EdgeMem: LLM-Free Agent Memory Construction and Retrieval via Evidence-Preserving Multi-Anchor Hypergraph

Abstract:Agent memory allows LLM agents to use earlier interactions when answering new queries. Existing methods often compress interaction histories into summaries or other LLM-generated representations. Repeated generation adds cost and can discard answer-bearing details before the system knows what a future query will require. We propose EdgeMem, an agent-memory method built around a simple principle: preserve original interaction turns and organize them through complementary content, temporal, and episodic cues. EdgeMem realizes this principle with a multi-anchor hypergraph constructed by lightweight local processing. Retrieval directly returns source evidence and reserves LLM use for final answer generation, combining structured access to multi-session histories with faithful retention of the original conversation. Experiments on LoCoMo and LongMemEval-S show strong retrieval and memory-grounded question answering; on LoCoMo, EdgeMem achieves the highest strict-judge score among seven reproduced systems under a shared prompt (61.01 versus 58.70), while construction and retrieval require no generative-LLM calls. Overall, EdgeMem shows that preserving and organizing source evidence provides an effective and efficient foundation for agent memory without generative memory management.

10 pages, 5 figures, and 5 tables. An earlier implementation is available at this https URL ; it differs substantially from the version described in this paper. The repository will be updated with the corresponding implementation after peer review

Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)

arXiv:2609.05553 [cs.AI]

(or arXiv:2609.05553v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.05553 arXiv-issued DOI via DataCite (pending registration)