{
  "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": "2609.15309",
  "title": "When Agents Slow Down: Understanding LLM Agents' Test-Time Strategies via Elo-per-token Analysis",
  "summary": "The method replaces uninstrumented agent budget allocation and single-score leaderboards with a per-token best-solution curve aggregated across tasks via a Bradley-Terry Elo model, using independent sampling as a reference to locate the point where marginal gains diminish. It costs additional evaluation infrastructure: tasks must expose continuous intermediate scores, trajectories must be logged per token, and Elo estimation plus reference sampling add compute and engineering effort on top of the agent runs themselves. It can fail when benchmarks lack intermediate scoring, when the independent-sampling reference is unrepresentative of the task, or when the empirically estimated inflection point does not transfer across models, tasks, or budget regimes.",
  "rationale": "The parallel-session splitting insight is directly actionable on a single GPU or API budget, but the inflection point is task- and agent-specific, so the reader must validate it on their own workload before adopting it as a production budgeting rule.",
  "technique": "Elo-per-token scaling analysis with parallel-session budget splitting",
  "family": "avaliacao_de_agente",
  "recommendation": "testar",
  "published": "2026-09-14",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.15309",
  "independent_implementations": 2,
  "total_implementations": 2,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces uninstrumented agent budget allocation and single-score leaderboards with a per-token best-solution curve aggregated across tasks via a Bradley-Terry Elo model, using independent sampling as a reference to locate the point where marginal gains diminish. It costs additional evaluation infrastructure: tasks must expose continuous intermediate scores, trajectories must be logged per token, and Elo estimation plus reference sampling add compute and engineering effort on top of the agent runs themselves. It can fail when benchmarks lack intermediate scoring, when the independent-sampling reference is unrepresentative of the task, or when the empirically estimated inflection point does not transfer across models, tasks, or budget regimes.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.15309",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Splitting a 100M-token budget across parallel sessions at the measured scaling inflection point gained +264 Elo over one long session and +355 Elo over ten short sessions on FrontierCS Polyomino Packing.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.15309",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
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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,
      "source_excerpt": ""
    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "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": "unavailable",
  "core_kind": "",
  "equations_fetched_at": "2026-10-06",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}