AI学会自己管记忆，性能翻4倍
大模型做长任务时经常忘事，不是记不住，而是不会管记忆——该记什么、什么时候翻、怎么整理，这些被称为元记忆的能力。研究者把文件操作变成模型自己能调用的动作，让模型自己决定怎么管理记忆，然后通过两个自动循环来优化：一个循环让强模型审查完整任务轨迹，自动调整记忆结构（比如提示词、文件格式）；另一个循环从大量任务中挑出模型做对的记忆决策，用来训练模型本身。在三个长时游戏任务中，单靠优化记忆管理，不碰任务动作，就把基础模型性能提升了2到4倍，让一个320亿参数的开源模型追平了Claude和Gemini的顶级版本。这不是你明天能用的工具，但它揭示了一个趋势：记忆管理是可以独立学习的技能，未来AI的长时能力可能不再靠堆参数，而是靠学会怎么用记忆。
📄 原文摘要(英文)
Memory expertise is a learned skill: knowing what to encode, when to retrieve, and how to organize knowledge--a capacity known in cognitive science as metamemory. We bring this perspective to LLMs by treating memory management as a trainable skill. We promote file-system operations to first-class memory actions alongside task actions, letting the model itself decide how to manage its memory. This memory skill improves along two axes: the structure that supports it (prompts, file schemas, action vocabulary), and the proficiency of the model exercising it. Both axes resist manual optimization: episodes in long-horizon tasks run for thousands of steps, and a single memory mistake can hide long before it surfaces, making human review of full trajectories impractical. We introduce AutoMem, a framework that automates both axes. In the first loop, a strong LLM reviews complete agent trajectories and iteratively revises the memory structure that shapes how the agent interacts with its memory files. In the second loop, the agent's own good memory decisions are identified from many episodes and used as training signal to sharpen the model's memory proficiency directly. Across three procedurally generated long-horizon games (Crafter, MiniHack, and NetHack), optimizing memory alone--without modifying the model's task-action behavior--improved the base agent's performance ~2x-4x, bringing a 32B open-weight model competitive with frontier systems such as Claude Opus 4.5 and Gemini 3.1 Pro Thinking. Our results show that memory management is an independently learnable skill, and a high-leverage objective yielding large gains on long-horizon tasks.