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Paper Citation Record · LEDGER

Towards Precise Scaling Laws for Video Diffusion Transformers

As of 13 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 0 inbound Pith citation observations for arXiv:2411.17470.

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

pith.paper-citation-record.v1
2411.17470 v2

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measured 63 of 63 reference resolution

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measured 0 of 0 inbound itemization

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63 of 63 outbound references displayed

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Outbound references

Observation 6959664b-9adb-4de5-be0e-a573a00b1a82 · outbound

This paper cites DeepSeek LLM: Scaling Open-Source Language Models with Longtermism.

Towards Precise Scaling Laws for Video Diffusion Transformers DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

Reference 1

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Observation 121e8797-2caf-4cb6-9959-b24eda427fd7 · outbound

This paper cites u-$\mu$P: The Unit-Scaled Maximal Update Parametrization.

Towards Precise Scaling Laws for Video Diffusion Transformers u-$\mu$P: The Unit-Scaled Maximal Update Parametrization

Reference 2

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Observation 523ba06a-a3aa-4b23-ab8f-f07675bb14a4 · outbound

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

Towards Precise Scaling Laws for Video Diffusion Transformers Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Reference 3

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Observation 86828b5b-13e7-4606-aa7e-e8d188465b3d · outbound

This paper cites Align your latents: High-resolution video synthesis with la- tent diffusion models.

Towards Precise Scaling Laws for Video Diffusion Transformers Align your latents: High-resolution video synthesis with la- tent diffusion models

Reference 4

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Observation 23ae8ba5-13e2-4e27-8e80-3375ba641bb3 · outbound

This paper cites Video generation models as world simulators, 2024.

Towards Precise Scaling Laws for Video Diffusion Transformers Video generation models as world simulators, 2024

Reference 5

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Observation 2e5c425c-659c-4459-bf58-d83a745250ba · outbound

This paper cites Videocrafter2: 9 Overcoming data limitations for high-quality video diffu- sion models.

Towards Precise Scaling Laws for Video Diffusion Transformers Videocrafter2: 9 Overcoming data limitations for high-quality video diffu- sion models

Reference 6

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Observation 5f67f53a-93e8-4dcb-a53a-c8f44139d549 · outbound

This paper cites PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis.

Towards Precise Scaling Laws for Video Diffusion Transformers PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis

Reference 8

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Observation 27030f13-9a31-4096-90a1-2e1f9b33cc0a · outbound

This paper cites Panda-70m: Captioning 70m videos with multiple cross-modality teachers.

Towards Precise Scaling Laws for Video Diffusion Transformers Panda-70m: Captioning 70m videos with multiple cross-modality teachers

Reference 9

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Observation 239e98e6-c1dc-4962-b088-5800c70fbf7c · outbound

This paper cites Seine: Short-to-long video diffu- sion model for generative transition and prediction.

Towards Precise Scaling Laws for Video Diffusion Transformers Seine: Short-to-long video diffu- sion model for generative transition and prediction

Reference 10

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Observation 48708aa3-a572-46ae-8c7a-4fb831e2474c · outbound

This paper cites Adversarial Video Generation on Complex Datasets.

Towards Precise Scaling Laws for Video Diffusion Transformers Adversarial Video Generation on Complex Datasets

Reference 11

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Observation 4ba04073-6539-46c9-bf6f-3e2d34f5ec09 · outbound

This paper cites The Llama 3 Herd of Models.

Towards Precise Scaling Laws for Video Diffusion Transformers The Llama 3 Herd of Models

Reference 12

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Observation fcb6e01f-93f8-46e6-a087-fff832362720 · outbound

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

Towards Precise Scaling Laws for Video Diffusion Transformers Scaling recti- fied flow transformers for high-resolution image synthesis

Reference 13

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Observation 9dd5c5e8-c743-4fee-a509-75eef8c6e838 · outbound

This paper cites Lumina-T2X: Transforming Text into Any Modality, Resolution, and Duration via Flow-based Large Diffusion Transformers.

Towards Precise Scaling Laws for Video Diffusion Transformers Lumina-T2X: Transforming Text into Any Modality, Resolution, and Duration via Flow-based Large Diffusion Transformers

Reference 14

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Observation f0625430-8b40-479d-b21c-e00cb918bcaa · outbound

This paper cites Emu Video: Factorizing Text-to-Video Generation by Explicit Image Conditioning.

Towards Precise Scaling Laws for Video Diffusion Transformers Emu Video: Factorizing Text-to-Video Generation by Explicit Image Conditioning

Reference 16

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Observation 0441af0c-0cc6-41de-a59e-fed8017eb81b · outbound

This paper cites Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour.

Towards Precise Scaling Laws for Video Diffusion Transformers Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Reference 17

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Observation 69a9fec3-811e-4398-8b6d-48bba4422a5c · outbound

This paper cites Latent Video Diffusion Models for High-Fidelity Long Video Generation.

Towards Precise Scaling Laws for Video Diffusion Transformers Latent Video Diffusion Models for High-Fidelity Long Video Generation

Reference 18

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Observation 073df9a6-4ee6-463a-8f99-ba741dea8c5e · outbound

This paper cites Scaling Laws for Autoregressive Generative Modeling.

Towards Precise Scaling Laws for Video Diffusion Transformers Scaling Laws for Autoregressive Generative Modeling

Reference 20

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Observation ac41a400-3674-404e-90dc-b7815959f048 · outbound

This paper cites StreamingT2V: Consistent, Dynamic, and Extendable Long Video Generation from Text.

Towards Precise Scaling Laws for Video Diffusion Transformers StreamingT2V: Consistent, Dynamic, and Extendable Long Video Generation from Text

Reference 21

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Observation 1e98f59e-12d8-4299-8fb0-3b28d16be15e · outbound

This paper cites Local Lipschitz Bounds of Deep Neural Networks.

Towards Precise Scaling Laws for Video Diffusion Transformers Local Lipschitz Bounds of Deep Neural Networks

Reference 22

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Observation b81af0fb-8aa1-4a35-a82c-f5b818c30df7 · outbound

This paper cites Deep Learning Scaling is Predictable, Empirically.

Towards Precise Scaling Laws for Video Diffusion Transformers Deep Learning Scaling is Predictable, Empirically

Reference 23

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Observation 05201b75-7e64-42ba-a163-e90c5599d204 · outbound

This paper cites Denoising dif- fusion probabilistic models.

Towards Precise Scaling Laws for Video Diffusion Transformers Denoising dif- fusion probabilistic models

Reference 24

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Observation 34aa8a0c-fe31-45a6-a434-57e4226866e0 · outbound

This paper cites Imagen Video: High Definition Video Generation with Diffusion Models.

Towards Precise Scaling Laws for Video Diffusion Transformers Imagen Video: High Definition Video Generation with Diffusion Models

Reference 25

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Observation a399adbe-96a3-4e3c-a374-be1b23908883 · outbound

This paper cites Training Compute-Optimal Large Language Models.

Towards Precise Scaling Laws for Video Diffusion Transformers Training Compute-Optimal Large Language Models

Reference 26

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Observation ac75d370-f0fd-461a-9257-8a71e4f11fd1 · outbound

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

Towards Precise Scaling Laws for Video Diffusion Transformers CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers

Reference 27

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Observation 5674c9ff-7cf3-4108-9623-f4809b357269 · outbound

This paper cites DepthCrafter: Generating Consistent Long Depth Sequences for Open-world Videos.

Towards Precise Scaling Laws for Video Diffusion Transformers DepthCrafter: Generating Consistent Long Depth Sequences for Open-world Videos

Reference 28

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Observation 5f808a54-7347-4b75-96ba-73401d1c1386 · outbound

This paper cites Scaling Laws for Neural Language Models.

Towards Precise Scaling Laws for Video Diffusion Transformers Scaling Laws for Neural Language Models

Reference 29

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Observation 89143203-6888-476f-b378-3a6c0eaa9a69 · outbound

This paper cites Klingai, 2024.

Towards Precise Scaling Laws for Video Diffusion Transformers Klingai, 2024

Reference 30

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Observation e703bb33-e5cd-493a-b05e-823776b64579 · outbound

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

Towards Precise Scaling Laws for Video Diffusion Transformers VideoPoet: A Large Language Model for Zero-Shot Video Generation

Reference 31

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Observation 290835e3-5c72-4468-a4f5-ed8240a28293 · outbound

This paper cites On the scalability of diffusion-based text-to-image generation.

Towards Precise Scaling Laws for Video Diffusion Transformers On the scalability of diffusion-based text-to-image generation

Reference 32

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Observation 8487dc0d-9166-45f7-a414-4bf0d7732189 · outbound

This paper cites Scaling laws for diffusion transformers.

Towards Precise Scaling Laws for Video Diffusion Transformers Scaling laws for diffusion transformers

Reference 33

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Observation 5f009bd5-a877-460a-884d-dbbf479fee0f · outbound

This paper cites MarDini: Masked Autoregressive Diffusion for Video Generation at Scale.

Towards Precise Scaling Laws for Video Diffusion Transformers MarDini: Masked Autoregressive Diffusion for Video Generation at Scale

Reference 34

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Observation 98c15704-7791-411b-ab39-520ae39c2635 · outbound

This paper cites VDT: General-purpose Video Diffusion Transformers via Mask Modeling.

Towards Precise Scaling Laws for Video Diffusion Transformers VDT: General-purpose Video Diffusion Transformers via Mask Modeling

Reference 35

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Observation fe7e8a26-a7e7-4441-b964-838b303adaf5 · outbound

This paper cites SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers.

Towards Precise Scaling Laws for Video Diffusion Transformers SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 36

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Observation e2e0e4c3-8574-464b-b192-af4da32c6d1a · outbound

This paper cites Latte: Latent Diffusion Transformer for Video Generation.

Towards Precise Scaling Laws for Video Diffusion Transformers Latte: Latent Diffusion Transformer for Video Generation

Reference 37

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Observation 427014aa-0976-4f90-9497-e97d0f9c1a8a · outbound

This paper cites An Empirical Model of Large-Batch Training.

Towards Precise Scaling Laws for Video Diffusion Transformers An Empirical Model of Large-Batch Training

Reference 38

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Observation 6a5edc9d-e900-49f9-b244-471ee9fdbdb8 · outbound

This paper cites Bigger is not Always Better: Scaling Properties of Latent Diffusion Models.

Towards Precise Scaling Laws for Video Diffusion Transformers Bigger is not Always Better: Scaling Properties of Latent Diffusion Models

Reference 39

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Observation 21b465df-66bc-4b3f-bef9-0cd4dae2f1e8 · outbound

This paper cites Sora, 2024.

Towards Precise Scaling Laws for Video Diffusion Transformers Sora, 2024

Reference 40

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source=pdf_text observed=2026-08-12T12:58:57.234566Z digest=sha256:eea8718588b978d357c1bed666cfb361983952546301c5490a305950adba9cf1

Observation d17b79ed-2c06-4b43-8b34-a9140e7474e3 · outbound

This paper cites Scalable diffusion models with transformers.

Towards Precise Scaling Laws for Video Diffusion Transformers Scalable diffusion models with transformers

Reference 42

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source=pdf_text observed=2026-08-12T12:58:57.241828Z digest=sha256:ac9de0f986e631f1c50bff4413cca3806fb1eefda40cdb015b5812b09c796cb2

Observation 1bb1d455-877d-4e43-aee1-aec96878df42 · outbound

This paper cites Movie Gen: A Cast of Media Foundation Models.

Towards Precise Scaling Laws for Video Diffusion Transformers Movie Gen: A Cast of Media Foundation Models

Reference 43

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source=pdf_text observed=2026-08-12T12:58:57.245236Z digest=sha256:2f7b4009b23ad1e7a789b84aec57edfb11e0d61d2f2f06b68c5e59866155ffaf

Observation 43828e3a-0e2e-47ae-a80a-29bdf1ab1509 · outbound

This paper cites Tempo- ral generative adversarial nets with singular value clipping.

Towards Precise Scaling Laws for Video Diffusion Transformers Tempo- ral generative adversarial nets with singular value clipping

Reference 44

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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.

source=pdf_text observed=2026-08-12T12:58:57.249522Z digest=sha256:db03545290b3d9c3bd94e21dadefb7b2181ae360128365636f5d03735aa1e705

Observation 8dae8a06-b1a9-44d4-b608-45a4c9204f79 · outbound

This paper cites Fast High-Resolution Image Synthesis with Latent Adversarial Diffusion Distillation.

Towards Precise Scaling Laws for Video Diffusion Transformers Fast High-Resolution Image Synthesis with Latent Adversarial Diffusion Distillation

Reference 45

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source=pdf_text observed=2026-08-12T12:58:57.253471Z digest=sha256:83aa407c9fe611d8975204f8b2f80b0abc9444dd95959456a810b6df85a7ba28

Observation d2dfee5a-d7fc-4ba3-8374-c14028255dba · outbound

This paper cites Measuring the effects of data parallelism on neural network training.

Towards Precise Scaling Laws for Video Diffusion Transformers Measuring the effects of data parallelism on neural network training

Reference 46

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raw_fallback, observed 2026-08-12T12:58:58.114943Z

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.

source=pdf_text observed=2026-08-12T12:58:57.257816Z digest=sha256:c822b8dd94b743cbcf63e63d2be6ac01c84c73e63071025502f00cb24c28bd60

Observation fe91a8cf-b0f1-47ee-b514-f21d73011611 · outbound

This paper cites Power Scheduler: A Batch Size and Token Number Agnostic Learning Rate Scheduler.

Towards Precise Scaling Laws for Video Diffusion Transformers Power Scheduler: A Batch Size and Token Number Agnostic Learning Rate Scheduler

Reference 47

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source=pdf_text observed=2026-08-12T12:58:57.261939Z digest=sha256:d64707fb29c668d864f77a0466ba296067a115f498b8c29db8e71a336052f45c

Observation b03d27a5-8d73-4733-b022-ab8807ce6ae3 · outbound

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

Towards Precise Scaling Laws for Video Diffusion Transformers Make-A-Video: Text-to-Video Generation without Text-Video Data

Reference 48

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source=pdf_text observed=2026-08-12T12:58:57.265902Z digest=sha256:e255c0a62b06503288e51a431e546b11f8ebb96389ca5f651a36ac28d54aecc3

Observation 3e98d66c-2eef-48e9-a91d-d09074420dd7 · outbound

This paper cites Don't Decay the Learning Rate, Increase the Batch Size.

Towards Precise Scaling Laws for Video Diffusion Transformers Don't Decay the Learning Rate, Increase the Batch Size

Reference 49

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source=pdf_text observed=2026-08-12T12:58:57.270766Z digest=sha256:08b8ef7dd05de722a1d2d71f8576516e99d089e3a1f8c0164832591b0c678aa2

Observation f51b78f6-d8af-438b-afaa-050ceb8d494f · outbound

This paper cites Denoising Diffusion Implicit Models.

Towards Precise Scaling Laws for Video Diffusion Transformers Denoising Diffusion Implicit Models

Reference 50

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source=pdf_text observed=2026-08-12T12:58:57.275848Z digest=sha256:ee3c1d3bb495e08108315264321f57f6a81565a52c2c4ff0e1b5bb7bacc51d12

Observation 681fb9ba-e4f2-4b34-8b25-418904736e6a · outbound

This paper cites Video-Infinity: Distributed Long Video Generation.

Towards Precise Scaling Laws for Video Diffusion Transformers Video-Infinity: Distributed Long Video Generation

Reference 51

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source=pdf_text observed=2026-08-12T12:58:57.281092Z digest=sha256:18cc8c195c984a7254abe2759becf6738080bb546a3034c24af564dd237af58c

Observation 55ce4d28-063a-442a-b43a-e8bf89e8976e · outbound

This paper cites Mocogan: Decomposing motion and content for video generation.

Towards Precise Scaling Laws for Video Diffusion Transformers Mocogan: Decomposing motion and content for video generation

Reference 52

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source=pdf_text observed=2026-08-12T12:58:57.286579Z digest=sha256:32e2adf0f84439730bef086c47ac974d99089d02990b06e3c7b7c3be4d692289

Observation 94561091-ef8d-4d61-aba3-5d5ec211b40f · outbound

This paper cites Phenaki: Variable length video generation from open domain textual descriptions.

Towards Precise Scaling Laws for Video Diffusion Transformers Phenaki: Variable length video generation from open domain textual descriptions

Reference 53

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verified fuzzy
raw_fallback, observed 2026-08-12T12:58:58.096353Z

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.

source=pdf_text observed=2026-08-12T12:58:57.290684Z digest=sha256:dd85235e45fcbc7245cb9c68724c2b7f0e1f3ea9ecee09db782a43b87975bf29

Observation d62a95e3-e629-4f28-8429-ffcb2c61a67c · outbound

This paper cites Generating videos with scene dynamics.

Towards Precise Scaling Laws for Video Diffusion Transformers Generating videos with scene dynamics

Reference 54

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raw_fallback, observed 2026-08-12T12:58:58.083579Z

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.

source=pdf_text observed=2026-08-12T12:58:57.294695Z digest=sha256:b1ea10f8f9de78881e76dd82cc7312b5174867b4089fc3ef8ccafc654673e88c

Observation 0f79c6ea-effe-4c7b-9f36-98830d650975 · outbound

This paper cites Gen-L-Video: Multi-Text to Long Video Generation via Temporal Co-Denoising.

Towards Precise Scaling Laws for Video Diffusion Transformers Gen-L-Video: Multi-Text to Long Video Generation via Temporal Co-Denoising

Reference 55

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source=pdf_text observed=2026-08-12T12:58:57.299416Z digest=sha256:bc085fcc733cb7455642c3c2537ea8626c6a2bdc073fcd96dd3235e54adbbe30

Observation 4eeb25bb-cdaf-4e4d-9070-769665fcd4cf · outbound

This paper cites Adapting to smoothness: A more universal algorithm for on- line convex optimization.

Towards Precise Scaling Laws for Video Diffusion Transformers Adapting to smoothness: A more universal algorithm for on- line convex optimization

Reference 56

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raw_fallback, observed 2026-08-12T12:58:58.070711Z

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.

source=pdf_text observed=2026-08-12T12:58:57.303450Z digest=sha256:0490cc495b42d9e6997a051ce0f30a829759095c5bf15d0be70e13b95f637e7e

Observation a63ae0b9-8093-4009-b810-b9cf60a0ed87 · outbound

This paper cites Show-o: One Single Transformer to Unify Multimodal Understanding and Generation.

Towards Precise Scaling Laws for Video Diffusion Transformers Show-o: One Single Transformer to Unify Multimodal Understanding and Generation

Reference 57

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source=pdf_text observed=2026-08-12T12:58:57.307392Z digest=sha256:5975d7b3333418e61c6f0e541c5a09849d40f8e108e8143f1022aff9a0d74069

Observation ada8b994-365b-48ae-b22e-69089c4a60e2 · outbound

This paper cites VideoGPT: Video Generation using VQ-VAE and Transformers.

Towards Precise Scaling Laws for Video Diffusion Transformers VideoGPT: Video Generation using VQ-VAE and Transformers

Reference 58

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source=pdf_text observed=2026-08-12T12:58:57.312178Z digest=sha256:939d075f4008df448117df7c12fc2d7144584505127dc26ad560baf77b9049a6

Observation a3381a06-ed1b-4b35-9dc9-e8900b1ff2a3 · outbound

This paper cites Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer.

Towards Precise Scaling Laws for Video Diffusion Transformers Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer

Reference 59

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source=pdf_text observed=2026-08-12T12:58:57.316711Z digest=sha256:0a120f81e56b7c1ab59d4476966a202709753002e661d32fcb1b0b4940592047

Observation efdaa03c-4afe-486d-b26e-1de95e965cc3 · outbound

This paper cites Rerender a video: Zero-shot text-guided video-to-video translation.

Towards Precise Scaling Laws for Video Diffusion Transformers Rerender a video: Zero-shot text-guided video-to-video translation

Reference 60

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raw_fallback, observed 2026-08-12T12:58:58.055536Z

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.

source=pdf_text observed=2026-08-12T12:58:57.322749Z digest=sha256:3e2b442bd06d9c83bcf0e34b9390ccaf651cff6528e31df9d8fa8c333b1e5ac0

Observation e0f695d4-8db7-4e38-90d8-76b3c2e43858 · outbound

This paper cites Space-time diffusion features for zero-shot text-driven motion transfer.

Towards Precise Scaling Laws for Video Diffusion Transformers Space-time diffusion features for zero-shot text-driven motion transfer

Reference 61

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raw_fallback, observed 2026-08-12T12:58:58.043210Z

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.

source=pdf_text observed=2026-08-12T12:58:57.328218Z digest=sha256:09585f2410e298346723006ccc9a372eb524ce84f606fed39b9cd37a51403a4b

Observation ac178b8d-662a-4fbb-a554-74a39c0478d9 · outbound

This paper cites NUWA-XL: Diffusion over Diffusion for eXtremely Long Video Generation.

Towards Precise Scaling Laws for Video Diffusion Transformers NUWA-XL: Diffusion over Diffusion for eXtremely Long Video Generation

Reference 62

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source=pdf_text observed=2026-08-12T12:58:57.332537Z digest=sha256:b5196aee5feec2676f045af336bc51e31094fa1378761b6052f4500b38a12433

Observation 16c40c50-f5a8-44e9-8276-668a4013d80a · outbound

This paper cites Generating Videos with Dynamics-aware Implicit Generative Adversarial Networks.

Towards Precise Scaling Laws for Video Diffusion Transformers Generating Videos with Dynamics-aware Implicit Generative Adversarial Networks

Reference 63

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source=pdf_text observed=2026-08-12T12:58:57.337071Z digest=sha256:24170479f6b4261b0af747c77332516a3538f7ab04c2cb1a059b64479c885f1f

Observation 2b883efa-dfc3-4297-a227-f26424a6a543 · outbound

This paper cites Tora: Trajectory-oriented Diffusion Transformer for Video Generation.

Towards Precise Scaling Laws for Video Diffusion Transformers Tora: Trajectory-oriented Diffusion Transformer for Video Generation

Reference 64

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-12T12:58:57.341054Z digest=sha256:0f8e2c9cc85e5d7c60eac9f043062049e825626ec7e23c44ad78d1aac3e85b7d

Observation 911ded9e-c48a-461a-9aed-eb8f0a5676cd · outbound

This paper cites Moviedreamer: Hier- archical generation for coherent long visual sequence.

Towards Precise Scaling Laws for Video Diffusion Transformers Moviedreamer: Hier- archical generation for coherent long visual sequence

Reference 65

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source=pdf_text observed=2026-08-12T12:58:57.345097Z digest=sha256:6d02ff78a8209f721e6374aa1e6383665b5d5f0901a4d4fd8898cf407c4044cc

Observation c661acd3-f623-4a74-8240-08e034d3f65b · outbound

This paper cites Open-sora: Democratizing efficient video production for all, 2024.

Towards Precise Scaling Laws for Video Diffusion Transformers Open-sora: Democratizing efficient video production for all, 2024

Reference 66

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raw_fallback, observed 2026-08-12T12:58:58.030147Z

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.

source=pdf_text observed=2026-08-12T12:58:57.349217Z digest=sha256:ded6bb44bdf8d9319b729a4d293b0e2d20f1ccd52c6f9177d0b1f3f2d653c869

Observation a764416c-a39e-4bc0-b39f-f87f97179d32 · outbound

This paper cites StoryDiffusion: Consistent Self-Attention for Long-Range Image and Video Generation.

Towards Precise Scaling Laws for Video Diffusion Transformers StoryDiffusion: Consistent Self-Attention for Long-Range Image and Video Generation

Reference 67

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source=pdf_text observed=2026-08-12T12:58:57.353357Z digest=sha256:3539786a30470d7c4edfcf1978dcfb815b9d9a6b315ed8c230cea237080b504c

Pith citing papers

No inbound Pith citation observations are available.