Submit Your Paper
Deadline: February 14, 11:59pm AoE5-page full papers or 3-page tiny papers (excluding references and supplementary). Non-archival, double-blind review.
About the Workshop
Associative Memory has re-emerged as a unifying principle linking classical energy-based models with transformers, diffusion models, and memory-augmented agents. This workshop brings together theorists and practitioners to develop a unified perspective on memory as the core substrate of intelligent behavior. This is the third iteration of the workshop, following ICLR 2025 and NeurIPS 2023.
Read more about the workshop scope
Rooted in early mathematical and neuroscientific formulations, associative memory (AM) provides a principled view of collective computation through attractor dynamics and energy landscapes. Recent advances have significantly expanded this framework, revealing associative retrieval as a form of attention, inference, and optimization within deep learning systems. Beyond classical recall, modern AM models support test-time regression, continual adaptation, and reasoning over structured domains such as graphs, manifolds, and probability distributions.
Despite rapid progress, research on memory, reasoning, and adaptation remains fragmented across communities spanning energy-based learning, optimization theory, neuroscience-inspired computation, generative modeling, and agentic AI. By fostering cross-disciplinary dialogue and shared evaluation paradigms, the workshop aims to catalyze a coherent research agenda for next-generation memory-augmented and agentic AI systems.
Submission details
We invite submissions on novel research results (theoretical and empirical), benchmarks, demos, visualizations, software frameworks, and work-in-progress research. Submissions should be made anonymously on OpenReview using this workshop's LaTeX template. Reviews will not be shared publicly. See the call for papers for more details.
Supplementary materials after references are allowed and don't count toward the page limit. Submissions should be anonymized. Accepted papers will be made public on OpenReview. The reviewing process is double-blind with standard ICLR conflict of interest rules. Preference given to new, unpublished work.
Invited Speakers
Panelists (tentative)
We end our workshop with a panel discussion exploring how Memory can improve LLMs.
Organizers
Schedule
View detailed schedule for April 26 or 27, 2026.
Contact Us
Questions? Email us at nfam2026@gmail.com.