{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "An agent looks good in demos, but you cannot tell which product tasks it fails.",
    "change": "Use the study's evaluation idea to inspect a small set of tasks with human-checked answers.",
    "baseline": "Your current evaluator and a manually reviewed reference set.",
    "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": "Agreement with human review, missed failures, false alarms, and evaluation cost per task.",
    "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": "2610.11514",
  "title": "SSCBench: Evaluating the Evidential Validity of Fault-Injection Tests for Tool-Using LLM Agents",
  "summary": "The method replaces reporting raw fault-adoption rates from fault-injection tests of tool-using agents with a measurement protocol that records whether counterevidence to an injected fault could become visible before the agent first relies on the affected fact, and whether the execution actually realized that condition. It costs additional trajectory instrumentation and temporal annotation of observation visibility and first-use events, plus benchmark construction or adaptation effort (the authors instantiated it over 1,191 faulted executions in two tau-bench environments). It can fail in that automated trajectory analysis recovers adoption without reliably recovering the first faulty-reliance event, so temporal diagnosis may require manual annotation, and conclusions remain tied to the specific fault operators and environments studied.",
  "rationale": "Any team already running or planning fault-injection reliability tests on tool-using agents can adopt this reporting discipline at negligible compute cost, and it directly prevents misinterpreting adoption rates as failures when timely counterevidence was never available.",
  "technique": "Evidential validity protocol for fault-injection evaluation",
  "family": "avaliacao_de_agente",
  "recommendation": "adotar",
  "published": "2026-10-08",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.11514",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces reporting raw fault-adoption rates from fault-injection tests of tool-using agents with a measurement protocol that records whether counterevidence to an injected fault could become visible before the agent first relies on the affected fact, and whether the execution actually realized that condition. It costs additional trajectory instrumentation and temporal annotation of observation visibility and first-use events, plus benchmark construction or adaptation effort (the authors instantiated it over 1,191 faulted executions in two tau-bench environments). It can fail in that automated trajectory analysis recovers adoption without reliably recovering the first faulty-reliance event, so temporal diagnosis may require manual annotation, and conclusions remain tied to the specific fault operators and environments studied.",
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      "result": "",
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      "source_url": "https://arxiv.org/abs/2610.11514",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
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      "dimension": "compatibility",
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    },
    {
      "dimension": "security",
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    },
    {
      "dimension": "data_and_training",
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    {
      "dimension": "reproducibility",
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  ],
  "risks": [],
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  "open_questions": [],
  "independent_tests": [],
  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
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  "equations_fetched_at": "",
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}