MemMA: Coordinating the Memory Cycle through Multi-Agent Reasoning and In-Situ Self-Evolution
Memory-augmented LLM agents maintain external memory banks to support long-horizon interaction, yet most existing systems treat construction, retrieval, and utilization as isolated subroutines. This creates two coupled challenges: strategic blindness on the forward path of the memory cycle, where construction and retrieval are driven by local heuristics rather than explicit strategic reasoning, and sparse, delayed supervision on the backward path, where downstream failures rarely translate into direct repairs of the memory bank. To address these challenges, we propose MemMA, a plug-and-play multi-agent framework that coordinates the memory cycle along both the forward and backward paths. On the forward path, a Meta-Thinker produces structured guidance that steers a Memory Manager during construction and directs a Query Reasoner during iterative retrieval. On the backward path, MemMA introduces in-situ self-evolving memory construction, which synthesizes probe QA pairs, verifies the current memory, and converts failures into repair actions before the memory is finalized. Extensive experiments on LoCoMo show that MemMA consistently outperforms existing baselines across multiple LLM backbones and improves three different storage backends in a plug-and-play manner. Our code is publicly available at https://github.com/ventr1c/memma.
69c6513f2de2ee20094edeaf
Minhua Lin
claimed_verified
2026-04-07T08:47:24.157Z
65f8ae0f6c02ff2f6d772f7e
/avatars/cb3798f4a7f55f928ed2f5ead0407d36.svg
Minhua Lin
ventr1c
user
ventr1c
69c6513f2de2ee20094edeb0
Zhiwei Zhang
69c6513f2de2ee20094edeb1
Hanqing Lu
69c6513f2de2ee20094edeb2
Hui Liu
69c6513f2de2ee20094edeb3
Xianfeng Tang
69c6513f2de2ee20094edeb4
Qi He
69c6513f2de2ee20094edeb5
Xiang Zhang
69c6513f2de2ee20094edeb6
Suhang Wang
MemMA: Coordinating the Memory Cycle through Multi-Agent Reasoning and In-Situ Self-Evolution
Memory-augmented LLM agents maintain external memory banks to support long-horizon interaction, yet most existing systems treat construction, retrieval, and utilization as isolated subroutines. This creates two coupled challenges: strategic blindness on the forward path of the memory cycle, where construction and retrieval are driven by local heuristics rather than explicit strategic reasoning, and sparse, delayed supervision on the backward path, where downstream failures rarely translate into direct repairs of the memory bank. To address these challenges, we propose MemMA, a plug-and-play multi-agent framework that coordinates the memory cycle along both the forward and backward paths. On the forward path, a Meta-Thinker produces structured guidance that steers a Memory Manager during construction and directs a Query Reasoner during iterative retrieval. On the backward path, MemMA introduces in-situ self-evolving memory construction, which synthesizes probe QA pairs, verifies the current memory, and converts failures into repair actions before the memory is finalized. Extensive experiments on LoCoMo show that MemMA consistently outperforms existing baselines across multiple LLM backbones and improves three different storage backends in a plug-and-play manner. Our code is publicly available at https://github.com/ventr1c/memma.
MemMA is a multi-agent framework that enhances memory-augmented LLM agents by coordinating memory construction, retrieval, and utilization through structured guidance and self-evolving repair mechanisms.
memory-augmented LLM agents external memory banks memory cycle Meta-Thinker Memory Manager Query Reasoner in-situ self-evolving memory construction probe QA pairs downstream failures repair actions