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Paper Citation Record · LEDGER

ViD-GPT: Introducing GPT-style Autoregressive Generation in Video Diffusion Models

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2406.10981.

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

pith.paper-citation-record.v1
2406.10981 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:03:14.996741Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T20:27:22.783676Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 6b526176-ebda-4030-9f11-a1a7e8a1767a · inbound

Unified Video Action Model cites this paper.

Unified Video Action Model ViD-GPT: Introducing GPT-style Autoregressive Generation in Video Diffusion Models

Reference 18

Resolution
verified exact
arxiv_id, observed 2026-05-13T17:50:29.733860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-13T17:50:29.675358Z digest=sha256:ecb2b2c42abeff99b18ce3112800a1e7ae34690afc4c626126ca951e952e32f2

Observation a96975dc-4aeb-444e-9db7-10a4edf6f0c6 · inbound

Long-Context State-Space Video World Models cites this paper.

Long-Context State-Space Video World Models ViD-GPT: Introducing GPT-style Autoregressive Generation in Video Diffusion Models

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-07T14:03:14.996741Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:03:14.996741Z digest=sha256:53133074111f891f12b94ac85b0bd15dd2f716d000896b3a108e2a3a08200d71

Observation 9fe474e8-794c-45fa-b6ea-aea7aff9f106 · inbound

Video World Models with Long-term Spatial Memory cites this paper.

Video World Models with Long-term Spatial Memory ViD-GPT: Introducing GPT-style Autoregressive Generation in Video Diffusion Models

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-07T10:28:37.579879Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:28:37.579879Z digest=sha256:e82c16fc08c28af5ed0ea48b52dd197f4d9250ea8ff898edf5da667e43f16bc4

Observation 7a8cc20c-fdd8-473c-91b6-84cb02db06b7 · inbound

Hunyuan-GameCraft: High-dynamic Interactive Game Video Generation with Hybrid History Condition cites this paper.

Hunyuan-GameCraft: High-dynamic Interactive Game Video Generation with Hybrid History Condition ViD-GPT: Introducing GPT-style Autoregressive Generation in Video Diffusion Models

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-06T23:35:01.995520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:35:01.995520Z digest=sha256:23095f518219573fc7d9e9d1d476c641f16f69480b33a983e616f1983fb642ce

Observation b033239e-d88d-4a8f-89dd-46da93d05877 · inbound

LoViC: Efficient Long Video Generation with Context Compression cites this paper.

LoViC: Efficient Long Video Generation with Context Compression ViD-GPT: Introducing GPT-style Autoregressive Generation in Video Diffusion Models

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-06T16:39:22.906941Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T16:39:22.906941Z digest=sha256:cc351cd826894b6b9b70d4c78e28ccb02a06e1fd78ca64fb40b2eac94e6045e2

Observation ae3038c0-872c-4636-be66-323e54d6ebd4 · inbound

StarPose: 3D Human Pose Estimation via Spatial-Temporal Autoregressive Diffusion cites this paper.

StarPose: 3D Human Pose Estimation via Spatial-Temporal Autoregressive Diffusion ViD-GPT: Introducing GPT-style Autoregressive Generation in Video Diffusion Models

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-06T05:19:30.168927Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T05:19:30.168927Z digest=sha256:ad528cb5b2ffb3beeb84e3e28a7662d72800fa42b462afc7d0f8a97d67e3a238

Observation 8855a3fc-8ce9-4d51-8efd-a89d56a2551c · inbound

BIFE: Better Interaction, Fewer Errors for Minute-Long Video Generation cites this paper.

BIFE: Better Interaction, Fewer Errors for Minute-Long Video Generation ViD-GPT: Introducing GPT-style Autoregressive Generation in Video Diffusion Models

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-03T19:41:56.337223Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T19:41:56.337223Z digest=sha256:cf788b7439f8484f650a27e763318ddc51a2bdcc500e16aa7ac42312f2547174

Observation 86a7ef8b-4e70-4894-baab-f7e938850f91 · inbound

Learning Physics from Pretrained Video Models: A Multimodal Continuous and Sequential World Interaction Models for Robotic Manipulation cites this paper.

Learning Physics from Pretrained Video Models: A Multimodal Continuous and Sequential World Interaction Models for Robotic Manipulation ViD-GPT: Introducing GPT-style Autoregressive Generation in Video Diffusion Models

Reference 16

Resolution
verified exact
arxiv_id, observed 2026-05-15T21:30:20.926227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-15T21:22:41.935691Z digest=sha256:75045aab8b70422a58c8ffeb3b279acddf209e1237920eda0a2ee4c5fbc7f6fa

Observation 4d3e3561-75f3-4ceb-a85e-91759b8477e9 · inbound

Efficient Video Diffusion Models: Advancements and Challenges cites this paper.

Efficient Video Diffusion Models: Advancements and Challenges ViD-GPT: Introducing GPT-style Autoregressive Generation in Video Diffusion Models

Reference 273

Resolution
verified exact
arxiv_id, observed 2026-05-10T09:03:26.111933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-10T08:28:29.706249Z digest=sha256:96d89479b7388344901fcacfd18bd4d8a1403280fce9f4112a6db0dd0efbe39e

Observation bc135bba-4dc1-415d-b394-a999b6f0ad3e · inbound

Ada-Diffuser: Latent-Aware Adaptive Diffusion for Decision-Making cites this paper.

Ada-Diffuser: Latent-Aware Adaptive Diffusion for Decision-Making ViD-GPT: Introducing GPT-style Autoregressive Generation in Video Diffusion Models

Reference 293

Resolution
verified exact
arxiv_id, observed 2026-05-20T20:59:01.972424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-20T20:54:31.025488Z digest=sha256:85eb97b9e14372287322067856e08da14391d4017471864fd1df5ed9fea0e399

Observation a7f1120a-d747-4d7f-84b0-b72872c62208 · inbound

DisCo: World Models with Discrete Camera Motion Control cites this paper.

DisCo: World Models with Discrete Camera Motion Control ViD-GPT: Introducing GPT-style Autoregressive Generation in Video Diffusion Models

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-07-02T20:27:22.785256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-27T20:21:20.257532Z digest=sha256:e5f7ad964f32ed826dd5fab1130c58b15de0dae97daa7413a88972e1dfed5495