{
  "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": "2610.28578",
  "title": "Sage-A1.5: From Long-Horizon Execution to Scientific Exploration with Capability Composition",
  "summary": "The method replaces single-agent, single-pass long-horizon execution with a 35B MoE model that decomposes tasks, delegates to isolated-context subagents running in parallel, and verifies progress through evidence validation and reflective strategy revision. The cost is substantial: a 35B MoE exceeds comfortable serving on a single 24 GB GPU without aggressive quantization, so practical use implies third-party API spend or degraded local deployment, plus the orchestration layer adds system complexity and multiplied token consumption per task. Failure modes include unproductive delegation loops, verification that latches onto flawed evidence, runaway API costs on exploratory tasks, and unverifiable claims since the abstract reports no quantitative benchmark results.",
  "rationale": "The capability-composition pattern (delegation, parallel subagents, reflective verification) is implementable at small scale via APIs, but the abstract offers no quantified gains and the 35B model itself does not fit the reader's hardware, so workload-specific validation is required before adoption.",
  "technique": "Agentic orchestration with verification and subagent delegation",
  "family": "orquestracao_multiagente",
  "recommendation": "testar",
  "published": "2026-10-20",
  "as_of": "2026-10-21",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.28578",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 122,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces single-agent, single-pass long-horizon execution with a 35B MoE model that decomposes tasks, delegates to isolated-context subagents running in parallel, and verifies progress through evidence validation and reflective strategy revision. The cost is substantial: a 35B MoE exceeds comfortable serving on a single 24 GB GPU without aggressive quantization, so practical use implies third-party API spend or degraded local deployment, plus the orchestration layer adds system complexity and multiplied token consumption per task. Failure modes include unproductive delegation loops, verification that latches onto flawed evidence, runaway API costs on exploratory tasks, and unverifiable claims since the abstract reports no quantitative benchmark results.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.28578",
      "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",
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      "source_page": null,
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    },
    {
      "dimension": "security",
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      "finding": "",
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      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
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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": ""
}