Paper Citation Record · LEDGER

Scaling Laws for Speculative Decoding

As of 21 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 2 inbound Pith citation observations for arXiv:2505.07858.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.07858 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:17:34.858836Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-29T07:43:30.763192Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-02T12:26:56.815339Z

Reference resolution

38 of 38 outbound references displayed

verified exact0
verified fuzzy8
unresolved30
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a67170a9-3092-4871-9b02-9002644661bf · outbound

This paper cites Scaling Laws for Neural Language Models.

Scaling Laws for Speculative Decoding Scaling Laws for Neural Language Models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T23:17:34.692295Z

Source-reported events for the cited work

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Observation 08a3fb10-7212-4093-98b1-23f90fffc150 · outbound

This paper cites Training Compute-Optimal Large Language Models.

Scaling Laws for Speculative Decoding Training Compute-Optimal Large Language Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-15T23:17:34.698002Z

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Observation 0414dbde-3ea7-4e33-be25-afc34a79fc76 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Scaling Laws for Speculative Decoding DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T23:17:34.703410Z

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Observation cd5aad38-7b3a-461e-b73e-6bffb6354da4 · outbound

This paper cites Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads.

Scaling Laws for Speculative Decoding Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T23:17:34.707831Z

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Observation 3e38735e-b35f-42e0-9165-49add1098637 · outbound

This paper cites EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty.

Scaling Laws for Speculative Decoding EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T23:17:34.712667Z

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Observation 0697af12-cf29-409d-9c86-10738bb96f97 · outbound

This paper cites EAGLE-2: Faster Inference of Language Models with Dynamic Draft Trees.

Scaling Laws for Speculative Decoding EAGLE-2: Faster Inference of Language Models with Dynamic Draft Trees

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T23:17:34.717314Z

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Observation 67644dfa-ed9d-4859-8220-5a1700418004 · outbound

This paper cites EAGLE-3: Scaling up Inference Acceleration of Large Language Models via Training-Time Test.

Scaling Laws for Speculative Decoding EAGLE-3: Scaling up Inference Acceleration of Large Language Models via Training-Time Test

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T23:17:34.722062Z

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Observation 91750ef8-97a3-4786-b597-d1a21f227b13 · outbound

This paper cites Training language models to follow instructions with human feedback.

Scaling Laws for Speculative Decoding Training language models to follow instructions with human feedback

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T23:17:34.728131Z

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Observation 8058b0dd-85ef-402a-8912-bb93b4974eb4 · outbound

This paper cites GPT-4 Technical Report.

Scaling Laws for Speculative Decoding GPT-4 Technical Report

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T23:17:34.732629Z

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Observation 9b9b99f0-8ea2-42b3-906b-a003d50206be · outbound

This paper cites Blockwise parallel decoding for deep autoregressive models.

Scaling Laws for Speculative Decoding Blockwise parallel decoding for deep autoregressive models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T23:17:34.737671Z

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Observation 84848e63-b412-450d-b0ec-b9d466c18d10 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Scaling Laws for Speculative Decoding Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T23:17:34.741787Z

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Observation e3c0ffc9-2712-4bd6-a95c-ed3888dbb4be · outbound

This paper cites Vicuna: An open-source chatbot impressing GPT-4 with 90%* ChatGPT quality.See https://vicuna.

Scaling Laws for Speculative Decoding Vicuna: An open-source chatbot impressing GPT-4 with 90%* ChatGPT quality.See https://vicuna

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:17:35.379528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8ca9a76e-cd65-4833-b831-b6d21a3ddaa8 · outbound

This paper cites Qwen2.5 Technical Report.

Scaling Laws for Speculative Decoding Qwen2.5 Technical Report

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T23:17:34.749630Z

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Observation cab6ca2c-6854-46e5-bab9-a66f86be8b15 · outbound

This paper cites The Llama 3 Herd of Models.

Scaling Laws for Speculative Decoding The Llama 3 Herd of Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T23:17:34.753513Z

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Observation bdb7d2f0-7d43-499b-bbef-0c962bc49c2a · outbound

This paper cites Judging LLM-as-a-judge with MT-bench and chatbot arena.

Scaling Laws for Speculative Decoding Judging LLM-as-a-judge with MT-bench and chatbot arena

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:17:35.365358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2533a5d2-186f-4888-b034-3d84c368f853 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Scaling Laws for Speculative Decoding Evaluating Large Language Models Trained on Code

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T23:17:34.762563Z

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Observation 3a0c90d6-a40b-45c9-8f80-4708c4b0837d · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Scaling Laws for Speculative Decoding Training Verifiers to Solve Math Word Problems

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T23:17:34.767055Z

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Observation 5221ba61-5eba-457e-a7d6-9f0da7eeab90 · outbound

This paper cites Alpaca: A strong, replicable instruction- following model.

Scaling Laws for Speculative Decoding Alpaca: A strong, replicable instruction- following model

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T23:17:34.771616Z

Source-reported events for the cited work

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Observation 4ba6577e-1e5a-4ae8-9d1a-3c3e1261052b · outbound

This paper cites Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond.

Scaling Laws for Speculative Decoding Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T23:17:34.776007Z

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Observation f165359b-4346-4764-ac16-32fac7ff8bb4 · outbound

This paper cites Natural questions: a benchmark for question answering research.

Scaling Laws for Speculative Decoding Natural questions: a benchmark for question answering research

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T23:17:34.780100Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 198686b6-5b97-41ce-929e-5391c9d2956b · outbound

This paper cites Accelerating Large Language Model Decoding with Speculative Sampling.

Scaling Laws for Speculative Decoding Accelerating Large Language Model Decoding with Speculative Sampling

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T23:17:34.784721Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39fc45a9-4233-48a9-9c43-843035a55741 · outbound

This paper cites Fast inference from transformers via speculative decoding.

Scaling Laws for Speculative Decoding Fast inference from transformers via speculative decoding

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T23:17:34.788732Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e8c69f8-3cbd-4c85-b248-1496aebbca62 · outbound

This paper cites Online Speculative Decoding.

Scaling Laws for Speculative Decoding Online Speculative Decoding

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T23:17:34.793543Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d8d4680-1461-4681-b914-d5373c44cb94 · outbound

This paper cites Lookahead: An inference acceleration framework for large language model with lossless generation accuracy.

Scaling Laws for Speculative Decoding Lookahead: An inference acceleration framework for large language model with lossless generation accuracy

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:17:35.326769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 495df7bc-fceb-492f-87d9-19961c7906b3 · outbound

This paper cites Ouroboros: Speculative decoding with large model enhanced drafting.

Scaling Laws for Speculative Decoding Ouroboros: Speculative decoding with large model enhanced drafting

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:17:35.312743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 585b9b35-7c6c-4cd5-a061-e62d9f26b8eb · outbound

This paper cites Break the Sequential Dependency of LLM Inference Using Lookahead Decoding.

Scaling Laws for Speculative Decoding Break the Sequential Dependency of LLM Inference Using Lookahead Decoding

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T23:17:34.805214Z

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Observation 69275d1a-5eef-44ff-873e-3934041833df · outbound

This paper cites DistillSpec: Improving Speculative Decoding via Knowledge Distillation.

Scaling Laws for Speculative Decoding DistillSpec: Improving Speculative Decoding via Knowledge Distillation

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T23:17:34.809724Z

Source-reported events for the cited work

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Observation 919b5ee7-ad91-42b6-8971-575f9918e862 · outbound

This paper cites CLLMs: Consistency large language models.

Scaling Laws for Speculative Decoding CLLMs: Consistency large language models

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:17:35.297745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 599f3793-47c3-4f1f-a4e7-3001c3020c51 · outbound

This paper cites Sequoia: Scalable, Robust, and Hardware-aware Speculative Decoding.

Scaling Laws for Speculative Decoding Sequoia: Scalable, Robust, and Hardware-aware Speculative Decoding

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T23:17:34.819505Z

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Observation 4e46eef2-056f-4480-952b-b9c56bfeb498 · outbound

This paper cites SSSD: Simply-Scalable Speculative Decoding.

Scaling Laws for Speculative Decoding SSSD: Simply-Scalable Speculative Decoding

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-15T23:17:34.823908Z

Source-reported events for the cited work

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Observation a4c654dc-573c-45f7-8f64-bed91df0d204 · outbound

This paper cites Better & Faster Large Language Models via Multi-token Prediction.

Scaling Laws for Speculative Decoding Better & Faster Large Language Models via Multi-token Prediction

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T23:17:34.828410Z

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Observation 6c33076b-ef44-4a9b-a953-ea5cdbf7edb2 · outbound

This paper cites Clover: Regressive Lightweight Speculative Decoding with Sequential Knowledge.

Scaling Laws for Speculative Decoding Clover: Regressive Lightweight Speculative Decoding with Sequential Knowledge

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-15T23:17:34.832568Z

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Observation a0a4cfb5-8c27-4c91-8edc-5e46e8e01416 · outbound

This paper cites Clover-2: Accurate Inference for Regressive Lightweight Speculative Decoding.

Scaling Laws for Speculative Decoding Clover-2: Accurate Inference for Regressive Lightweight Speculative Decoding

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T23:17:34.837115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4aac5eb-856d-4673-ad0d-f4cedbaacc14 · outbound

This paper cites Learning Harmonized Representations for Speculative Sampling.

Scaling Laws for Speculative Decoding Learning Harmonized Representations for Speculative Sampling

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-15T23:17:34.842401Z

Source-reported events for the cited work

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Observation e9d577f8-60cd-402e-8e88-4999a2143842 · outbound

This paper cites Sinkhorn distance minimization for knowledge distillation.

Scaling Laws for Speculative Decoding Sinkhorn distance minimization for knowledge distillation

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:17:35.283112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6f9b6511-d788-4cd0-9606-8bd5b455d814 · outbound

This paper cites Multi-level optimal transport for universal cross-tokenizer knowledge distillation on language models.

Scaling Laws for Speculative Decoding Multi-level optimal transport for universal cross-tokenizer knowledge distillation on language models

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:17:35.269428Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9329acb0-aed3-4e51-9f02-71eda2311630 · outbound

This paper cites Sinkd: Sinkhorn distance minimization for knowledge distillation.

Scaling Laws for Speculative Decoding Sinkd: Sinkhorn distance minimization for knowledge distillation

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-15T23:17:34.854943Z

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Observation 8d0c76ff-e751-4ad3-b56b-0bd0e9ca6b94 · outbound

This paper cites Kangaroo: Lossless self-speculative decoding for accelerating llms via double early exiting.

Scaling Laws for Speculative Decoding Kangaroo: Lossless self-speculative decoding for accelerating llms via double early exiting

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:17:35.247562Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

Observation 13276186-f9e5-44b5-b73c-09663e07f529 · inbound

Domino: Decoupling Causal Modeling from Autoregressive Drafting in Speculative Decoding cites this paper.

Domino: Decoupling Causal Modeling from Autoregressive Drafting in Speculative Decoding Scaling Laws for Speculative Decoding

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-06-29T07:53:14.179186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8e9f2e97-b40f-42ed-86b1-bc30d7b9b20a · inbound

Geometry-Aware Dataset Condensation for Diffusion Model Training cites this paper.

Geometry-Aware Dataset Condensation for Diffusion Model Training Scaling Laws for Speculative Decoding

Reference 86

Resolution: verified exact; arxiv_id, observed 2026-07-02T12:26:56.816800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.