{
  "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": "2609.15973",
  "title": "Discovery Foundation Models: Toward Open-Ended Discovery Intelligence",
  "summary": "The paper proposes replacing task-specified LLM problem solving with a general discovery framework in which models iteratively maintain a revisable research state, form and test hypotheses, and evolve reusable discovery skills, instantiated via the Zetema system and the GALILEO therapeutic-discovery loop. Adoption costs include substantial orchestration complexity (explicit state tracking, verification and experimental gating, external grounding, skill evolution), plus, for the full vision, robotic wet-lab infrastructure and ongoing API or compute spend far exceeding a single 24 GB GPU. Failure modes include ungrounded or unverified hypothesis generation without rigorous gating, process-centered evaluation metrics that do not yet predict real discovery outcomes, and a framework whose abstract reports no quantified results, so its benefit over standard tool-use pipelines is unestablished.",
  "rationale": "This is a conceptual framework and system proposal requiring lab-grade infrastructure and offering no validated quantified gains, so it merits monitoring rather than adoption under constrained infrastructure.",
  "technique": "Discovery Foundation Models: closed-loop research-state agent framework",
  "family": "orquestracao_multiagente",
  "recommendation": "observar",
  "published": "2026-09-14",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.15973",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 25,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The paper proposes replacing task-specified LLM problem solving with a general discovery framework in which models iteratively maintain a revisable research state, form and test hypotheses, and evolve reusable discovery skills, instantiated via the Zetema system and the GALILEO therapeutic-discovery loop. Adoption costs include substantial orchestration complexity (explicit state tracking, verification and experimental gating, external grounding, skill evolution), plus, for the full vision, robotic wet-lab infrastructure and ongoing API or compute spend far exceeding a single 24 GB GPU. Failure modes include ungrounded or unverified hypothesis generation without rigorous gating, process-centered evaluation metrics that do not yet predict real discovery outcomes, and a framework whose abstract reports no quantified results, so its benefit over standard tool-use pipelines is unestablished.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.15973",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
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      "finding": "",
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    },
    {
      "dimension": "latency",
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      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "risks": [],
  "minimum_test": [],
  "open_questions": [],
  "independent_tests": [],
  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "selector_failed",
  "core_kind": "",
  "equations_fetched_at": "2026-10-10",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}