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Paper Citation Record · LEDGER

D^2SD: Accelerating Speculative Decoding with Dual Diffusion Draft Models

As of 6 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2606.04446.

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

pith.paper-citation-record.v1
2606.04446 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-28T04:51:28.419612Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

34 of 34 outbound references displayed

  • verified exact12
  • verified fuzzy0
  • unresolved20
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch2

External citation measurements

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Outbound references

Observation c1a2b347-8e57-43c6-981f-aa113af26c14 · outbound

This paper cites DeepSeek-V3 Technical Report.

D^2SD: Accelerating Speculative Decoding with Dual Diffusion Draft Models DeepSeek-V3 Technical Report

Reference 1

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local_arxiv, observed 2026-07-02T10:56:52.700460Z

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Observation 668be06e-7882-46a6-95bd-37c74bd6f50b · outbound

This paper cites Qwen3 Technical Report.

D^2SD: Accelerating Speculative Decoding with Dual Diffusion Draft Models Qwen3 Technical Report

Reference 2

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local_arxiv, observed 2026-07-02T10:56:52.703182Z

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source=pdf_text observed=2026-06-28T04:51:28.419612Z digest=sha256:76891b63f674bde2edab5c097989a349b3d5bd68f8013ea6c22a441181a48db4

Observation d602b1f0-7d9a-43f3-b4ef-9aa9a16e46e9 · outbound

This paper cites GLM-5: from Vibe Coding to Agentic Engineering.

D^2SD: Accelerating Speculative Decoding with Dual Diffusion Draft Models GLM-5: from Vibe Coding to Agentic Engineering

Reference 3

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source=pdf_text observed=2026-06-28T04:51:28.419612Z digest=sha256:9d2a94b850167f8e652c6d5ffe32f79832cb9980f60aff1bb841f74b249f14af

Observation c8082b2f-913c-4fa1-a10f-eb0d6717d5ad · outbound

This paper cites Fast inference from transformers via speculative decoding.

D^2SD: Accelerating Speculative Decoding with Dual Diffusion Draft Models Fast inference from transformers via speculative decoding

Reference 4

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source=pdf_text observed=2026-06-28T04:51:28.419612Z digest=sha256:9bc5463bf945962a75d9b330d0d99d0673e6af2e90b1018af9e760abbb6c79a4

Observation fbe8194a-1bd9-45b2-90f5-a52ef2a6afde · outbound

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

D^2SD: Accelerating Speculative Decoding with Dual Diffusion Draft Models Accelerating Large Language Model Decoding with Speculative Sampling

Reference 5

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source=pdf_text observed=2026-06-28T04:51:28.419612Z digest=sha256:611ae6bcf4abf79f476cc9a7a92282e3d45d8d9371c7d544aab2d4c4b8feea1f

Observation b3904077-3084-49b0-a1ff-3979042a0d95 · outbound

This paper cites Specinfer: Accelerating large language model serving with tree-based speculative inference and verification.

D^2SD: Accelerating Speculative Decoding with Dual Diffusion Draft Models Specinfer: Accelerating large language model serving with tree-based speculative inference and verification

Reference 6

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source=pdf_text observed=2026-06-28T04:51:28.419612Z digest=sha256:a8a132b1e9194baa0224936cf86d83808467ac6ff20e9a72647a60a60324753b

Observation 0b5056c8-38e7-4d2a-b2e1-11655373fc27 · outbound

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

D^2SD: Accelerating Speculative Decoding with Dual Diffusion Draft Models Sequoia: Scalable, Robust, and Hardware-aware Speculative Decoding

Reference 7

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arxiv_id, observed 2026-07-02T10:56:52.695567Z

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source=pdf_text observed=2026-06-28T04:51:28.419612Z digest=sha256:5a3c5e424797055cdd3e31ba45339d629c77dd0672540079edd0e03c4a61ff25

Observation 0062e03a-12de-4c5f-92f0-253f6f85ca7a · outbound

This paper cites Eagle-3: Scaling up inference acceleration of large language models via training-time test.

D^2SD: Accelerating Speculative Decoding with Dual Diffusion Draft Models Eagle-3: Scaling up inference acceleration of large language models via training-time test

Reference 8

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source=pdf_text observed=2026-06-28T04:51:28.419612Z digest=sha256:2f20b671a70d3618303be2ef69bfbab9f59c053618f89c01c60b4cab9172e9ee

Observation 50768005-f73a-4a1a-9611-959a3dca293a · outbound

This paper cites DFlash: Block Diffusion for Flash Speculative Decoding.

D^2SD: Accelerating Speculative Decoding with Dual Diffusion Draft Models DFlash: Block Diffusion for Flash Speculative Decoding

Reference 9

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local_arxiv, observed 2026-07-02T10:56:52.697944Z

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source=pdf_text observed=2026-06-28T04:51:28.419612Z digest=sha256:9934355b4d0aef441037edb38c510ca11d8418620d22ed5ef377b29fa631a451

Observation bebd7537-2a76-44e4-bd6a-dc2203eda96a · outbound

This paper cites Fasttree: Optimizing attention kernel and runtime for tree-structured llm inference.

D^2SD: Accelerating Speculative Decoding with Dual Diffusion Draft Models Fasttree: Optimizing attention kernel and runtime for tree-structured llm inference

Reference 10

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source=pdf_text observed=2026-06-28T04:51:28.419612Z digest=sha256:91528155abc504ef662fe37144dd620187ebd595156af6ecfe4b9b28efebbb16

Observation 1d93fe2e-7a78-4ec9-874a-e765fac2ca1f · outbound

This paper cites Deft: Decoding with flash tree-attention for efficient tree-structured llm inference.

D^2SD: Accelerating Speculative Decoding with Dual Diffusion Draft Models Deft: Decoding with flash tree-attention for efficient tree-structured llm inference

Reference 11

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source=pdf_text observed=2026-06-28T04:51:28.419612Z digest=sha256:07d4706942e638ccbb240c59513ac46972acbe9eae6fb935100b66c8a7b1ced3

Observation 4f390458-d2e3-4bb8-bb02-2573723fee99 · outbound

This paper cites Magicdec: Breaking the latency-throughput tradeoff for long context generation with speculative decoding.

D^2SD: Accelerating Speculative Decoding with Dual Diffusion Draft Models Magicdec: Breaking the latency-throughput tradeoff for long context generation with speculative decoding

Reference 12

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source=pdf_text observed=2026-06-28T04:51:28.419612Z digest=sha256:f8f463f38a099ed6c29a603777010f47eff0d5f18f9a16487f741a8c427265d6

Observation 3c48d962-f621-49cd-8fa1-d6aab29f963a · outbound

This paper cites Glide with a cape: A low-hassle method to accelerate speculative decoding.

D^2SD: Accelerating Speculative Decoding with Dual Diffusion Draft Models Glide with a cape: A low-hassle method to accelerate speculative decoding

Reference 13

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source=pdf_text observed=2026-06-28T04:51:28.419612Z digest=sha256:01776af5aa0fee23eafcebed10b9da1d29ee42caffe6e9b0904fe837a74fa3d4

Observation 33d2bd10-3de2-418b-a7d7-6ca98f4f38c7 · outbound

This paper cites Eagle-2: Faster inference of language models with dynamic draft trees.

D^2SD: Accelerating Speculative Decoding with Dual Diffusion Draft Models Eagle-2: Faster inference of language models with dynamic draft trees

Reference 14

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source=pdf_text observed=2026-06-28T04:51:28.419612Z digest=sha256:101f21c9f1759dd38e03eae04a0ac114278bccf91d51f6a9049746648be90120

Observation 176f0543-8c52-43e6-b65d-2940e0aeac04 · outbound

This paper cites Medusa: Simple llm inference acceleration framework with multiple decoding heads.

D^2SD: Accelerating Speculative Decoding with Dual Diffusion Draft Models Medusa: Simple llm inference acceleration framework with multiple decoding heads

Reference 15

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source=pdf_text observed=2026-06-28T04:51:28.419612Z digest=sha256:0b09f4f894f371d1aa3b88c0aab1f455d5ca7d2bfa5f0549f3cf2a058a46a866

Observation 3f0c4cef-4606-42d4-af42-198eecc58dbf · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

D^2SD: Accelerating Speculative Decoding with Dual Diffusion Draft Models Training Verifiers to Solve Math Word Problems

Reference 16

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-06-28T04:51:28.419612Z digest=sha256:ae8fae82ef4a7ecfbadc4e38684ff9e78b09bcb864075df6d1b72039ed89d230

Observation c4976bd1-c18c-48ef-a98f-249d11d03054 · outbound

This paper cites Measuring mathematical problem solving with the math dataset.

D^2SD: Accelerating Speculative Decoding with Dual Diffusion Draft Models Measuring mathematical problem solving with the math dataset

Reference 17

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source=pdf_text observed=2026-06-28T04:51:28.419612Z digest=sha256:04693cef6d167ac5e016a0fab69bb118f00e7cb30ac64d4349f7b8fa9ce210a6

Observation 494e50ca-a2ee-4b04-a666-b7d4b0128526 · outbound

This paper cites American Invitational Mathematics Examination – AIME 2025.

D^2SD: Accelerating Speculative Decoding with Dual Diffusion Draft Models American Invitational Mathematics Examination – AIME 2025

Reference 18

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source=pdf_text observed=2026-06-28T04:51:28.419612Z digest=sha256:90d22878517b58c80fe4bbf5d0010203f5a2caa85eebc53df9ffb5100e2428c0

Observation 68d6f51f-4b7c-4e97-abad-0495973711a5 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

D^2SD: Accelerating Speculative Decoding with Dual Diffusion Draft Models Evaluating Large Language Models Trained on Code

Reference 19

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-06-28T04:51:28.419612Z digest=sha256:9b7cc3c1e662ff1aced14da3d9cb4fef34df1c7026c03f9776facb0d7ac82fa2

Observation 728c4f68-80e4-46ba-ae3e-07efed1428b8 · outbound

This paper cites Program Synthesis with Large Language Models.

D^2SD: Accelerating Speculative Decoding with Dual Diffusion Draft Models Program Synthesis with Large Language Models

Reference 20

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-06-28T04:51:28.419612Z digest=sha256:86d483cb0f534db7dd633d5929538b99c7e381f98402a8afeb55b5673a35eaed

Observation 0a7fd022-77a8-4966-905a-4481592079bc · outbound

This paper cites Livecodebench: Holistic and contamination free evaluation of large language models for code.

D^2SD: Accelerating Speculative Decoding with Dual Diffusion Draft Models Livecodebench: Holistic and contamination free evaluation of large language models for code

Reference 21

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source=pdf_text observed=2026-06-28T04:51:28.419612Z digest=sha256:2831f209d0e79680da23e2bfbfa0059a944a7d3f6bc179fd2e6c86570fc300c9

Observation 6482d9b5-5468-4a67-b817-1275e757bea0 · outbound

This paper cites Judging llm-as-a-judge with mt-bench and chatbot arena.

D^2SD: Accelerating Speculative Decoding with Dual Diffusion Draft Models Judging llm-as-a-judge with mt-bench and chatbot arena

Reference 22

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source=pdf_text observed=2026-06-28T04:51:28.419612Z digest=sha256:7941deb64b99543bab49bb6f953057848302586d91d6a224a74fb89d5180aeb2

Observation 55389453-755f-4ca2-8950-f2fa818f9d29 · outbound

This paper cites Hashimoto.

D^2SD: Accelerating Speculative Decoding with Dual Diffusion Draft Models Hashimoto

Reference 23

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source=pdf_text observed=2026-06-28T04:51:28.419612Z digest=sha256:884667815e0019d2717a74a4652b4ffa1e1cd9bfa996aa28bd5b29456dbf775b

Observation f946c407-089e-4f4d-9fe5-4ba490d65da6 · outbound

This paper cites The perfect blend: Redefining rlhf with mixture of judges, 2024.

D^2SD: Accelerating Speculative Decoding with Dual Diffusion Draft Models The perfect blend: Redefining rlhf with mixture of judges, 2024

Reference 24

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source=pdf_text observed=2026-06-28T04:51:28.419612Z digest=sha256:82c488664a2c7b91f6040d495baad32a5d975f94e63f627448ad9da3be71da1d

Observation f5a7e651-9a1e-49e9-97a7-4d5fdfe66da7 · outbound

This paper cites Flashattention-2: Faster attention with better parallelism and work partitioning.

D^2SD: Accelerating Speculative Decoding with Dual Diffusion Draft Models Flashattention-2: Faster attention with better parallelism and work partitioning

Reference 25

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source=pdf_text observed=2026-06-28T04:51:28.419612Z digest=sha256:1e4b4c9fc450ad3ad3ade83637202b579dd62ce6c7da6a0bb11ad17076068f60

Observation 29f300ca-e383-448a-a437-789a48fde32f · outbound

This paper cites Flashinfer: Efficient and customizable attention engine for llm inference serving.

D^2SD: Accelerating Speculative Decoding with Dual Diffusion Draft Models Flashinfer: Efficient and customizable attention engine for llm inference serving

Reference 26

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source=pdf_text observed=2026-06-28T04:51:28.419612Z digest=sha256:999b3654eb450d60cbe7afa5ffe0163ccfa542cfcacde3bfbe9011ec12fcc71b

Observation d7426a78-4006-4fdc-9041-e43a7dc84afe · outbound

This paper cites Eagle: speculative sampling requires rethinking feature uncertainty.

D^2SD: Accelerating Speculative Decoding with Dual Diffusion Draft Models Eagle: speculative sampling requires rethinking feature uncertainty

Reference 27

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source=pdf_text observed=2026-06-28T04:51:28.419612Z digest=sha256:e47acb653f2c2144904622f57e05c54393912e7ed96e732a0ea9a089ad37724b

Observation e7714e36-1b25-475a-b53b-09a443357960 · outbound

This paper cites Break the sequential dependency of llm inference using lookahead decoding.

D^2SD: Accelerating Speculative Decoding with Dual Diffusion Draft Models Break the sequential dependency of llm inference using lookahead decoding

Reference 28

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source=pdf_text observed=2026-06-28T04:51:28.419612Z digest=sha256:2f158f912db3768bf7d128722d707215e67f3a3209eaa0d4f744a9efebce924a

Observation 4271e623-492a-4ec4-949d-f308e4d304fb · outbound

This paper cites Online Speculative Decoding.

D^2SD: Accelerating Speculative Decoding with Dual Diffusion Draft Models Online Speculative Decoding

Reference 29

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arxiv_id, observed 2026-07-02T10:56:52.668974Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-06-28T04:51:28.419612Z digest=sha256:274f6844c871222bc07177ca1ebb0fb11ef49ac775cfc4d20449e2e63d85aeae

Observation 0f68fead-2712-4484-83f2-aae27cb1cf23 · outbound

This paper cites LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Models.

D^2SD: Accelerating Speculative Decoding with Dual Diffusion Draft Models LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Models

Reference 30

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source=pdf_text observed=2026-06-28T04:51:28.419612Z digest=sha256:d6b4ee20ce1ab891ca9991b45fe12754b723b6b34140d6e8cb221f26c054eddb

Observation fbdfa311-cd4d-4daf-83d2-98fadafc5b3c · outbound

This paper cites LLaDA2.1 : Speeding up text diffusion via token editing.

D^2SD: Accelerating Speculative Decoding with Dual Diffusion Draft Models LLaDA2.1 : Speeding up text diffusion via token editing

Reference 31

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source=pdf_text observed=2026-06-28T04:51:28.419612Z digest=sha256:61e3bd61ca97564e19e002aebe97fe29cc1cb80bc61c3636f5329691f4902c28

Observation 043d9fe5-5e77-4e92-9974-a5af8567d049 · outbound

This paper cites Diffuspec: Unlocking diffusion language models for speculative decoding.

D^2SD: Accelerating Speculative Decoding with Dual Diffusion Draft Models Diffuspec: Unlocking diffusion language models for speculative decoding

Reference 32

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arxiv_id, observed 2026-07-02T10:56:52.671827Z

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source=pdf_text observed=2026-06-28T04:51:28.419612Z digest=sha256:ac8accfc4b3a29c498fa8233934bf051cebea6c9fe873813a694ff9b1f290201

Observation 17f94c82-6834-44be-9b80-e8fec74d0c88 · outbound

This paper cites Pard: Accelerating llm inference with low-cost parallel draft model adaptation, 2025.

D^2SD: Accelerating Speculative Decoding with Dual Diffusion Draft Models Pard: Accelerating llm inference with low-cost parallel draft model adaptation, 2025

Reference 33

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source=pdf_text observed=2026-06-28T04:51:28.419612Z digest=sha256:5488384d4056479bf70c1cc172c7dbef6ebf5ea0ad3afc3a7e478c8db585500c

Observation 7d625fcb-16ac-4dc8-87b6-173994959032 · outbound

This paper cites Accelerating Speculative Decoding with Block Diffusion Draft Trees.

D^2SD: Accelerating Speculative Decoding with Dual Diffusion Draft Models Accelerating Speculative Decoding with Block Diffusion Draft Trees

Reference 34

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source=pdf_text observed=2026-06-28T04:51:28.419612Z digest=sha256:b7dddafa696600f1b1ea099974bf518b03c6da20374424406772f261403b0b8c

Pith citing papers

No inbound Pith citation observations are available.