Paper Citation Record · LEDGER

Exploring Diffusion Transformer Designs via Grafting

As of 15 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 6 inbound Pith citation observations for arXiv:2506.05340.

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

pith.paper-citation-record.v1
2506.05340 v2

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:29:05.175338Z

measured 58 of 58 standing notices

One-hop event checks from named stored sources.

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measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-18T05:30:11.389756Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T05:30:54.985115Z

Reference resolution

52 of 52 outbound references displayed

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External citation measurements

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Outbound references

Observation acc38b6f-2d25-45e7-a3c1-d1533a902d3f · outbound

This paper cites Scalable diffusion models with transformers.

Exploring Diffusion Transformer Designs via Grafting Scalable diffusion models with transformers

Reference 1

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Observation 0002ab3c-6d29-4261-8d3e-796d82d86fe7 · outbound

This paper cites Video generation models as world simulators.

Exploring Diffusion Transformer Designs via Grafting Video generation models as world simulators

Reference 2

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Observation 6f3eef75-6580-4523-89c6-db09473afdc4 · outbound

This paper cites Photorealistic Video Generation with Diffusion Models.

Exploring Diffusion Transformer Designs via Grafting Photorealistic Video Generation with Diffusion Models

Reference 3

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Observation 2ba889be-cf29-4cc7-803d-e5db347b625e · outbound

This paper cites Plant grafting: new mechanisms, evolutionary implications.

Exploring Diffusion Transformer Designs via Grafting Plant grafting: new mechanisms, evolutionary implications

Reference 4

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Observation 18183868-c903-43d3-9f38-7b313ddccc01 · outbound

This paper cites Lolcats: On low-rank linearizing of large language models.

Exploring Diffusion Transformer Designs via Grafting Lolcats: On low-rank linearizing of large language models

Reference 5

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Observation 94797a61-bc86-452a-bea2-c392b43fe5eb · outbound

This paper cites The mamba in the llama: Distilling and accelerating hybrid models.

Exploring Diffusion Transformer Designs via Grafting The mamba in the llama: Distilling and accelerating hybrid models

Reference 6

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Observation c0c183aa-0522-41d3-ad9b-67a1c40a39ac · outbound

This paper cites Transformers to ssms: Distilling quadratic knowledge to subquadratic models.

Exploring Diffusion Transformer Designs via Grafting Transformers to ssms: Distilling quadratic knowledge to subquadratic models

Reference 7

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Observation d510c7d8-8333-4afd-8576-7cd0d242cd2d · outbound

This paper cites Monarch mixer: A simple sub-quadratic gemm- based architecture.

Exploring Diffusion Transformer Designs via Grafting Monarch mixer: A simple sub-quadratic gemm- based architecture

Reference 8

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Observation 97a8d313-ba5d-485a-b317-d495f6d975e9 · outbound

This paper cites Sparse upcycling: Training mixture-of-experts from dense checkpoints.

Exploring Diffusion Transformer Designs via Grafting Sparse upcycling: Training mixture-of-experts from dense checkpoints

Reference 9

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Observation 2913771c-b7b8-4015-8c15-738690116fc4 · outbound

This paper cites Scaling Laws for Neural Language Models.

Exploring Diffusion Transformer Designs via Grafting Scaling Laws for Neural Language Models

Reference 10

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Observation 0e6c5894-a10a-4bb4-8f3d-58f8d59efbea · outbound

This paper cites Pixart- P: Weak-to-strong training of diffusion transformer for 4k text-to-image generation, 2024.

Exploring Diffusion Transformer Designs via Grafting Pixart- P: Weak-to-strong training of diffusion transformer for 4k text-to-image generation, 2024

Reference 11

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Observation 8f241aaa-52ad-4d5f-b5e5-f90a471df643 · outbound

This paper cites Denoising diffusion probabilistic models.

Exploring Diffusion Transformer Designs via Grafting Denoising diffusion probabilistic models

Reference 12

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Observation 642437e1-9bf9-46a3-ab2f-4871eb3bc840 · outbound

This paper cites Flow Matching for Generative Modeling.

Exploring Diffusion Transformer Designs via Grafting Flow Matching for Generative Modeling

Reference 13

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Observation 71506ef5-5010-47e6-84c0-c93b3603437b · outbound

This paper cites Pixart- α: Fast training of diffusion transformer for photorealistic text-to-image synthesis.

Exploring Diffusion Transformer Designs via Grafting Pixart- α: Fast training of diffusion transformer for photorealistic text-to-image synthesis

Reference 14

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Observation 541a6d75-0ff1-4469-8929-9cd7b445f45b · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Exploring Diffusion Transformer Designs via Grafting Imagenet: A large-scale hierarchical image database

Reference 15

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Observation f78835da-258e-4843-8b7e-b90d5e5495ca · outbound

This paper cites Geneval: An object-focused framework for evaluating text-to-image alignment.

Exploring Diffusion Transformer Designs via Grafting Geneval: An object-focused framework for evaluating text-to-image alignment

Reference 16

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Observation 1e730da0-e004-4c44-898a-926aed34feac · outbound

This paper cites Distilling the knowledge in a neural network.

Exploring Diffusion Transformer Designs via Grafting Distilling the knowledge in a neural network

Reference 17

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Observation 2092e7dd-0a20-4b0c-9e35-ad8c8a81b167 · outbound

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Exploring Diffusion Transformer Designs via Grafting Unresolved cited work

Reference 18

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Observation a8244702-d76e-4287-b609-5b158240de7c · outbound

This paper cites Benign overfitting in linear regression.

Exploring Diffusion Transformer Designs via Grafting Benign overfitting in linear regression

Reference 19

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Observation 680badb0-9672-4f0f-a3dc-60a4d999f1eb · outbound

This paper cites Solar 10.7 b: Scaling large language models with simple yet effective depth up-scaling.

Exploring Diffusion Transformer Designs via Grafting Solar 10.7 b: Scaling large language models with simple yet effective depth up-scaling

Reference 20

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Observation 7c8f6e76-0076-4334-b7e7-9ef5694c857d · outbound

This paper cites Compute better spent: Replacing dense layers with structured matrices.

Exploring Diffusion Transformer Designs via Grafting Compute better spent: Replacing dense layers with structured matrices

Reference 21

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Observation 08f7ddc7-0ea9-48d4-864e-4fbed37c4b57 · outbound

This paper cites The impact of depth on compositional generalization in transformer language models.

Exploring Diffusion Transformer Designs via Grafting The impact of depth on compositional generalization in transformer language models

Reference 22

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Observation 73430180-c280-477c-9544-f9178ffc6258 · outbound

This paper cites Mechanistic Design and Scaling of Hybrid Architectures.

Exploring Diffusion Transformer Designs via Grafting Mechanistic Design and Scaling of Hybrid Architectures

Reference 23

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Observation f68bc3b7-2d58-4537-b05b-2049462507a3 · outbound

This paper cites Systems and Algorithms for Convolutional Multi-Hybrid Language Models at Scale.

Exploring Diffusion Transformer Designs via Grafting Systems and Algorithms for Convolutional Multi-Hybrid Language Models at Scale

Reference 24

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Observation 0b35f614-cc56-4897-9ddb-62513ba9b0f9 · outbound

This paper cites Durrant, Jerome Ku, Michael Poli, Greg Brockman, Daniel Chang, Gabriel A.

Exploring Diffusion Transformer Designs via Grafting Durrant, Jerome Ku, Michael Poli, Greg Brockman, Daniel Chang, Gabriel A

Reference 25

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Observation e877c025-2c86-4ede-941f-8173887b35c3 · outbound

This paper cites Longformer: The Long-Document Transformer.

Exploring Diffusion Transformer Designs via Grafting Longformer: The Long-Document Transformer

Reference 26

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Observation 5c45670f-4a33-462a-b46e-50f3855034f4 · outbound

This paper cites Generating Long Sequences with Sparse Transformers.

Exploring Diffusion Transformer Designs via Grafting Generating Long Sequences with Sparse Transformers

Reference 27

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Observation dba04c42-501c-4756-91b6-5806306e115f · outbound

This paper cites Transformers are ssms: Generalized models and efficient algorithms through structured state space duality.

Exploring Diffusion Transformer Designs via Grafting Transformers are ssms: Generalized models and efficient algorithms through structured state space duality

Reference 28

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Observation 8ed27e29-7b88-4628-b3d1-16bc81589178 · outbound

This paper cites Lora: Low-rank adaptation of large language models.

Exploring Diffusion Transformer Designs via Grafting Lora: Low-rank adaptation of large language models

Reference 29

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Observation 62e69093-bc73-424b-b220-c29700e85811 · outbound

This paper cites Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps.

Exploring Diffusion Transformer Designs via Grafting Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps

Reference 30

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Observation cd3c74e9-3aa6-4673-8910-9d94435f1248 · outbound

This paper cites Bk-sdm: A lightweight, fast, and cheap version of stable diffusion.

Exploring Diffusion Transformer Designs via Grafting Bk-sdm: A lightweight, fast, and cheap version of stable diffusion

Reference 31

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Exploring Diffusion Transformer Designs via Grafting TinyFusion: Diffusion Transformers Learned Shallow

Reference 32

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Observation 3f2f5dea-5882-4e86-a423-60bd10e0f243 · outbound

This paper cites All are worth words: A vit backbone for diffusion models.

Exploring Diffusion Transformer Designs via Grafting All are worth words: A vit backbone for diffusion models

Reference 33

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Observation f6ede002-b1e1-4445-9693-e6b30a9feb6e · outbound

This paper cites SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformers.

Exploring Diffusion Transformer Designs via Grafting SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformers

Reference 34

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This paper cites Diffusion models without attention.

Exploring Diffusion Transformer Designs via Grafting Diffusion models without attention

Reference 35

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Observation f9768a86-9725-449e-a29b-ded41e60736b · outbound

This paper cites Scalable Diffusion Models with State Space Backbone.

Exploring Diffusion Transformer Designs via Grafting Scalable Diffusion Models with State Space Backbone

Reference 36

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Observation 8544d02f-cb4e-48a8-b696-9635672310d2 · outbound

This paper cites ZigMa: A DiT-style Zigzag Mamba Diffusion Model.

Exploring Diffusion Transformer Designs via Grafting ZigMa: A DiT-style Zigzag Mamba Diffusion Model

Reference 37

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Exploring Diffusion Transformer Designs via Grafting DiM: Diffusion Mamba for Efficient High-Resolution Image Synthesis

Reference 38

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This paper cites DiG: Scalable and Efficient Diffusion Models with Gated Linear Attention.

Exploring Diffusion Transformer Designs via Grafting DiG: Scalable and Efficient Diffusion Models with Gated Linear Attention

Reference 39

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Exploring Diffusion Transformer Designs via Grafting Seaweed-7b: Cost-effective training of video generation foundation model

Reference 40

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Observation c1afb0fe-67dc-4a6d-bd6c-fc68199eb0f7 · outbound

This paper cites A survey on video diffusion models.

Exploring Diffusion Transformer Designs via Grafting A survey on video diffusion models

Reference 41

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Observation 395f94d2-57d6-4913-b52d-57c77c222d6a · outbound

This paper cites Matten: Video Generation with Mamba-Attention.

Exploring Diffusion Transformer Designs via Grafting Matten: Video Generation with Mamba-Attention

Reference 42

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This paper cites LinGen: Towards High-Resolution Minute-Length Text-to-Video Generation with Linear Computational Complexity.

Exploring Diffusion Transformer Designs via Grafting LinGen: Towards High-Resolution Minute-Length Text-to-Video Generation with Linear Computational Complexity

Reference 43

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This paper cites Scaling Diffusion Transformers to 16 Billion Parameters.

Exploring Diffusion Transformer Designs via Grafting Scaling Diffusion Transformers to 16 Billion Parameters

Reference 44

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Exploring Diffusion Transformer Designs via Grafting Star: Syn- thesis of tailored architectures

Reference 45

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Observation 08c57e6b-7b12-4d86-878d-6a1237331c3a · outbound

This paper cites CLEAR: Conv-Like Linearization Revs Pre-Trained Diffusion Transformers Up.

Exploring Diffusion Transformer Designs via Grafting CLEAR: Conv-Like Linearization Revs Pre-Trained Diffusion Transformers Up

Reference 46

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Source-reported events for the cited work

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Observation cd191463-0216-4f84-963f-05e85a2ca15b · outbound

This paper cites LinFusion: 1 GPU, 1 Minute, 16K Image.

Exploring Diffusion Transformer Designs via Grafting LinFusion: 1 GPU, 1 Minute, 16K Image

Reference 47

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This paper cites EDiT: Efficient Diffusion Transformers with Linear Compressed Attention.

Exploring Diffusion Transformer Designs via Grafting EDiT: Efficient Diffusion Transformers with Linear Compressed Attention

Reference 48

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This paper cites FFN Fusion: Rethinking Sequential Computation in Large Language Models.

Exploring Diffusion Transformer Designs via Grafting FFN Fusion: Rethinking Sequential Computation in Large Language Models

Reference 49

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Observation ae8d36cf-1ba5-4303-a094-c5c464ec6999 · outbound

This paper cites Hadzic, Taran Kota, Jimming He, Cristobal Eyzaguirre, Zane Durante, Manling Li, Jiajun Wu, and Fei-Fei Li.

Exploring Diffusion Transformer Designs via Grafting Hadzic, Taran Kota, Jimming He, Cristobal Eyzaguirre, Zane Durante, Manling Li, Jiajun Wu, and Fei-Fei Li

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:29:06.266190Z

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Observation 1d41be1d-0fde-4c0e-96e2-4aab1c3d975e · outbound

This paper cites Eagle 2.5: Boosting long-context post-training for frontier vision-language models.

Exploring Diffusion Transformer Designs via Grafting Eagle 2.5: Boosting long-context post-training for frontier vision-language models

Reference 51

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This paper cites Hyena hierarchy: Towards larger convolutional language models.

Exploring Diffusion Transformer Designs via Grafting Hyena hierarchy: Towards larger convolutional language models

Reference 52

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Pith citing papers

Observation 4891e28d-626d-4305-8fa6-0820b87ffeac · inbound

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs cites this paper.

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs Exploring Diffusion Transformer Designs via Grafting

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d02162dc-df72-4902-a149-d271281b1622 · inbound

DisCa: Accelerating Video Diffusion Transformers with Distillation-Compatible Learnable Feature Caching cites this paper.

DisCa: Accelerating Video Diffusion Transformers with Distillation-Compatible Learnable Feature Caching Exploring Diffusion Transformer Designs via Grafting

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T07:30:44.377023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 97b63802-a0ae-4443-a1e2-581244fca16d · inbound

DC-DiT: Adaptive Compute and Elastic Inference for Visual Generation via Dynamic Chunking cites this paper.

DC-DiT: Adaptive Compute and Elastic Inference for Visual Generation via Dynamic Chunking Exploring Diffusion Transformer Designs via Grafting

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-15T15:10:05.854700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b8e85676-81e2-46e2-9258-b53210ef4574 · inbound

Linearizing Vision Transformer with Test-Time Training cites this paper.

Linearizing Vision Transformer with Test-Time Training Exploring Diffusion Transformer Designs via Grafting

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-05-09T06:25:48.708330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3fd91422-b6de-4380-b5ee-c27cef3e8bf6 · inbound

Continuous Latent Diffusion Language Model cites this paper.

Continuous Latent Diffusion Language Model Exploring Diffusion Transformer Designs via Grafting

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:11:10.638089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2d7e9ca2-225d-4b42-b249-86370124688f · inbound

CoReDiT: Spatial Coherence-Guided Token Pruning and Reconstruction for Efficient Diffusion Transformers cites this paper.

CoReDiT: Spatial Coherence-Guided Token Pruning and Reconstruction for Efficient Diffusion Transformers Exploring Diffusion Transformer Designs via Grafting

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-15T04:49:44.387534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.