{
  "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": "2610.30537",
  "title": "PLE: Physical-Engineering's Last Exam",
  "summary": "PLE replaces saturated knowledge-based and toy engineering benchmarks with 85 long-horizon, agentic tasks that require completing realistic, billable engineering deliverables across electrical, civil, structural, and mechanical domains. It costs significant evaluation resources, as the top configuration spends 27M tokens and 63 minutes per task on average, making it expensive for routine iteration. It can fail to provide a stable signal for incremental product improvements because the tasks are extremely difficult, with even frontier models scoring only 50.6%.",
  "rationale": "This is a high-cost, research-grade benchmark that is useful for understanding the long-term capabilities of frontier models but is not a practical evaluation tool for a small team with a limited cloud budget and a 24 GB GPU.",
  "technique": "PLE: agentic benchmark for professional engineering tasks",
  "family": "avaliacao_de_agente",
  "recommendation": "observar",
  "published": "2026-10-21",
  "as_of": "2026-10-24",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.30537",
  "independent_implementations": 3,
  "total_implementations": 4,
  "stars": 138,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "PLE replaces saturated knowledge-based and toy engineering benchmarks with 85 long-horizon, agentic tasks that require completing realistic, billable engineering deliverables across electrical, civil, structural, and mechanical domains. It costs significant evaluation resources, as the top configuration spends 27M tokens and 63 minutes per task on average, making it expensive for routine iteration. It can fail to provide a stable signal for incremental product improvements because the tasks are extremely difficult, with even frontier models scoring only 50.6%.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.30537",
      "source_page": null,
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      "source_url": "https://arxiv.org/abs/2610.30537",
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    }
  ],
  "exposure_map": [
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    },
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      "dimension": "latency",
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    },
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      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "security",
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      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "reproducibility",
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      "finding": "",
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      "limitation": "",
      "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": "",
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  "equations_source_sha256": "",
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}