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Paper Citation Record · LEDGER

Towards Chunk-Wise Generation for Long Videos

As of 12 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2411.18668.

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

pith.paper-citation-record.v1
2411.18668 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T11:11:52.136614Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+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

30 of 30 outbound references displayed

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  • verified fuzzy9
  • unresolved21
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6deccf3c-9644-4f84-8d6c-7246b6df6751 · outbound

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

Towards Chunk-Wise Generation for Long Videos Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Reference 1

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Observation d2adc6c2-350f-4cf7-b45a-19b43094854b · outbound

This paper cites Emerg- ing properties in self-supervised vision transformers.

Towards Chunk-Wise Generation for Long Videos Emerg- ing properties in self-supervised vision transformers

Reference 2

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Observation ff646f98-332d-4a4c-8793-f853c87eec05 · outbound

This paper cites Videocrafter2: Overcoming data limitations for high-quality video diffu- sion models.

Towards Chunk-Wise Generation for Long Videos Videocrafter2: Overcoming data limitations for high-quality video diffu- sion models

Reference 3

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Observation bc0ed4ac-52f3-495d-b810-f86a23f3bde9 · outbound

This paper cites Control-a- video: Controllable text-to-video diffusion models with mo- tion prior and reward feedback learning, 2024.

Towards Chunk-Wise Generation for Long Videos Control-a- video: Controllable text-to-video diffusion models with mo- tion prior and reward feedback learning, 2024

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation b1dd4b0d-9967-4a93-9e36-696aa6d95008 · outbound

This paper cites Generative adversarial nets.

Towards Chunk-Wise Generation for Long Videos Generative adversarial nets

Reference 5

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 9de4b07a-3c29-4c95-a8c1-e384d9385910 · outbound

This paper cites AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning.

Towards Chunk-Wise Generation for Long Videos AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning

Reference 6

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Observation ef0e1c46-7740-42bf-8405-1ccd95fd6cac · outbound

This paper cites Denoising dif- fusion probabilistic models.

Towards Chunk-Wise Generation for Long Videos Denoising dif- fusion probabilistic models

Reference 7

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Observation 895c75ae-0d8c-41c1-81ac-8bb628799d9a · outbound

This paper cites Vbench: Comprehensive bench- mark suite for video generative models.

Towards Chunk-Wise Generation for Long Videos Vbench: Comprehensive bench- mark suite for video generative models

Reference 8

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Observation 33a7be06-7b5e-4eb1-bfb5-fae8ede5089e · outbound

This paper cites Elucidating the design space of diffusion-based generative models.

Towards Chunk-Wise Generation for Long Videos Elucidating the design space of diffusion-based generative models

Reference 9

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Observation 773a7575-2607-48b2-b67c-115f1c664f0c · outbound

This paper cites FIFO-Diffusion: Generating Infinite Videos from Text without Training.

Towards Chunk-Wise Generation for Long Videos FIFO-Diffusion: Generating Infinite Videos from Text without Training

Reference 10

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Observation 0dc10f3c-39fc-4eb2-b484-f002e9a88bcf · outbound

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

Towards Chunk-Wise Generation for Long Videos VideoPoet: A Large Language Model for Zero-Shot Video Generation

Reference 11

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Observation f97c8577-1f85-4e29-93db-3b1197e4681f · outbound

This paper cites Open-sora-plan, 2024.

Towards Chunk-Wise Generation for Long Videos Open-sora-plan, 2024

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation c2e80aca-341b-472d-9b7f-e1fee4cd7279 · outbound

This paper cites aesthetic-predictor.

Towards Chunk-Wise Generation for Long Videos aesthetic-predictor

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation d18bfcda-a372-4a92-8923-cf0bc47cf535 · outbound

This paper cites Amt: All-pairs multi-field transforms for efficient frame interpolation.

Towards Chunk-Wise Generation for Long Videos Amt: All-pairs multi-field transforms for efficient frame interpolation

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 3cda880c-f228-42f2-8032-ac04a190fbc5 · outbound

This paper cites Pseudo Numerical Methods for Diffusion Models on Manifolds.

Towards Chunk-Wise Generation for Long Videos Pseudo Numerical Methods for Diffusion Models on Manifolds

Reference 15

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Observation 9327c4d9-2c11-4f41-9001-a7787ab9e142 · outbound

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

Towards Chunk-Wise Generation for Long Videos Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps

Reference 16

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Observation e1c6c4fa-c7c1-4d74-875a-5fd72fa8fdb9 · outbound

This paper cites Scalable diffusion models with transformers.

Towards Chunk-Wise Generation for Long Videos Scalable diffusion models with transformers

Reference 17

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Observation 5a06b7bc-f90e-4989-9590-ab77a60d5514 · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

Towards Chunk-Wise Generation for Long Videos Learning transferable visual models from natural language supervi- sion

Reference 18

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Observation 628339fd-d702-4bbc-9bc4-b73dbda6c8d3 · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

Towards Chunk-Wise Generation for Long Videos Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 19

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Observation 7a508ef1-e9d1-41a8-8578-68e9141c205f · outbound

This paper cites ConsistI2V: Enhancing Visual Consistency for Image-to-Video Generation.

Towards Chunk-Wise Generation for Long Videos ConsistI2V: Enhancing Visual Consistency for Image-to-Video Generation

Reference 20

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Observation 8aa0ef68-bdc1-41ce-947f-08be4808a00f · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

Towards Chunk-Wise Generation for Long Videos High-resolution image synthesis with latent diffusion models

Reference 21

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Observation 29ad6fe1-c1e2-40c2-93b5-b31a21450ee9 · outbound

This paper cites U- net: Convolutional networks for biomedical image segmen- tation.

Towards Chunk-Wise Generation for Long Videos U- net: Convolutional networks for biomedical image segmen- tation

Reference 22

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Observation 8a3b6530-ecb4-45d7-b5e0-a4de2393a5a0 · outbound

This paper cites Denoising Diffusion Implicit Models.

Towards Chunk-Wise Generation for Long Videos Denoising Diffusion Implicit Models

Reference 23

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Observation 6f113df8-9ceb-412a-bf98-5e03a32bc4ce · outbound

This paper cites Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation.

Towards Chunk-Wise Generation for Long Videos Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation

Reference 24

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Observation f95ad6d7-4238-493b-a81e-e6cfcfefc142 · outbound

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

Towards Chunk-Wise Generation for Long Videos ModelScope Text-to-Video Technical Report

Reference 25

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Observation b605e088-94a8-4fff-9b43-0cfd0b6ac3b7 · outbound

This paper cites NUWA-Infinity: Autoregressive over Autoregressive Generation for Infinite Visual Synthesis.

Towards Chunk-Wise Generation for Long Videos NUWA-Infinity: Autoregressive over Autoregressive Generation for Infinite Visual Synthesis

Reference 26

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Observation 73b0e0a6-9475-492b-a316-e2de0df150e6 · outbound

This paper cites Freeinit: Bridging initialization gap in video dif- fusion models.

Towards Chunk-Wise Generation for Long Videos Freeinit: Bridging initialization gap in video dif- fusion models

Reference 27

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation bac21d8c-b2d1-4039-bb4b-a8c678fc9ac0 · outbound

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

Towards Chunk-Wise Generation for Long Videos CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer

Reference 28

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Observation efceeb06-a411-4c85-ab80-80fa2faac988 · outbound

This paper cites Magvit: Masked generative video transformer.

Towards Chunk-Wise Generation for Long Videos Magvit: Masked generative video transformer

Reference 29

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation afdcb13c-091e-438a-b927-0962ed38e2d7 · outbound

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

Towards Chunk-Wise Generation for Long Videos Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation

Reference 30

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Pith citing papers

No inbound Pith citation observations are available.