{
  "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": "2610.01256",
  "title": "DeFA: Dependency-Guided Failure Attribution for LLM Agents",
  "summary": "DeFA replaces manual or single-pass LLM inspection of agent trajectories with a pipeline that builds an event dependency graph, constructs a failure propagation graph, and then attributes the decisive error, responsible agent, and error category, using segmentation plus cross-segment summaries for long traces. The cost is additional LLM inference per trajectory (graph construction and per-segment diagnosis), added pipeline complexity, and latency that makes it suitable for offline debugging rather than real-time control. It can fail when dependency extraction itself is erroneous on messy real-world logs, when benchmarks do not transfer to the reader's domain, and when the reported 6-15 point downstream gain does not replicate on tasks unlike the evaluated ones.",
  "rationale": "It runs entirely through third-party LLM APIs on existing trajectory logs, so it fits constrained infrastructure, but the accuracy and downstream-gain claims require validation on the reader's own agents and failure modes before production adoption.",
  "technique": "Dependency-guided agent failure attribution (DeFA)",
  "family": "recuperacao_de_falha",
  "recommendation": "testar",
  "published": "2026-10-01",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.01256",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 131,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "DeFA replaces manual or single-pass LLM inspection of agent trajectories with a pipeline that builds an event dependency graph, constructs a failure propagation graph, and then attributes the decisive error, responsible agent, and error category, using segmentation plus cross-segment summaries for long traces. The cost is additional LLM inference per trajectory (graph construction and per-segment diagnosis), added pipeline complexity, and latency that makes it suitable for offline debugging rather than real-time control. It can fail when dependency extraction itself is erroneous on messy real-world logs, when benchmarks do not transfer to the reader's domain, and when the reported 6-15 point downstream gain does not replicate on tasks unlike the evaluated ones.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.01256",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Highest responsible-agent and exact-step accuracy on Who&When and its Pro text subset across backbones; diagnoses fed into Trace2Skill improve downstream task accuracy by 6-15 percentage points over the native pipeline.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.01256",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compute",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "latency",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "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": ""
}