{
  "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": "2610.23787",
  "title": "DLite: History-free Speculative Decoding for Agentic AI",
  "summary": "DLite replaces conventional draft models that maintain their own historical KV cache with a history-free draft that reuses the target model's contextual hidden states and a lightweight sequential head, trained with a two-stage strategy. It costs the training of a custom draft head, access to the target model's hidden states, and integration effort into the serving stack, though it reduces draft-side KV memory overhead. It can fail on workloads where acceptance length does not reach the reported 2-6 range, and it is inapplicable to API-only models that do not expose hidden states.",
  "rationale": "The speedup claim is significant for agentic multi-turn workloads, but adoption requires self-hosted model access to hidden states and custom draft training, so it warrants small-scale validation on the reader's own workloads rather than immediate production adoption.",
  "technique": "History-free speculative decoding via target hidden-state reuse",
  "family": "decodificacao_especulativa",
  "recommendation": "testar",
  "published": "2026-10-16",
  "as_of": "2026-10-19",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.23787",
  "independent_implementations": 2,
  "total_implementations": 3,
  "stars": 132,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "DLite replaces conventional draft models that maintain their own historical KV cache with a history-free draft that reuses the target model's contextual hidden states and a lightweight sequential head, trained with a two-stage strategy. It costs the training of a custom draft head, access to the target model's hidden states, and integration effort into the serving stack, though it reduces draft-side KV memory overhead. It can fail on workloads where acceptance length does not reach the reported 2-6 range, and it is inapplicable to API-only models that do not expose hidden states.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.23787",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Achieves 2-6 token acceptance length and up to 70% speedup over the state-of-the-art speculative decoding method DSpark on general-purpose, long-context, and agentic workloads.",
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      "source_url": "https://arxiv.org/abs/2610.23787",
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      "source_excerpt": ""
    }
  ],
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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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      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "security",
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      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
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      "source_page": null,
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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": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}