elvis@omarsar0 · X斯坦福大学提出 AutoMem，将智能体的记忆管理从固定模块变为可训练技能。模型自主决定编码内容、检索时机以及笔记组织方式，文件系统操作升级为一级动作。AutoMem 采用双循环机制：强 LLM 审查完整轨迹并重写记忆结构（提示词、模式、动作词表）；同时利用智能体自身良好的记忆决策作为训练信号。仅优化记忆（不改任务动作），便在 Crafter、MiniHack、NetHack 上取得 2–4 倍提升，使 32B 开放模型性能媲美 Claude Opus 4.5 和 Gemini 3.1 Pro Thinking。论文：arxiv.org/abs/2607.01224。
// AutoMem //
I quite like this idea of metamemory.
（bookmark it）
This new research from Stanford treats agent's memory management as a trainable skill instead of a fixed module.
The model decides what to encode， when to retrieve， and how to organize its own notes， with file-system operations promoted to first-class actions right alongside task actions.
AutoMem automates this on two loops. A strong LLM reviews full trajectories and rewrites the memory structure （prompts， schemas， action vocabulary）. Then the agent's own good memory decisions across episodes become training signal to sharpen its proficiency.