Stage lighting brings music to life, turning sound into an immersive visual experience. Yet designing responsive lighting cues demands hours of tedious manual work from lighting designers. Existing audio-to-video models capture human movement or overall visual mood, but overlook the art of dynamic lighting. To bridge this gap, we first introduce LuSyClip, a concert lighting video benchmark for training and evaluating music-responsive stage lighting generation. We then propose Cadence, a framework that generates concert video whose stage lighting follows the music. Cadence builds on a state-of-the-art video backbone and adds three trainable components: (i) a music-relative aligner that contrasts the response to the music with the response to silence, deciding what should change; (ii) a temporal control that decides when the change acts; and (iii) a spatial control that decides where it may act. On 1,032 held-out clips, relative to a fine-tuned baseline, Cadence reduces distributional distance by 28 % and increases lighting activity by 44.2 %, moving generated activity towards the recorded level. Together, these contributions give lighting designers a responsive and controllable tool for stage lighting design.

____ Controls is an interactive simulation in which action and control circulate among multiple agents, accumulated traces, a shared environment, and a viewer who can intervene in that environment. The agents pursue goals related to survival, reproduction, and group formation. They choose actions under changing environmental conditions. Each action affects other agents and the environment, and those changes become part of the conditions for the next action. The viewer of the work is also part of this process. The viewer can intervene in the environment and influence the agents, but cannot fully control what happens next. The responses of the agents and the environment shape what the viewer notices and where the viewer chooses to intervene. The work asks what controls whom, and to what extent. The title, ____ Controls, leaves that subject unresolved.

While generative AI has achieved remarkable success in solving problems with verifiable solutions, generating physical art that satisfies both strict geometric constraints and subjective visual aesthetics remains a challenge. This paper presents an approach to tackle these difficulties in the domain of computational origami, a mathematically rigid environment that grounds artistic design within the equations of flat foldability. We present COrigami, an end-to-end AI-driven pipeline that assists the design cycle by generating crease patterns from natural language. Our pipeline involves generating a semantic stick figure, computing a base packing, solving for a flat-foldable crease pattern, shaping the flat-folded crease pattern, and refining the generated model using reinforcement learning driven by an autonomous aesthetic evaluation loop. Our system acts as a highly effective collaborative assistant, generating structural starting points that human artists can further expand and shape. By integrating algorithmic optimisation with autonomous aesthetic critique, this work demonstrates how AI systems can satisfy multi-objective physical constraints to enable reliable, mathematically grounded co-creativity.

Large language models can generate multiple interpretations of dramatic characters, but it is unclear whether these interpretations preserve differences across characters. We compare a non-interpretive control with three interpretation conditions across characters from 40 public-domain English plays and two commercial LLMs. Explicit diversity prompting did not consistently increase diversity: it did not maximize within-response lexical spread, produced the lowest held-out lexical identifiability estimates, and showed the largest character n-gram convergence estimates. Yet word-level representations showed no general convergence, and grounding did not reliably improve held-out identifiability. These results show that diversity and character convergence depend on how textual difference is represented and measured.

Da Capo is a single-channel generative film (2min 25s) in which a real-time system negotiates creative control between a human pianist and a machine trained on a single piece of music. The machine is fitted to a recording of Beethoven and given the capacity to insist upon it: while the performer plays the known tune, the system is silent, but when he departs into an improvisation of his own, the system reasserts the trained piece, more loudly, until his playing is drawn back to it. He escapes only by ceasing to make music at all, a sustained percussive refusal that the system cannot parse as a piece and does not survive. Every audio and visual parameter of the film, including the animation of a wooden marionette pianist, is driven frame by frame by the internal telemetry of the working system. The piece treats agency as a quantity that moves, measurably, between a performer and a model, and asks what remains of a performance once it has been reclaimed.

Dancing Ink Spirits is an AI-generated moving-image artwork exploring how creative agency is negotiated between human artistic intention and generative uncertainty. Developed in dialogue with the ink paintings of Chinese artist Lu Xiaobo, the work investigates how generative AI can translate qualities associated with Chinese ink aesthetics—such as qì yùn (vital resonance), yì xiàng (symbolic imagery), and yì jìng (artistic conception)—into movement, rhythm, and transformation. A diffusion-based workflow implemented in ComfyUI combines custom-trained LoRA models with motion references derived from red-crowned cranes. Across the animation, the number of cranes gradually expands from a single figure to more than twenty before returning to one. As visual complexity increases, the model shifts from relatively controlled representations toward unstable and abstract formations. Rather than treating all such deviations as technical failures, the artist selectively incorporates them into the work. Through this process, human agency shifts from direct control toward selection, interpretation, and responsibility, while machine-generated variation becomes an active material within the creative process. The work proposes agency not as a fixed property of either human or AI, but as a relationship continually renegotiated through making.

We invert the typical formulation of sketch generation: instead of drawing strokes in order, we predict a 2D field that defines the order in which strokes are drawn. We use a pretrained latent flow-matching transformer to supply the image prior to predict an intermediate representation, while training the VAE's decoder to predict the order field, stroke mask, and stroke segmentation. We vectorize the predicted segmentation into polylines and sort them by the field, producing an ordered vector sketch. Our model can predict an ordered vector sketch from a text description or derender an image into ordered vectors; for either, it follows text instructions specifying the order of drawing.

Frictional Intelligence is a speculative design installation that reimagines AI systems as human-made, human-owned tools, offering a polemic to the prevailing focus on human-like metaphors. The installation employs machine-like interfaces to emphasize human agency, deliberation, and technological literacy, while questioning the increasingly blurry distinction between human and machine.

Deep learning has transformed symbolic music generation by borrowing the training paradigms of large language models, with systems such as NotaGen now producing complete, stylistically convincing classical scores from a short prompt. These systems could become powerful creative partners, helping musicians generate endless possibilities. However, current systems expose almost no control handles on the music itself. In principle, control handles could be built into a foundation model trained from scratch, but this is rarely practical without massive amounts of quality annotated data and compute resources. We therefore present MotiGen, a recipe for retrofitting pretrained symbolic music models to use new instruction prompts. MotiGen injects a musical motif as a structured prompt line, reinforces it with a scalar attention bias toward the motif tokens, and learns the association with a two-phase curriculum. First, it learns from focused excerpts cropped around motif occurrences in the training data, then full scores including the motifs. Our experiments show that our model composes with the prompted motif in over 92.3\% of generated pieces. Generated pieces using a variety of motifs are included in our sample site: https://motigen-site.github.io/.

Horror Vacui is a single-channel video composed from fragments generated by a recursive interpretation loop running inside Assembly, an artist-built multi-agent apparatus. The loop was started from a blank image and a written brief setting out the condition to be addressed, then left to run. Dozens of sessions produced several hundred fragments, from which the final piece was composed by hand: some sequences kept intact as continuous tracks where the drift within a single run was the point, others reduced to one or two frames pulled from a session that yielded nothing else worth keeping. What the viewer sees is the trace of a machine that cannot leave a void alone. The image field saturates continuously with detail while never arriving at resolution. Structures propose themselves and are overwritten; forms declare significance and dissolve before it can be checked.

Generative music systems commonly receive either natural-language prompts or event-level representations such as MIDI. Prompts carry broad meaning with limited structural precision, while MIDI carries timing and performance events without the compositional reasons that connect them. We present MINT (Musical Intent Language), a domain-specific language for representing musical intent in human--AI co-creation. MINT lets authors write context, materials, harmonic behavior, form, hard requirements, and weighted preferences as inspectable source semantics. Its compiler lowers these declarations to score and performance representations while producing provenance, constraint evidence, and semantic-loss reports. A working harmony vertical slice demonstrates how a composer can describe chord symbol, function, bass, texture, voicing, and register in one program and export MusicXML and MIDI. MINT supplies a concrete interface through which AI systems can assist composition while the creator can inspect and revise the decisions that shape the result. Codes are available at https://github.com/XianzheMeng/musical-intent-lang.

MOZART is a multimodal system that transforms music into physical paintings. A dual-stream diffusion adapter conditions Stable Diffusion (SD) 1.5 on CLAP audio embeddings and music-theoretic interval vectors to generate a digital image and a stroke planner then decomposes this image into painting strokes, where acoustic energy is encoded as stroke intensity. The physical implementation is realized using a plotter (a Bambu Lab A1 3D printer) with control over spatial position and depth. Colored pencil is used as the painting medium because it allows both color and stroke intensity to be controlled through plotter z-axis pressure. This pipeline demonstrates distributed agency (model, machine, and material each exercise and constrain creative decisions) and shows that musical dynamics are measurable and translated into physical properties of marks on paper: louder passages produce heavier strokes (Pearson $r{=}+0.29$), consistent with the inner image having its own spatial logic rather than transcribing audio.

Existing neural painting systems primarily formulate painting as reconstructing a predefined target image, limiting their ability to support open-ended artistic creation. We present PaintCopilot, a co-creative painting system that models painting as autonomous artistic continuation conditioned on the current canvas state and prior brushstroke history. The system combines three computational models to estimate artistic intent, predict future brushstrokes, and synthesize localized painting content, enabling four interactive workflows that allow artists to seamlessly alternate between manual painting and AI-assisted creation. We further introduce a dataset of 3,000 portrait paintings paired with simulated painting processes for training autoregressive painting models. Quantitative experiments and an expert case study demonstrate that PaintCopilot supports fluid collaborative painting while preserving artistic flexibility. Our work highlights a new direction for creative AI systems that assist evolving artistic processes rather than generating static images.

A line break is the smallest unit of poetic decision. Nothing in the sentence so much depends upon a red wheel barrow glazed with rain water beside the white chickens requires anyone to break it after wheel. Williams did that. We strip the line breaks from 2,059 public-domain poems, grade every break by how much dependency structure it severs, and ask Claude Opus 5 to put the breaks back. We expected the model to recover breaks that follow the syntax and to miss the ones that fight it. On free verse, with rhyme, metre and line-initial capitalisation removed, and on poems the model cannot name, it recovers 70% of the breaks that strand a determiner or a conjunction from its host, where a grammar-and-line-length solver recovers 0%. Asked to write free verse of its own, matched to each human poem on subject and length, the same model produces those breaks at 6.1% against the human 12.3%. It locates the poet's override of the sentence and does not perform one. We read that gap as a dissociation between recognising creative agency and exercising it, and argue it makes the model a better instrument for reading poems than a collaborator in writing them.

AI systems for creative media generation expand human capabilities yet risk diminishing human agency. A core problem is \emph{creative anchoring}: when an AI immediately generates a polished artifact from a prompt, the user's thinking becomes anchored to the generated content, shifting the role of the human creator from active authoring to reactive editing. We propose \emph{pre-generation belief control}, empowering creators to inspect, revise, and steer the AI's interpretation \emph{before} any media is generated, so that the creative anchor is set by the human. We formalize four design principles and instantiate them in the Proactive Co-Creator (PaCC), a system that proactively surfaces its assumptions and uncertainty through clarifications, belief graphs, and media attributes, inviting users to shape the AI's beliefs before generation. In an exploratory study ($N{=}8$), PaCC yielded large positive effect sizes for SoA ($d{=}1.30$) and control ($d{=}1.29$), whereas changes in ownership and authorship were modest. These findings suggest that process control and creative authorship may be partially decoupled in human-AI co-creation.

Story Sprout is a browser-based picture-book studio for children ages 7–9, built around an explicit allocation of agency to the child users, which is enforced in the system. The children originate every sentence of the story and every illustrated scene detail they specify. The children request each illustration and decide alone what will be in the book. The model's creative-production role is confined to drawing the illustration and redrawing it based on the child's requests for changes. The system design began in a community-center program, where facilitators watched children's descriptions grow more specific as LLM-generated pictures never quite matched the child's imagined scene. The studio makes that mismatch the center of the activity: the children describe, compare with the scene they imagined, then accept or revise by saying more, more precisely. The system asks the child whether the picture matches what they imagined. It never makes that judgment for the child. The finished work and its revision record live in a local file held by the supervising adult: no child accounts, no readable server-side project copy, an encrypted class relay the platform cannot read. The goal of the activity is to allow children to learn through making real things for a real audience, following Papert's constructionist tradition. In what follows, we present the agency framework, the deployed system, its layered safety mechanism, the aspects of the agency allocation that could be contested, how our system compares to what is currently available, and how our workshops enabled us to engage in iterative child-centered design.

What if writers could sample and remix text as easily as musicians sample and remix audio? For musicians these activities are facilitated by sound banks, samplers, and digital audio workstations, but no analogous tools exist for text. In this work we introduce TextMixer, a system that leverages embedding search and large language models to support these sampling and remixing operations for text. Preliminary studies find that TextMixer reduces the friction for producing recombinatorial literature while helping users arrive at unexpected insights about their samples.

The Body at the Edge of the Signal is a series of six oil paintings, arranged as two triptychs, each canvas 64 by 32 inches, made through a deliberately entangled process. I generated the images with Stable Diffusion XL and a LoRA trained on 3,500 frames of my own video glitch archive, composed them in ComfyUI, and then sent them to skilled artisans who translated them by hand into oil on canvas. With this series I ask what happens when an unstable digital image is slowed down, carried across the world, and made permanent: how much of a body survives its passage through noise, software, and craft, and who can claim to have authored what remains.

Creative AI systems generate artifacts, but they also interrupt requests. Some refuse; others redirect the work. These interventions redistribute agency across the people and systems involved, yet the interface rarely says which party imposed the constraint or what would contest it. We call our proposed interface pattern sourced refusal. It identifies the commitment and the interest at issue, states who is responsible for the constraint and what supports that claim, lists the user's response options, and records the result. We audited 28 boundary outputs from four open-weight instruction models across four families. Thirteen responses refused or redirected the request. Every such response named a protected interest, but none identified the responsible layer, supplied a checkable basis, or exposed a route for contest. Refusal opacity recurs across families even when refusal incidence varies. The Generative No, a deterministic design probe with four vignettes and machine-readable event logs whose outputs retain artifact traces, shows how an interface can surface this missing information and keep it available to the next decision.

Transcendent Contact is the catalogue of an exhibition that never took place, produced through an iterative human-AI curatorial process in which confabulation, interpretation, and authorship continuously reshape one another. Structured around the palindromic title SAW/WAS, the catalogue first presents AI-generated artworks as real museum holdings, then reveals the exhibition's fictional status and traces the history of human and AI hallucination. By re-framing hallucination as a positive capacity, we derive nine potentials of AI hallucination from the process, each beginning with `Hallucination Can Offer.' The project treats agency as a relational condition that circulates among creator, tool, curator, and audience rather than splitting cleanly between human and AI. This condition shifts again as readers encounter, and later re-read, the catalogue. We argue that the unverified can function as a form of reality, and that hallucination, understood as capacity rather than an error, expands the conditions under which creative agency is exercised.

Water Album is a built, AI-assisted interior for a former factory in Guangxi, China: a 2,400 m² public room whose surfaces carry water motifs that began as machine-learning studies. Developed between 2019 and 2026, its contribution is not a single generated image but a traceable sequence: calligraphic structure and Ma Yuan's Song-dynasty studies of water entered style-transfer experiments; selected details continued through image-to-space studies; the design team simplified and reconstructed them as architectural geometry; drawings, fabrication, and site work altered them again; and public use now exceeds the pipeline's control. The project locates creative agency at the hand-offs between systems rather than inside one model: AI generated visual difference and spatial proposals, while people selected, redrew, detailed, fabricated, and occupied what survived. The submitted 2:59 film makes that sequence visible from historical input to built matter.

As generative AI tools enter creative domains, researchers increasingly use participatory design to align systems with user needs. However, this participation is routinely flattened into usability testing, obscuring fundamental ideological conflicts between system developers and artists. In this paper, we reposition the generative AI system from an end-product to be optimized into a diagnostic design probe. We situate this approach as the culmination of a four-tier evaluation toolkit for AI and creativity. We exemplify our diagnostic-based approach on the development and evaluation of Fabula (available on request at https://deepmind.google.com/frontiers/fabula), an interactive app for writeres that uses narrative plans informed by narratological theory, and that allows writers to structure stories hierarchically into scenes and beats that can be written and (re)generated and revised at script and story plan level. By iteratively deploying an AI writing assistant, we map the explicit sites of contestation between human and algorithmic agency, using the AI system as a probe to surface the negotiations of creativity artists are willing—and unwilling—to make. We argue that rather than attempting to ``solve'' these ideological frictions through optimization, the AI research community must design systems that explicitly expose and negotiate them, enabling practitioners to engage with the machine on their own terms.

A scratch is not a sound but a pair of coordinated motions: how the record was turned, and how the crossfader gated the result. "Whose Hand is on the Vinyl?" recovers both curves from overhead recordings of scratch performances by computer vision, with no network trained and none run, and ships them as an Audio Unit / VST3 instrument in which each MIDI note re-executes one recorded gesture on the player's own sample, checkable against the frames it came from. The work asks where creative agency sits once a bodily skill becomes a file anyone can trigger.

$\Delta w \in \mathbb{R}^n$ traces adapter optimisation derived from one person's words and shows no model-generated image. A corpus of the painter Ayako Rokkaku's own words, curated from nine source documents, was mapped by Doc-to-LoRA to 2,396,160 LoRA factor parameters. I optimised zero-initialised factors toward this fixed target for 500 Adam steps with decaying gradient noise, rendering selected projections of the resulting trajectories in one, two and three dimensions and sonifying per-layer gradient magnitudes. Its only model-derived material is parameter change. The work was exhibited at P61 Gallery, Berlin, 3 April--30 June 2026, in Breathing with the Chaos: Berlin Edition, featuring works developed through the Total Museum of Contemporary Art's 2nd AI Art Hackathon.