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Paper Citation Record · LEDGER

MobileWan: Closing the Quality Gap for Mobile Video Diffusion

As of 9 August 2026, this Paper Citation Record lists 86 of 86 outbound references and 0 inbound Pith citation observations for arXiv:2607.06173.

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

pith.paper-citation-record.v1
2607.06173 v2

Coverage vector

measured 86 of 86 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T08:21:50.176074Z

measured 86 of 86 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.

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measured 0 of 1 external citation measurements

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Reference resolution

86 of 86 outbound references displayed

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Outbound references

Observation 0665dd74-9a36-4f68-82f0-8e3ec53f0d3f · outbound

This paper cites InICLR, 2026.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion InICLR, 2026

Reference 1

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source=pdf_text observed=2026-08-02T08:21:42.373506Z digest=sha256:d8850a9bff6d437ed4d6170ea901ba46a834a68ab0f24353da548f58e541e8e3

Observation 83000d4c-0299-4422-9f21-25adf2816fb6 · outbound

This paper cites Video generation models as world simulators.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Video generation models as world simulators

Reference 2

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Observation 56b5f97e-e07f-47f9-9f4a-600f90e82440 · outbound

This paper cites HunyuanVideo: A Systematic Framework For Large Video Generative Models.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion HunyuanVideo: A Systematic Framework For Large Video Generative Models

Reference 3

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Observation e19a6d29-3ef8-4d3c-a132-7e3238ea37b5 · outbound

This paper cites Cogvideox: Text-to-video diffusion models with an expert transformer.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Cogvideox: Text-to-video diffusion models with an expert transformer

Reference 4

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Observation b64368e7-2503-42e9-aa5a-210f8c96651d · outbound

This paper cites Wan: Open and Advanced Large-Scale Video Generative Models.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Wan: Open and Advanced Large-Scale Video Generative Models

Reference 5

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Observation c1bb6a8d-a37d-4065-9b0e-01ac40fccbad · outbound

This paper cites Efficient Video Diffusion Models: Advancements and Challenges.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Efficient Video Diffusion Models: Advancements and Challenges

Reference 6

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Observation b8b0dbe1-bc09-47bf-ab64-c809dd43a7f3 · outbound

This paper cites Moalign: Motion-centric representation alignment for video diffusion models.arXiv preprint arXiv:2510.19022, 2025.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Moalign: Motion-centric representation alignment for video diffusion models.arXiv preprint arXiv:2510.19022, 2025

Reference 7

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Observation 329de75c-2e64-409b-86ad-fe1e2eb22bbe · outbound

This paper cites A Systematic Post-Train Framework for Video Generation.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion A Systematic Post-Train Framework for Video Generation

Reference 8

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source=pdf_text observed=2026-08-02T08:21:43.194828Z digest=sha256:ddf2da8418b2c10f3c2dbb448670b67968fa60eb4259ee60556a25e79250fd4b

Observation a66d33af-1f81-4ef0-8fbd-1c72abcbb54e · outbound

This paper cites Mobile-oriented video diffusion: Enabling text-to-video generation on mobile devices without retraining, compression, or pruning.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Mobile-oriented video diffusion: Enabling text-to-video generation on mobile devices without retraining, compression, or pruning

Reference 9

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source=pdf_text observed=2026-08-02T08:21:43.311128Z digest=sha256:c4edb69c352bbd11231c0fa83cf3caec430e11cea34b9bb465f71a7f6596d630

Observation 62c4e14f-5f44-4f6b-83a2-085791d9ccb0 · outbound

This paper cites Tamingdiffusiontransformer for real-time mobile video generation.arXiv preprint arXiv:2507.13343, 2025.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Tamingdiffusiontransformer for real-time mobile video generation.arXiv preprint arXiv:2507.13343, 2025

Reference 10

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Observation 9d64e9b7-858d-4eec-bc0e-28b2de3a3fe7 · outbound

This paper cites S2dit: Sandwichdiffusiontransformerformobilestreaming video generation.arXiv preprint arXiv:2601.12719, 2026.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion S2dit: Sandwichdiffusiontransformerformobilestreaming video generation.arXiv preprint arXiv:2601.12719, 2026

Reference 11

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Observation 200ea078-6346-4511-a56c-bebbd434e54a · outbound

This paper cites Mobile video diffusion.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Mobile video diffusion

Reference 12

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Observation ae2a47d7-3cd2-4b42-965a-48382f2dedc5 · outbound

This paper cites Snapgen-v: Generating a five-second video within five seconds on a mobile device.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Snapgen-v: Generating a five-second video within five seconds on a mobile device

Reference 13

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Observation 10ad7b12-a8f0-49a7-bbf7-d283b8a0cc85 · outbound

This paper cites Sana-video: Efficient video generation with block linear diffusion transformer.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Sana-video: Efficient video generation with block linear diffusion transformer

Reference 14

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Observation 83872c8b-05ba-4e65-9a27-3e4e0afa0092 · outbound

This paper cites Attention surgery: An efficient recipe to linearize your video diffusion transformer.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Attention surgery: An efficient recipe to linearize your video diffusion transformer

Reference 15

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Observation 8c41ed9e-8110-43c0-a5a1-0b325df28a87 · outbound

This paper cites Rehyat: Recurrent hybrid attention for video diffusion transformers.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Rehyat: Recurrent hybrid attention for video diffusion transformers

Reference 16

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source=pdf_text observed=2026-08-02T08:21:43.831980Z digest=sha256:84bd94d64ac63499b621a0fbe061fafc2bd5178e09c0b8eecdcf8c99c7919eab

Observation 41222ae2-c9b0-4745-bf05-c5803cd5d191 · outbound

This paper cites MoViE: Mobile Diffusion for Video Editing.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion MoViE: Mobile Diffusion for Video Editing

Reference 17

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Observation 2945076f-930a-4104-ac06-ef6db7f7fac0 · outbound

This paper cites Clockwork diffusion: Efficient generation with model-step distillation.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Clockwork diffusion: Efficient generation with model-step distillation

Reference 18

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Observation 7f33ebbc-33da-48c5-acba-a2739fbf7f03 · outbound

This paper cites Amd-hummingbird: Towards an efficient text-to-video model.arXiv preprint arXiv:2503.18559, 2025.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Amd-hummingbird: Towards an efficient text-to-video model.arXiv preprint arXiv:2503.18559, 2025

Reference 19

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Observation c77e72b4-ffbd-437c-b03f-e349d0404ac4 · outbound

This paper cites Stable video diffusion: Scaling latent video diffusion models to large datasets, 2023.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Stable video diffusion: Scaling latent video diffusion models to large datasets, 2023

Reference 20

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Observation 28edac6e-cc20-40fd-9a52-b3ee690f6fbd · outbound

This paper cites On-device Sora: Enabling Training-Free Diffusion-based Text-to-Video Generation for Mobile Devices.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion On-device Sora: Enabling Training-Free Diffusion-based Text-to-Video Generation for Mobile Devices

Reference 21

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source=pdf_text observed=2026-08-02T08:21:44.482775Z digest=sha256:2480eaaed70abd0a51635be29445e47ef8503a994f6f29a94f52bcc2a25932ee

Observation 12a83501-4239-4b27-83a0-a050ed05312e · outbound

This paper cites SANA 1.5: Efficient scaling of training-time and inference-time compute in linear diffusion transformer.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion SANA 1.5: Efficient scaling of training-time and inference-time compute in linear diffusion transformer

Reference 22

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source=pdf_text observed=2026-08-02T08:21:44.579584Z digest=sha256:2c39c06d1619d1b83f5f0d3980b82eec1c4f13a33602cf9a81ec2201ec4b2709

Observation 03f24a78-e79d-40ef-b751-d55d4644aee9 · outbound

This paper cites FastLightGen: Fast and light video generation with fewer steps and parameters.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion FastLightGen: Fast and light video generation with fewer steps and parameters

Reference 23

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Observation 8cc07399-c155-4ecb-8c8c-2999ae66ac29 · outbound

This paper cites Hadzic, Manling Li, AgrimGupta,StefanoMassaroli,AzaliaMirhoseini,JuanCarlosNiebles,StefanoErmon,andLiFei-Fei.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Hadzic, Manling Li, AgrimGupta,StefanoMassaroli,AzaliaMirhoseini,JuanCarlosNiebles,StefanoErmon,andLiFei-Fei

Reference 24

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Observation 6bf77c7b-9a36-4be9-bf29-2009f876e8b3 · outbound

This paper cites Freeman, and Taesung Park.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Freeman, and Taesung Park

Reference 25

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Observation 36e77ffd-78d1-4460-bc10-8c1c1423bc1d · outbound

This paper cites Classifier-Free Diffusion Guidance.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Classifier-Free Diffusion Guidance

Reference 26

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source=pdf_text observed=2026-08-02T08:21:45.070933Z digest=sha256:48aa3078addf9352b5cf596249f53dbc7bb8b64e2eb5e1fd72b9d726ab59dff5

Observation e9479b95-a838-4f2b-bc25-37c03f4e374e · outbound

This paper cites Decoupled DMD: CFG augmentation as the spear, distribution matching as the shield.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Decoupled DMD: CFG augmentation as the spear, distribution matching as the shield

Reference 27

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Observation 2ab4b322-6347-43e0-ae9d-3589fed12c9a · outbound

This paper cites Barron, and Ben Mildenhall.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Barron, and Ben Mildenhall

Reference 28

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Observation c141a6ff-0ae5-4f5b-8e05-b154f7be9d84 · outbound

This paper cites Jacobs, Alexei A.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Jacobs, Alexei A

Reference 29

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source=pdf_text observed=2026-08-02T08:21:45.508798Z digest=sha256:64e9720f2a7072e54a5ec32c90461b79131850af411bbf6aa32d66ffc98af121

Observation 7e536701-efda-4270-8828-2845afc3a97d · outbound

This paper cites Compositional visual generation with energy based models.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Compositional visual generation with energy based models

Reference 30

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Observation 75520106-f25e-4328-a66a-4da674b0534a · outbound

This paper cites Scaling rectified flow transformers for high-resolution image synthesis.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Scaling rectified flow transformers for high-resolution image synthesis

Reference 31

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Observation 63afad3a-441d-43d7-a8a4-3579a1aa6234 · outbound

This paper cites Faster video diffusion with trainable sparse attention.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Faster video diffusion with trainable sparse attention

Reference 32

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Observation 5b13600d-0b8e-44ab-83a4-80c44de019e3 · outbound

This paper cites an unresolved cited work.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Unresolved cited work

Reference 33

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Observation bd880515-8111-4f02-8d9b-800108660d4c · outbound

This paper cites Nvidia fastgen: Fast generation from diffusion models, 2026.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Nvidia fastgen: Fast generation from diffusion models, 2026

Reference 34

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Observation f3754048-2bd2-4f90-bb7c-12c5c315ba85 · outbound

This paper cites Lightx2v: Light video generation inference framework.https://github.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Lightx2v: Light video generation inference framework.https://github

Reference 35

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Observation 52b4da6b-3b5d-4bfb-8c22-39a2a12d65f9 · outbound

This paper cites https://github.com/madebyollin/ taehv, 2025.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion https://github.com/madebyollin/ taehv, 2025

Reference 36

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Observation e1007310-c2f6-4b11-be91-246cb1f4faac · outbound

This paper cites Abenchmark dataset and evaluation methodology for video object segmentation.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Abenchmark dataset and evaluation methodology for video object segmentation

Reference 37

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Observation cac7b691-cd6c-4f62-8b97-9c64b0d3711c · outbound

This paper cites The 2018 DAVIS Challenge on Video Object Segmentation.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion The 2018 DAVIS Challenge on Video Object Segmentation

Reference 38

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Observation f1660d8c-dd6f-4733-ade8-87c522b109f6 · outbound

This paper cites VBench: Comprehensive benchmark suite for video generative models.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion VBench: Comprehensive benchmark suite for video generative models

Reference 39

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source=pdf_text observed=2026-08-02T08:21:46.961784Z digest=sha256:05660550aed84812b7254e43ba6876d58176678412f38585e3d95bce326155c8

Observation d96ad724-1b77-4b9d-a8ca-c43a3971daf2 · outbound

This paper cites Fastforward: Neural network quantization for research and prototyping.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Fastforward: Neural network quantization for research and prototyping

Reference 40

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Observation 717b6340-37b6-40f5-8f41-4c2c4397acae · outbound

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

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Open-Sora Plan: Open-Source Large Video Generation Model

Reference 41

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Observation 15d01596-1caa-4748-8e61-e0ada1bb6a3e · outbound

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

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Open-Sora: Democratizing Efficient Video Production for All

Reference 42

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source=pdf_text observed=2026-08-02T08:21:47.256075Z digest=sha256:901e516ce90262a0be52de931a0f52f305444af99f7db529b05df1fa5d574f72

Observation f663fc7e-f958-4037-889d-c75f2ee01e2c · outbound

This paper cites LTX-Video: Realtime Video Latent Diffusion.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion LTX-Video: Realtime Video Latent Diffusion

Reference 43

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source=pdf_text observed=2026-08-02T08:21:47.395667Z digest=sha256:1302080b5e34f6e2371e72e7c4a9698fcbe62469e90f5e275a036a5a7a3f3f96

Observation c0193c03-4d62-4bea-bfd3-c68e9c0fd49a · outbound

This paper cites Pyramidal flow matching for efficient video generative modeling.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Pyramidal flow matching for efficient video generative modeling

Reference 44

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source=pdf_text observed=2026-08-02T08:21:47.456145Z digest=sha256:ca2282563e107fed172d47b5e739fed91e674c3c04359ccf5c032350d6c26c6a

Observation a0e029a5-6f1e-44f4-881e-082c12d34802 · outbound

This paper cites M4V: Multimodal Mamba for Efficient Text-to-Video Generation.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion M4V: Multimodal Mamba for Efficient Text-to-Video Generation

Reference 45

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source=pdf_text observed=2026-08-02T08:21:47.529772Z digest=sha256:806387cfbf6d6f63a9036af0bc72178fc74b6adcd0d3771d75fe1d11a0aa92fe

Observation 9178de47-31b3-46f5-8d09-ac9f11859376 · outbound

This paper cites Fast video generation with sliding tile attention.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Fast video generation with sliding tile attention

Reference 46

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source=pdf_text observed=2026-08-02T08:21:47.596923Z digest=sha256:0a9c99d89cfd57a51293ff05c3dd90172c48172d6a0eb82aa007db9637cbfd40

Observation d0093cfa-ae8b-4960-a083-9a1f7b9fa08f · outbound

This paper cites Tinyfusion: Diffusion transformers learned shallow.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Tinyfusion: Diffusion transformers learned shallow

Reference 47

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source=pdf_text observed=2026-08-02T08:21:47.673235Z digest=sha256:4acc2dc38cf2257c5d0770d77c1ba57adbb5ea5a5b754cc7cdb5537b148586d2

Observation 895c4d68-5510-4889-b087-bcefb0f74013 · outbound

This paper cites Learnable sparsity for vision generative models.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Learnable sparsity for vision generative models

Reference 48

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source=pdf_text observed=2026-08-02T08:21:47.783195Z digest=sha256:836e9b1f4fdaf0f5a080fa2affe4463a426dea99507c1803485d30a78fb1af89

Observation 213050b8-77ea-48bd-a201-f03b98fd6e0e · outbound

This paper cites OBS-diff: Accurate pruning for diffusion models in one-shot.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion OBS-diff: Accurate pruning for diffusion models in one-shot

Reference 49

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source=pdf_text observed=2026-08-02T08:21:47.888725Z digest=sha256:480f863b14c9f68e0a82126039547f103182caf7ccbf03a8e6d087fe41f75794

Observation 2d82b61d-5f11-4516-8166-d05b3d864973 · outbound

This paper cites Generating long sequences with sparse transformers, 2019.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Generating long sequences with sparse transformers, 2019

Reference 50

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source=pdf_text observed=2026-08-02T08:21:47.999897Z digest=sha256:cbff4f82b5180ef2b49f79cb19ab3b86ded705cdace1987c7d412dc1e84ac4b5

Observation e8c13547-a651-4c66-a64f-d4b6a99ae267 · outbound

This paper cites Peters, and Arman Cohan.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Peters, and Arman Cohan

Reference 51

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source=pdf_text observed=2026-08-02T08:21:48.089461Z digest=sha256:33b7c77e8b41c4163a6439e6cf7fb4256a934b7ea7b27c66e71ab686f86a1621

Observation ee7cb08b-764b-4645-8c95-8517b67b7a8f · outbound

This paper cites Bigbird: Transformers for longer sequences.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Bigbird: Transformers for longer sequences

Reference 52

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source=pdf_text observed=2026-08-02T08:21:48.195577Z digest=sha256:bd0f150e04cb0dfd1fec543e26e40ae7862ea22de57fc6a6e7d8427a6477d575

Observation 7add25e1-0049-4df2-b56b-39e214cb040d · outbound

This paper cites Efficient content-based sparse attention with routing transformers.Transactions of the Association for Computational Linguistics, 2021.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Efficient content-based sparse attention with routing transformers.Transactions of the Association for Computational Linguistics, 2021

Reference 53

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source=pdf_text observed=2026-08-02T08:21:48.254528Z digest=sha256:4857c9a9a2e47bc6600179cd96da32fa24ce10a9d36c6531031dcad226eb5cda

Observation 4f7b4d4d-1f2e-434b-8794-2a34313f336e · outbound

This paper cites Object-centric diffusion for efficient video editing.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Object-centric diffusion for efficient video editing

Reference 54

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source=pdf_text observed=2026-08-02T08:21:48.389814Z digest=sha256:c0d67c2165f67fe5064f0bce43a9212575eb2865201c32d5606e9b42782682f6

Observation f83acb98-93e9-42a8-8751-9fadda3b57a2 · outbound

This paper cites Li, Madian Khabsa, Han Fang, and Hao Ma.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Li, Madian Khabsa, Han Fang, and Hao Ma

Reference 55

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source=pdf_text observed=2026-08-02T08:21:48.435544Z digest=sha256:50dc0da0d6584ba4cacad22dc7e99152f866d0676fbc5f1fe4982fcfdd630508

Observation 74345b2e-bc12-457c-b947-7395511620c8 · outbound

This paper cites Reformer: The efficient transformer.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Reformer: The efficient transformer

Reference 56

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source=pdf_text observed=2026-08-02T08:21:48.502184Z digest=sha256:3452c54286746487ba7d48640c80f66c757a146fd7d652de630307315b3c2bb7

Observation e37b488a-8504-481f-9884-e318fbfb8f72 · outbound

This paper cites Transformers are rnns: fast autoregressive transformers with linear attention.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Transformers are rnns: fast autoregressive transformers with linear attention

Reference 57

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source=pdf_text observed=2026-08-02T08:21:48.616499Z digest=sha256:dc809f0b6c8c5b5748a2fcb433b297fb0857c2bac0bb3dd466bbeda158c2ec76

Observation 621ec1a0-61ff-4575-a854-8d7fa16e2e92 · outbound

This paper cites Rethinking attention with performers.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Rethinking attention with performers

Reference 58

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source=pdf_text observed=2026-08-02T08:21:48.730873Z digest=sha256:1b07437727e3215234a2eb83cbe1555a2e011e2b25ef935e74ec5b3c55ba0bb2

Observation 9fcbf503-79ae-4cdf-8829-71ae2ee8eda9 · outbound

This paper cites Nyströmformer: A nyström-based algorithm for approximating self-attention.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Nyströmformer: A nyström-based algorithm for approximating self-attention

Reference 59

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source=pdf_text observed=2026-08-02T08:21:48.874547Z digest=sha256:3b4b5425679f22fdf7fa248c6c01a8eec113ed9ecaf7ae62264889895099090b

Observation 6733f197-370d-4e9c-8fd8-88ca2237714d · outbound

This paper cites Flashattention: Fast and memory-efficient exact attention with IO-awareness.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Flashattention: Fast and memory-efficient exact attention with IO-awareness

Reference 60

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source=pdf_text observed=2026-08-02T08:21:48.984639Z digest=sha256:d0cf08f38dd6097e25b65bb4989f68f37edb9f8076c9f7971a5b00184db4ce0e

Observation 21105a20-cfb1-4887-b021-2564722ccb9f · outbound

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

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Flashattention-2: Faster attention with better parallelism and work partitioning

Reference 61

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source=pdf_text observed=2026-08-02T08:21:49.048638Z digest=sha256:5805370551d85b273ba2c00fa72fa4f4b15351eda6b3e64d0cebceb0daae9a93

Observation 8c669b83-e667-44be-b956-8d57c750d110 · outbound

This paper cites HLA:HadamardLinear Attention, 2026.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion HLA:HadamardLinear Attention, 2026

Reference 62

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source=pdf_text observed=2026-08-02T08:21:49.183611Z digest=sha256:d803ca5769a8703e0ca216a99800879cee0fd6d868381965431802040e1b4f3a

Observation 10008844-d1ac-4460-9728-21a1255f4c0f · outbound

This paper cites Gonzalez, Jianfei Chen, and Jun Zhu.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Gonzalez, Jianfei Chen, and Jun Zhu

Reference 63

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source=pdf_text observed=2026-08-02T08:21:49.343265Z digest=sha256:6317eec814e72c1211063e1f84f74b447c4cc278788eacbcd766abf61c4898bb

Observation 2ad55bbe-251e-4430-8b58-ffd66485bb6c · outbound

This paper cites Liteattention: A temporal sparse attention for diffusion transformers, 2025.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Liteattention: A temporal sparse attention for diffusion transformers, 2025

Reference 64

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source=pdf_text observed=2026-08-02T08:21:49.489047Z digest=sha256:da784e8a64fed1ae5ccdd1367f86e5e9dfffff89ee86511c1973e91e5e571090

Observation 9cdefb9a-1204-408e-a774-4c09df607f78 · outbound

This paper cites Adaptor: Adaptive token reduction for video diffusion transformers.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Adaptor: Adaptive token reduction for video diffusion transformers

Reference 65

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source=pdf_text observed=2026-08-02T08:21:49.585118Z digest=sha256:716960b3b0a942b3fb15dd0603bddc7ed7fe5a73d23a41e1f9c090c69cb00d5a

Observation fcc849a4-9aeb-4aa4-bc0f-17182650aa79 · outbound

This paper cites Astraea: A token-wise acceleration framework for video diffusion transformers.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Astraea: A token-wise acceleration framework for video diffusion transformers

Reference 66

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source=pdf_text observed=2026-08-02T08:21:49.676883Z digest=sha256:c3462ce60cbe43151dea074ef8e16790efa99b07b2b16cac9c9cdc0617d1fe94

Observation f3091fbb-4930-4616-aa1a-970ff59818ca · outbound

This paper cites TPDiff: Temporal pyramid video diffusion model.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion TPDiff: Temporal pyramid video diffusion model

Reference 67

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source=pdf_text observed=2026-08-02T08:21:49.776506Z digest=sha256:4463da27fbdc81b230463a5482deafd2c1bb3c6edb876c9c7d03d9c24a3659ed

Observation 419ae7e7-3235-4804-b14f-8455ee8b9189 · outbound

This paper cites PyramidalWan: On making pretrained video model pyramidal for efficient inference.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion PyramidalWan: On making pretrained video model pyramidal for efficient inference

Reference 68

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source=pdf_text observed=2026-08-02T08:21:49.977802Z digest=sha256:daf2dcf0bb3360cf71fa00fcba64b7a7340927131ca05198ea855080e4344263

Observation a2ca59ec-a324-4b23-ac28-59ab87993b94 · outbound

This paper cites Fast high-resolution image synthesis with latent adversarial diffusion distillation.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Fast high-resolution image synthesis with latent adversarial diffusion distillation

Reference 69

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source=pdf_text observed=2026-08-02T08:21:50.108254Z digest=sha256:1196adcb1ea1ec5b52d1f6f4efff7349c537534b4695a71f953e677749584463

Observation 746a97f2-ffbe-40ac-b7b8-3c2d3aaf9e5b · outbound

This paper cites Align your flow: Scaling continuous-time flow map distillation.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Align your flow: Scaling continuous-time flow map distillation

Reference 70

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source=pdf_text observed=2026-08-02T08:21:50.112167Z digest=sha256:a420a97a334f1f5aee709fae8ce49f733abea29510bb09f3c3f4c973aeec500c

Observation afff3e65-0637-49e6-8843-30302922a7a6 · outbound

This paper cites Sana-sprint: One-step diffusion with continuous-time consistency distillation.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Sana-sprint: One-step diffusion with continuous-time consistency distillation

Reference 71

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source=pdf_text observed=2026-08-02T08:21:50.115963Z digest=sha256:790378dc6032e16c4446254dfc0440fb330ea35974e047bde4992c8cb2fb127f

Observation 2f384f97-88ea-4529-b0c9-1bc9adbf0c53 · outbound

This paper cites Twinflow: Realizing one-step generation on large models with self-adversarial flows.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Twinflow: Realizing one-step generation on large models with self-adversarial flows

Reference 72

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source=pdf_text observed=2026-08-02T08:21:50.120597Z digest=sha256:0fb1285c1e0c298138abf6f63c13cd61c349e60499e78b8fb9c66ab06987d202

Observation 63eeaf85-85e2-4820-b70b-64f91e2b6ec1 · outbound

This paper cites Metaxas, Sergey Tulyakov, and Jian Ren.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Metaxas, Sergey Tulyakov, and Jian Ren

Reference 73

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source=pdf_text observed=2026-08-02T08:21:50.124480Z digest=sha256:a91527d7d91c20438b1a260e250272c212727c50dbf43d3285fcad559185e16f

Observation 32b42b70-542d-46a6-a358-63fdb248e063 · outbound

This paper cites Diffusion adversarial post-training for one-step video generation.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Diffusion adversarial post-training for one-step video generation

Reference 74

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source=pdf_text observed=2026-08-02T08:21:50.128458Z digest=sha256:a90fc10f717d6da2f25b8b4652fcc52cb75114ee61b2c9ac12332ea07e2363af

Observation 26c233d2-db87-427b-b0f5-f444462b1afb · outbound

This paper cites Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Models.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Models

Reference 75

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source=pdf_text observed=2026-08-02T08:21:50.132158Z digest=sha256:a50f7dc1c0ddcf03d43fad6802c269e11a1a36b75b8ecc6057578a0f07d1e9e0

Observation 5c8da603-62b1-435c-9a23-1c50cfcdb00d · outbound

This paper cites Phaseddmd: Few-stepdistributionmatchingdistillationviascorematching within subintervals, 2026.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Phaseddmd: Few-stepdistributionmatchingdistillationviascorematching within subintervals, 2026

Reference 76

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source=pdf_text observed=2026-08-02T08:21:50.136768Z digest=sha256:0f0d1a91299da84772d3fad15d5a8e04edb56cd87bb4cfa463a7e372b071329a

Observation 8704d43a-1e2e-4f96-a6b0-f1ab322eb20e · outbound

This paper cites MagicDistillation: Weak-to-strong video distillation for large-scale few-step synthesis, 2025.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion MagicDistillation: Weak-to-strong video distillation for large-scale few-step synthesis, 2025

Reference 77

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Observation eb4ab00e-4d25-461e-8567-c8b2da711457 · outbound

This paper cites Transition matching distillation for fast video generation, 2026.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Transition matching distillation for fast video generation, 2026

Reference 78

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Observation a45c92bb-cffa-4cef-9630-6ee949c24d9c · outbound

This paper cites HiStream: Efficient high-resolution video generation via redundancy-eliminated streaming, 2025.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion HiStream: Efficient high-resolution video generation via redundancy-eliminated streaming, 2025

Reference 79

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Observation ab4308f2-105f-4161-b820-e4a4c3a22eb1 · outbound

This paper cites Large scale diffusion distillation via score-regularized continuous-time consistency.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Large scale diffusion distillation via score-regularized continuous-time consistency

Reference 80

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Observation 4d5a3b0d-8da9-475d-90f3-1f0dd436d42d · outbound

This paper cites Cosmos world foundation model platform for physical ai, 2025.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Cosmos world foundation model platform for physical ai, 2025

Reference 81

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Observation b7d20e67-52dd-4e3d-bb90-f634afb71444 · outbound

This paper cites Leanvae: An ultra-efficient reconstruction vae for video diffusion models, 2025.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Leanvae: An ultra-efficient reconstruction vae for video diffusion models, 2025

Reference 82

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Observation cd9d7904-7a15-4d0b-ac30-c9880b53610e · outbound

This paper cites WF-VAE: Enhancing video vae by wavelet-driven energy flow for latent video diffusion model.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion WF-VAE: Enhancing video vae by wavelet-driven energy flow for latent video diffusion model

Reference 83

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Observation 77514c81-80eb-4b56-9621-4e4cc0b995bd · outbound

This paper cites Improved video vae for latent video diffusion model.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Improved video vae for latent video diffusion model

Reference 84

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Observation d2f634a8-fe5f-446c-9fba-df038803177c · outbound

This paper cites Turbo-vaed: Fast and stable transfer of video-vaes to mobile devices.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion Turbo-vaed: Fast and stable transfer of video-vaes to mobile devices

Reference 85

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Observation b353ad8a-d5ca-4d40-a22f-4d0e875ef7ca · outbound

This paper cites CV-VAE: A compatible video VAE for latent generative video models.

MobileWan: Closing the Quality Gap for Mobile Video Diffusion CV-VAE: A compatible video VAE for latent generative video models

Reference 86

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