MACE: Memory-Agent Co-Evolution with Adaptive Memory Graphs for Multi-Agent Systems

LLM based multiagent systems (MAS) accumulate collaboration experience through task execution, including task conditions, agent actions, intermediate artifacts, repairs, and outcomes (Wu et al., 2023). Reusing this experience can improve later collaboration, yet existing memory mechanisms often treat experience as isolated records or static graph context. They therefore miss two requirements of multiagent reuse. The first is functional structure, because a useful experience depends on how conditions, actions, and outcomes support one another and how this support extends across experiences. The second is adaptive use, because the same memory unit may need different selection, format, placement, and strength for different agents and execution states. We propose MACE, a memory agent co evolution framework that jointly organizes experience and learns how agents should use it. MACE introduces MemGoG, which stores experience as functional graph units with typed links across units. It then uses a Need Aware Memory Composer to build a compact working graph, an Adaptive Agent Memory Coupler to render role specific memory patches, and a Feedback Co Evolver to update unit utility, graph links, and agent side use policies after execution. Across eight benchmarks, MACE achieves an average score of 81.11%, improving over the strongest controlled baseline by 2.14%.