Motivation and Scope
Generative AI is evolving from offline, single modality models into interactive agentic systems that perceive, decide, and act in the real world. This shift marks a transition from static generation to dynamic, context-aware interaction. As these systems move toward deployment on edge devices such as mobile phones, augmented reality glasses, and robots, they face constraints in compute, memory, and latency. Beyond efficiency and responsiveness, a new frontier is emerging: agents equipped with persistent memory that enables long-term adaptation and personalization.

This workshop explores a timely and focused question. How do we build generative agents that are not only efficient and responsive but also able to accumulate, recall, and adapt based on personal memory over time? We aim to bring together perspectives from generative modeling, agentic learning, efficient model design, and memory systems to close the gap between lab scale prototypes and real-world deployment. x000D
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Key Themes x000D
Personal Memory Systems for AI Assistants: Architectures for persistent memory, retrieval-augmented generation, and long-term personalization. x000D
Real-World Adaptation Few-shot generalization, continual learning, and task inference for evolving agent behavior. x000D
Grounded and Trustworthy Generation: Techniques for hallucination mitigation, constraint-aware generation, and safety under uncertainty. x000D
Deployment on Edge Platforms: Challenges and solutions for deploying generative agents on mobile, AR, and robotics platforms. x000D
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This focused workshop aligns with emerging themes at NeurIPS including agentic learning, trustworthy AI, efficient multimodal generation, and embodied intelligence. It will spotlight the systems, algorithms, and design decisions needed to make generative AI truly adaptive and persistent, outside the data center and into the wild.