{
  "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": "2610.10042",
  "title": "Learning to Accumulate Knowledge with Mutual Information",
  "summary": "It replaces hand-written or prompt-curated knowledge banks for experience reuse in LLM agents with a curator model trained via GRPO on mutual-information-inspired and marginal-success rewards. The cost is an offline RL training run on collected agent trajectories, plus curator inference at curation time; the executor and retrieval step remain unchanged. It can fail if training trajectories poorly represent deployment tasks, if MI-based feedback rewards novelty that does not transfer, or if the tuned curator does not generalize to new domains.",
  "rationale": "The quality gains are substantial, but adoption requires workload-specific RL training of a curator on the reader's own task trajectories, which fits a single 24 GB GPU only after small-scale validation.",
  "technique": "RL-trained knowledge bank curator for LLM agents",
  "family": "memoria_e_contexto",
  "recommendation": "testar",
  "published": "2026-10-07",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.10042",
  "independent_implementations": 0,
  "total_implementations": 2,
  "stars": 121,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "It replaces hand-written or prompt-curated knowledge banks for experience reuse in LLM agents with a curator model trained via GRPO on mutual-information-inspired and marginal-success rewards. The cost is an offline RL training run on collected agent trajectories, plus curator inference at curation time; the executor and retrieval step remain unchanged. It can fail if training trajectories poorly represent deployment tasks, if MI-based feedback rewards novelty that does not transfer, or if the tuned curator does not generalize to new domains.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.10042",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "On ALFWorld and WebShop, mean success rates of 54.0% and 42.0% with k=10 retrieved entries, exceeding GRPO by 16.9 and 18.7 percentage points; learned banks also beat prompt-based and human-written banks with a frozen executor.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.10042",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
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      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
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    },
    {
      "dimension": "compute",
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      "source_url": "",
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    },
    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
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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": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}