{
  "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": "2610.31650",
  "title": "EpiSEM-Bench: Evaluating Safety and Evidence Recovery in LLM Agents under Missing Evidence",
  "summary": "EpiSEM-Bench replaces informal or anecdotal safety testing of tool-using agents with an executable benchmark of 300 matched safe/unsafe task pairs that suppress safety-critical facts and audit whether agents seek missing evidence before acting. It costs benchmark integration and API or inference spend to run the 300 tasks against one's agent stack, with modest engineering effort to wire the tool responses and audit harness into an existing evaluation pipeline. It can fail to predict production behavior because its six domains and synthetic evidence-suppression setup may not match a team's real tools, and a passing score does not guarantee robustness to omission patterns outside the benchmark.",
  "rationale": "A team deploying tool-using agents on APIs or a single GPU can run this benchmark to quantify omission-driven unsafe execution in its own stack, but the result requires validation against the team's actual tools and tasks before it informs production gating.",
  "technique": "Executable safety benchmark for agents under missing evidence",
  "family": "avaliacao_de_agente",
  "recommendation": "testar",
  "published": "2026-10-22",
  "as_of": "2026-10-23",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.31650",
  "independent_implementations": 2,
  "total_implementations": 3,
  "stars": 132,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "EpiSEM-Bench replaces informal or anecdotal safety testing of tool-using agents with an executable benchmark of 300 matched safe/unsafe task pairs that suppress safety-critical facts and audit whether agents seek missing evidence before acting. It costs benchmark integration and API or inference spend to run the 300 tasks against one's agent stack, with modest engineering effort to wire the tool responses and audit harness into an existing evaluation pipeline. It can fail to predict production behavior because its six domains and synthetic evidence-suppression setup may not match a team's real tools, and a passing score does not guarantee robustness to omission patterns outside the benchmark.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.31650",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
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      "dimension": "compute",
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      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "latency",
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      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "operations",
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      "finding": "",
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      "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": ""
}