{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Tool errors or unavailable services cause your agent to abandon recoverable tasks.",
    "change": "Inject repeatable tool failures and change only the recovery policy.",
    "baseline": "Your current retry and fallback behavior under the same injected failures.",
    "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": "Recovered tasks, duplicate side effects, retries, time to recovery, and total cost.",
    "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": "2609.39578",
  "title": "Thinking Outside the Box: Can Language Models Rely on External Guidance Selectively?",
  "summary": "The method replaces blind adherence to fixed agent workflows with counterfactual supervised fine-tuning and outcome-based reinforcement learning that teach a model to follow reliable guidance and override misleading guidance, evaluated on the Box^2-Bench benchmark. It costs fine-tuning compute on open-weight models (feasible at small scale with parameter-efficient methods), requires constructing counterfactual training data from bad workflows, and adds training and evaluation complexity without changing inference cost. It can fail if the training distribution of unreliable guidance does not match production failure modes, since frontier models remain vulnerable to misleading guidance and the learned selectivity may not transfer to unseen workflow or memory-corruption patterns.",
  "rationale": "Selective reliance addresses a real production risk in workflow-driven agents, but the abstract reports no quantified improvement and the benefit depends on whether the reader's guidance-failure modes resemble the training distribution, so small-scale validation is warranted before adoption.",
  "technique": "Selective reliance on fallible external guidance",
  "family": "recuperacao_de_falha",
  "recommendation": "testar",
  "published": "2026-09-30",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.39578",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 121,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
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      "result": "",
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      "source_url": "https://arxiv.org/abs/2609.39578",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
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      "dimension": "compatibility",
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    },
    {
      "dimension": "security",
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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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      "source_page": null,
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    }
  ],
  "risks": [],
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  "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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}