{
  "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": "2609.26293",
  "title": "Dual-Frontier: When Can an Agent Trust Its World Model?",
  "summary": "The method replaces unconditional trust in a learned world model's predictions with a verify-then-promote gate: a model-guided decision is admitted only when its predicted advantage exceeds a certified bound on decision-relevant model error, otherwise evidence is redirected to model verification. The cost is additional machinery: action-conditioned value bounds, calibrated gates, and simultaneous confidence sequences must be estimated and maintained, adding implementation complexity and extra verification rollouts that consume compute or API budget. It can fail if the certified error bounds are miscalibrated or too loose (admitting bad decisions) or too conservative (rejecting good decisions and stalling progress), and the identifiability result means passive data alone cannot correct a wrong bound.",
  "rationale": "The gating principle is implementable on constrained infrastructure and directly relevant to tool-use agents, but its benefit depends on bound calibration in the reader's specific workload, so it warrants small-scale validation before production adoption.",
  "technique": "Certified gating of world-model-guided decisions",
  "family": "recuperacao_de_falha",
  "recommendation": "testar",
  "published": "2026-09-22",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.26293",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 216,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces unconditional trust in a learned world model's predictions with a verify-then-promote gate: a model-guided decision is admitted only when its predicted advantage exceeds a certified bound on decision-relevant model error, otherwise evidence is redirected to model verification. The cost is additional machinery: action-conditioned value bounds, calibrated gates, and simultaneous confidence sequences must be estimated and maintained, adding implementation complexity and extra verification rollouts that consume compute or API budget. It can fail if the certified error bounds are miscalibrated or too loose (admitting bad decisions) or too conservative (rejecting good decisions and stalling progress), and the identifiability result means passive data alone cannot correct a wrong bound.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
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      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
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  ],
  "exposure_map": [
    {
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      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
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    },
    {
      "dimension": "security",
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      "finding": "",
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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": [],
  "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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}