Stanford's Serena Yeung-Levy team has open-sourced the AutoMem framework (paper 2607.01224), turning "memory management" from an engineering trick into an independently trainable cognitive skill. The researchers give an LLM a real file system as its "external notebook", and put the five operations — read, write, search, append, create file — on the same level as in-game actions, calling them "memory actions", letting the model decide for itself when to record, what to record, and what to look up. AutoMem designs two layers of automated optimization loops. The first layer uses a strong LLM as a meta-reviewer, reading the complete game trajectory, iteratively rewriting prompts, file structure, and available operations — turning NetHack's runaway file growth from repeated appends into coordinate-keyed overwrites, cutting the per-step character growth from 138 down to 6. The second layer filters good memory-operation fragments from the model's own successful trajectories, using LoRA to train a separate "memory expert", internalizing the habit of "look up before recording" into the parameters. The game strategy model's weights are left untouched throughout, so memory capability stacks cleanly on top of the original ability. On three long-horizon procedural-generation games — Crafter, MiniHack, NetHack — optimizing only the memory item lifts the base agent's score 2–4x; the 32B open-source model, on all three games, overtakes the Qwen2.5-72B-Instruct with twice the parameters, and approaches the level of Claude Opus 4.5 and Gemini 3.1 Pro Thinking. Inefficient-action rate drops 32–65%, redundant-write rate drops 68–83%. The most noteworthy thing about this work isn't another SOTA, but a methodological signal: outside the parameter-scale and compute arms race, "how to manage memory" can be split out and optimized on its own — and the leverage may be higher than stacking parameters. The current engineering bottleneck of Agent systems often isn't the model itself, but memory and state management in long-horizon tasks; AutoMem offers a reusable engineering paradigm — treat memory operations as a first-class citizen, decouple the memory expert and the policy model for fine-tuning, and bring meta-AI into the automated tuning closed loop.