{
  "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": "2610.03102",
  "title": "Ask, Relax, or Act? Evaluating Actionable Indeterminacy in LLM Preference Reasoning",
  "summary": "The framework replaces ad hoc uncertainty handling (ask-or-guess prompting) with a formal decision rule: act only when an action is valid across all admissible preferences, clarify when no shared action exists, and propose minimum-cost constraint repair when infeasible, evaluated on matched solver-grounded pairs. Adoption costs solver-backed task formulation and explicit output schemas in prompts; for users it mainly means restructuring prompts to state required response content, which is cheap but requires a verifiable notion of admissibility per domain. Failures include models intervening when a justified action already exists, correct decision labels paired with unusable actions, questions, or repairs, and decision choices that shift with response-format requirements.",
  "rationale": "The explicit-response-requirement prompting finding is directly applicable on a single GPU or via APIs, but the act/clarify/repair decision rule needs workload-specific validation since the paper shows models reliably misjudge when intervention is unnecessary.",
  "technique": "Solver-grounded act/clarify/repair decision benchmark with explicit response requirements",
  "family": "avaliacao_de_agente",
  "recommendation": "testar",
  "published": "2026-10-02",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.03102",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 121,
  "citations": null,
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      "claim_id": "claim-01",
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      "baseline": "",
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    }
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      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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    },
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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": [],
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  "open_questions": [],
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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": "",
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