{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A multi-step product task fails because the agent loses track of dependencies.",
    "change": "Try the proposed planning step on tasks with checkable intermediate outcomes.",
    "baseline": "The same agent solving the same tasks without the new planning step.",
    "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": "Task completion, unnecessary steps, planning tokens, response time, and total cost.",
    "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": "2609.35559",
  "title": "From Search to Research: Exploring Search Scaling in Autonomous Quantitative Factor Mining",
  "summary": "The method replaces single-trajectory, sequentially refined agent loops with multiple parallel research trajectories (and, where applicable, deeper search budgets) for tasks such as code-generating quantitative research. The cost is multiplied API or GPU inference spend per task, roughly linear in the number of parallel branches, plus orchestration and candidate-selection logic. Gains can fail to materialize if the base model is too weak to diagnose failures and preserve the task hypothesis, since the paper shows early research states materially determine final quality and weak trajectories are not reliably rescued by more search.",
  "rationale": "Parallel test-time search is implementable on a 24 GB GPU or API budget and the paper provides evidence it beats sequential refinement at equal cost, but the benefit is workload-dependent and contingent on base-model capability, so it warrants a small-scale trial before production adoption.",
  "technique": "Parallel search scaling for autonomous research agents",
  "family": "planejamento_e_decomposicao",
  "recommendation": "testar",
  "published": "2026-09-28",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.35559",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 122,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces single-trajectory, sequentially refined agent loops with multiple parallel research trajectories (and, where applicable, deeper search budgets) for tasks such as code-generating quantitative research. The cost is multiplied API or GPU inference spend per task, roughly linear in the number of parallel branches, plus orchestration and candidate-selection logic. Gains can fail to materialize if the base model is too weak to diagnose failures and preserve the task hypothesis, since the paper shows early research states materially determine final quality and weak trajectories are not reliably rescued by more search.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.35559",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Parallel search outperforms sequential search under the same iteration budget in quantitative factor-mining tasks; deeper search narrows cross-model performance gaps, but no multiplicative factor is reported.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.35559",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
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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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      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
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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": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}