{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "You already need task-specific training and want to reduce its memory or compute cost.",
    "change": "Change only the training technique on a small controlled run after checking prerequisites.",
    "baseline": "Your current training recipe with the same model, data split, and evaluation.",
    "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": "Held-out quality, peak memory, training time, and full compute cost to the target quality.",
    "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": "2610.06404",
  "title": "Quantifying the Stability of Multi-Step Reasoning via Error Amplification",
  "summary": "The method replaces standard fine-tuning for multi-step reasoning tasks with chain-of-thought length compression plus quantization-aware training that penalizes input-Jacobian spectral norms. Cost is confined to training time: modified data construction and an added regularization objective, with no inference-time overhead and feasible fine-tuning of small models on a single 24 GB GPU. It can fail because the theoretical guarantees are proven only for transformers on simple synthetic function-prediction tasks, and the validated gains are modest, task-specific (graph algorithms, symbolic state tracking), and may not transfer to general-domain reasoning with API-only models where fine-tuning access is limited.",
  "rationale": "The technique is cheap to implement during fine-tuning of small models and shows measurable gains on long reasoning chains, but its narrow validation domains and small effect sizes require workload-specific confirmation before production adoption.",
  "technique": "Stability regularization via CoT compression and Jacobian-aware QAT",
  "family": "treino_eficiente",
  "recommendation": "testar",
  "published": "2026-10-05",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.06404",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces standard fine-tuning for multi-step reasoning tasks with chain-of-thought length compression plus quantization-aware training that penalizes input-Jacobian spectral norms. Cost is confined to training time: modified data construction and an added regularization objective, with no inference-time overhead and feasible fine-tuning of small models on a single 24 GB GPU. It can fail because the theoretical guarantees are proven only for transformers on simple synthetic function-prediction tasks, and the validated gains are modest, task-specific (graph algorithms, symbolic state tracking), and may not transfer to general-domain reasoning with API-only models where fine-tuning access is limited.",
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      "result": "",
      "baseline": "",
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      "source_url": "https://arxiv.org/abs/2610.06404",
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    },
    {
      "claim_id": "claim-02",
      "statement": "Average improvement of 3.5% over baselines across seven evaluations, rising to 8.2% on longer-length inputs; stability measure reduced 3-8x in ablations.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.06404",
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  ],
  "exposure_map": [
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      "dimension": "compute",
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    },
    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
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      "finding": "",
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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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      "finding": "",
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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": "",
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}