Agent Memory Management
Overview
It gives language and embodied agents persistent state beyond a context window, supporting personalization, multi-step work, and adaptation as facts change. Systems range from raw-log and vector-search stores to episodic-semantic hierarchies, temporal or provenance-aware graphs, and learned controllers that decide what to write, retrieve, consolidate, or discard. Evaluations use long-dialogue question answering benchmarks such as LoCoMo and LongMemEval, alongside tool-use, coding, and GUI tasks, measuring accuracy as well as token cost, latency, retention, and stale or unsafe recall. The central challenge is balancing useful evidence and traceability against bounded storage and computation: retention cannot help if retrieval fails, while compression, updates, and sharing can lose, stale, or expose information, and no design works best across all workloads.
Latest papers
449 papers68 papers in the last four weeks, up 8% on the four weeks before. 0.4% of all new papers.