让大模型自主管理记忆存储，跨场景表现更优
Exploring Cross-Scenario Generality of Agentic Memory Systems: Diagnostics and a Strong Baseline
- 让代理主动控制记忆存取，而非依赖固定流程
- 在5种不同任务中，自管理记忆系统表现最佳
- 适合需要多场景通用记忆的智能体开发
大语言模型代理在运行中积累的历史数据常超出上下文窗口限制，催生了大量记忆系统研究。然而现有设计多针对单一场景（如多轮对话或单条轨迹），缺乏在部署中面对多样化任务时的泛化能力证据。本文在五种场景下评估了八种记忆系统及一个用于搜索问题的代理框架：单轮问答、多轮对话、代理轨迹问答、记忆压力测试与长周期代理任务。结果表明，通过工具调用自主管理纯文本文件存储的代理框架，在跨任务排名中表现最优，说明记忆性能关键在于赋予代理对存储与检索的主动控制权，而非依赖固定管道的被动存储。基于此洞察，我们提出AutoMEM，一种具有自管理工具接口的代理记忆框架，在所评估系统中实现了最佳跨场景泛化能力。
原文摘要 · Abstract (English)
LLM agents accumulate histories that outgrow their context windows, motivating a growing literature on memory systems. Yet most existing designs are tuned to a single scenario (multi-session chat or a single trajectory format), and there is little evidence that they generalize across the heterogeneous trajectories agents encounter in deployment. We revisit eight memory systems plus an agentic harness for search problems, on five scenarios: single-turn QA, multi-session chat, agentic-trajectory QA, memory stress tests, and long-horizon agentic tasks. The harness, which self-manages flat text-file storage via tool calls, achieves the best cross-task ranking, suggesting that memory performance hinges on giving the agent active control over storage and retrieval rather than on a passive store behind a fixed pipeline. We instantiate this insight in AutoMEM, an agentic memory harness with a self-managed tool interface that achieves the best cross-scenario generality among the systems we evaluate.
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