AutoMem: Automated Learning of Memory as a Cognitive Skill
Score
59Why selected
The paper proposes a novel automated memory optimization framework with significant experimental results and generality, offering important value for advancing LLM long-term interaction capabilities.
AI commentary
「This work for the first time treats memory management as an independently learnable cognitive skill, opening a new path to improve LLM autonomy and efficiency in long-horizon tasks.」
Quick overview
AutoMem proposes treating memory management as a trainable skill for LLMs, automating two loops: one uses a strong LLM to review full trajectories and iteratively revise memory structures, the other extracts good memory decisions from many episodes as training signals. In three long-horizon games (Crafter, MiniHack, NetHack), optimizing memory alone improves baseline performance ~2-4x, making a 32B open-weight model competitive with Claude Opus 4.5 and Gemini 3.1 Pro Thinking. Results show memory management is an independently learnable skill with high leverage for long-horizon tasks.
- Promotes file-system operations to first-class memory actions alongside task actions, letting the model decide memory management
- Dual-loop automation: structure loop (strong LLM reviews trajectories to improve memory structures) and proficiency loop (trains memory skill from good decisions)
- In three procedurally generated long-horizon games, optimizing memory alone yields 2-4x improvement, making 32B model competitive with frontier systems
Full text
Computer Science > Artificial Intelligence
arXiv:2607.01224v1 (cs) [Submitted on 1 Jul 2026]Title:AutoMem: Automated Learning of Memory as a Cognitive Skill
Authors:Shengguang Wu, Hao Zhu, Yuhui Zhang, Xiaohan Wang, Serena Yeung-Levy View a PDF of the paper titled AutoMem: Automated Learning of Memory as a Cognitive Skill, by Shengguang Wu and 4 other authors View PDF HTML (experimental)Abstract: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.
| Comments: | Project Website: this https URL |
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multiagent Systems (cs.MA) |
| Cite as: | arXiv:2607.01224 [cs.AI] |
| (or arXiv:2607.01224v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.01224 Focus to learn more arXiv-issued DOI via DataCite |
Submission history
From: Shengguang Wu [view email][v1] Wed, 1 Jul 2026 17:57:03 UTC (2,111 KB)
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