{
  "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": "2609.24197",
  "title": "H-Spec: Parallel Speculative Decoding Without a Drafter-Side KV Cache",
  "summary": "H-Spec replaces block-diffusion drafters that maintain a separate drafter-side KV cache (projecting target hidden states at every position) with a hybrid Mamba-attention drafter that reuses the target model's KVs in place and injects target hidden states only at the last input position. The cost is a specialized drafter architecture combining Mamba and attention modules that must be trained per target model, plus the engineering complexity of integrating target-KV reuse into the serving stack. It can fail if a trained drafter is unavailable for the reader's specific target model, if the acceptance-rate gains do not transfer to the reader's task distribution, or if the implementation is not available in the reader's inference framework (e.g., vLLM, SGLang).",
  "rationale": "The paper demonstrates a real per-request memory and concurrency benefit, but adopting it requires training or obtaining a target-specific hybrid drafter and framework-level integration that is not yet a turnkey option for a constrained-infrastructure team.",
  "technique": "Drafter-KV-free hybrid parallel speculative decoding",
  "family": "decodificacao_especulativa",
  "recommendation": "observar",
  "published": "2026-09-21",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.24197",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 3,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
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      "claim_id": "claim-02",
      "statement": "H-Spec improves over the best baseline by 5.0-13.3% in mean accepted length, 5.3-12.6% in batch-size-1 inter-token latency speedup, and sustains higher throughput with lower KV cache utilization under concurrent serving, across three target models.",
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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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    },
    {
      "dimension": "security",
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    },
    {
      "dimension": "data_and_training",
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    },
    {
      "dimension": "reproducibility",
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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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}