{
  "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": "2610.30790",
  "title": "Agentic Knowledge-Model Co-Evolution via Consistency Regularization",
  "summary": "This technique replaces single-view experience analysis with a multi-view consistency check, where knowledge candidates are only adopted if they provide utility gains across different perturbations of the same experience. It requires significant infrastructure to support interleaved cycles of self-distillation and reinforcement learning, which increases computational cost and engineering complexity. Adoption can fail if the consistency regularization filters out necessary domain-specific nuances or if the overhead of maintaining multiple evolution views exceeds the available performance budget.",
  "rationale": "The requirement for interleaved reinforcement learning and self-distillation cycles exceeds the reader's constrained infrastructure and limited cloud budget.",
  "technique": "Consistency-Regularized Knowledge-Model Co-Evolution",
  "family": "treino_eficiente",
  "recommendation": "observar",
  "published": "2026-10-22",
  "as_of": "2026-10-24",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.30790",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 132,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "This technique replaces single-view experience analysis with a multi-view consistency check, where knowledge candidates are only adopted if they provide utility gains across different perturbations of the same experience. It requires significant infrastructure to support interleaved cycles of self-distillation and reinforcement learning, which increases computational cost and engineering complexity. Adoption can fail if the consistency regularization filters out necessary domain-specific nuances or if the overhead of maintaining multiple evolution views exceeds the available performance budget.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.30790",
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    },
    {
      "claim_id": "claim-02",
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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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      "finding": "",
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      "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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      "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,
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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": ""
}