{
  "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": "2609.20186",
  "title": "To Copy or Not to Copy: Controlling Speculative Decoding via Intrinsic Model Signals",
  "summary": "The method replaces fixed speculative-decoding drafting strategies (pure neural drafting such as EAGLE3, or heuristic n-gram copy triggering) with a runtime switch driven by lightweight probes trained on the target model's hidden states to detect copy intent. It costs the existing EAGLE-style drafter infrastructure plus training and maintaining per-model probes, and adds probe-inference overhead at each switching decision; memory cost is small but engineering complexity increases. It can fail if probe accuracy degrades on domains or models different from the training distribution, and the 15% gain depends on workloads containing genuine copy-intensive spans, offering little benefit on free-form generation.",
  "rationale": "The reader already able to run EAGLE3-class speculative decoding on a single GPU can validate the probe-based switch cheaply, but the gain is workload-dependent and requires training probes, so adoption should follow workload-specific testing rather than immediate production use.",
  "technique": "Probe-guided switching between neural and copy drafting",
  "family": "decodificacao_especulativa",
  "recommendation": "testar",
  "published": "2026-09-17",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.20186",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
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      "source_url": "https://arxiv.org/abs/2609.20186",
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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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    },
    {
      "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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}