{
  "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": "2609.21827",
  "title": "RheoSampling: Resolving the One-Hot Dilemma in Stochastic Dynamic-Tree Speculative Decoding",
  "summary": "It replaces the deterministic top-K tree expansion of dynamic-tree speculative decoding (which collapses to one-hot distributions at T>0) with a decoupled scheme that uses sampled tokens with proxy probabilities for construction and true probabilities for verification. The cost is added implementation complexity (OT-based verification, equivalence-class analysis, sparse drafting) on top of an already complex EAGLE-3-style draft-model stack, and it only matters when serving with temperature-based sampling. It can fail if the acceptance-rate gains do not materialize on the reader's specific model and draft head, if no maintained implementation exists, or if the reader's workload is predominantly greedy, where existing dynamic-tree methods already work.",
  "rationale": "The technique addresses a real bottleneck only for teams already running or evaluating tree-based speculative decoding with nonzero temperature, so it warrants a workload-specific pilot rather than immediate adoption.",
  "technique": "Lossless stochastic dynamic-tree speculative decoding",
  "family": "decodificacao_especulativa",
  "recommendation": "testar",
  "published": "2026-09-18",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.21827",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
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      "dimension": "latency",
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    },
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      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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    },
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      "dimension": "security",
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    },
    {
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    },
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      "dimension": "reproducibility",
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  ],
  "risks": [],
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
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}