{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "An agent looks good in demos, but you cannot tell which product tasks it fails.",
    "change": "Use the study's evaluation idea to inspect a small set of tasks with human-checked answers.",
    "baseline": "Your current evaluator and a manually reviewed reference set.",
    "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": "Agreement with human review, missed failures, false alarms, and evaluation cost per task.",
    "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": "2609.29007",
  "title": "When Does Action Credit Need Updating?",
  "summary": "The method replaces full recomputation of action credits after every policy update with a gating mechanism that reuses historical credit, transports it with a first-order estimator built on old interventional trajectories, or resamples only when the update affects action-distinguishing branches. It costs implementation complexity: the pipeline requires stored interventional trajectories, branch-sensitivity estimation, and an existing credit-assignment loop, and the first-order transport approximation adds estimation error when policy drift is large. It can fail when updates shift action rankings in ways the branch-sensitivity signal does not capture, or when historical data is too sparse for credit transport to reduce error.",
  "rationale": "The cost saving is real but presupposes a repeated credit-estimation pipeline over interventional trajectories that most small teams operating a single-GPU tool agent do not run, so it merits monitoring rather than immediate adoption.",
  "technique": "Decision-Sufficient Credit Gate for action credit reuse",
  "family": "avaliacao_de_agente",
  "recommendation": "observar",
  "published": "2026-09-24",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.29007",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces full recomputation of action credits after every policy update with a gating mechanism that reuses historical credit, transports it with a first-order estimator built on old interventional trajectories, or resamples only when the update affects action-distinguishing branches. It costs implementation complexity: the pipeline requires stored interventional trajectories, branch-sensitivity estimation, and an existing credit-assignment loop, and the first-order transport approximation adds estimation error when policy drift is large. It can fail when updates shift action rankings in ways the branch-sensitivity signal does not capture, or when historical data is too sparse for credit transport to reduce error.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.29007",
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    },
    {
      "claim_id": "claim-02",
      "statement": "DSC-Gate reduces mean new tool steps from 472 to 286 (39.4% reduction) while changing mean regret by only +0.00004 relative to a gap-based gate, on an independent test set and confirmed after a real tool-agent parameter update.",
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      "source_url": "https://arxiv.org/abs/2609.29007",
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    }
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      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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      "source_page": null,
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    },
    {
      "dimension": "security",
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      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
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      "source_page": null,
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    },
    {
      "dimension": "reproducibility",
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      "next_check": "",
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      "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": "",
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}