{
  "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": "2609.40284",
  "title": "cua-speedrun: Standardized Benchmarking of the Speed of Computer-Use Agents",
  "summary": "cua-speedrun replaces ad hoc, infrastructure-confounded CUA evaluation setups (varying VM and container configurations across benchmarks) with a uniform virtual machine pipeline, common agent interface, and standardized task sets spanning four benchmarks. The cost is adopting its fixed harness and environment, which constrains agents to its interface and may not reflect production latency or tooling; it evaluates agents rather than improving them. It can fail to transfer if real deployment environments differ from the standardized VM, since the paper shows environment I/O latency and harness choice materially alter measured speed, so rankings may not hold in the reader's actual setting.",
  "rationale": "A constrained-infrastructure team deploying or selecting computer-use agents needs reproducible speed and cost comparisons, and this benchmark plus its reduced task sets provide that without requiring any cluster or model training.",
  "technique": "Standardized speed benchmarking harness for computer-use agents",
  "family": "avaliacao_de_agente",
  "recommendation": "adotar",
  "published": "2026-09-30",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.40284",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 10,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
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      "result": "",
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      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.40284",
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      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Evaluation task sets of most CUA benchmarks can be reduced without degrading overall statistical power, lowering benchmarking cost; findings include that higher reasoning effort can speed task completion for some models and that no open-weight model is on the performance-speed-cost frontier.",
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      "source_url": "https://arxiv.org/abs/2609.40284",
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      "source_excerpt": ""
    }
  ],
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      "dimension": "compute",
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      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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    },
    {
      "dimension": "security",
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    },
    {
      "dimension": "data_and_training",
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    },
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      "dimension": "reproducibility",
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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",
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  "equations_fetched_at": "",
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}