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Paper Citation Record · LEDGER

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving

As of 15 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 5 inbound Pith citation observations for arXiv:2506.13497.

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

pith.paper-citation-record.v1
2506.13497 v1

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:05:06.570203Z

measured 62 of 62 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T05:50:05.685350Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-16T22:21:18.726162Z

Reference resolution

57 of 57 outbound references displayed

  • verified exact0
  • verified fuzzy18
  • unresolved38
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c09a5e5e-d814-4ed1-ab2f-5e7d317a18d1 · outbound

This paper cites Deepspeed-inference: enabling efficient inference of transformer models at unprecedented scale.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving Deepspeed-inference: enabling efficient inference of transformer models at unprecedented scale

Reference 1

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raw_fallback, observed 2026-08-15T20:05:07.270814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-15T20:05:06.255892Z digest=sha256:4d1d3d83f2b0cdc218179068dc812d71cca24d348ff0fad570f0f3a12ef2a80a

Observation 94aae542-5cd2-4d01-806a-9ebb3ebd61f7 · outbound

This paper cites Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Reference 2

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T20:05:06.261017Z digest=sha256:5d708630adaa4e9e9b4762dc924d051e06473f050b1f0a79f0f8914676ff58c0

Observation 828659ad-2bf4-48ac-892d-6e32b7519cd2 · outbound

This paper cites Striped Attention: Faster Ring Attention for Causal Transformers.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving Striped Attention: Faster Ring Attention for Causal Transformers

Reference 3

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source=pdf_text observed=2026-08-15T20:05:06.265472Z digest=sha256:a94e0667e9c4b2cc620647d6b5d33d6b3e029b351d0c299954e6c9217116407b

Observation b4a73874-3515-4696-8bcb-5fe23b0f43b8 · outbound

This paper cites Pixart- 𝑠𝑖𝑔𝑚𝑎: Weak-to-strong training of diffusion transformer for 4k text- to-image generation.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving Pixart- 𝑠𝑖𝑔𝑚𝑎: Weak-to-strong training of diffusion transformer for 4k text- to-image generation

Reference 4

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raw_fallback, observed 2026-08-15T20:05:07.257922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-15T20:05:06.270988Z digest=sha256:0dfbe38a6ab4ace51cc84c933a026e9a1cab4c891dafe4d5be4685e5f1c5476b

Observation a7ae735a-73f9-4479-b255-dcd01a0bd2da · outbound

This paper cites $\Delta$-DiT: A Training-Free Acceleration Method Tailored for Diffusion Transformers.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving $\Delta$-DiT: A Training-Free Acceleration Method Tailored for Diffusion Transformers

Reference 5

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T20:05:06.275248Z digest=sha256:6d3b942cdf6d44222b20bd607232b7730066ca1fdca3030c3f0e7272be808e88

Observation 7cab8db1-c386-4cdb-b789-a0f4acde07f1 · outbound

This paper cites Flashattention: Fast and memory-efficient exact attention with io- awareness.Advances in Neural Information Processing Systems, 2022.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving Flashattention: Fast and memory-efficient exact attention with io- awareness.Advances in Neural Information Processing Systems, 2022

Reference 6

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raw_fallback, observed 2026-08-15T20:05:07.245832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-15T20:05:06.279204Z digest=sha256:b8c8d547c2545a9bc884606bc697e4962c03aaf9e5cbd67f65bbdb487ee51bbe

Observation 5e3a6cfc-2a7f-4aa2-b222-41677c3b4395 · outbound

This paper cites Addison-Wesley, 2013.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving Addison-Wesley, 2013

Reference 7

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raw_fallback, observed 2026-08-15T20:05:07.232935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-15T20:05:06.283294Z digest=sha256:26f342dcf92b2029386667926f92d7e3e529c3796e267185e497f1198d189427

Observation 3bcb97a0-d7f1-4d54-ae57-efeb117cd17d · outbound

This paper cites xDiT: an Inference Engine for Diffusion Transformers (DiTs) with Massive Parallelism.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving xDiT: an Inference Engine for Diffusion Transformers (DiTs) with Massive Parallelism

Reference 8

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source=pdf_text observed=2026-08-15T20:05:06.314626Z digest=sha256:fa8347c8982a318d44bbde70f3fb9b3d15a868464d51ac3433ce3d005d4f3a23

Observation 86101e07-82d0-46b6-b1c6-95170c33cf35 · outbound

This paper cites an unresolved cited work.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving Unresolved cited work

Reference 9

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source=pdf_text observed=2026-08-15T20:05:06.373071Z digest=sha256:52cbb0a9c5efc541ce3d76a695ca2da372ce188b01aa38deec5f95f28853b8cb

Observation 7e27dc85-5be4-44fe-a8a1-c201f82d6e72 · outbound

This paper cites Reevaluating amdahl’s law.Communications of the ACM, 31(5):532–533, 1988.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving Reevaluating amdahl’s law.Communications of the ACM, 31(5):532–533, 1988

Reference 10

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raw_fallback, observed 2026-08-15T20:05:07.209870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-15T20:05:06.383705Z digest=sha256:67151c45c25c376528243a12b39ab9842565faa4a69d5eecf10f32f90f250742

Observation f3d74ff0-29ce-44d0-b1ef-37740abf3c8b · outbound

This paper cites Denoising diffusion prob- abilistic models.Advances in neural information processing systems, 33:6840–6851, 2020.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving Denoising diffusion prob- abilistic models.Advances in neural information processing systems, 33:6840–6851, 2020

Reference 11

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source=pdf_text observed=2026-08-15T20:05:06.388256Z digest=sha256:7ceb9d0a71df52e44321f6d58281a2edbf5e9f87e7855818260bf7425d251363

Observation 1d89c164-e0b3-46e0-992b-f5fc0d686405 · outbound

This paper cites FlashDecoding++: Faster Large Language Model Inference on GPUs.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving FlashDecoding++: Faster Large Language Model Inference on GPUs

Reference 12

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source=pdf_text observed=2026-08-15T20:05:06.391434Z digest=sha256:8913a018f8334ae98a7586807bcef4d00cad7a61b32a1d1300081dcf8b3f1047

Observation 2e0ac90f-a911-4c1e-95cc-3e0b02964cc2 · outbound

This paper cites an unresolved cited work.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving Unresolved cited work

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-15T20:05:06.395037Z digest=sha256:5f83415f01b10208b31583e6faa9f3f9097e503ca42b3385a9e097ef456e575f

Observation e05d157c-4b43-483b-9d7f-2d1f0c8fb6e7 · outbound

This paper cites an unresolved cited work.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving Unresolved cited work

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-15T20:05:06.399663Z digest=sha256:121a2c3b2f97cbd1f94d9e7bfd11bf65bfeb51ebf8c7999f3bddf75f80677ebf

Observation 2b28b665-4e13-4260-b651-c556cecf1b01 · outbound

This paper cites MemServe: Context Caching for Disaggregated LLM Serving with Elastic Memory Pool.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving MemServe: Context Caching for Disaggregated LLM Serving with Elastic Memory Pool

Reference 15

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source=pdf_text observed=2026-08-15T20:05:06.403634Z digest=sha256:7a9b55390064c822141fe5b8ebb2f7540f0e9eb5278ba6964aaa68f91bab8e6b

Observation a1981db8-ea96-454b-9268-7f2ef1bc6c00 · outbound

This paper cites Inference without Interference: Disaggregate LLM Inference for Mixed Downstream Workloads.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving Inference without Interference: Disaggregate LLM Inference for Mixed Downstream Workloads

Reference 16

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source=pdf_text observed=2026-08-15T20:05:06.408108Z digest=sha256:5cf548b2ea1f6fee5cf6c13f2ed66297c5137164a9e5c75be78f9fa6a15d80b0

Observation fbe8a1b0-52fe-455c-a9ed-96937d17beb7 · outbound

This paper cites Reducing activation recomputation in large transformer models.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving Reducing activation recomputation in large transformer models

Reference 17

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source=pdf_text observed=2026-08-15T20:05:06.412347Z digest=sha256:1490d16f6279c06faafc1c06f4934a11fe86fbb31c724dee234ed3ceb49b17f3

Observation a7303ded-12f3-40b5-8960-706a99dd863c · outbound

This paper cites an unresolved cited work.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving Unresolved cited work

Reference 18

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source=pdf_text observed=2026-08-15T20:05:06.416218Z digest=sha256:d231612404c4636a47fad596c6bde4cde51522e1f8c96ba7cfb95b46c328efce

Observation 09665ae3-4dec-4ee0-8318-85a5adf32044 · outbound

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

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving Efficient memory management for large language model serving with pagedattention

Reference 19

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source=pdf_text observed=2026-08-15T20:05:06.420247Z digest=sha256:00bb89ca7992ba8b8eb8e585cc32e40ceeea539c60bdfef3a4b7f1c01f52249f

Observation 952f23a5-0eba-4af8-bb54-21dd21d43d11 · outbound

This paper cites Distrifusion: Distributed parallel inference for high-resolution diffusion models.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving Distrifusion: Distributed parallel inference for high-resolution diffusion models

Reference 20

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source=pdf_text observed=2026-08-15T20:05:06.423782Z digest=sha256:f6f4e852962f1f51eb15a8dc1005712117e98a69393f7748527b3f9eb6954cc1

Observation a0c45c94-03ed-4298-807c-f99269844f3c · outbound

This paper cites Sequence Parallelism: Long Sequence Training from System Perspective.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving Sequence Parallelism: Long Sequence Training from System Perspective

Reference 21

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source=pdf_text observed=2026-08-15T20:05:06.427406Z digest=sha256:20eefd8a06881158339e8a8ceab47ead58d35c0346c973e066b935fc8d522aa8

Observation 776d4615-57c6-40fd-a9f9-3beffd53da02 · outbound

This paper cites SwiftDiffusion: Efficient Diffusion Model Serving with Add-on Modules.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving SwiftDiffusion: Efficient Diffusion Model Serving with Add-on Modules

Reference 22

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source=pdf_text observed=2026-08-15T20:05:06.431371Z digest=sha256:77ec0fa1f1b1931fda14f9aebcd128e410047dc1ccb0ef4b1cf6c5969ee81e95

Observation af10252a-5900-40b1-89c2-42ca4bdd52ce · outbound

This paper cites A survey of convolutional neural networks: analysis, applications, and prospects.IEEE transactions on neural networks and learning systems, 33(12):6999–7019, 2021.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving A survey of convolutional neural networks: analysis, applications, and prospects.IEEE transactions on neural networks and learning systems, 33(12):6999–7019, 2021

Reference 23

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source=pdf_text observed=2026-08-15T20:05:06.435356Z digest=sha256:3717c952c0f3ee119be71c188c53d83923f140a107ef43989762d2e017acf19e

Observation 0dd30c49-bbdd-4909-b771-32f1353fb0bb · outbound

This paper cites Train big, then compress: Rethinking model size for efficient training and inference of transformers.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving Train big, then compress: Rethinking model size for efficient training and inference of transformers

Reference 24

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-15T20:05:06.439215Z digest=sha256:b3ae16341e26abe7ac71a4802d9d2bda2792b8d33792f3c4eb832da8b0851624

Observation a6432ccc-f1db-4715-9c72-16c4fe52b485 · outbound

This paper cites Open-Sora Plan: Open-Source Large Video Generation Model.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving Open-Sora Plan: Open-Source Large Video Generation Model

Reference 25

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source=pdf_text observed=2026-08-15T20:05:06.442913Z digest=sha256:001829a040c8240a06fece3d9ec4ee1ff883c90d5c5f4ef5a4eb058f10228898

Observation 450a7e55-a72c-4049-af69-f0861dc39779 · outbound

This paper cites Ring Attention with Blockwise Transformers for Near-Infinite Context.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving Ring Attention with Blockwise Transformers for Near-Infinite Context

Reference 26

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source=pdf_text observed=2026-08-15T20:05:06.446955Z digest=sha256:ff41ebc01a2d23bf40d677a57d77a91b848be5c406218352a23e657bab597ac6

Observation 8eb1b57e-b2f9-4a53-ace4-a9aca481b5de · outbound

This paper cites Deepcache: Accelerat- ing diffusion models for free.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving Deepcache: Accelerat- ing diffusion models for free

Reference 27

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raw_fallback, observed 2026-08-15T20:05:07.115490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-15T20:05:06.451188Z digest=sha256:c347f90997b24ca38843eedaaa20766f8210e04fcf9db82eff5e18062834fb77

Observation fd8191b9-2d46-4a62-8345-626b778740f3 · outbound

This paper cites Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models

Reference 28

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source=pdf_text observed=2026-08-15T20:05:06.454723Z digest=sha256:df2fffb194fd61680e0f17ffbdc2552769a82e09f4c2a631d831e8c64c52d14c

Observation ef0325db-7c96-4ea0-9f29-35f78e5c1ded · outbound

This paper cites The design process for google’s training chips: Tpuv2 and tpuv3.IEEE Micro, 41(2):56–63, 2021.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving The design process for google’s training chips: Tpuv2 and tpuv3.IEEE Micro, 41(2):56–63, 2021

Reference 29

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raw_fallback, observed 2026-08-15T20:05:07.103618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-15T20:05:06.459888Z digest=sha256:4827e13e74c00c465c3abcbab06bd3f866fecbe2df5e95e2738045fa2c8badee

Observation 7d4bd50a-5f2c-499d-8c1b-33771e994039 · outbound

This paper cites NCCL.https://docs.nvidia.com/deeplearning/nccl/user- guide/docs/overview.html.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving NCCL.https://docs.nvidia.com/deeplearning/nccl/user- guide/docs/overview.html

Reference 30

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raw_fallback, observed 2026-08-15T20:05:07.090777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-15T20:05:06.464126Z digest=sha256:7fa7a6f0e0dbaebdf998b13374da7107bf2f98409cd5a98d19f39b2e7be2dc1c

Observation 9c56a28e-6aa1-4617-a7a0-e656c98e93d8 · outbound

This paper cites an unresolved cited work.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving Unresolved cited work

Reference 31

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-15T20:05:06.468166Z digest=sha256:3f80b515f064cde9050266ce62031eb2652895ac24ea442b048193b3c89b412a

Observation 515dfd6f-029b-4467-b878-13b93cd913bc · outbound

This paper cites Splitwise: Efficient generative LLM inference using phase splitting.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving Splitwise: Efficient generative LLM inference using phase splitting

Reference 32

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source=pdf_text observed=2026-08-15T20:05:06.472326Z digest=sha256:5b06aa6ad5a249a3631bdeee81ab5761d3ad07269a4e035e51420efef998d4b1

Observation f976183c-5317-4335-985d-282c1099f744 · outbound

This paper cites Scalable diffusion models with transformers.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving Scalable diffusion models with transformers

Reference 33

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source=pdf_text observed=2026-08-15T20:05:06.476265Z digest=sha256:78391ef4012f5cc89fa2c9fc1108c0a87cf0d00094f9f41513111f031da14ece

Observation 42600dd4-2549-455e-96b7-992e377d7a46 · outbound

This paper cites Variational autoencoder.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving Variational autoencoder

Reference 34

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raw_fallback, observed 2026-08-15T20:05:07.057332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-15T20:05:06.480002Z digest=sha256:41789f32f49433307cef21365e9182c86197d833ac681dc9247cd2455effb99f

Observation 809ed3dd-6705-4ed1-9ba5-c7688ce26c2f · outbound

This paper cites Seagull: An Infrastructure for Load Prediction and Optimized Resource Allocation.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving Seagull: An Infrastructure for Load Prediction and Optimized Resource Allocation

Reference 35

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source=pdf_text observed=2026-08-15T20:05:06.483437Z digest=sha256:c94a407345987db71a1117b45ea8fd48ffd4c45bded38a1b2a6d3c5441aa4506

Observation fc481b10-0b48-4be1-a105-321f9ce13c9c · outbound

This paper cites A remark on stirling’s formula.The American mathematical monthly, 62(1):26–29, 1955.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving A remark on stirling’s formula.The American mathematical monthly, 62(1):26–29, 1955

Reference 36

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-15T20:05:06.486911Z digest=sha256:9382d8498b233aca9e012857ec64d41c9b4285a9b95a64e8d0e927303b75433f

Observation ae3ab585-60f0-4e23-aab9-ff770706ee90 · outbound

This paper cites an unresolved cited work.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving Unresolved cited work

Reference 37

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source=pdf_text observed=2026-08-15T20:05:06.490338Z digest=sha256:716eba1c9e27c8c2fe289e1cef2c0abc44004fbb529c1ed868b204fbc04f6b6e

Observation d853f804-380e-479a-837c-d3899bdeed23 · outbound

This paper cites FORA: Fast-Forward Caching in Diffusion Transformer Acceleration.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving FORA: Fast-Forward Caching in Diffusion Transformer Acceleration

Reference 38

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source=pdf_text observed=2026-08-15T20:05:06.493639Z digest=sha256:0aa5133a829fd203faea2bc8f3932a6bb66071bae71044f65e5ec396881bf37e

Observation 9717fb87-54e7-4425-84f3-b90446bb1b6e · outbound

This paper cites Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Reference 39

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source=pdf_text observed=2026-08-15T20:05:06.496920Z digest=sha256:98229f386901a9a6b1ab56c6b87e3ac588de5086673780d48a0504a6c636e7c3

Observation 99f9ae0c-df2e-46f9-8b48-d7b9731a8442 · outbound

This paper cites Fundamentals of queueing theory, volume 399.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving Fundamentals of queueing theory, volume 399

Reference 40

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raw_fallback, observed 2026-08-15T20:05:07.019142Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-15T20:05:06.500900Z digest=sha256:301c68f1238a922bd23ea103ce42c4b2c82ea83fa3e378e7a6ac5987e8a79a6e

Observation 5e8346e6-8498-41a0-9e8f-2e61557b7111 · outbound

This paper cites Rpc: Remote procedure call protocol specification version 2.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving Rpc: Remote procedure call protocol specification version 2

Reference 41

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raw_fallback, observed 2026-08-15T20:05:07.005583Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-15T20:05:06.504641Z digest=sha256:dbe64e7ca35f983e043a5fba6f02cc207f2c1abd7fe16adbb197dcc1487ce421

Observation 39163bc3-5ad0-4b37-af08-44df92dbeaea · outbound

This paper cites One Embedder, Any Task: Instruction-Finetuned Text Embeddings.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving One Embedder, Any Task: Instruction-Finetuned Text Embeddings

Reference 42

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source=pdf_text observed=2026-08-15T20:05:06.508413Z digest=sha256:3649d6a9df3eddf29ae9f5d58b17fff0cf559ed97998fad97fbf067761853f8c

Observation 1f8c96ef-7309-463a-a795-4d3ed50d2009 · outbound

This paper cites Videosys: An easy and efficient system for video generation, 2024.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving Videosys: An easy and efficient system for video generation, 2024

Reference 43

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raw_fallback, observed 2026-08-15T20:05:06.989202Z

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source=pdf_text observed=2026-08-15T20:05:06.512520Z digest=sha256:436bbfc76e8a5b4ca945976bc7bf4ab0c29e87b961cc5782742925f88efbff8b

Observation f28349fa-4562-4a07-8c44-ed35d4692974 · outbound

This paper cites New and old results for the m/d/c queue.AEU- International Journal of Electronics and Communications, 60(2):125–130, 2006.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving New and old results for the m/d/c queue.AEU- International Journal of Electronics and Communications, 60(2):125–130, 2006

Reference 44

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raw_fallback, observed 2026-08-15T20:05:06.975467Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-15T20:05:06.516173Z digest=sha256:8ffb1eb518db52a522b72a034546250946de32882b627cc8d9f9c63268078a26

Observation 3ceda22f-ceb0-4af3-a2cf-929ddfc0923d · outbound

This paper cites PipeFusion: Patch-level Pipeline Parallelism for Diffusion Transformers Inference.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving PipeFusion: Patch-level Pipeline Parallelism for Diffusion Transformers Inference

Reference 45

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source=pdf_text observed=2026-08-15T20:05:06.519810Z digest=sha256:62865607932120ff08a2b4e6185551d47069d80a3bbe1c7f081bad27f8b57276

Observation 4a57b75d-e3bb-4783-8b24-1a5b2f3cb3ec · outbound

This paper cites NVLink.https://en.wikipedia.org/wiki/NVLink.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving NVLink.https://en.wikipedia.org/wiki/NVLink

Reference 46

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raw_fallback, observed 2026-08-15T20:05:06.960296Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-15T20:05:06.523938Z digest=sha256:4863fdc72757893f3ebc57527cb6475d38c800d4237fa81d6a7b5a22253774d5

Observation 293b7419-54dd-475d-89be-088c6e2838c9 · outbound

This paper cites Cache me if you can: Accelerating diffusion models through block caching.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving Cache me if you can: Accelerating diffusion models through block caching

Reference 47

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source=pdf_text observed=2026-08-15T20:05:06.527751Z digest=sha256:ba1d2dfb2bc6892b1ecbf844f825b18006a0fd25815626ca88a3bf17b318b3c6

Observation 63df851d-b387-463a-8250-45dd56ba37ea · outbound

This paper cites Preact: Predictive resource allocation for bursty workloads in a co-located data center.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving Preact: Predictive resource allocation for bursty workloads in a co-located data center

Reference 48

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raw_fallback, observed 2026-08-15T20:05:06.939121Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-15T20:05:06.531612Z digest=sha256:004262eceb8b55ce840ed9d57d7a12788cd0d96e4f8279cb66a95d11dc87a650

Observation 46ae53c2-2565-4ed4-b3ec-0021dd8296f1 · outbound

This paper cites CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer

Reference 49

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source=pdf_text observed=2026-08-15T20:05:06.535684Z digest=sha256:fa3821e9c9fadbdbc515eb6c978f77b922966b09c982768cea196adfa873b392

Observation eafb86e6-4a05-45d1-8638-5eafa4f9a10f · outbound

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

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving Orca: A distributed serving system for {Transformer-Based} generative models

Reference 50

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raw_fallback, observed 2026-08-15T20:05:06.923834Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-15T20:05:06.540557Z digest=sha256:3106a2b292d2efec39dbdfbef15a9a839d5d22e09db1a9f535422b4ae20fab30

Observation a9393754-35b7-4b19-b409-61a895cd667a · outbound

This paper cites DiTFastAttn: Attention Compression for Diffusion Transformer Models.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving DiTFastAttn: Attention Compression for Diffusion Transformer Models

Reference 51

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source=pdf_text observed=2026-08-15T20:05:06.545013Z digest=sha256:063297cf64a40fba2602c229687e57cecd2a56f609f549043188182a4316919a

Observation 9bf367cf-2d7e-4562-965a-12c78c0ebb7c · outbound

This paper cites Faster Diffusion via Temporal Attention Decomposition.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving Faster Diffusion via Temporal Attention Decomposition

Reference 52

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source=pdf_text observed=2026-08-15T20:05:06.549223Z digest=sha256:5fb525d506212196c9a2921d2acbd36018ceb3866f7c52c9148dc45bcbb785be

Observation 3c7775af-a8e7-4625-ad2d-14f2edcf4b52 · outbound

This paper cites DSP: Dynamic Sequence Parallelism for Multi-Dimensional Transformers.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving DSP: Dynamic Sequence Parallelism for Multi-Dimensional Transformers

Reference 53

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source=pdf_text observed=2026-08-15T20:05:06.553354Z digest=sha256:be27867e82a9ce1e0f352b06ce244fcbce843a14996f767d9bfb38e02dc5e1a8

Observation 84245383-cba9-4a01-a43b-05d640fb7874 · outbound

This paper cites Real-Time Video Generation with Pyramid Attention Broadcast.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving Real-Time Video Generation with Pyramid Attention Broadcast

Reference 54

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source=pdf_text observed=2026-08-15T20:05:06.557898Z digest=sha256:d4d4eeb989d330b9ef74cde34abfaec3faa96f67274de52a1ab383a90a863535

Observation 2103e915-1e4a-4c46-ab00-cf103ed9f552 · outbound

This paper cites Open-sora: Democratizing efficient video production for all, March 2024.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving Open-sora: Democratizing efficient video production for all, March 2024

Reference 55

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source=pdf_text observed=2026-08-15T20:05:06.562133Z digest=sha256:ac25dfa7b221bbc91c01d011625f942a77bca91dbc7c536398bb04ca2972061a

Observation 8f99295a-6bb4-48b5-9981-c6af53d2edc7 · outbound

This paper cites Open-Sora: Democratizing Efficient Video Production for All.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving Open-Sora: Democratizing Efficient Video Production for All

Reference 56

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source=pdf_text observed=2026-08-15T20:05:06.565942Z digest=sha256:1e5247c6123f2c84c52e1e6aca95fea660375c50500156d0a6dc41d3a991ad81

Observation 11ed2b9a-3168-48be-a6e4-6bc18ce08a01 · outbound

This paper cites DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving.

DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving

Reference 57

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no resolver link, observed 2026-08-15T20:05:06.570203Z

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source=pdf_text observed=2026-08-15T20:05:06.570203Z digest=sha256:649b6ec4973b5cc65de9fd954f6affd4b86e907a17493830665b76e61f778a06

Pith citing papers

Observation 9a2ef9ec-b0c1-4e4e-a98b-33d034fd302d · inbound

TetriServe: Efficiently Serving Mixed DiT Workloads cites this paper.

TetriServe: Efficiently Serving Mixed DiT Workloads DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving

Reference 2025

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source=pdf_text observed=2026-08-04T12:55:10.421426Z digest=sha256:98e88a510371b37f170ea31fbee30425b59bd78ca4c95aa5a0324166edc2db27

Observation f6f3628e-fe3b-432f-b7ef-a3c3167ffe19 · inbound

Cornfigurator: Automated Planning for Any-to-Any Multimodal Model Serving cites this paper.

Cornfigurator: Automated Planning for Any-to-Any Multimodal Model Serving DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving

Reference 16

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arxiv_id, observed 2026-05-16T22:21:18.728402Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-16T22:20:48.239186Z digest=sha256:d7c9414016682fe83049c2573fe6fcdd4d21e6fe8dcb93346caa298cb5155438

Observation 1b0d1033-9fa6-4c35-ade5-39a3c39b7347 · inbound

TempoNet: Slack-Quantized Transformer-Guided Reinforcement Scheduler for Adaptive Deadline-Centric Real-Time Dispatchs cites this paper.

TempoNet: Slack-Quantized Transformer-Guided Reinforcement Scheduler for Adaptive Deadline-Centric Real-Time Dispatchs DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving

Reference 40

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arxiv_id, observed 2026-05-15T21:11:37.593778Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-15T21:11:07.026338Z digest=sha256:b4747eeee8793b270e86eab714646e841c1b8e469d2aceb0024dd69a089c6a8f

Observation 4bbc630b-c53c-4645-aa4b-496032172d23 · inbound

GENSERVE: Efficient Co-Serving of Heterogeneous Diffusion Model Workloads cites this paper.

GENSERVE: Efficient Co-Serving of Heterogeneous Diffusion Model Workloads DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving

Reference 15

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arxiv_id, observed 2026-05-10T22:00:47.892092Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-10T20:22:20.395250Z digest=sha256:7d21d3926de55d3247d0492b4147e4b6ff1d2a17c2b85ea5661fa4dff2a4e42d

Observation b65cc737-39d8-4e89-85c3-5b3c43cb3795 · inbound

OpScale: Operator-level Provisioning and Autoscaling for LLM Serving cites this paper.

OpScale: Operator-level Provisioning and Autoscaling for LLM Serving DDiT: Dynamic Resource Allocation for Diffusion Transformer Model Serving

Reference 23

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source=pdf_text observed=2026-08-14T05:50:05.685350Z digest=sha256:7f6a8e246efae10d96aba5e0db8af2ae3db00df09aad615a02090a39765752ab