{
  "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": "2610.13632",
  "title": "Humanize Olympiad Agents: A Multi-Session Workflow for Olympiad Problem Solving with Solve, Review, Wiki, Verify",
  "summary": "It replaces single-session agent attempts on hard reasoning problems with a multi-session pipeline that adds an independent reviewer, a task-specific structured wiki, and a Lean-based verifier checking proofs against trusted statements. The cost is substantially higher inference spending (multiple sessions plus reviewer and verifier calls per problem), added orchestration complexity, and dependence on frontier models and a Lean formalization stack. Failure modes include reviewer errors propagating as authoritative feedback, wiki content being incomplete or wrong, verifier gaps when no formal statement exists for a task, and inference costs that scale poorly on a limited budget.",
  "rationale": "The feedback-loop orchestration pattern is implementable with third-party APIs at small scale, but the reported results rely on unavailable frontier models and unverifiable benchmark claims, so the reader should validate the pattern on their own workload before committing.",
  "technique": "Multi-session solve-review-wiki-verify agent workflow",
  "family": "orquestracao_multiagente",
  "recommendation": "testar",
  "published": "2026-10-09",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.13632",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 132,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
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      "baseline": "",
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      "source_url": "https://arxiv.org/abs/2610.13632",
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    {
      "claim_id": "claim-02",
      "statement": "Claims perfect scores on IMO/IPhO/IOI 2026, 100% on PutnamBench (672 problems), and 82.6% (251/304) on Lean-Eval; with Kimi-K3, matches or exceeds a GPT-5.6 baseline. These are absolute benchmark scores, not multiplicative gains over a baseline, and the cited models and dates make the claims currently unverifiable.",
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      "source_url": "https://arxiv.org/abs/2610.13632",
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  "exposure_map": [
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      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
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      "source_page": null,
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    },
    {
      "dimension": "security",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
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      "source_page": null,
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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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}