{
  "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": "2610.22194",
  "title": "AutoSFT: An Agentic System for Automated Data Research in Supervised Fine-Tuning",
  "summary": "It replaces manual SFT data engineering (adjusting proportions, pruning, repairing, and synthesizing rows) with a meta agent that separates execution from analysis, gathers proposals from independent subagents, and calls a synthesis subagent for gaps in the data pool. The cost is a multi-round loop in which the base model is retrained from scratch each round, plus orchestration complexity and API or GPU spend per iteration; a single 24 GB GPU constrains how large the retrained model can be. It can fail through context overflow in the agent loop, subagent proposals that do not transfer to held-out benchmarks, and compute cost scaling with rounds without guaranteed convergence.",
  "rationale": "The approach is actionable with a small team using API-based agents and modest local fine-tuning, but its gains are benchmark-specific and require repeated full retraining, so it warrants a bounded pilot rather than direct production adoption.",
  "technique": "Multi-agent automated SFT data curation",
  "family": "orquestracao_multiagente",
  "recommendation": "testar",
  "published": "2026-10-15",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.22194",
  "independent_implementations": 2,
  "total_implementations": 3,
  "stars": 132,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
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      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.22194",
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    },
    {
      "claim_id": "claim-02",
      "statement": "Analysis subagents lift best IFEval by +6.5 points; synthesis adds +3.4 IFEval and +2.9 IFBench; transfer to code yields +3.7 MBPP+ over a single agent that does not improve.",
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  "exposure_map": [
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      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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    },
    {
      "dimension": "security",
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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": "",
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}