{
  "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": "2610.20563",
  "title": "Scaling Discovery Requires Forgetting",
  "summary": "The method replaces continuous long-horizon agent search (single-agent iteration, parallel agents, or communicating teams that accumulate full history) with periodic restarts: all agent sessions and candidate history are discarded and the search resumes from the best candidate found so far. It costs no extra compute or memory relative to the baseline strategies, since it operates at fixed budget, but adds orchestration logic for checkpointing best candidates, scheduling resets, and re-seeding agents; reset frequency becomes a new hyperparameter. It can fail when the discarded history contained information needed to escape the current basin, when the best-so-far candidate is a local optimum that restarts cannot leave, or when task verifiers are noisy enough that the retained candidate is misjudged; the reported gains are also limited to four specific open-ended tasks and may not transfer.",
  "rationale": "The technique is cheap to implement on top of API-based multi-agent search and shows quality gains at fixed budget, but the reader must validate reset schedules and restart behavior on their own workload before relying on it in production.",
  "technique": "Periodic state reset in multi-agent search",
  "family": "orquestracao_multiagente",
  "recommendation": "testar",
  "published": "2026-10-14",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.20563",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces continuous long-horizon agent search (single-agent iteration, parallel agents, or communicating teams that accumulate full history) with periodic restarts: all agent sessions and candidate history are discarded and the search resumes from the best candidate found so far. It costs no extra compute or memory relative to the baseline strategies, since it operates at fixed budget, but adds orchestration logic for checkpointing best candidates, scheduling resets, and re-seeding agents; reset frequency becomes a new hyperparameter. It can fail when the discarded history contained information needed to escape the current basin, when the best-so-far candidate is a local optimum that restarts cannot leave, or when task verifiers are noisy enough that the retained candidate is misjudged; the reported gains are also limited to four specific open-ended tasks and may not transfer.",
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      "source_url": "https://arxiv.org/abs/2610.20563",
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    },
    {
      "claim_id": "claim-02",
      "statement": "Up to 15% improvement over parallel communicating agents and up to 39% over a single agent, at equal compute budget, across four discovery tasks (heuristic programming, kernel optimization, model training).",
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      "result": "",
      "baseline": "",
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    }
  ],
  "exposure_map": [
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      "dimension": "latency",
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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    },
    {
      "dimension": "data_and_training",
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      "next_check": "",
      "source_url": "",
      "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": "",
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  "equations_source_url": "",
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}