{
  "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": "2609.29154",
  "title": "A Wrong Turn Does Not Ruin the Journey: Deviation-Guided Skill Self-Evolution for LLM Agents",
  "summary": "SkillPivot replaces coarse whole-trajectory reflection (or forcing failed runs to imitate a fixed successful trajectory) with localized skill updates generated by contrasting the student's erroneous suffix against a teacher's successful continuation from the same prefix. It costs an extra stronger teacher model to complete trajectories, deviation-point detection (execution validity, goal progress, action diversity), and additional API calls per failed trajectory, plus a skill store the agent must maintain. It can fail if deviation-point detection mislocates the breakdown (editing still-valid guidance), if no teacher stronger than the student is available within budget, or if skill updates overfit to the three evaluated benchmarks and do not transfer to the reader's task distribution.",
  "rationale": "The method runs entirely at inference/API level with no training, fitting a 24 GB or API-only budget, but its unquantified benchmark claims and dependence on a stronger teacher require workload-specific validation before production adoption.",
  "technique": "Deviation-guided skill self-evolution for LLM agents",
  "family": "recuperacao_de_falha",
  "recommendation": "testar",
  "published": "2026-09-24",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.29154",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "SkillPivot replaces coarse whole-trajectory reflection (or forcing failed runs to imitate a fixed successful trajectory) with localized skill updates generated by contrasting the student's erroneous suffix against a teacher's successful continuation from the same prefix. It costs an extra stronger teacher model to complete trajectories, deviation-point detection (execution validity, goal progress, action diversity), and additional API calls per failed trajectory, plus a skill store the agent must maintain. It can fail if deviation-point detection mislocates the breakdown (editing still-valid guidance), if no teacher stronger than the student is available within budget, or if skill updates overfit to the three evaluated benchmarks and do not transfer to the reader's task distribution.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.29154",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "SkillPivot 'consistently outperforms competing skill-evolution methods, improves multiple agent models, and produces compact, transferable skill updates' on ToolQA, LogicBench, and WildClawBench; no quantified numbers are given in the abstract.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.29154",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
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      "finding": "",
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      "next_check": "",
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    },
    {
      "dimension": "compute",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "latency",
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      "finding": "",
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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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      "next_check": "",
      "source_url": "",
      "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,
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    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
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      "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": "",
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}