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Paper Citation Record · LEDGER

BlockServe: Block-Grained Continuous Batching for High-Throughput Diffusion LLM Serving

As of 8 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2607.08930.

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

pith.paper-citation-record.v1
2607.08930 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-13T05:43:12.690359Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

21 of 21 outbound references displayed

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External citation measurements

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Outbound references

Observation b03ec011-2f9d-4d74-9362-1893d5dec339 · outbound

This paper cites DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference.

BlockServe: Block-Grained Continuous Batching for High-Throughput Diffusion LLM Serving DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference

Reference 1

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source=pdf_text observed=2026-07-13T05:43:12.690359Z digest=sha256:3f70f97c940b064df5972085192ba98c98d92671c225ae0377d9e9fb82fe6446

Observation 84881377-2ebd-48c7-bd80-deea8eb904fd · outbound

This paper cites Orca: A distributed serving system for{Transformer-Based}generative models,.

BlockServe: Block-Grained Continuous Batching for High-Throughput Diffusion LLM Serving Orca: A distributed serving system for{Transformer-Based}generative models,

Reference 2

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source=pdf_text observed=2026-07-13T05:43:12.690359Z digest=sha256:36073e8b17b213ca7985a6f2ebe5c8aa6f4a3e93331367e01615473da04c1522

Observation 71a5e5c0-422b-4e7a-a2ab-7f522470adb6 · outbound

This paper cites Efficient memory management for large language model serving with pagedattention,.

BlockServe: Block-Grained Continuous Batching for High-Throughput Diffusion LLM Serving Efficient memory management for large language model serving with pagedattention,

Reference 3

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Observation 082abff3-f51f-4d0e-b99b-55703f42637b · outbound

This paper cites Large Language Diffusion Models.

BlockServe: Block-Grained Continuous Batching for High-Throughput Diffusion LLM Serving Large Language Diffusion Models

Reference 4

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source=pdf_text observed=2026-07-13T05:43:12.690359Z digest=sha256:416bd60c390acba3a43c5f00ba1bd54ccaa294e44be684512a3a2b690bf08d8d

Observation b7b2ae5b-9534-4c9a-bbc1-18ea11d7ed81 · outbound

This paper cites Dream 7B: Diffusion Large Language Models.

BlockServe: Block-Grained Continuous Batching for High-Throughput Diffusion LLM Serving Dream 7B: Diffusion Large Language Models

Reference 5

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source=pdf_text observed=2026-07-13T05:43:12.690359Z digest=sha256:29fd5b5a2a4d1e765b007b70ce30e9f6a68ce6218246fbfe84387770f4fac455

Observation bcb52f80-ea89-435c-b650-0393da7e0fcd · outbound

This paper cites Fast-dLLM: Training-free Acceleration of Diffusion LLM by Enabling KV Cache and Parallel Decoding.

BlockServe: Block-Grained Continuous Batching for High-Throughput Diffusion LLM Serving Fast-dLLM: Training-free Acceleration of Diffusion LLM by Enabling KV Cache and Parallel Decoding

Reference 6

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source=pdf_text observed=2026-07-13T05:43:12.690359Z digest=sha256:06e4511dd1336783a56b38e7581cc76303af0307b370d55ae9435b3595d2f9f7

Observation d4ad8f6e-ec23-4eec-be11-31e37ec220d3 · outbound

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

BlockServe: Block-Grained Continuous Batching for High-Throughput Diffusion LLM Serving SARATHI: Efficient LLM Inference by Piggybacking Decodes with Chunked Prefills

Reference 7

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source=pdf_text observed=2026-07-13T05:43:12.690359Z digest=sha256:6ce052123f124faed4223add1367e618962aa9c0360fe3a81bfa31da162e754b

Observation 82c58ec2-eff8-437d-947a-a276263c2572 · outbound

This paper cites Taming{Throughput-Latency}tradeoff in{LLM}inference with{Sarathi-Serve},.

BlockServe: Block-Grained Continuous Batching for High-Throughput Diffusion LLM Serving Taming{Throughput-Latency}tradeoff in{LLM}inference with{Sarathi-Serve},

Reference 8

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source=pdf_text observed=2026-07-13T05:43:12.690359Z digest=sha256:2f2881c5e50c9961c437ef5c5a1572649c0df90f72f2ce3e65c68b7f09f42c84

Observation bc1b0424-c7f8-47eb-a39b-01ee5b80a7ff · outbound

This paper cites Roformer: En- hanced transformer with rotary position embedding,.

BlockServe: Block-Grained Continuous Batching for High-Throughput Diffusion LLM Serving Roformer: En- hanced transformer with rotary position embedding,

Reference 9

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source=pdf_text observed=2026-07-13T05:43:12.690359Z digest=sha256:176b2b91ca0bf48ae8fb0ec48b153139186af797c40637d7a414afecb7bba46b

Observation 4b4d85dc-c94d-4c05-a7b6-8f27ad4ec0f9 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

BlockServe: Block-Grained Continuous Batching for High-Throughput Diffusion LLM Serving Training Verifiers to Solve Math Word Problems

Reference 10

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source=pdf_text observed=2026-07-13T05:43:12.690359Z digest=sha256:7199c60c79f737b46b5f275f57bd14e97d9ac295b4a591771fcf80f53af462aa

Observation 4136d42f-2270-4595-afb0-7257c46dbdfa · outbound

This paper cites Evaluating Large Language Models Trained on Code.

BlockServe: Block-Grained Continuous Batching for High-Throughput Diffusion LLM Serving Evaluating Large Language Models Trained on Code

Reference 11

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source=pdf_text observed=2026-07-13T05:43:12.690359Z digest=sha256:e6d75a95a9673aa2c8d736946d92a28ea6167330fa03f40c3a9bd08d48b9bf03

Observation 1b8b9055-a96c-4542-8d31-800358d06ebc · outbound

This paper cites Program Synthesis with Large Language Models.

BlockServe: Block-Grained Continuous Batching for High-Throughput Diffusion LLM Serving Program Synthesis with Large Language Models

Reference 12

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source=pdf_text observed=2026-07-13T05:43:12.690359Z digest=sha256:03dd627909820029ee858639624fbd7511be72a6b02cfdb883daf00e948c3cab

Observation 9ea17427-2774-439e-bda6-a46811c11c2e · outbound

This paper cites Measuring Mathematical Problem Solving With the MATH Dataset.

BlockServe: Block-Grained Continuous Batching for High-Throughput Diffusion LLM Serving Measuring Mathematical Problem Solving With the MATH Dataset

Reference 13

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source=pdf_text observed=2026-07-13T05:43:12.690359Z digest=sha256:709a051eb9ea07d6fe342d532273c2a5b1bf53d69b4424d92e5b695527c1b562

Observation eebdbd75-6120-4b82-9524-964c89883b00 · outbound

This paper cites Truthfulqa: Measuring how models mimic human falsehoods,.

BlockServe: Block-Grained Continuous Batching for High-Throughput Diffusion LLM Serving Truthfulqa: Measuring how models mimic human falsehoods,

Reference 14

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source=pdf_text observed=2026-07-13T05:43:12.690359Z digest=sha256:18877144a189f8036dbd487256b2ef071cc890f19c77cbe9ee331921fd53202f

Observation 8518fc48-37d5-47c0-9583-53ebfc64825d · outbound

This paper cites Reference-guided verdict: Llms-as-judges in automatic evaluation of free-form qa,.

BlockServe: Block-Grained Continuous Batching for High-Throughput Diffusion LLM Serving Reference-guided verdict: Llms-as-judges in automatic evaluation of free-form qa,

Reference 15

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source=pdf_text observed=2026-07-13T05:43:12.690359Z digest=sha256:c14dab6946979a099dfe25a43c070f0aa12bb2ed7168f03b3a83386546589164

Observation 34cf0982-6e35-44ef-abd3-6a9ae38feb72 · outbound

This paper cites Struc- tured denoising diffusion models in discrete state-spaces,.

BlockServe: Block-Grained Continuous Batching for High-Throughput Diffusion LLM Serving Struc- tured denoising diffusion models in discrete state-spaces,

Reference 16

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Observation 5330e205-ea96-4015-8aeb-97af415f72ba · outbound

This paper cites Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution.

BlockServe: Block-Grained Continuous Batching for High-Throughput Diffusion LLM Serving Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution

Reference 17

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Observation 22d17a10-ac90-4c5e-8784-06d1918e8d61 · outbound

This paper cites Simple and effective masked diffusion language models,.

BlockServe: Block-Grained Continuous Batching for High-Throughput Diffusion LLM Serving Simple and effective masked diffusion language models,

Reference 18

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source=pdf_text observed=2026-07-13T05:43:12.690359Z digest=sha256:ade2ef1971b5ed69f368c4cb61052fc253f1abbe0bea8fd7b38723d8abdd4e27

Observation e374e6c1-e0a7-4947-82b1-04f2d2c588e9 · outbound

This paper cites Beyond Autoregression: Discrete Diffusion for Complex Reasoning and Planning.

BlockServe: Block-Grained Continuous Batching for High-Throughput Diffusion LLM Serving Beyond Autoregression: Discrete Diffusion for Complex Reasoning and Planning

Reference 19

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Observation d68501d4-3ffc-408e-a2d0-360d58fedf83 · outbound

This paper cites {DistServe}: Disaggregating prefill and decoding for goodput-optimized large language model serving,.

BlockServe: Block-Grained Continuous Batching for High-Throughput Diffusion LLM Serving {DistServe}: Disaggregating prefill and decoding for goodput-optimized large language model serving,

Reference 20

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Observation 02fa7e2b-ef3c-4213-bf7c-be58bddb8c5a · outbound

This paper cites Taming the memory footprint crisis: System design for production diffusion llm serving,.

BlockServe: Block-Grained Continuous Batching for High-Throughput Diffusion LLM Serving Taming the memory footprint crisis: System design for production diffusion llm serving,

Reference 21

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source=pdf_text observed=2026-07-13T05:43:12.690359Z digest=sha256:f2d38727b820d148c30f0c84156de844913b60635963298b87110262172be160

Pith citing papers

No inbound Pith citation observations are available.