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arXiv AI By Chengxin Yu, Zhaoxin Fan, Faguo Wu, Hongwei Zheng, Yun Zhou, Zhiyu Li

CoMem: Collective-Individual Memory Synergy for Evolutionary Multi-Agent Systems

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CoMem introduces a memory architecture for multi-agent systems that blends private experience with shared knowledge. It includes Private Experience Sedimentation to retain useful individual memories, Collective Wisdom Curation to filter widely proven ideas for sharing, and Parallel Dual-Stream Retrieval to draw from both personal and group memories while maintaining diversity. Experiments on ALFWorld and PDDL benchmarks demonstrate that CoMem improves overall performance and reduces memory pollution.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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