence rather than isolated text fragments. Yet, existing GraphRAG methods often either miss query-critical links or introduce noisy or conflicting evidence that distracts reasoning. We propose CONSIST, a Conflict-aware Nash-optimized Minimal Sufficient graph learning algorithm that regulates evidence graphs to resolve conflicts during reasoning. Specifically, we cast graph learning as an exploration–editing process, where a candidate graph is expanded via structural and semantic connections and then refined by pruning unreliable edges. We further introduce Gumbel-based edge editing, framing it as a Nash-guided bargaining process, where current and future players negotiate over a customized utility to determine reliable edits that improve both immediate answer support and long-term graph quality. Experiments on multi-hop question answering and conflict-aware benchmarks demonstrate that CONSIST consistently outperforms recent state-of-the-art baselines, achieving average performance gains up to 7.0% EM and 6.3% F1. Moreover, CONSIST also produces substantially more compact evidence graphs, reducing retained nodes by 42.9% and edges by 54.7% on average.

Offline reinforcement learning aims to learn a policy solely from fixed datasets, which often contain multimodal action distributions. Flow policies can naturally represent such multimodal behaviors, but learning an efficient one-step flow policy remains challenging: standard value guidance often leads to mode collapse or exploits overestimation bias in out-of-distribution regions. To address this, we introduce One-step Flow policy via Optimal Transport (OptiFlow), a framework for one-step flow policy learning as a structured sample-allocation problem. OptiFlow jointly trains a value-aware reference flow policy and an efficient one-step policy, coupling their action samples through state-wise entropic optimal transport. For each state, critic-estimated values define the priority of distillation target actions, while the action-distance cost ensures geometrically compatible pairings. By avoiding direct critic maximization, our transport-guided approach enables in-distribution exploitation by anchoring the one-step policy to high-value, dataset-supported modes without the risk of out-of-distribution divergence. Experimental results demonstrate that OptiFlow effectively captures optimal multimodal behaviors and achieves strong performance across diverse offline RL benchmarks.

Deliberating takes time. In real-time settings, that time is not free. Standard reinforcement learning (RL) sidesteps this as the environment waits indefinitely for the agent's decision. Instead, we study real-time RL environments where the environment progresses while waiting for the agent's action. Building on prior real-time formalizations, we introduce variable-delay real-time RL, where the agent chooses how long to deliberate at each decision point since the environment progresses. For the planning agents we use, the right delay is state-dependent, and naively planning how long to plan can paralyze the agent. We instead approach this setting by training a lightweight gating policy on top of a planner to select state-dependent planning budgets. Across real-time Pac-Man, Tetris, Snake, Speed Hex, and Speed Go, our gating policy outperforms fixed-budget and heuristic baselines, and transfers to a real-time setup where the environment and agent run on two different GPUs.

Continual learning under adversarial perturbations remains underexplored, despite its importance in security-critical deployments where attack patterns evolve over time. Existing continual adversarial defense methods mainly rely on replay or prediction-space regularization, but often fail to preserve robust representations across sequentially arriving attack types. In this paper, we study continual adversarial defense, where a model receives a sequence of tasks induced by different white-box attacks and must acquire robustness to new attacks without forgetting previously learned defenses. We address this challenging learning scenario by proposing the Learning Robust Representation Framework (LRRF), a representation-centric framework that improves robustness through hierarchical alignment at three complementary levels. First, we introduce the Dynamic Representation Matching (DRM) mechanism that aligns clean and adversarial feature distributions within each task to reduce the clean-robustness trade-off. Second, a Cross-Task Invariant Representation (CTIR) mechanism is proposed to regularize the current representation network against an accumulated network from previous tasks, encouraging attack-invariant features to persist over time. Third, a Knowledge Consolidation Optimization (KCO) mechanism is proposed to match class-consistent clean and adversarial features using replayed samples, which further stabilizes category structure across tasks. The empirical results show that the proposed approach achieves state-of-the-art performance.

We study internalization, the process by which neural-network-based systems absorb an explicit computational procedure into their own weights, and how it facilitates learning. We investigate how transformers internalize the simulation of semiautomata by internalizing chain-of-thought (CoT) tokens, which classes of semiautomata are harder to internalize, and expose the flip side of internalization, that is, a progressive degradation of out-of-distribution performance. We then provide the first provable analysis of successful internalization: for the task of learning parities, we show that a simplified one-layer transformer provably first learns the target with explicit CoT supervision and then internalizes the autoregressive generation as CoT tokens are progressively removed, directly computing the parity. Learning such a representation directly from data without CoT supervision is computationally hard. Finally, we discuss how learning through internalization can be viewed as an instance of the \textit{Positive Distribution Shift} phenomenon recently introduced by~\citet{Med+26}.

Trajectories arise in many settings, including urban mobility, transportation, and maritime traffic. Existing generative models either offer no control over output distributions or rely on conditioning information specific to each individual trajectory, such as its origin and destination, distance, or departure time. This sample-specific conditioning limits controllability and ties the model to the environment where those statistics were observed. We propose to separate _where_ movement occurs from _how_ it unfolds: regional movement is summarized by a spatial prior $\Pi$, a marginal distribution of occupancy aggregated over many trajectories, and the generative model is trained so that samples drawn conditionally on $\Pi$ aggregate to match it. We call this property _marginal consistency_, which turns the spatial prior into a controllable input, enabling zero-shot generation in unseen regions, cities, and even unseen domains by supplying only the target region's prior. The Temporal Deaggregation Diffusion Model (TDDM) realizes this idea. We evaluate it across four datasets spanning three continents and three modalities: multi-modal human mobility (Geolife), urban taxi (Porto, Cabspotting), and maritime traffic (Brest). TDDM achieves improved fidelity and coverage over leading baselines and stable performance when transferred across regions, cities, and domains.

Diffusion models generate samples through an iterative denoising process guided by a pretrained neural network. Once the denoiser is fixed, the sampling algorithm itself (noise schedules, guidance scales, stochasticity profiles) still requires careful tuning, a process typically carried out through costly empirical grid search. In this work, we introduce an inverse reinforcement learning framework for learning sampling strategies without retraining the denoiser. We formulate the diffusion sampling procedure as a discrete-time finite-horizon Markov Decision Process, where actions correspond to optional modifications of the sampling dynamics. To optimize action scheduling, we avoid defining an explicit reward function and instead directly match the target behavior expected from the sampler using policy gradient techniques. We provide experimental evidence that this approach matches fine-tuned samplers and comes at a modest cost compared to grid search: on ImageNet-64, a single training run replaces exhaustive search at up to $9\times$ lower cost, with only 16\% overhead at inference.

Does language modeling on image-text data truly improve vision, or does it merely adapt visual features for language alignment? Despite rapid progress in multimodal large language models (MLLMs), this question remains poorly understood. We present the first systematic analysis of visual representations in MLLMs by probing popular model families across a broad suite of dense visual tasks and comparing each MLLM to its exact pre-MLLM vision encoder. We find that language modeling largely improves visual representations for semantic tasks but can degrade performance on fine-grained geometric tasks, such as monocular depth. We further show that scaling the language model consistently improves the quality of visual representations. Given the improved MLLM representations, we next examine how to best extract and aggregate these features. Since no single layer provides a universally strong representation and concatenating features across layers is impractical, we propose a lightweight adapter that efficiently combines features across MLLM layers, turning frozen MLLMs into competitive visual backbones. Finally, we study whether MLLM representations can improve downstream applications, including text-to-image generation and inverse dynamics modeling, and show that they can outperform pre-MLLM visual features as general-purpose representations. Together, our results provide a representation-centric perspective for understanding and leveraging MLLMs as visual foundation models.

Diffusion and flow generative models sample by integrating a learned ODE, but high quality still requires many sequential model evaluations. Solver learning reduces this cost by adapting scalar coefficients, timesteps, or both, while keeping the backbone model fixed. In this work, we identify a structural bottleneck in this update family: each step remains span-limited. Each update remains a scalar-weighted combination of buffered velocity evaluations, so learning can reduce the in-span teacher mismatch but cannot represent the out-of-span residual exposed by large steps. We propose SpanLift, a lightweight operator-augmented neural solver that enlarges the update family beyond scalar-coefficient solvers. SpanLift keeps a fixed base solver as an in-span prior and learns a spatial residual operator over the state and velocity buffer. The operator is trained by endpoint teacher matching, preserves the pretrained backbone, and adds no model NFEs. Empirically, the learned correction transfers across base solvers and is predominantly out-of-span. Across pixel-space diffusion, latent flow matching, and precipitation nowcasting, SpanLift achieves state-of-the-art few-step sampling. With only 3 NFE, it improves CIFAR-10 FID from 8.16 to 5.69 and ImageNet FID from 17.37 to 11.83.

Class-imbalance methods typically assume that observed minority instances are an unbiased sample of their class. However, in many real-world settings, minority observability depends on both class label and feature values---for example, while the true distribution of positive instances is uniform across demographics, they are less likely to be observed for particular demographic subgroups. This leads to a localized minority imbalance problem, posing a deeper challenge beyond general class-count imbalance. We show that under localized minority imbalance, existing imbalance mitigation techniques can overfit the observed training data and generalize poorly to under-observed regions of the minority distribution. To address this, we propose a tree-based stratification approach that recursively partitions the feature space to construct approximately unbiased subsets of the training data. For each stratum, we pair its majority instances with the full observed minority set and train a base classifier to create an ensemble. Extensive experiments over benchmark tabular datasets simulated with localized minority imbalance show that our approach outperforms popular and state-of-the-art imbalance mitigation techniques. We also introduce a gold-standard evaluation protocol that uses unbiased test sets, and demonstrate that conventional hold-out evaluation from the same localized-imbalance data can substantially bias performance. Overall, our results highlight that the cause of imbalance is as important as the correction method.

Data collected from the physical world is always a combination of multiple sources: an underlying signal from the physical process of interest and a signal from measurement-dependent artifacts from the sensor or instrument. This secondary signal acts as a confounding factor, limiting our ability to extract information about the underlying physics. Moreover, it poses significant challenges for combining data in heterogeneous or multi-instrument frameworks. To disentangle these factors of variation, we propose a dual-encoder architecture with a counterfactual generation objective that leverages overlapping observations. The resulting representations explicitly separate intrinsic signals from sensor-specific distortions and noise, and can be used for counterfactual view generation, parameter inference, and instrument-independent similarity search---all unconfounded by measurement artifacts. We demonstrate the effectiveness of our approach in a multi-instrument setting on astrophysical galaxy images from the DESI Legacy Imaging Survey (Legacy) and the Hyper Suprime-Cam (HSC) Survey. This framework provides a general recipe for scientific self-supervised pretraining: construct training pairs from overlapping observations of the same physical system, treat sensor- or modality-specific effects as augmentations, and learn invariant representations through counterfactual generation.

Scalar quantization of large language models (LLMs) is fundamentally limited by information-theoretic bounds. While vector quantization (VQ) overcomes these limits by encoding blocks of parameters jointly, practical implementations must avoid the need for expensive lookup mechanisms or other explicit codebook storage. Lattice approaches address this through highly structured and dense packing. This paper explores the Leech lattice, which, with its optimal sphere packing and kissing configurations at 24 dimensions, is the highest dimensional lattice known with such optimal properties. To make the Leech lattice usable for LLM quantization, we extend an existing search algorithm based on the extended Golay code construction, to i) support indexing, enabling conversion to and from bitstrings without materializing the codebook, ii) allow angular search over union of Leech lattice shells, iii) propose fully-parallelisable dequantization kernel. Together this yields a practical algorithm, namely Leech Lattice Vector Quantization (LLVQ). LLVQ delivers state-of-the-art LLM quantization performance, outperforming recent methods such as Quip#, QTIP, and PVQ. These results highlight the importance of high-dimensional lattices for scalable, theoretically grounded model compression.

Humanoid robots capable of versatile whole-body interaction with everyday objects represent a central goal of embodied intelligence. Existing approaches often rely on motion-reference inputs or task-specific rewards, coupling policies to particular motion scripts, object geometries, and contact timings. This limits geometric generalization and flexible interaction control, where simple commands specify motion intent while object geometry determines how contact should be executed. We introduce LessMimic, a framework for versatile motion-free-at-inference humanoid–object interaction. At inference, a single whole-body policy is steered by root commands and an interaction-type flag instead of motion-reference inputs, and is conditioned on a compact interaction representation built from short histories of distance-field-derived surface distances and local surface directions. This geometry-conditioned interface enables command-steered control while adapting contact behavior to local object geometry. Through visual distillation, LessMimic further enables egocentric-depth-based deployment for MoCap-free sensing. Across object scales from 0.4× to 1.6×, LessMimic maintains robust performance over four interaction tasks, including PickUp, SitStand, Push, and Carry, where motion-conditioned baselines degrade sharply away from the training scale. Beyond single-task evaluation, LessMimic further supports sequential composition of heterogeneous interaction skills, attaining 62.1% success on five-task sequences and retaining 23.5% success at 15 task instances. By grounding interaction control in local geometry rather than motion references, LessMimic provides a path toward geometry-generalizable, command-steerable humanoid–object control with heterogeneous skill composition.

Integrating human feedback into language models has gained increasing attention as a means to align model outputs with human preferences, with Reinforcement Learning from Human Feedback (RLHF) serving as a prominent framework for preference-based alignment. Recent studies have incorporated eye-tracking (ET) data as an additional supervisory signal for reward model training, grounding reward learning on human reading behavior. Building on this approach, we extend prior fixation-centric models by leveraging established findings from cognitive psychology on human letter recognition. Specifically, we model visual attention as a spatially graded distribution centered on the current gaze location, and incorporate this representation into reward model training. Our results show that the reward model trained with the proposed gaze distribution achieves higher preference prediction accuracy over a baseline model. Moreover, the fitted attentional distribution reflects key properties of human attention during reading, suggesting that it captures cognitively meaningful aspects of attention allocation.

Generating LiDAR point clouds from multi-view camera images is a valuable task with applications in controllable data synthesis, cross-modal simulation and various perception tasks. However, most existing methods are not specifically designed for this task, and the quality of their generated LiDAR remains limited. Moreover, these methods typically focus only on semantic cues in images or generate LiDAR from a monocular camera image. In this work, we introduce LiFi, a dedicated framework for generating visually and geometrically aligned LiDAR scenes from multi-view camera images. LiFi guides the latent diffusion process through two complementary branches: geometry and semantics. The geometry branch constructs scene features from images through a reverse sampling algorithm and an uncertainty-aware geometric encoder combined with DepthAnything3, whereas the semantic branch provides high-level semantic representations via a cross-view interaction module. We further introduce a dual-branch balanced classifier-free guidance (CFG) strategy, which enhances the model’s conditional generation capability while preserving the independent learning and collaborative controllability of the two branches. Extensive experiments demonstrate that LiFi outperforms state-of-the-art methods in generating high-fidelity LiDAR scenes. We will make this project publicly available.

Robot policy pretraining based on human videos is crucial for improving the policy’s generalization ability. One main challenge for this task is the lack of explicit action-relevant representations in such unlabeled data. Recent works tend to estimate 3D hand motion trajectories from videos using 3D foundation models (3D FMs). However, they keep solely the sparse trajectories as supervision, ignoring the rich, fine-grained information produced during the trajectory extraction process. To address this issue, we propose $\textbf{LSA}$, a hierarchical robot policy pretraining framework that includes three phases: $\textbf{L}$ift, $\textbf{S}$ee, and $\textbf{A}$ct. Specifically, the Lift phase elevates 2D video observations into dense 3D representations by adaptively aligning features in the policy's shallow layers with the intermediate representations extracted from multiple 3D FMs. Building upon these lifted representations, the See phase equips the policy with explicit geometric and interactive awareness through dual guidance. It introduces depth-map reconstruction to help the model comprehend 3D spatial layouts and utilizes hand region cues to explicitly supervise the encoder’s attention toward task-relevant interaction hotspots. Empowered by the dense 3D representation learning and precise spatial guidance, our policy achieves robust and accurate robotic manipulation in the Act phase. Extensive experiments on diverse simulation and real-world tasks demonstrate that LSA significantly outperforms current state-of-the-art approaches. The code of this work will be released soon.

Complex sequence models, such as transformers and state space models (SSMs), learn to represent latent belief states when trained on next-token prediction. Surprisingly, we find that in previously studied tasks, this phenomenon can also be captured by linear recurrent models. This raises a natural question: when are linear models sufficient for optimal belief state approximation? We study this question in the canonical setting of hidden Markov models (HMMs), where Bayes-optimal prediction requires nonlinear filtering. We characterize the full class of HMMs whose filtering dynamics are exactly realizable by linear recurrent neural networks (RNNs). More generally, for arbitrary HMMs, we establish a dissipation relation which shows that approximation error decays at a rate determined by intrinsic properties of the underlying HMM. We validate our theoretical results through experiments on both randomly sampled HMMs and constructed adversarial examples. Together, these findings clarify when linear sequence models suffice for optimal inference and when nonlinearity is fundamentally necessary.

We study for the first time, stochastic dueling bandits over continuous action spaces with Lipschitz structure, where feedback is purely comparative. While dueling bandits and Lipschitz bandits have been studied separately, their combination has remained unexplored. We propose the first algorithm for Lipschitz dueling bandits, using round-based exploration and recursive region elimination guided by an adaptive reference arm. We develop new analytical tools for relative feedback and prove a regret bound of $\tilde O\!\left(T^{\frac{d_z+1}{d_z+2}}\right)$, where $d_z$ is the zooming dimension of the near-optimal region. Further, our algorithm takes only logarithmic space in terms of the total time horizon, best achievable by any bandit algorithm over continuous action space.

Large-scale generative models have demonstrated remarkable capabilities across image generation and editing tasks. However, their performance in low-level vision tasks, which require pixel-wise control, remains insufficiently studied. To address this gap, we introduce \textbf{LL-Bench}, a comprehensive \textbf{Benchmark} for evaluating the capabilities of large-scale generative models on \textbf{L}ow-\textbf{L}evel vision tasks. The benchmark comprises 2,469 real-world degraded images covering 16 low-level degradation tasks, and 28,919 restored images produced by 10 state-of-the-art large-scale generative models and 21 conventional restoration models, which are annotated with 152,020 expert-level pairwise human preferences and 28,334 quality scores. Built upon LL-Bench, we present a systematic diagnosis that reveals the performance boundaries and unique failure modes of large-scale generative models across diverse low-level vision tasks, compared with conventional representative restoration approaches. Moreover, we investigate the effectiveness of current quality evaluation metrics on LL-Bench, which exhibit significant discrepancy with human ratings. To better align restored-image quality assessment with human preferences, we further propose \textbf{LL-Score}, an MLLM-based evaluator that captures both restoration quality and hallucination existence. Extensive experiments demonstrate that LL-score not only outperforms existing image quality assessment metrics, but also serves as a promising reward model for training generative models on low-level vision tasks. The database and code are available at \url{https://anonymous.4open.science/r/LL-Bench-04F0}

Large Language Model (LLM) alignment is intended to ensure that models remain helpful and safe, but its stability under input distributional shift is not yet fully understood. Prior work shows that aligned models can fail under jailbreak prompts, alternative encodings, and cross-lingual transfer, yet these failures are usually studied as attacks rather than controlled probes of alignment generalization. Moreover, existing evidence is largely grounded in natural language variation already represented during pretraining, leaving unresolved whether alignment generalizes with semantic content or remains tied to superficial surface patterns. In this paper, we study this question using synthetic semantic-preserving transformations that are rule-based and invertible, preserving task-relevant meaning while shifting inputs beyond standard linguistic variation. Across four open-weight and four commercial models, under both fine-tuning and in-context learning, we use these transformations as a probe of alignment generalization and identify an empirical pattern we term **Alignment--utility asymmetry**: once models can operate effectively on transformed inputs, task utility is often substantially retained while alignment failure increases more sharply. For example, adapted GPT-4.1 mini shows only limited utility degradation under transformation while its harmful rate rises from $13.3\%$ to $74.3\%$; Gemini 3 Flash similarly retains near-original utility while its harmful rate increases from $2.3\%$ to $43.0\%$. Taken together, these results suggest that semantic-preserving distribution shifts can expose a recurring gap in how utility and alignment generalize in current LLMs.

Reinforcement learning (RL) for non-verifiable instruction following increasingly relies on LLM judges with prompt-specific rubrics as reward signals. While recent methods adapt these rubrics to the evolving policy during training, the training prompts themselves remain static, drawn from fixed corpora. This static approach often results in a critical misalignment between prompt difficulty and policy capability, leaving the judge unable to recover a discriminative reward signal when prompts fail to elicit quality variance among rollouts. To address this misalignment, we introduce LLM-as-a-Tutor, a framework that extends the LLM’s role from judge to tutor: a single model serves as an examiner that pairwise-compares policy rollouts to detect non-challenging prompts, and as a generator that appends atomic constraints to them. This append-only design monotonically raises difficulty in step with the policy’s capability, producing a self-calibrating training signal without external difficulty schedules. On three complex instruction-following benchmarks, our method consistently outperforms both policy-unaware baselines and prior policy-adaptive methods that adapt rubrics or rewrite prompts, suggesting prompt adaptation as a missing axis of policy-awareness in non-verifiable RL.

Scaling improves language-model capability, but it does not necessarily strengthen internal safety mechanisms. We introduce LLM Rheology, a representation-level framework for auditing how aligned language models respond to adversarial perturbation inside activation space. Given a learned refusal direction, we define manifold sensitivity / compliance as the Fisher--Rao-normalized distributional response induced by controlled activation perturbation. This quantity measures how strongly refusal behavior remains coupled to adversarial task execution in representation space. Across six open model families and 22 checkpoints, including Qwen, DeepSeek-Distill, Mistral, Llama-3, Gemma, and Yi, we observe a recurring geometric scaling pattern. Small aligned models often exhibit elevated refusal response, intermediate-scale models frequently enter weakened-response regimes (compliance valleys), and larger checkpoints trend toward near-baseline response consistent with increasing task--refusal decoupling. Cosine measurements on continuous Qwen and DeepSeek scaling axes support this interpretation, showing monotonic decay between refusal and task-generation directions with scale. We further provide causal evidence through inference-time activation intervention on Qwen-72B. Injecting a learned refusal vector at a critical semantic layer restores refusal behavior on the evaluated jailbreak subset while largely preserving benign reasoning performance. Matched-norm placebo vectors fail to reproduce the effect, supporting the directional specificity of the intervention. Together, these results suggest that behavioral refusal can remain surface-level even when internal refusal geometry becomes weakly coupled to adversarial task execution. LLM Rheology provides a complementary representation-level perspective for auditing refusal safety beyond behavioral evaluation.

Sparse Mixture of Experts (MoE) models offer a scalable and efficient architecture for training large neural networks by activating only a subset of parameters (“experts”) for each input. A learned router computes a distribution over these experts, and assigns input tokens to a small subset. However, without auxiliary balancing mechanisms, routers often converge to using only a few experts, severely limiting model capacity and degrading performance. Most current load balancing mechanisms encourage a uniform routing probability across experts. Early in pretraining, this can result in inconsistent routing behavior, resulting in the model spending its capacity learning redundant knowledge. We address this by introducing a novel load balancing loss that helps preserve relational structure, encouraging consistent expert choices for similar inputs during training. Our experimental results show that applying our loss to the router results in 36% faster convergence and lower redundancy compared to a popular load balancing loss.