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Paper Citation Record · LEDGER

Mitigating Compounding Error via Video Representation Regularization

As of 21 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2607.27036.

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

pith.paper-citation-record.v1
2607.27036 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-30T13:09:35.981096Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

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

Source: cited_works

Reference resolution

33 of 33 outbound references displayed

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Outbound references

Observation e7d44bbe-f3eb-4543-9990-6f66f27d4d06 · outbound

This paper cites LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics.

Mitigating Compounding Error via Video Representation Regularization LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics

Reference 2

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source=pdf_text observed=2026-07-30T13:09:35.542295Z digest=sha256:5785f8fd57c6b4851f2293db2a06c46124d7ff122ef71fa7866a03d03b70aedc

Observation c1e9c168-1385-4f3e-9280-73977f94de49 · outbound

This paper cites Navigation World Models.

Mitigating Compounding Error via Video Representation Regularization Navigation World Models

Reference 3

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Observation f7707265-aef6-4426-92ed-4f0a25fc3107 · outbound

This paper cites DOLLAR: Few-Step Video Generation via Distillation and Latent Reward Optimization.

Mitigating Compounding Error via Video Representation Regularization DOLLAR: Few-Step Video Generation via Distillation and Latent Reward Optimization

Reference 6

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Observation 72aa4d30-974b-4edd-a36d-204674dbea1e · outbound

This paper cites Pre-trained language model representations for language generation.

Mitigating Compounding Error via Video Representation Regularization Pre-trained language model representations for language generation

Reference 7

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Observation 2cfa8645-05de-444f-942e-29cd67afae8d · outbound

This paper cites MineRL: A Large-Scale Dataset of Minecraft Demonstrations.

Mitigating Compounding Error via Video Representation Regularization MineRL: A Large-Scale Dataset of Minecraft Demonstrations

Reference 9

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source=pdf_text observed=2026-07-30T13:09:35.590051Z digest=sha256:694d764d04031f385014a21e6624974107fb0e39296bc87388689f04962e50bb

Observation c908fad0-b6bc-48aa-9e2a-f802fc04bc78 · outbound

This paper cites CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers.

Mitigating Compounding Error via Video Representation Regularization CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers

Reference 13

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source=pdf_text observed=2026-07-30T13:09:35.617774Z digest=sha256:abc8ad40b4f33d9fa56d8904023807bf5f8e05f3bb8a2439ae4e2dd23ee77f09

Observation b6b2dd56-3fde-4ed6-a669-9fb82948fdbc · outbound

This paper cites GAIA-1: A Generative World Model for Autonomous Driving.

Mitigating Compounding Error via Video Representation Regularization GAIA-1: A Generative World Model for Autonomous Driving

Reference 14

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source=pdf_text observed=2026-07-30T13:09:35.624634Z digest=sha256:a016347668990964860584235ae0f5e31114c084ce24ab44224e219c6b8f63ee

Observation 4f9864d8-9707-4790-8746-dae35c25a2fd · outbound

This paper cites Yang Jin, Zhicheng Sun, Ningyuan Li, Kun Xu, Hao Jiang, Nan Zhuang, Quzhe Huang, Yang Song, Yadong Mu, and Zhouchen Lin.

Mitigating Compounding Error via Video Representation Regularization Yang Jin, Zhicheng Sun, Ningyuan Li, Kun Xu, Hao Jiang, Nan Zhuang, Quzhe Huang, Yang Song, Yadong Mu, and Zhouchen Lin

Reference 15

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source=pdf_text observed=2026-07-30T13:09:35.632980Z digest=sha256:ff2503fc3a001826c93e463f5fa022084425b0b4bcee990a63a93537f3c6069b

Observation bfaae049-88be-449f-8811-556737900834 · outbound

This paper cites URLhttps://proceedings.neurips.cc/paper_files/paper/ 2024/file/e304d374c85e385eb217ed4a025b6b63-Paper-Conference.pdf.

Mitigating Compounding Error via Video Representation Regularization URLhttps://proceedings.neurips.cc/paper_files/paper/ 2024/file/e304d374c85e385eb217ed4a025b6b63-Paper-Conference.pdf

Reference 16

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source=pdf_text observed=2026-07-30T13:09:35.639932Z digest=sha256:a7a67ffbc6be2d6a0a8b00ea4ad5e46ba1941c73dcddb43b0a00dcf33127e3d5

Observation cac93e91-c435-4b80-bbf8-8730a60a296c · outbound

This paper cites Genie Envisioner: A Unified World Foundation Platform for Robotic Manipulation.

Mitigating Compounding Error via Video Representation Regularization Genie Envisioner: A Unified World Foundation Platform for Robotic Manipulation

Reference 17

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source=pdf_text observed=2026-07-30T13:09:35.645585Z digest=sha256:944844bc3699c7d4a4af9051e46f4b5ce4bf1def5a0a6ab9598e60ec0a000e7a

Observation da4ff7fc-4b49-40fd-8690-8a8370701303 · outbound

This paper cites LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels.

Mitigating Compounding Error via Video Representation Regularization LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels

Reference 18

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source=pdf_text observed=2026-07-30T13:09:35.650949Z digest=sha256:2b8314d175a5947f6345d0dc0fd99d3a20c445fb7ff2967bbdf7ad8d7988bbb7

Observation 33320717-a92a-4917-8568-30419176d014 · outbound

This paper cites Cosmos World Foundation Model Platform for Physical AI.

Mitigating Compounding Error via Video Representation Regularization Cosmos World Foundation Model Platform for Physical AI

Reference 19

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source=pdf_text observed=2026-07-30T13:09:35.684747Z digest=sha256:2fa60caf83e3c1f9bb345801839c6883111a64b536132d3ee306707d3923350e

Observation 082b2ace-c474-48e5-853f-c0f329e986ea · outbound

This paper cites Representative Language Generation.

Mitigating Compounding Error via Video Representation Regularization Representative Language Generation

Reference 20

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source=pdf_text observed=2026-07-30T13:09:35.734743Z digest=sha256:bd42bdcc7d64b8944a5a19f4e3941e79809b3ed5c06ee307bfad34089ef996ee

Observation 4bc04e04-7e83-45e0-87ec-a3979fb9dc6d · outbound

This paper cites Freenoise: Tuning-free longer video diffusion via noise rescheduling.

Mitigating Compounding Error via Video Representation Regularization Freenoise: Tuning-free longer video diffusion via noise rescheduling

Reference 21

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source=pdf_text observed=2026-07-30T13:09:35.774781Z digest=sha256:d627b4f844be71e1e3845be0278961e6a04bc2456bcf1daf969ef56067522a15

Observation aa843729-8093-4aa9-b6f8-64a2664689ec · outbound

This paper cites Cosmos-Drive-Dreams: Scalable Synthetic Driving Data Generation with World Foundation Models.

Mitigating Compounding Error via Video Representation Regularization Cosmos-Drive-Dreams: Scalable Synthetic Driving Data Generation with World Foundation Models

Reference 22

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source=pdf_text observed=2026-07-30T13:09:35.844757Z digest=sha256:c586c68464afffb1da3071097e105fbf25b44a7f990d37e48f6af902c18cb055

Observation 46bbb23f-8ac6-4a0f-8491-feb67b5a1c6d · outbound

This paper cites Make-A-Video: Text-to-Video Generation without Text-Video Data.

Mitigating Compounding Error via Video Representation Regularization Make-A-Video: Text-to-Video Generation without Text-Video Data

Reference 23

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source=pdf_text observed=2026-07-30T13:09:35.884739Z digest=sha256:73db8a0431136ec6e914b096ea3b23e8ba9285099cfd8122568ea5dd556a90b7

Observation 4c8bb7bf-335b-447d-8a20-6165804242ac · outbound

This paper cites History-Guided Video Diffusion.

Mitigating Compounding Error via Video Representation Regularization History-Guided Video Diffusion

Reference 24

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source=pdf_text observed=2026-07-30T13:09:35.904126Z digest=sha256:95dea17881bade9e9c425f1f5438a4db5f4dac2b4c8179775b1b7e3053b171ef

Observation abb2848c-d03d-4805-b925-864a464a1da5 · outbound

This paper cites Diffusion Models Are Real-Time Game Engines.

Mitigating Compounding Error via Video Representation Regularization Diffusion Models Are Real-Time Game Engines

Reference 25

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Observation 2a49ca13-aafa-461d-97bf-d7733f5cfce6 · outbound

This paper cites ModelScope Text-to-Video Technical Report.

Mitigating Compounding Error via Video Representation Regularization ModelScope Text-to-Video Technical Report

Reference 26

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source=pdf_text observed=2026-07-30T13:09:35.922005Z digest=sha256:444608acb10e287e514c09403647bb72a2dede1647739c16c218996c290faf07

Observation 6743bc7c-44a3-49cc-aac1-599fcd5478eb · outbound

This paper cites Diffuse and Disperse: Image Generation with Representation Regularization.

Mitigating Compounding Error via Video Representation Regularization Diffuse and Disperse: Image Generation with Representation Regularization

Reference 27

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Observation 071d8046-9fa8-4a2e-9dd2-79fd8c6b2d1b · outbound

This paper cites Progressive Autoregressive Video Diffusion Models.

Mitigating Compounding Error via Video Representation Regularization Progressive Autoregressive Video Diffusion Models

Reference 28

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source=pdf_text observed=2026-07-30T13:09:35.936709Z digest=sha256:56ccb56915c11719411024bb186ccd6b9f2f843d64424c19b806ce138d7b6aeb

Observation c407142e-4449-4333-ad9d-1eb333f46729 · outbound

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

Mitigating Compounding Error via Video Representation Regularization CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer

Reference 29

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source=pdf_text observed=2026-07-30T13:09:35.942835Z digest=sha256:03ad697323859bd3da4b0bb4070c276882c7ce784c1236b58dbbf65364e51083

Observation fbc31150-b2c4-41ba-9e95-76274a060ffb · outbound

This paper cites Gamefactory: Creating new games with generative interactive videos.arXiv preprint arXiv:2501.08325, 2025a.

Mitigating Compounding Error via Video Representation Regularization Gamefactory: Creating new games with generative interactive videos.arXiv preprint arXiv:2501.08325, 2025a

Reference 30

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Observation 8c8aa7d6-cd78-4983-87f8-bafd509edfae · outbound

This paper cites 13 A Preprint Boyang Zheng, Nanye Ma, Shengbang Tong, and Saining Xie.

Mitigating Compounding Error via Video Representation Regularization 13 A Preprint Boyang Zheng, Nanye Ma, Shengbang Tong, and Saining Xie

Reference 31

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source=pdf_text observed=2026-07-30T13:09:35.965615Z digest=sha256:5414297e6a16e17a6bbb5a0a2db3dd91d3617839a4e942983524633a2b1a2139

Observation ccfab96b-c440-4f63-8fa9-9a5d48a6d903 · outbound

This paper cites Diffusion Transformers with Representation Autoencoders.

Mitigating Compounding Error via Video Representation Regularization Diffusion Transformers with Representation Autoencoders

Reference 32

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source=pdf_text observed=2026-07-30T13:09:35.971697Z digest=sha256:df072c4072aebf2c789f2de040fc4c731fff4c966a21942721d1e61b7f2d615e

Observation e57ae3e6-5878-4652-9e3f-ef6272308e7e · outbound

This paper cites DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning.

Mitigating Compounding Error via Video Representation Regularization DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning

Reference 33

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Observation e978b968-a126-445f-93ee-deb7bc5f3efe · outbound

This paper cites Flexible Diffusion Modeling of Long Videos.

Mitigating Compounding Error via Video Representation Regularization Flexible Diffusion Modeling of Long Videos

Reference 2018

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source=pdf_text observed=2026-07-30T13:09:35.601823Z digest=sha256:f3ee30f1b27f4e932194c6647d2b47a845e778f5fd66ee7141e64ffab1b5a118

Observation 38230e96-c332-4e24-a8bd-e6a55f3d8e64 · outbound

This paper cites World Models.

Mitigating Compounding Error via Video Representation Regularization World Models

Reference 2019

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Observation ba1694d6-fda1-4ea4-ae2c-594ce443d94b · outbound

This paper cites Video Diffusion Models.

Mitigating Compounding Error via Video Representation Regularization Video Diffusion Models

Reference 2022

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source=pdf_text observed=2026-07-30T13:09:35.610850Z digest=sha256:18a151edc174e10a1a0474adf029024f1898ba33da9bead1cc04e24b54501731

Observation 8d550b77-a1fd-4f98-b0a9-dd1a9ad82c1f · outbound

This paper cites Junliang Guo, Yang Ye, Tianyu He, Haoyu Wu, Yushu Jiang, Tim Pearce, and Jiang Bian.

Mitigating Compounding Error via Video Representation Regularization Junliang Guo, Yang Ye, Tianyu He, Haoyu Wu, Yushu Jiang, Tim Pearce, and Jiang Bian

Reference 2023

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source=pdf_text observed=2026-07-30T13:09:35.583674Z digest=sha256:494dadaa2052b13a44bb3210b2a0e5962830f2256e18cd8e5e0db18d8b3140da

Observation 4682cbba-a32e-4fd2-b7f0-0d0a203d2913 · outbound

This paper cites Revisiting Feature Prediction for Learning Visual Representations from Video.

Mitigating Compounding Error via Video Representation Regularization Revisiting Feature Prediction for Learning Visual Representations from Video

Reference 2024

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source=pdf_text observed=2026-07-30T13:09:35.555769Z digest=sha256:c53ac1f80f348cc62f327902b2ede078816c0fcd01171e9fcc6dfd93268dfb23

Observation c431b515-135f-4a46-a51b-11c5a6b7bd51 · outbound

This paper cites V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning.

Mitigating Compounding Error via Video Representation Regularization V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning

Reference 2025

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source=pdf_text observed=2026-07-30T13:09:35.533052Z digest=sha256:b52a7917e2e6cbd8b336769dc48aa579d0fb3100838a5d350fc272613f9e38ad

Observation 0f13f13f-81df-4bd2-82e3-57338cefa2c0 · outbound

This paper cites Self-Forcing++: Towards Minute-Scale High-Quality Video Generation.

Mitigating Compounding Error via Video Representation Regularization Self-Forcing++: Towards Minute-Scale High-Quality Video Generation

Reference 2026

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source=pdf_text observed=2026-07-30T13:09:35.561958Z digest=sha256:6835375c7a10b7e290ee5567038e7ec492638d61e28eb72645f693154f760f18

Pith citing papers

No inbound Pith citation observations are available.