Finally a good paper testing if file-system based memory for LLM agents is worth it. First, what does this look like? Deployed agents keep long-term memory as a folder of markdown files they read and reorganize with ordinary file tools. Two assumptions had never been checked. That an agent can keep a growing store organized as memories accumulate, conflict, and go stale. And whether the organization pays for itself. Organized stores roughly halve retrieval cost when the material is large. No agent in the study converted organization into better answers, and in the growth study the store degraded for every management agent except the strongest one. Changing the tool set alone reshapes the memory store as strongly as swapping the model. Paper: Track more trending AI papers in our academy: