Paper Citation Record · LEDGER

V-RAE: Rethinking Video Latent Spaces for Generation

As of 20 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2608.13556.

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

pith.paper-citation-record.v1
2608.13556 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T04:16:07.560939Z

measured 32 of 32 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.

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

32 of 32 outbound references displayed

verified exact0
verified fuzzy5
unresolved27
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e1ff04b0-f62e-4edf-8228-4f445fd41fea · outbound

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

V-RAE: Rethinking Video Latent Spaces for Generation Cosmos World Foundation Model Platform for Physical AI

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:07.419865Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4447f9fa-a3c3-431d-8097-f730f0dfeb96 · outbound

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

V-RAE: Rethinking Video Latent Spaces for Generation V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:07.432474Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bdcfce0e-4edc-4c09-a229-1410ffb3817a · outbound

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

V-RAE: Rethinking Video Latent Spaces for Generation Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:07.437307Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 822e9cf7-499b-4321-8c06-45437b1d7d2c · outbound

This paper cites The latent perturbation is used only as a reconstruction-training augmentation; evaluation, latent-statistics estimation, and latent video generation all use clean latents.

V-RAE: Rethinking Video Latent Spaces for Generation The latent perturbation is used only as a reconstruction-training augmentation; evaluation, latent-statistics estimation, and latent video generation all use clean latents

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:08.235615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7be6db4f-9549-4388-8706-111c5887fa99 · outbound

This paper cites The Kinetics Human Action Video Dataset.

V-RAE: Rethinking Video Latent Spaces for Generation The Kinetics Human Action Video Dataset

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:07.449932Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c640daf3-f0a5-4252-9f5c-4bb9c82b55b5 · outbound

This paper cites VideoPoet: A Large Language Model for Zero-Shot Video Generation.

V-RAE: Rethinking Video Latent Spaces for Generation VideoPoet: A Large Language Model for Zero-Shot Video Generation

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:07.453726Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b88751b-242a-4767-b78d-6090785898a2 · outbound

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

V-RAE: Rethinking Video Latent Spaces for Generation HunyuanVideo: A Systematic Framework For Large Video Generative Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:07.458705Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32c377c0-0683-4177-a23f-c8d74756ed76 · outbound

This paper cites Atoken: A unified tokenizer for vision.arXiv preprint arXiv:2509.14476,.

V-RAE: Rethinking Video Latent Spaces for Generation Atoken: A unified tokenizer for vision.arXiv preprint arXiv:2509.14476,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:07.463799Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1715a996-a8eb-4e7c-93d8-7855af826bc1 · outbound

This paper cites Open-MAGVIT2: An Open-Source Project Toward Democratizing Auto-regressive Visual Generation.

V-RAE: Rethinking Video Latent Spaces for Generation Open-MAGVIT2: An Open-Source Project Toward Democratizing Auto-regressive Visual Generation

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:07.468746Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24aedaf7-f34f-43c1-988b-43d5371d167e · outbound

This paper cites DINOv3.

V-RAE: Rethinking Video Latent Spaces for Generation DINOv3

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:07.478488Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bce3e1be-f1a3-454b-b373-e8c803aa75d7 · outbound

This paper cites Improved Baselines with Representation Autoencoders.

V-RAE: Rethinking Video Latent Spaces for Generation Improved Baselines with Representation Autoencoders

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:07.483649Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c927a75-48a4-446e-a8db-1c66ec61b5ed · outbound

This paper cites UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild.

V-RAE: Rethinking Video Latent Spaces for Generation UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:07.492798Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a1d4876-fec9-4f39-b268-392b5243f904 · outbound

This paper cites Scaling text-to-image diffusion transformers with representation autoencoders.arXiv preprint arXiv:2601.16208,.

V-RAE: Rethinking Video Latent Spaces for Generation Scaling text-to-image diffusion transformers with representation autoencoders.arXiv preprint arXiv:2601.16208,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:07.501394Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dea9f47f-51ca-4dea-94e4-9db511d75c7a · outbound

This paper cites SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features.

V-RAE: Rethinking Video Latent Spaces for Generation SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:07.504838Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ed848e9-08e3-4044-99f2-b9f7936e713e · outbound

This paper cites Towards Accurate Generative Models of Video: A New Metric & Challenges.

V-RAE: Rethinking Video Latent Spaces for Generation Towards Accurate Generative Models of Video: A New Metric & Challenges

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:07.509532Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d46f0a2-d88e-4157-87d3-ebf0d5b62093 · outbound

This paper cites Larp: Tokenizing videos with a learned autoregressive generative prior.

V-RAE: Rethinking Video Latent Spaces for Generation Larp: Tokenizing videos with a learned autoregressive generative prior

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:08.260044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c0fc0f33-5340-4164-8dc2-b8294f5720c0 · outbound

This paper cites VidTwin: Video VAE with Decoupled Structure and Dynamics.

V-RAE: Rethinking Video Latent Spaces for Generation VidTwin: Video VAE with Decoupled Structure and Dynamics

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:07.523981Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c801366-1054-466b-b96e-bbc3f9721aa7 · outbound

This paper cites Making Reconstruction FID Predictive of Diffusion Generation FID.

V-RAE: Rethinking Video Latent Spaces for Generation Making Reconstruction FID Predictive of Diffusion Generation FID

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:07.532657Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e19e2ac3-fd39-4026-88f7-8aa24b5b4913 · outbound

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

V-RAE: Rethinking Video Latent Spaces for Generation Cogvideox: Text-to-video diffusion models with an expert transformer

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:08.247634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 07ba8789-d4c7-4792-84dd-94679d813625 · outbound

This paper cites Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation.

V-RAE: Rethinking Video Latent Spaces for Generation Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:07.540481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ce2d571-5fb6-4834-853f-f71141afcf86 · outbound

This paper cites Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think.

V-RAE: Rethinking Video Latent Spaces for Generation Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:07.544422Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d574185f-1288-415a-aa4d-085b3074756b · outbound

This paper cites Diffusion Transformers with Representation Autoencoders.

V-RAE: Rethinking Video Latent Spaces for Generation Diffusion Transformers with Representation Autoencoders

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:07.548558Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 668e9aa1-a0a4-4e1e-bc87-0ea517977c57 · outbound

This paper cites Efficient universal perception encoder.arXiv preprint arXiv:2603.22387,.

V-RAE: Rethinking Video Latent Spaces for Generation Efficient universal perception encoder.arXiv preprint arXiv:2603.22387,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:07.552563Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f503f54a-b211-4ffd-9eeb-50438244f92e · outbound

This paper cites Only the input channel count and corresponding time shift change for EUPE-B.

V-RAE: Rethinking Video Latent Spaces for Generation Only the input channel count and corresponding time shift change for EUPE-B

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:08.221888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4a60b787-5796-4cda-9d61-5ab9045089ec · outbound

This paper cites Representation entanglement for generation: Training diffusion transformers is much easier than you think.Advances in Neural Information Processing Systems, 38:7714–7743, 2025a.

V-RAE: Rethinking Video Latent Spaces for Generation Representation entanglement for generation: Training diffusion transformers is much easier than you think.Advances in Neural Information Processing Systems, 38:7714–7743, 2025a

Reference 2004

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:07.528689Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation caceaa4c-1ffa-4ba9-be4c-a72b7725b96e · outbound

This paper cites RoFormer: Enhanced Transformer with Rotary Position Embedding.

V-RAE: Rethinking Video Latent Spaces for Generation RoFormer: Enhanced Transformer with Rotary Position Embedding

Reference 2012

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:07.497419Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0fe687e-725d-44d6-b5c2-0b3ce4674630 · outbound

This paper cites Dera: Decoupled representation alignment for video tokenization.arXiv preprint arXiv:2512.04483,.

V-RAE: Rethinking Video Latent Spaces for Generation Dera: Decoupled representation alignment for video tokenization.arXiv preprint arXiv:2512.04483,

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:07.445929Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ddcbd4b4-db9c-40cf-b329-2bc79b53a10b · outbound

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

V-RAE: Rethinking Video Latent Spaces for Generation Wan: Open and Advanced Large-Scale Video Generative Models

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:07.514157Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d972246-bfff-44ac-bf89-e1863b9d3ed9 · outbound

This paper cites A Short Note about Kinetics-600.

V-RAE: Rethinking Video Latent Spaces for Generation A Short Note about Kinetics-600

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:07.441723Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a781aca-ad0e-49de-a977-ae39dad674cd · outbound

This paper cites V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised Learning.

V-RAE: Rethinking Video Latent Spaces for Generation V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised Learning

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:07.473562Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff700b73-2f1e-4f9f-8c12-40d2fd0a3c5c · outbound

This paper cites CoVLA: Comprehensive vision-language-action dataset for autonomous driving.

V-RAE: Rethinking Video Latent Spaces for Generation CoVLA: Comprehensive vision-language-action dataset for autonomous driving

Reference 2025

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T04:16:08.273629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 83a764d7-a9ff-4c8b-a812-46ac2f97e315 · outbound

This paper cites Improving the Diffusability of Autoencoders.

V-RAE: Rethinking Video Latent Spaces for Generation Improving the Diffusability of Autoencoders

Reference 2026

Resolution: unresolved; no resolver link, observed 2026-08-14T04:16:07.488440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.