Beyond Retrieval: Analytic Memory for Multimodal Agents

TL;DR - An arXiv paper introducing "analytic memory" for long-term multimodal agents — memory that supports computing over accumulated observations (filtering, aggregation, ranking, temporal comparison) rather than only retrieving them. It matters because current agent memory systems stop at retrieval, limiting questions that require reasoning across many past interactions.

• Formalizes analytic memory as a complement to retrieval memory, which today is mostly summaries and indexes returning query-relevant content at varying granularity.
• AdaMM extracts provenance-linked attribute-value observations from dialogue, images, and contextual metadata without requiring application-defined schemas, then discovers recurring field structures and materializes them for analytical queries.
• A memory-aware planner decomposes incoming queries into retrieval vs. analytic operations and routes each to the appropriate tool.
• Reported gains of up to 11.3% on MemEye and 7.3% on MemGallery, two long-term multimodal memory benchmarks.