{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A support or data assistant repeats work across related customer requests.",
    "change": "Try the paper's memory change in a replay of past tasks, with separate memory per customer.",
    "baseline": "Your current memory pipeline plus a no-memory control using the same agent.",
    "steps": [
      "Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.",
      "Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.",
      "Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.",
      "Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test."
    ],
    "measure": "Task success, stale memories, retrieval misses, response latency, and all memory maintenance costs.",
    "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation."
  },
  "builder_review": null,
  "arxiv_id": "2610.22273",
  "title": "Librarian Agents: Training LLMs to Curate Long-Context Workspaces that Anticipate Future Queries",
  "summary": "Replaces reactive retrieval (standard RAG) and heuristic precomputed structures like knowledge graphs with a builder agent that proactively reorganizes raw corpora into a hierarchical filesystem of synthetic files and links, optimized via RL for downstream answering accuracy. Costs include RL training of the builder policy, substantial upfront token/compute spend for workspace construction that is only amortized across repeated queries, and added pipeline complexity. Can fail when query volume is too low to amortize construction, when the corpus changes frequently (stale workspaces), or when transferring the trained builder to domains whose structure differs from the RL training distribution.",
  "rationale": "The core idea (proactive corpus curation into a navigable filesystem) can be prototyped with API-driven agents without RL training, but the reported gains depend on a trained builder and repeated-query workloads, so the reader should validate on their own query distribution before adopting.",
  "technique": "RL-trained agents curating reusable document workspaces",
  "family": "memoria_e_contexto",
  "recommendation": "testar",
  "published": "2026-10-15",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.22273",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 10,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
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      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.22273",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Outperforms strongest retrieval and precomputation baselines by 33 percentage points on BrowseComp-Plus; 31-point average accuracy improvement zero-shot on BRIGHT.",
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    }
  ],
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      "dimension": "operations",
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    {
      "dimension": "compatibility",
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      "dimension": "security",
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    {
      "dimension": "data_and_training",
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      "dimension": "reproducibility",
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  ],
  "risks": [],
  "minimum_test": [],
  "open_questions": [],
  "independent_tests": [],
  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
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}