{
  "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": "2609.17985",
  "title": "RideWay: Benchmarking Efficient Task Completion for Tool-Using Language Agents",
  "summary": "RideWay replaces binary task-completion scoring of tool-using agents with a success-gated utility that discounts trajectories for excess tool calls and user-facing turns, calibrated against human preferences. Adoption costs a task-specific reference-effort annotation per task, preference data to fit penalty weights, and instrumentation to log turns and calls, none of which requires cluster-scale compute. It can fail because count-based penalties do not capture why extra calls occurred: the metric is at chance level when trajectories differ only in tool-call counts, and the 58-task ridehailing scope plus per-domain penalty fitting limits transfer to other agent workloads without recalibration.",
  "rationale": "The metric is cheap to implement on a small team's evaluation harness, but its penalty weights and domain coverage require validation on the reader's own agent workload before production use.",
  "technique": "Success-gated efficiency metric for tool-using agents",
  "family": "avaliacao_de_agente",
  "recommendation": "testar",
  "published": "2026-09-16",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.17985",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "RideWay replaces binary task-completion scoring of tool-using agents with a success-gated utility that discounts trajectories for excess tool calls and user-facing turns, calibrated against human preferences. Adoption costs a task-specific reference-effort annotation per task, preference data to fit penalty weights, and instrumentation to log turns and calls, none of which requires cluster-scale compute. It can fail because count-based penalties do not capture why extra calls occurred: the metric is at chance level when trajectories differ only in tool-call counts, and the 58-task ridehailing scope plus per-domain penalty fitting limits transfer to other agent workloads without recalibration.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.17985",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Efficiency Utility achieves 78.7% accuracy on held-out human paired preferences (90.6% when trajectories differ in dialogue turns, chance-level when they differ only in tool calls); the fitted penalty for excess turns is about twice that for excess tool calls.",
      "basis": "inferred",
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      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.17985",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
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      "next_check": "",
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    },
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      "dimension": "compute",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "latency",
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      "source_page": null,
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "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,
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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": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
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}