{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Your coding assistant makes changes that still need substantial repair before merging.",
    "change": "Try the technique on held-out issues from a repository with reliable tests.",
    "baseline": "The same coding agent, repository snapshot, tools, and token budget.",
    "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": "Correct patches, regression failures, human repair time, and total cost per accepted change.",
    "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": "2610.11858",
  "title": "Trajectory-Guided Fault Localization for Agent Skill Evolution",
  "summary": "The method replaces manual inspection of agent skill failures and generic LLM-driven revision with a fault-localization step that compares abstracted failure and success trajectories across repeated runs to identify suspicious actions and targeted edit sites before generating revisions. It costs additional inference for multiple trajectory rollouts, trajectory abstraction, and the localization pipeline, plus integration complexity for logging and skill-file management; no foundation-model training or cluster is required. It can fail when failure and success trajectories are insufficiently sampled to isolate causal actions, when abstractions obscure the true fault, or when localized edits overfit to the benchmark tasks and degrade skill generality.",
  "rationale": "The technique targets a real production problem (maintaining reusable skill libraries for code agents) and is feasible with API-level access, but the abstract reports only comparative improvements without quantified magnitudes, so the reader should validate trajectory-sampling cost and revision quality on their own workloads before adoption.",
  "technique": "Trajectory-guided fault localization for skill evolution",
  "family": "agentes_de_codigo",
  "recommendation": "testar",
  "published": "2026-10-08",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.11858",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 10,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces manual inspection of agent skill failures and generic LLM-driven revision with a fault-localization step that compares abstracted failure and success trajectories across repeated runs to identify suspicious actions and targeted edit sites before generating revisions. It costs additional inference for multiple trajectory rollouts, trajectory abstraction, and the localization pipeline, plus integration complexity for logging and skill-file management; no foundation-model training or cluster is required. It can fail when failure and success trajectories are insufficiently sampled to isolate causal actions, when abstractions obscure the true fault, or when localized edits overfit to the benchmark tasks and degrade skill generality.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.11858",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
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      "finding": "",
      "interpretation": "",
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      "next_check": "",
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    },
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    {
      "dimension": "latency",
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      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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      "source_page": null,
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    },
    {
      "dimension": "security",
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      "finding": "",
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      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
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      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
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      "next_check": "",
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      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "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": "",
  "exposures_source_sha256": ""
}