Multimodal Memory
Overview
Multimodal memory studies how AI agents preserve and use information from conversations, images, audio, and video across long periods and changing tasks. Common approaches include hierarchical episodic and semantic memories, event or entity graphs, retrieval-augmented systems, and learned policies that decide what to store, compress, or retrieve. Evaluations use long-video question answering, personal archives, multi-session conversations, and interactive agent tasks, where systems still struggle with fine visual details, cross-session identity, and tracking changing facts. A central tension is how to reduce storage and inference costs without losing source evidence, temporal context, or the ability to verify an answer.
Latest papers
71 papers12 papers in the last four weeks, up 33% on the four weeks before. 0.1% of all new papers.