{
  "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": "2610.00917",
  "title": "Finding the Right Fit: Model-Harness Interactions across Agent Tasks",
  "summary": "The paper replaces the practice of selecting an agent stack from model-only leaderboards or defaulting to a vendor's own harness with an empirical evaluation procedure that scores model-harness pairings on the target tasks. The cost is engineering effort to run multiple harness-model configurations on your own workload, since the released adapters cover four harnesses and results do not transfer reliably across tasks. The failure mode is overfitting selection to a benchmark: rankings reverse across harnesses and tasks, so a pairing chosen from public results may be wrong for your workload, and a malformed-tool-call-prone model paired with a lean scaffold can degrade sharply in production.",
  "rationale": "The reader can adopt agent stacks via APIs and harness adapters without training, but the paper's core finding is that no pairing is universally best, so the correct action is to run a small benchmark of candidate model-harness pairs on their own tasks before committing.",
  "technique": "Joint model-harness-task evaluation for agent selection",
  "family": "avaliacao_de_agente",
  "recommendation": "testar",
  "published": "2026-10-01",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.00917",
  "independent_implementations": 0,
  "total_implementations": 2,
  "stars": 13,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
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    {
      "claim_id": "claim-02",
      "statement": "On Terminal-Bench 4, GPT under the PI harness scores higher than under DSH at less than a quarter of the cost per task; openJiuwen gives Kimi its best score on all three benchmarks by 5.61 to 11.11 points, and Claude leads GPT by 7.94 points in OpenHands but trails it by 30.16 points in PI.",
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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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      "dimension": "reproducibility",
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  ],
  "risks": [],
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  "open_questions": [],
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
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