Agent Memory
Overview
Agent memory studies how AI systems preserve and use information beyond the current context, from personal facts and changing state to task procedures and spatial experience. Common approaches include retrieval over raw histories, summaries and vector or knowledge graphs, alongside methods that organize episodic, semantic, and procedural memory or learn when to update, compress, and forget. Evaluation spans conversational benchmarks such as LoCoMo and LongMemEval as well as multi-session web, coding, and embodied tasks, with growing attention to interference, stale information, provenance, privacy, and whether retrieved evidence actually improves later actions. A central tension is balancing reliable, generalizable memory with limited context, latency, and maintenance cost; strong recall on question-answering tests does not yet guarantee robust use in evolving environments.
Latest papers
190 papers37 papers in the last four weeks, up 95% on the four weeks before. 0.2% of all new papers.