{
  "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": "2609.39903",
  "title": "OSWorld-Science: A Benchmark of Computer Use Agents for Learning and Using Scientific Software",
  "summary": "Replaces ad hoc or general-purpose agent evaluations (e.g., generic OSWorld tasks) with 146 expert-designed scientific software tasks featuring artifact-based evaluators that inspect molecular structures, segmentation masks, plots, and numerical outputs with partial credit. Adopting it costs setup effort for its harness and evaluation environments, plus compute/API spend to run agents, but requires no model training. Its findings indicate even state-of-the-art VLMs fail on many scientific workflows, so agents validated only on this benchmark may still underperform on the reader's specific software stack, and benchmark coverage across only a few domains may not transfer to other scientific tools.",
  "rationale": "The reader building or buying computer-use agents for scientific software needs verifiable, domain-specific evaluation before production deployment, and this benchmark plus its partial-credit evaluators can be run on a single GPU or via APIs without training infrastructure.",
  "technique": "Benchmark for scientific computer-use agents",
  "family": "avaliacao_de_agente",
  "recommendation": "testar",
  "published": "2026-09-30",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.39903",
  "independent_implementations": 3,
  "total_implementations": 4,
  "stars": 135,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "Replaces ad hoc or general-purpose agent evaluations (e.g., generic OSWorld tasks) with 146 expert-designed scientific software tasks featuring artifact-based evaluators that inspect molecular structures, segmentation masks, plots, and numerical outputs with partial credit. Adopting it costs setup effort for its harness and evaluation environments, plus compute/API spend to run agents, but requires no model training. Its findings indicate even state-of-the-art VLMs fail on many scientific workflows, so agents validated only on this benchmark may still underperform on the reader's specific software stack, and benchmark coverage across only a few domains may not transfer to other scientific tools.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.39903",
      "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": "latency",
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    },
    {
      "dimension": "operations",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "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": ""
}