{
  "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": "2610.01275",
  "title": "Know When to Hold 'em: Correct-Token Retention in Uniform-State Diffusion Language Models",
  "summary": "CTR-Reg replaces the standard USDM training objective (which penalizes errors only at corrupted positions) with an auxiliary loss rewarding retention of unperturbed clean tokens, requiring no sampler change. It costs an extra loss term during training or fine-tuning, which means it cannot be applied to third-party API models and presumes the capacity to train or fine-tune a diffusion language model. It can fail if the workload uses masked rather than uniform-state diffusion, and retention gains may not transfer to tasks or model scales beyond the six benchmarks evaluated.",
  "rationale": "The method is a training-time fix for a specific class of diffusion language models, so it only matters to a constrained-infrastructure practitioner if they already fine-tune USDMs, which is unlikely given that production text generation is dominated by autoregressive API or open-weight models.",
  "technique": "Correct-Token Retention Regularization for diffusion LMs",
  "family": "treino_eficiente",
  "recommendation": "observar",
  "published": "2026-10-01",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.01275",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 121,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "CTR-Reg replaces the standard USDM training objective (which penalizes errors only at corrupted positions) with an auxiliary loss rewarding retention of unperturbed clean tokens, requiring no sampler change. It costs an extra loss term during training or fine-tuning, which means it cannot be applied to third-party API models and presumes the capacity to train or fine-tune a diffusion language model. It can fail if the workload uses masked rather than uniform-state diffusion, and retention gains may not transfer to tasks or model scales beyond the six benchmarks evaluated.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.01275",
      "source_page": null,
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    },
    {
      "claim_id": "claim-02",
      "statement": "With five greedy-tail steps, generative perplexity more than halves for all three USDMs while diversity is preserved; clean-token accuracy improves 26.5 points and per-step revisions drop to 3-11 of 512 positions.",
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      "source_url": "https://arxiv.org/abs/2610.01275",
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    }
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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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    },
    {
      "dimension": "reproducibility",
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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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}