MemMA：通过多智能体推理与就地自演进协调内存周期

摘要

记忆增强型大型语言模型智能体通过维护外部记忆库来支持长程交互，然而现有系统大多将构建、检索与利用视为孤立子程序。这导致两个相互关联的挑战：在记忆周期的前向路径上存在策略盲区——构建与检索过程受局部启发式规则驱动而缺乏显式策略推理；在后向路径上则面临稀疏延迟监督——下游故障难以直接转化为记忆库的修复信号。为解决这些问题，我们提出MemMA，一种即插即用的多智能体框架，可协同管理记忆周期的双向路径。在前向路径中，元思考者生成结构化指导，既在构建阶段引导记忆管理器，又在迭代检索过程中指挥查询推理器。后向路径上，MemMA引入原位自演进记忆构建机制，通过合成探测性问答对验证当前记忆状态，并在记忆固化前将故障转化为修复动作。在LoCoMo数据集上的大量实验表明，MemMA在多种LLM骨干网络上均持续超越现有基线，并能以即插即用方式提升三种不同存储后端的性能。代码已开源：https://github.com/ventr1c/memma。

English

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：通过多智能体推理与就地自演进协调内存周期

MemMA: Coordinating the Memory Cycle through Multi-Agent Reasoning and In-Situ Self-Evolution

摘要

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