Memory management is a high-leverage, independently trainable skill for LLMs on long-horizon tasks—optimizing it alone can match frontier models' performance without modifying task-solving capabilities.
This paper treats memory management as a learnable skill for language models, similar to how humans develop expertise in organizing and retrieving information. The AutoMem framework automatically optimizes both the memory structure (file schemas, prompts) and the model's ability to use it, achieving 2-4x performance improvements on long-horizon tasks without changing the core task behavior.