{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A workflow needs specialist review, but extra agents may add more cost than useful work.",
    "change": "Test the proposed division of work against a single-agent implementation.",
    "baseline": "One agent with the same tools and an equal total token and time budget.",
    "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": "Accepted outputs, coordination failures, wall-clock time, and total cost across all agents.",
    "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": "2609.39551",
  "title": "RankEvolve: A Reliable Multi-Agent Auto-Research Harness for Evolving Ranking Models",
  "summary": "RankEvolve replaces ad hoc single-agent automation of the ML experimental loop with a compiled state-machine protocol (EOP) that gates phases and composes multiple black-box coding-agent products (e.g., Claude Code, Codex) to review and repair each other's work, plus a knowledge layer carrying negative results across iterations. It costs subscriptions or API fees for several frontier coding-agent products, orchestration engineering to build the runtime and benchmarks, and added iteration latency from cross-agent review cycles. Even after adoption, roughly one in ten executions can carry a silent critical defect (data leakage, disconnected gradients, unwired train/eval flags), so expensive runs still require independent validation, and the absolute accuracy ceiling of 62.5% means the harness reduces but does not eliminate invalid experiments.",
  "rationale": "The execution-accuracy gain from heterogeneous agent composition is statistically supported and directly relevant to a small team automating experiments on constrained infrastructure, but the residual silent-defect rate and multi-product cost mean it should be validated on the reader's own workload before production reliance.",
  "technique": "Runtime-enforced multi-agent auto-research harness",
  "family": "orquestracao_multiagente",
  "recommendation": "testar",
  "published": "2026-09-30",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.39551",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 131,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "RankEvolve replaces ad hoc single-agent automation of the ML experimental loop with a compiled state-machine protocol (EOP) that gates phases and composes multiple black-box coding-agent products (e.g., Claude Code, Codex) to review and repair each other's work, plus a knowledge layer carrying negative results across iterations. It costs subscriptions or API fees for several frontier coding-agent products, orchestration engineering to build the runtime and benchmarks, and added iteration latency from cross-agent review cycles. Even after adoption, roughly one in ten executions can carry a silent critical defect (data leakage, disconnected gradients, unwired train/eval flags), so expensive runs still require independent validation, and the absolute accuracy ceiling of 62.5% means the harness reduces but does not eliminate invalid experiments.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.39551",
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    },
    {
      "claim_id": "claim-02",
      "statement": "Heterogeneous composition of coding-agent products raises all-oracle execution accuracy from 45.8% (best single product) to 62.5% (+16.7 points, 95% CI [6.6, 26.7]), with a 10.4% silent critical-defect rate; a 12-iteration run on HSTU reported +4.48% NDCG@10 over the published anchor on MovieLens-20M.",
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      "source_url": "https://arxiv.org/abs/2609.39551",
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    }
  ],
  "exposure_map": [
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    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
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      "source_page": null,
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    },
    {
      "dimension": "security",
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    },
    {
      "dimension": "data_and_training",
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    },
    {
      "dimension": "reproducibility",
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      "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": "",
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}