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

arXiv:2609.15009v1 Announce Type: new Abstract: Designing effective memory mechanisms is crucial for advancing LLM-driven Multi-Agent Systems (MAS), helping agents learn together and perform better over time. While recent work has led to strong cooperation skills, most methods still use flat, unstructured memories, which easily get filled with noise and erase differences between agents. To address this, we introduce the concept of collective-individual memory synergy and propose CoMem, an architecture that unifies both private experience and shared knowledge for multi-agent learning. CoMem features:(i) Private Experience Sedimentation, which lets each agent keep and update its own useful memories over time;(ii) Collective Wisdom Curation, which carefully selects only widely proven ideas to be shared among agents;(iii)Parallel Dual-Stream Retrieval, which allows agents to draw both from their own memory and the group's wisdom, using clustering to ensure diversity.Experiments on ALFWorld and PDDL benchmarks show that CoMem achieves strong overall performance and robustly avoids memory pollution.

Related

• RecMem: Recurrence-based Memory Consolidation for Efficient and Effective Long-Running LLM Agents
• AMA: Adaptive Memory via Multi-Agent Collaboration
• Mesh Memory Protocol: Semantic Infrastructure for Multi-Agent LLM Systems
• Learning What to Retain: Gated-Memory Routing for Efficient Collaboration in Multi-Agent LLM Systems
• Rashomon Memory: Towards Argumentation-Driven Retrieval for Multi-Perspective Agent Memory

Source: arXiv cs.AI | 2026-09-15