AutoMem: Automated Learning of Memory as a Cognitive Skill

Key points

• The file system, including read, write, search, append, and create, is promoted to a first-class memory action equivalent to task actions, so every memory decision is traceable.
• In Outer-loop 1, a meta-LLM reviews trajectories of up to 100,000 steps and automatically improves the agent scaffold, including the code, prompts, and memory schema.
• In Outer-loop 2, good memory decisions from the agent are extracted and used to LoRA fine-tune a dedicated memory specialist model, while the task model remains frozen.
• Memory optimization alone yields a 2x to 4x performance gain without changing task-model weights, showing that memory has more leverage than model scale.
• The optimized 32B agent surpasses 72B and reaches the level of Claude Opus 4.5 and Gemini 3.1 Pro Thinking.
• This is the first case that turns the cognitive-science concept of metamemory into a concrete optimization target for LLM agents.

Paper links

External research summaries. These are not HDATF publications or measured product results.