{
  "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": "2610.12939",
  "title": "Surfing the Activation Wave: Analyzing the Information and Geometry of LLM Hidden States for Speculative Decoding",
  "summary": "The method replaces fixed, position-based heuristics for choosing which decoder layers feed the draft model in EAGLE-style speculative decoding with a data-driven selection based on matrix entropy (informativeness), CKA (diversity), and token-entropy filtering. The cost is a one-time offline analysis pass over calibration data plus added pipeline complexity; inference architecture and memory footprint are unchanged since only the layer subset used by the draft head differs. It can fail if the calibration distribution shifts, if the gains are small relative to the already-competitive default rule (which the paper itself concedes can remain competitive), or if the statistics are computed on too few or poorly filtered tokens.",
  "rationale": "The reader runs single-GPU inference where speculative decoding is directly deployable, and layer selection is a cheap offline tuning step, but the abstract provides no quantified speedup, so the gain must be validated against the strong default heuristic on the reader's own model and workload.",
  "technique": "Entropy- and CKA-based layer selection for speculative decoding",
  "family": "decodificacao_especulativa",
  "recommendation": "testar",
  "published": "2026-10-09",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.12939",
  "independent_implementations": 2,
  "total_implementations": 3,
  "stars": 126,
  "citations": null,
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