{
  "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": "2610.20503",
  "title": "ReaDy: Recurrent States as Dynamic Context for Speculative Drafting in Hybrid Linear Attention Large Language Models",
  "summary": "ReaDy replaces the target-hidden-feature conditioning used by feature-based drafters (e.g., DSpark) with position-specific context retrieved from the target model's committed recurrent states via learned queries and a lightweight state reader. The cost is an additional adaptation stage, low-rank state readers, and fused operators on top of a hybrid linear attention target model, and the demonstrated results depend on a 27B-parameter model on an 80GB B200, which exceeds the reader's single 24GB GPU budget. It can fail for the reader because it applies only to HLA-architecture models, requires training the reader against a specific target, and its speedup is unvalidated at model sizes and hardware the reader can actually run.",
  "rationale": "The method is tightly coupled to hybrid linear attention targets and was validated only on a 27B model on B200-class hardware, so it is relevant to track but not deployable or testable within the reader's infrastructure today.",
  "technique": "Recurrent-state context retrieval for speculative drafting",
  "family": "decodificacao_especulativa",
  "recommendation": "observar",
  "published": "2026-10-14",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.20503",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "ReaDy replaces the target-hidden-feature conditioning used by feature-based drafters (e.g., DSpark) with position-specific context retrieved from the target model's committed recurrent states via learned queries and a lightweight state reader. The cost is an additional adaptation stage, low-rank state readers, and fused operators on top of a hybrid linear attention target model, and the demonstrated results depend on a 27B-parameter model on an 80GB B200, which exceeds the reader's single 24GB GPU budget. It can fail for the reader because it applies only to HLA-architecture models, requires training the reader against a specific target, and its speedup is unvalidated at model sizes and hardware the reader can actually run.",
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      "result": "",
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    },
    {
      "claim_id": "claim-02",
      "statement": "Average 3.16x decoding speedup over autoregressive decoding with Qwen3.8-27B on a single NVIDIA B200; 7.97% throughput and 8.50% acceptance-length improvement over DSpark.",
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    }
  ],
  "exposure_map": [
    {
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    },
    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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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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}