{
  "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": "2610.06563",
  "title": "HERA: Harness-Environment Co-Evolution for Reliable Agentic Abstention",
  "summary": "HERA replaces static, hand-tuned agent harnesses and fixed training task sets with an automated loop that mutates solvable tasks into verifiable infeasible ones and co-evolves the harness against its own failures. The cost is the compute and engineering needed to run the task-mutation pipeline and iterative harness-evaluation cycles, plus dependence on environments where feasibility can be programmatically verified. It can fail when production tasks lack verifiable feasibility signals, when environment mutations do not represent real infeasibility modes, or when a transferred harness is miscalibrated for a domain's actual abstention threshold.",
  "rationale": "The abstention gains and cross-model transfer are promising and API-compatible, but the reader must validate that their production environment supports verifiable task mutations before the co-evolution loop produces a useful harness.",
  "technique": "Harness-environment co-evolution for agentic abstention",
  "family": "recuperacao_de_falha",
  "recommendation": "testar",
  "published": "2026-10-05",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.06563",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 131,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "HERA replaces static, hand-tuned agent harnesses and fixed training task sets with an automated loop that mutates solvable tasks into verifiable infeasible ones and co-evolves the harness against its own failures. The cost is the compute and engineering needed to run the task-mutation pipeline and iterative harness-evaluation cycles, plus dependence on environments where feasibility can be programmatically verified. It can fail when production tasks lack verifiable feasibility signals, when environment mutations do not represent real infeasibility modes, or when a transferred harness is miscalibrated for a domain's actual abstention threshold.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.06563",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Evolved harness raises abstention accuracy from 61.7% to 83.3% and feasible-task completion from 68.3% to 76.7%; it transfers to 19 other LLMs (+15.3 abstention points on average) and lets smaller models match stronger ones at an estimated 85% lower cost.",
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      "result": "",
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      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.06563",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
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      "finding": "",
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      "dimension": "compute",
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    },
    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
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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": "",
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}