{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Your self-hosted model spends too long generating answers token by token.",
    "change": "Check whether a compatible draft model and serving integration exist before renting compute.",
    "baseline": "The same target with speculation off, then your existing drafter if you have one.",
    "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 draft length, tokens per second, tail latency, output quality, and total GPU cost.",
    "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": "2610.12570",
  "title": "Contextual Correction of Parallel Draftsfor Speculative Decoding",
  "summary": "DCOCO replaces the top-1 commitment of parallel diffusion-based drafters (e.g., DFlash) by retaining multiple candidate tokens per draft position and scoring their contextual compatibility across adjacent positions in parallel, restoring intra-block dependencies without sequential drafting. It costs an additional lightweight correction stage per draft block, extra candidate storage, and integration into a diffusion-drafter serving stack, and it is not drop-in for conventional autoregressive speculative decoding. It can fail when the verifier or drafter backbone differs from the evaluated configurations, when the correction overhead erodes gains at low batch sizes, or when sequential correctors under load are the actual bottleneck it claims to avoid.",
  "rationale": "The claimed speedups are large and persistent under concurrency, but adopting DCOCO presupposes a diffusion-based drafter such as DFlash plus custom serving integration, so the reader should validate it on their own model and serving setup before production use.",
  "technique": "Parallel contextual correction for diffusion-based speculative drafts",
  "family": "decodificacao_especulativa",
  "recommendation": "testar",
  "published": "2026-10-08",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.12570",
  "independent_implementations": 2,
  "total_implementations": 3,
  "stars": 149,
  "citations": 0,
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    {
      "claim_id": "claim-01",
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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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      "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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}