{
  "id": 15308,
  "url": "https://arxiv.org/abs/2607.26637",
  "title": "Filesystem-Based Memory for LLM Agents: Organization, Evolution, and Sustainability",
  "summary": "Deployed LLM agents increasingly keep their long-term memory as a filesystem: a directory tree of markdown files that the agent itself reads, writes, and reorganizes through generic file tools. Yet research has largely passed over this medium: prior systems design bespoke memory representations and study retrieval over them, leaving the default's two working assumptions untested: that an agent can keep a growing store organized as memories accumulate, conflict, and go stale, and that this organi",
  "authors": "Sizhe Zhou, Sheldon Yu, Hui Wei, Junda Wu, Siru Ouyang, Yizhu Jiao",
  "category": "research",
  "topics": "agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-28T20:00:00.000Z",
  "fetched_at": "2026-08-01T05:10:57.676Z",
  "source_slug": "hf-daily",
  "source_name": "HuggingFace Daily Papers",
  "source_homepage": "https://huggingface.co/papers",
  "ethics_ai_record_url": "https://ethics.ai/record/15308",
  "original_url": "https://arxiv.org/abs/2607.26637",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}