Speculative decoding

Evidence in the last complete seven days

Source mentions

7

Previous seven days 4

Distinct sources

2

Previous 1

Comparable growth

Not comparable

Ranking appearances

0

Collection observations and undated records are separate from dated publication counts. Observed in 7d：3 · Undated records：0

Daily history · up to 90 days

Bars show dated source mentions. The latest UTC day may be incomplete. Zero mentions and missing collection coverage are different conditions.

Dotted markers indicate days with no record. They are not counted as zero mentions.

Source evidence

Sources in the evidence shown：arXiv 12 · HF Daily Papers 2 · GitHub Issues 1

1. arXiv · Published Nov 5, 2026

   Hybrid Spark: Adaptive Speculative Decoding That Lets the Content Choose the Drafter

   arXiv:2611.05611v1 Announce Type: new Abstract: Speculative decoding (SD) speeds up large language model (LLM) serving, but its gain depends on the workload's \emph{shape}, such as batch size and context length, and on its \emph{content}. A model-based drafter proposes a short block on any content, while a model-free drafter such as n-gram lookup proposes lo

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2. arXiv · Published Nov 4, 2026

   Towards Optimal Multi-draft Speculative Decoding

   arXiv:2502.18779v2 Announce Type: replace Abstract: Large Language Models (LLMs) have become an indispensable part of natural language processing tasks. However, autoregressive sampling has become an efficiency bottleneck. Multi-Draft Speculative Decoding (MDSD) is a recent approach where, when generating each token, a small draft model generates multiple dr

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3. arXiv · Published Nov 4, 2026

   ROCm InferaSim: Cut LLM Serving Trial Costs and Deploy the Best Configuration

   arXiv:2611.04032v1 Announce Type: new Abstract: Serving an LLM efficiently depends on the engine, how the model is split across GPUs, precision, scheduler settings, speculative decoding, KV-cache management, and prefill-decode disaggregation. The best configuration depends on the model, hardware, workload, and objective. Operators usually find it by trial an

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4. arXiv · Published Nov 4, 2026

   HybridSpec: Throughput-Aware Speculative Decoding for Hybrid GDN-MoE Models

   arXiv:2611.04055v1 Announce Type: new Abstract: In speculative decoding, parallel drafters such as DFlash make drafting cheap, so target verification dominates each round. A central choice is the verification length, the number of tokens that the target processes in each round. Verifying more draft tokens can accept more of them, but work after the first rej

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5. HF Daily Papers · Published Nov 3, 2026

   Adversarial Speculative Reasoning Improves Long Horizon Agents

   Speculative decoding has been treated purely as a systems optimization that speeds up large language model decoding without changing the model behavior. Under this view, the growing effort to train better draft models has been focused solely on acceptance rate. We argue that this view does not hold true for long-horizon agent reasoning. Measuring frontier op

6. GitHub Issues · Published Nov 2, 2026

   QuantizedCache.crop() raises IndexError across FP8, Quanto and HQQ, breaking speculative decoding

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7. arXiv · Published Nov 2, 2026

   TraceSpec: Understanding and Improving Speculative Decoding for LLM Agents

   arXiv:2610.41177v1 Announce Type: new Abstract: LLM agents execute tasks through repeated model calls over evolving histories of reasoning, actions, and tool feedback. Aggregate speculative-decoding metrics merge these distinct decisions, providing limited guidance on where draft supervision is needed. We present TraceSpec, a framework connecting trajectory

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8. HF Daily Papers · Published Nov 2, 2026

   SpecOPD: Branch-Parallel On-Policy Distillation for Speculative Decoding

   Speculative decoding (SD) accelerates large language model inference by reducing the sequential cost of token-by-token generation. Advanced SD methods reduce proposal cost with parallel drafters that predict entire blocks in one forward pass, but overall speedup still depends on acceptance length. On-policy distillation (OPD) can push well-trained drafters b

9. arXiv · Published Oct 26, 2026

   SPECMEM: Accelerating Agentic Tool Calling via Live Memory Management

   arXiv:2610.31840v1 Announce Type: new Abstract: Tool-calling agents are mostly optimized for accuracy; in deployment, however, their growing cost is latency: every tool call is decoded autoregressively. Speculative decoding accelerates this without changing the output, and retrieved past calls make strong drafts under real use, where each user's behavior ten

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10. arXiv · Published Oct 23, 2026

    Speculative Acceptance Does Not Decay with Position: A Full-Length Diagnostic on Long Chain-of-Thought with Independent Drafts

    arXiv:2610.30644v1 Announce Type: new Abstract: The claim that speculative-decoding acceptance rate decays as a chain-of-thought grows longer has become a de facto industry narrative, motivating draft-model co-design and position-aware training. Nearly all direct evidence comes from trained drafters whose offline training length is far shorter than inference

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11. arXiv · Published Oct 22, 2026

    Target Regeneration Shrinks the Gains of Draft-Training Objectives in Speculative Decoding

    arXiv:2610.29013v1 Announce Type: new Abstract: Speculative decoding accelerates large language models by verifying drafted tokens in parallel. New draft-training objectives are usually credited with the acceptance they add over cross-entropy on externally written responses, yet many pipelines let the target rewrite them. We ask how much of that gain survive

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12. arXiv · Published Oct 22, 2026

    Understanding Target Probability Reuse in Speculative Decoding

    arXiv:2610.29210v1 Announce Type: new Abstract: Speculative decoding uses a target model to verify a tree of draft tokens in parallel. We study whether the resulting next-token distributions can help select the next tree. For unrestricted target models, we show that guaranteed exact reuse requires a distribution computed at the next round's complete context.

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13. arXiv · Published Oct 20, 2026

    FlashMap: Flow Map Speculative Decoding

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14. arXiv · Published Oct 12, 2026

    Contextual Correction of Parallel Draftsfor Speculative Decoding

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15. arXiv · Published Oct 12, 2026

    Surfing the Activation Wave: Analyzing the Information and Geometry of LLM Hidden States for Speculative Decoding

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Review priority 65 / 100

This score orders research candidates. It is not search volume, a forecast or verified commercial demand.

• Evidence

  24 / 30

  Distinct dated articles or first observations; repeat crawling adds nothing.

• Publisher diversity

  16 / 25

  Conservative owner grouping; syndicated copies count once.

• Comparable change

  0 / 20

  Comparable complete weeks only; no percentage from a zero baseline.

• Recent evidence

  15 / 15

  Latest publication or first observation, not repeated fetch time.

• Name specificity

  9.7 / 10

  Explicit entities and technical concepts outrank ambiguous acronyms.

Limitations and interpretation

• First observation is a collection milestone, not proof that a word or product is new to the world.
• Counts describe monitored public sources, not the whole web or search demand.
• First observed here is not the invention date of a name or idea.
• Candidates come from public titles, summaries and entity URLs, not verified full-text analysis.
• Owner grouping is conservative; undisclosed syndication may remain.
• Repeat ranking appearances are observations, not new articles or independent endorsements.
• An established concept or common product name retained as a baseline, not a newly invented term.
• Both weeks need adequate, comparable collection coverage.
• Sources behind this term changed or lack a comparable baseline.
• Recent evidence includes collection over three days after publication.