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Paper Citation Record · LEDGER

Mitigating Compounding Error via Video Representation Regularization

As of 9 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 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

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:a6fd58e18797e004da2b7ad5ab511835d46e170869345fb2784af35f2bf37e14

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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source=pdf_text observed=2026-07-30T13:09:35.577926Z digest=sha256:00a62f21db42ad9448728cc1549787d2552d951bdc0bf382c9f474b63c9927ec

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=sha256:588d01cb516bd7e7b3956434471fcec8c899fcbe01674f72854a87990178b020

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:d5795d0592fb5876eab1ae5c35c1085f5b7f6f85abee3f3f7cfab4ec56634090

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:f459bf36f5109df1b5565c60d5b1acebc24158e6dc68a6209a27295aa6f6182d

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=sha256:2ed7dfaf564b6f4cdf4d8697e4dba1d47b60c22d8318c66bc2c8c345113d8e1a

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=sha256:516d5ffb0ab741e05133dd5e9e4e442d4b4c79d09e4fe49de368a30c393f51e0

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=sha256:0c8527295aefb9409f7bf75e7e4348de66707f5c88f4262fd686d001391a7fd7

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:ee6dd4773e23917e936f9fb55c3c05fefe536ac67d9b5093b6be146b632bb063

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=sha256:9fb6fe54d78417986ff43bfdf7eb1d685ccde68e9a9b16b8ad8cc87cec205d98

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=sha256:7d3286d8e4a793093e75747dc2980ac8bd395bfa3a758de9fb6dcd76652e4137

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=sha256:16e58fd0c518deab66706e87cd66a2b2e001b7b42100e7d64b5f1e972f9117fc

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:d46b8b73d3efd7c80fd2914dec0cdd343b819c4b5256d4dc8db463f49a8fdbe5

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=sha256:02cfb14ade9cf439f2add1c674c50b2c456a4da32df8e612bb8ae50c3831c2cd

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=sha256:7d99ba10d040f45c64aff3c339422de3395f834db2db545587f3d054c5552fe9

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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source=pdf_text observed=2026-07-30T13:09:35.911015Z digest=sha256:15308038da2639073379345ee5df0092cde2e90cb43dd557b383c8f0aa43a313

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:d7e78daab7c9da0ab2c80f0f791fae188300766ffd901def9a25a83228248988

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:fcd8949c7c6190c5c082b28c978372789454f1218a2458246846a3a9f5b454dd

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=sha256:96185770f64e847f22f524c1e29126c1cb411bcbf34c4b55299b617c368ad4d4

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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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=sha256:6bb94976c07defcc9324a68958948ae72b0bf7e6db13976c43f91343e27de07f

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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source=pdf_text observed=2026-07-30T13:09:35.981096Z digest=sha256:083ed3f60f03ba699c77fd2063bc4fde5e3c2c765eb3f7ce2b1c3e7f8ed49dae

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:e811d19cd8fe660cf06e99c3482eede5ee5cdb00dba3c78f0dc82b1a8dd770b9

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=sha256:8272aa549ec3866da77ccbe8d923c1e2a1981619eb53b13826216067c5027a62

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=sha256:46a971c9d411d3d12d840cbe5e01ed299085f4010e095e35940754c53dc556f3

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=sha256:0afcd3e524e6c612f760806728ad0e4752dc6bca888caef55f434ebac0c1dc00

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=sha256:9f0b69276a1f4de4aa28c85bc50a04441fb0c1f55b166299fec754d460c74b70

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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Pith citing papers

No inbound Pith citation observations are available.