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Paper Citation Record · LEDGER

MineDraft: A Framework for Batch Parallel Speculative Decoding

As of 9 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2603.18016.

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

pith.paper-citation-record.v1
2603.18016 v2

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T21:14:51.048500Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

37 of 37 outbound references displayed

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  • verified fuzzy0
  • unresolved37
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  • malformed identifier0
  • metadata mismatch0

External citation measurements

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Outbound references

Observation a69c6223-3e02-438f-828d-83bc75e5164b · outbound

This paper cites write newline.

MineDraft: A Framework for Batch Parallel Speculative Decoding write newline

Reference 1

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source=arxiv_source observed=2026-08-02T21:14:46.600284Z digest=sha256:a0914d99bc7234224fbd94400bd04734e1b9c7130e90b59ba939bfdce8cd19b3

Observation 1fe7abec-fc54-4930-b95b-570e5b69389c · outbound

This paper cites SARATHI: Efficient LLM Inference by Piggybacking Decodes with Chunked Prefills.

MineDraft: A Framework for Batch Parallel Speculative Decoding SARATHI: Efficient LLM Inference by Piggybacking Decodes with Chunked Prefills

Reference 2

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source=arxiv_source observed=2026-08-02T21:14:46.669096Z digest=sha256:c458bae37f5025cfc4326936ca95948379c2229261a38dee7116d24fa63fd947

Observation a3bce2f3-f5d4-45bc-8f8f-7d3d622eb094 · outbound

This paper cites Sharegpt dataset.

MineDraft: A Framework for Batch Parallel Speculative Decoding Sharegpt dataset

Reference 3

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source=arxiv_source observed=2026-08-02T21:14:46.786173Z digest=sha256:1d68cd6bfc92eaf3f0f07fe1d0b15403010cd76fde8fea9c3b28eaa5da41a70b

Observation 9b59e4c8-dbc5-4275-9cc0-58022c554d7d · outbound

This paper cites D., Chen, D., and Dao, T.

MineDraft: A Framework for Batch Parallel Speculative Decoding D., Chen, D., and Dao, T

Reference 4

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source=arxiv_source observed=2026-08-02T21:14:46.911164Z digest=sha256:b5bab6955a978cca130a2c90a99216a3db493b1905ba640267152ed6535b6038

Observation 90208bc0-7082-4ed5-9efb-46549c373407 · outbound

This paper cites Recurrent Drafter for Fast Speculative Decoding in Large Language Models.

MineDraft: A Framework for Batch Parallel Speculative Decoding Recurrent Drafter for Fast Speculative Decoding in Large Language Models

Reference 5

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source=arxiv_source observed=2026-08-02T21:14:47.037475Z digest=sha256:f557eeef6795f9629fa032900db75f6b8ab02739b87b8abf0e1fd1f2ff474d0f

Observation 9bc5af93-b56f-45c4-b4d8-8584ea99e97d · outbound

This paper cites DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models.

MineDraft: A Framework for Batch Parallel Speculative Decoding DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

Reference 6

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source=arxiv_source observed=2026-08-02T21:14:47.147459Z digest=sha256:40461b5678a8d7e3e04643d4c5fc2a3670d54266150945e8a5d512d27f40b7b9

Observation a042fb18-1d7f-4ee5-840e-dc4913762615 · outbound

This paper cites The Llama 3 Herd of Models.

MineDraft: A Framework for Batch Parallel Speculative Decoding The Llama 3 Herd of Models

Reference 7

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source=arxiv_source observed=2026-08-02T21:14:47.254920Z digest=sha256:1e77555b249c2cace35650cfc53850ade068dc1502e1ea6ff313ef0c52a99ab7

Observation cdba3d89-b08e-48d2-ae5b-0b62c228a920 · outbound

This paper cites an unresolved cited work.

MineDraft: A Framework for Batch Parallel Speculative Decoding Unresolved cited work

Reference 8

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source=arxiv_source observed=2026-08-02T21:14:47.407048Z digest=sha256:c2054cfdf40da1573c8dd72887f239142aedb9fe0d77ce914fba255519448af9

Observation e33df120-8630-4d14-b205-c7a1738320fe · outbound

This paper cites Specserve: Efficient and slo-aware large language model serving with adaptive speculative decoding.

MineDraft: A Framework for Batch Parallel Speculative Decoding Specserve: Efficient and slo-aware large language model serving with adaptive speculative decoding

Reference 9

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source=arxiv_source observed=2026-08-02T21:14:47.526576Z digest=sha256:66ce0b4294f7ec80e6a332b40f8dbcb99214f7cce70889cde8bd7ce58bc81528

Observation 75ebd1ac-313e-4d96-b7ed-013763a93896 · outbound

This paper cites Transformers v4.53.3.

MineDraft: A Framework for Batch Parallel Speculative Decoding Transformers v4.53.3

Reference 10

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source=arxiv_source observed=2026-08-02T21:14:47.624918Z digest=sha256:6db2a2250152cb66f7a196468e79ea684cd0c4969d60d2830af7945840a596bc

Observation cc54fd00-c9f8-43a8-a85e-b2442afe863d · outbound

This paper cites ibnzterrell/meta-llama-3.3-70b-instruct-awq-int4.

MineDraft: A Framework for Batch Parallel Speculative Decoding ibnzterrell/meta-llama-3.3-70b-instruct-awq-int4

Reference 11

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source=arxiv_source observed=2026-08-02T21:14:47.739237Z digest=sha256:c4abe46946daf9db62c3eefc4a573507cd97fa51e5ae242164350b65be26f77f

Observation b6a656f7-5fc2-4da8-a191-4c388593195e · outbound

This paper cites H., Gonzalez, J., Zhang, H., and Stoica, I.

MineDraft: A Framework for Batch Parallel Speculative Decoding H., Gonzalez, J., Zhang, H., and Stoica, I

Reference 12

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source=arxiv_source observed=2026-08-02T21:14:47.913177Z digest=sha256:8ee193bb5f0da86d1f7070a33ad46aaa6d6773e6ee85756c76f24eca765ec304

Observation c3876205-edbf-4a5c-afe9-93f9f1261392 · outbound

This paper cites Fast inference from transformers via speculative decoding.

MineDraft: A Framework for Batch Parallel Speculative Decoding Fast inference from transformers via speculative decoding

Reference 13

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source=arxiv_source observed=2026-08-02T21:14:48.072409Z digest=sha256:d8b73294f17097fe840612e36e42fd61a3b5ae490ae598100958a6927b035b0f

Observation d4947ebe-87da-4ed8-90e7-abb46008b96a · outbound

This paper cites EAGLE : Speculative sampling requires rethinking feature uncertainty.

MineDraft: A Framework for Batch Parallel Speculative Decoding EAGLE : Speculative sampling requires rethinking feature uncertainty

Reference 14

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source=arxiv_source observed=2026-08-02T21:14:48.196174Z digest=sha256:92a5e0b7dfe7fbfe18366efd1d6158e88cec69eba729f427b38fad0b7a404b44

Observation ad033fcc-896e-44f6-b886-6ccb33468e56 · outbound

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

MineDraft: A Framework for Batch Parallel Speculative Decoding EAGLE -2: Faster inference of language models with dynamic draft trees

Reference 15

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source=arxiv_source observed=2026-08-02T21:14:48.356425Z digest=sha256:ec51f4a6319f8a0d0325d33ad920915cfac162ba2223fe2214cb66fdf89c0048

Observation 0f8f9bde-28a3-49b5-bc53-db2b10fb0651 · outbound

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

MineDraft: A Framework for Batch Parallel Speculative Decoding EAGLE-3: Scaling up Inference Acceleration of Large Language Models via Training-Time Test

Reference 16

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source=arxiv_source observed=2026-08-02T21:14:48.491243Z digest=sha256:a51685530bcb2c789702e28cc1c368dc68895374b3f78392b5bf6b9b7f6c4a4b

Observation 6722a6bf-1307-4619-a1e6-304ebba110c7 · outbound

This paper cites AdaServe: Accelerating Multi-SLO LLM Serving with SLO-Customized Speculative Decoding.

MineDraft: A Framework for Batch Parallel Speculative Decoding AdaServe: Accelerating Multi-SLO LLM Serving with SLO-Customized Speculative Decoding

Reference 17

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source=arxiv_source observed=2026-08-02T21:14:48.629742Z digest=sha256:35c95bc300f1cb0241a2384939546194ed412910ab08e13836aec6c4fdd29969

Observation d534511e-9e55-458b-a9dd-25cf9b231862 · outbound

This paper cites PEARL : Parallel speculative decoding with adaptive draft length.

MineDraft: A Framework for Batch Parallel Speculative Decoding PEARL : Parallel speculative decoding with adaptive draft length

Reference 18

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source=arxiv_source observed=2026-08-02T21:14:48.746466Z digest=sha256:46e1cb2ab6ab66f5e83e59d7f40ad7b479f6156f47316a603ff4a5effba55a4e

Observation 4d6cfdfc-82f7-484e-b7f3-8669c44dbdaa · outbound

This paper cites TurboSpec: Closed-loop Speculation Control System for Optimizing LLM Serving Goodput.

MineDraft: A Framework for Batch Parallel Speculative Decoding TurboSpec: Closed-loop Speculation Control System for Optimizing LLM Serving Goodput

Reference 19

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source=arxiv_source observed=2026-08-02T21:14:48.916679Z digest=sha256:a376f4a6a1b30cb8bb034aa3c64842b08dfd4230ba8876ceec784bef808f96c6

Observation 2db38151-d015-48e6-a8ae-808ede11aafc · outbound

This paper cites NVIDIA Nsight Systems 2024.2.

MineDraft: A Framework for Batch Parallel Speculative Decoding NVIDIA Nsight Systems 2024.2

Reference 20

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source=arxiv_source observed=2026-08-02T21:14:49.052378Z digest=sha256:f1f7352b1e9b8a5ee39605ee0ab86140598f0ffb1067e8f7b887c7b77e3bc291

Observation b58c61b5-a5b7-40b3-8993-285bbda241c0 · outbound

This paper cites Suffixdecoding: Extreme speculative decoding for emerging ai applications.

MineDraft: A Framework for Batch Parallel Speculative Decoding Suffixdecoding: Extreme speculative decoding for emerging ai applications

Reference 21

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source=arxiv_source observed=2026-08-02T21:14:49.163280Z digest=sha256:f206243e92608dde3211e44240428a3d2f780ef46d58247fd318a1652b634d98

Observation 8dc45b6c-153e-401d-b5e2-27e2d4685cc8 · outbound

This paper cites Pytorch 2.7.0.

MineDraft: A Framework for Batch Parallel Speculative Decoding Pytorch 2.7.0

Reference 22

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source=arxiv_source observed=2026-08-02T21:14:49.273155Z digest=sha256:5dc48953bd42f758892e2c061353c9c652ab1b1218c2df8de3c0efdad1a0ada3

Observation 1e4581c2-f0a9-4a66-ac11-d89c5b5ce6e1 · outbound

This paper cites Closer Look at Efficient Inference Methods: A Survey of Speculative Decoding.

MineDraft: A Framework for Batch Parallel Speculative Decoding Closer Look at Efficient Inference Methods: A Survey of Speculative Decoding

Reference 23

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source=arxiv_source observed=2026-08-02T21:14:49.396548Z digest=sha256:1dc96d6c5ead84d43549b04454036a0c1db247188e650aa916ec62fc6bc1f32a

Observation abf001af-1e76-4868-af25-779fd8ccd8a4 · outbound

This paper cites Outrageously large neural networks: The sparsely-gated mixture-of-experts layer.

MineDraft: A Framework for Batch Parallel Speculative Decoding Outrageously large neural networks: The sparsely-gated mixture-of-experts layer

Reference 24

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source=arxiv_source observed=2026-08-02T21:14:49.542832Z digest=sha256:353ef673aa5bafc133dba6453c5c21452663e744b0d071ff83fd62f5d8b491a1

Observation 40505f20-96ee-4095-9f69-9daaa04512a8 · outbound

This paper cites Distributed speculative inference ( DSI ): Speculation parallelism for provably faster lossless language model inference.

MineDraft: A Framework for Batch Parallel Speculative Decoding Distributed speculative inference ( DSI ): Speculation parallelism for provably faster lossless language model inference

Reference 25

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source=arxiv_source observed=2026-08-02T21:14:49.663480Z digest=sha256:e7de5693e1900f31788dbbe5c62847ef378971921986d322d22c5c228ebd105b

Observation 84c3e34a-924f-4d06-8687-964e55e632fb · outbound

This paper cites [performance]: vllm eagle performance is worse than expected.

MineDraft: A Framework for Batch Parallel Speculative Decoding [performance]: vllm eagle performance is worse than expected

Reference 26

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source=arxiv_source observed=2026-08-02T21:14:49.791536Z digest=sha256:c104573c67782c4dd8037623a39db44812d293fbab945343f566b821665a2a80

Observation 525a5462-fea4-48d9-a2ae-a18be2bd0aa0 · outbound

This paper cites vllm v0.9.2.

MineDraft: A Framework for Batch Parallel Speculative Decoding vllm v0.9.2

Reference 27

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source=arxiv_source observed=2026-08-02T21:14:49.904704Z digest=sha256:42d1838b7db6673a3d9c975c7b42a1b7a3af355b96dadb55665dc0f426484ce7

Observation 6c4b610e-ef0b-44f1-9a79-82e169f1ce40 · outbound

This paper cites OPT -tree: Speculative decoding with adaptive draft tree structure.

MineDraft: A Framework for Batch Parallel Speculative Decoding OPT -tree: Speculative decoding with adaptive draft tree structure

Reference 28

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source=arxiv_source observed=2026-08-02T21:14:50.122042Z digest=sha256:294ae4ca0c6dbfd01a176d2d44c5f55b366dfd9e6afc2032c6741a05b3ca1c37

Observation 495bec5c-218c-4941-a7c9-27b0094c92b0 · outbound

This paper cites Minions: Accelerating Large Language Model Inference with Aggregated Speculative Execution.

MineDraft: A Framework for Batch Parallel Speculative Decoding Minions: Accelerating Large Language Model Inference with Aggregated Speculative Execution

Reference 29

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source=arxiv_source observed=2026-08-02T21:14:50.222538Z digest=sha256:aa612c27eee417ebedb58215f61c05cee8c06250679277f130424d6150b868b2

Observation 1460c91f-5374-4f7a-a1ba-68558e4f1b07 · outbound

This paper cites an unresolved cited work.

MineDraft: A Framework for Batch Parallel Speculative Decoding Unresolved cited work

Reference 30

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source=arxiv_source observed=2026-08-02T21:14:50.340335Z digest=sha256:fdbf45f4ef8364c2876b4fec61a30de462321eca2ed7a950207a9caf554f3c1b

Observation 68231890-d1ee-4f21-be50-100e38d583e2 · outbound

This paper cites Unlocking efficiency in large language model inference: A comprehensive survey of speculative decoding.

MineDraft: A Framework for Batch Parallel Speculative Decoding Unlocking efficiency in large language model inference: A comprehensive survey of speculative decoding

Reference 31

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source=arxiv_source observed=2026-08-02T21:14:50.472687Z digest=sha256:2b015c678ed1a57df00b9faab0df7304b1836ebbb2e3fe96f6f463033d4f0f30

Observation 4faeb111-b210-48ae-afd5-42d1a5d5ffcc · outbound

This paper cites ParallelSpec: Parallel Drafter for Efficient Speculative Decoding.

MineDraft: A Framework for Batch Parallel Speculative Decoding ParallelSpec: Parallel Drafter for Efficient Speculative Decoding

Reference 32

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source=arxiv_source observed=2026-08-02T21:14:50.581257Z digest=sha256:51f84a8171e6d0c319bef4f0b7e5c68e7f580ef4d31e33f16e7e8f56faf0fca2

Observation 33fecf6e-5328-4b72-b60b-df007142ea84 · outbound

This paper cites Qwen3 Technical Report.

MineDraft: A Framework for Batch Parallel Speculative Decoding Qwen3 Technical Report

Reference 33

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source=arxiv_source observed=2026-08-02T21:14:50.671835Z digest=sha256:c6c7b9658f60d844f2a53b3930ac8aa5b7abb73cdffa4a2dd61e4726a2c0e449

Observation c038e494-dc7d-4dab-a8b6-3e380776e45c · outbound

This paper cites Llm-tough-questions dataset: A collection of complex questions across 100 domains.

MineDraft: A Framework for Batch Parallel Speculative Decoding Llm-tough-questions dataset: A collection of complex questions across 100 domains

Reference 34

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source=arxiv_source observed=2026-08-02T21:14:50.763821Z digest=sha256:ae906c34036de6d52d2f7869891a64f439bb9ce8432a84c1aa66d74da5802980

Observation 758d793c-0b58-4959-ae17-1fc0ca9dd364 · outbound

This paper cites S., Kim, G.-W., Kim, S., and Chun, B.-G.

MineDraft: A Framework for Batch Parallel Speculative Decoding S., Kim, G.-W., Kim, S., and Chun, B.-G

Reference 35

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source=arxiv_source observed=2026-08-02T21:14:50.839904Z digest=sha256:55738b8b0de64216801add96b92ec8cca0f00917b9f6825ebde1a6b3e7f6de58

Observation 720e337c-508e-409e-bc07-bbbe1b19e5d4 · outbound

This paper cites Draft & verify: Lossless large language model acceleration via self-speculative decoding.

MineDraft: A Framework for Batch Parallel Speculative Decoding Draft & verify: Lossless large language model acceleration via self-speculative decoding

Reference 36

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source=arxiv_source observed=2026-08-02T21:14:50.941081Z digest=sha256:eddcad3cc18e926da48a162c270a942075f66748d2b02d77cff4ecd70c581c88

Observation 0acec2aa-c140-4ca7-ba60-34a9ff906c32 · outbound

This paper cites E., and Stoica, I.

MineDraft: A Framework for Batch Parallel Speculative Decoding E., and Stoica, I

Reference 37

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source=arxiv_source observed=2026-08-02T21:14:51.048500Z digest=sha256:f2940a1f142fc0f938f316b705076f710eefd2b8bc117c31425f273e69c8faf3

Pith citing papers

No inbound Pith citation observations are available.