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Paper Citation Record · LEDGER

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model

As of 21 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 5 inbound Pith citation observations for arXiv:2506.01546.

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

pith.paper-citation-record.v1
2506.01546 v1

Coverage vector

measured 59 of 59 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:47:50.172713Z

measured 64 of 64 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 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T18:31:52.876814Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T06:26:27.481686Z

Reference resolution

59 of 59 outbound references displayed

  • verified exact0
  • verified fuzzy11
  • unresolved48
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4a400a42-c8cb-4763-8778-83733654c949 · outbound

This paper cites World Models.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model World Models

Reference 1

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source=pdf_text observed=2026-08-07T11:46:58.980467Z digest=sha256:06546a0342e1972873093d6cf94a8f014faead891a7dc7e127b613b0faedcf51

Observation 667ad5d2-34b7-45a1-8b9d-09a2a97bfe85 · outbound

This paper cites A path towards autonomous machine intelligence version 0.9.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model A path towards autonomous machine intelligence version 0.9

Reference 2

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verified fuzzy
raw_fallback, observed 2026-08-07T11:48:04.307171Z

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.

source=pdf_text observed=2026-08-07T11:46:59.004195Z digest=sha256:12afc4bea5eb2a43122d4c27248bc2833a3de6b00ab4dd5b4d02d6ba6efb506f

Observation 6e5df58c-8867-414a-a7bf-127a8f47f90a · outbound

This paper cites Dream to Control: Learning Behaviors by Latent Imagination.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model Dream to Control: Learning Behaviors by Latent Imagination

Reference 3

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source=pdf_text observed=2026-08-07T11:46:59.031775Z digest=sha256:ee2c28104b1f4ba4b35b46570a4b45040b7001cbf3b208f6d2dca75b8f19a6ac

Observation e2f20f12-4f59-4009-a9f9-be48820a6009 · outbound

This paper cites Mastering Atari with Discrete World Models.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model Mastering Atari with Discrete World Models

Reference 4

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source=pdf_text observed=2026-08-07T11:46:59.069096Z digest=sha256:4570474f2ae644de40c0bc5db252526c8d766a79deb6e9ff30e3b27ab50eb4e6

Observation 35f31062-079c-41aa-b3e2-73cb92631b34 · outbound

This paper cites Mastering Diverse Domains through World Models.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model Mastering Diverse Domains through World Models

Reference 5

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source=pdf_text observed=2026-08-07T11:46:59.113086Z digest=sha256:14422a356de8815465ac79b184116573259806f42ad73e2c4be9a4a00631ad14

Observation 2661083e-36a0-44ac-9dda-f94985e1dd3f · outbound

This paper cites Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control

Reference 6

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source=pdf_text observed=2026-08-07T11:46:59.145898Z digest=sha256:9ef1c3795e1b9375c68658b85e0e5f831a7295b411ea1ca0eef5e845228440ba

Observation 72df8b87-f52b-490a-9783-f3f1daabd8aa · outbound

This paper cites Carla: An open urban driving simulator.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model Carla: An open urban driving simulator

Reference 7

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source=pdf_text observed=2026-08-07T11:46:59.180265Z digest=sha256:d8280b08fb61f8e714e71eac391e36cdcb291d7ccd6e72c78f5733a875c3f999

Observation 206a9620-d6d5-408f-9f2f-1fe6f85fa7b9 · outbound

This paper cites DeepMind Control Suite.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model DeepMind Control Suite

Reference 8

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source=pdf_text observed=2026-08-07T11:46:59.223222Z digest=sha256:3944c8903ed13c7b2ca3bc592914883dad12d6021cf2d926acd5b4110ad7d9f4

Observation 38e8555f-b215-4926-bc6d-1faec35ce97e · outbound

This paper cites GAIA-1: A Generative World Model for Autonomous Driving.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model GAIA-1: A Generative World Model for Autonomous Driving

Reference 9

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:46:59.260528Z digest=sha256:64f505600042a288dafd8284b7c2d8db46478d70f15d5ba7d91c098909b002af

Observation 9a7f9a72-f594-4e6a-a6e2-3e6e7f44912e · outbound

This paper cites DriveDreamer: Towards Real-world-driven World Models for Autonomous Driving.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model DriveDreamer: Towards Real-world-driven World Models for Autonomous Driving

Reference 10

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source=pdf_text observed=2026-08-07T11:46:59.294846Z digest=sha256:30d9495dfcceda1af582237ce57a46c8429528451e71c87064b897fd0314e92b

Observation 5db1b862-e40d-4a43-8d70-25241db47fe0 · outbound

This paper cites Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Driving.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Driving

Reference 11

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source=pdf_text observed=2026-08-07T11:46:59.339105Z digest=sha256:8c6e87dc093989127e4f57f6425463d5212817789d68be35b8f53504f648be69

Observation 262dda6a-1d24-44ec-a016-f4ba8098314e · outbound

This paper cites Vista: A generalizable driving world model with high fidelity and versatile controllability.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model Vista: A generalizable driving world model with high fidelity and versatile controllability

Reference 12

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source=pdf_text observed=2026-08-07T11:46:59.382275Z digest=sha256:d6b4b9d7f79a60c4de714ae2b7a2a91e1d02ac34bdd38bce480e0f3b8f889546

Observation c71186db-677f-4473-b182-7047880c81f1 · outbound

This paper cites Sora technical report.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model Sora technical report

Reference 13

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verified fuzzy
raw_fallback, observed 2026-08-07T11:48:04.255464Z

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.

source=pdf_text observed=2026-08-07T11:46:59.417902Z digest=sha256:6530e9fd788f3bfdb3dc70157f175b8788b64c5c6aadfc4caf7ef889720d1513

Observation 572766da-d16d-48a8-9c04-564eb04414dd · outbound

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

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model HunyuanVideo: A Systematic Framework For Large Video Generative Models

Reference 14

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source=pdf_text observed=2026-08-07T11:46:59.453238Z digest=sha256:c281731e8a9c735890231cc8f15d6feacd12dd9f81d95ef0e67ceb230d5e0710

Observation f03e5416-4985-4c3a-9193-515248b4f1f6 · outbound

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

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer

Reference 15

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source=pdf_text observed=2026-08-07T11:46:59.487751Z digest=sha256:3cedbadbc85ac133dad6fb29163046a3410e560c41ac0a85e5d5eb986790dce0

Observation 0591274a-a7a6-4698-8c4c-67247dc28ed7 · outbound

This paper cites nuscenes: A multimodal dataset for autonomous driving.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model nuscenes: A multimodal dataset for autonomous driving

Reference 16

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source=pdf_text observed=2026-08-07T11:46:59.523598Z digest=sha256:6ed61ea78bbcfdaddbec6ac80ec4be1145975e717c80ae128ac0c28d6c26c590

Observation c65f01ec-389a-4b34-a9b7-46ac7c3b2f9c · outbound

This paper cites Scalable diffusion models with transformers.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model Scalable diffusion models with transformers

Reference 17

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source=pdf_text observed=2026-08-07T11:46:59.558320Z digest=sha256:b6ddcd1a575ee75c7ef86d361a9cca98c4e82ea7be2ee750cea8814494f40c09

Observation eb12fa49-6f23-4817-bfb3-1f28b1900581 · outbound

This paper cites RIFLEx: A Free Lunch for Length Extrapolation in Video Diffusion Transformers.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model RIFLEx: A Free Lunch for Length Extrapolation in Video Diffusion Transformers

Reference 18

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source=pdf_text observed=2026-08-07T11:46:59.603343Z digest=sha256:d36a52d1ae9e75a8e86a7d28c0b4237850f56e0fad027b2059a53c7a57cc4c59

Observation f4e5c9dd-d371-4bbf-aa40-a1f67644afa9 · outbound

This paper cites Moviedreamer: Hierarchical generation for coherent long visual sequence.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model Moviedreamer: Hierarchical generation for coherent long visual sequence

Reference 19

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source=pdf_text observed=2026-08-07T11:46:59.649582Z digest=sha256:8b14773bbbaa4e7b9dd0ac0946b99df32ab8c612070e2258ad1c9657f9442685

Observation c178382e-d639-4159-85ff-6a66ebcca95c · outbound

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

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model NUWA-XL: Diffusion over Diffusion for eXtremely Long Video Generation

Reference 20

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source=pdf_text observed=2026-08-07T11:46:59.685586Z digest=sha256:2a493c9257ef09987fd1dd894da50c12bab5854b10241da9851e474499a60d47

Observation 72dc8356-d93f-482e-9ee0-4de84188d6ab · outbound

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

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model High- resolution image synthesis with latent diffusion models

Reference 21

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source=pdf_text observed=2026-08-07T11:46:59.718887Z digest=sha256:d7ad09c465e8b30ef87a0626181aa4fdbd7ccbda08f7ba7e1e57b8b2b30ac162

Observation af7f9952-bc94-4946-b1f0-223a4306affb · outbound

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

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model Align your latents: High-resolution video synthesis with latent diffusion models

Reference 22

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:46:59.771650Z digest=sha256:5b6c4266c2f7a5f8b4836edbec7bac8288a41b8f07b05f75347e92fb67386595

Observation ca61a6cb-8866-49dc-9559-61a6c6330d72 · outbound

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

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning

Reference 23

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source=pdf_text observed=2026-08-07T11:46:59.816834Z digest=sha256:90f4a4164468a9d0e8dedca332d8fc5780c1299c2233e5495c50fa33b9b58bfd

Observation 9b61f0f1-5a4f-4c2f-a8f8-fc1c130bdd69 · outbound

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

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Reference 24

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source=pdf_text observed=2026-08-07T11:46:59.862351Z digest=sha256:f5ee0a10fe99cca1df4402862168ceefca9f4cce4f1a3f436b1f912ef2b93fef

Observation 5adca448-c3bb-461f-997c-2bfb5c6fbb01 · outbound

This paper cites Vidu: a Highly Consistent, Dynamic and Skilled Text-to-Video Generator with Diffusion Models.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model Vidu: a Highly Consistent, Dynamic and Skilled Text-to-Video Generator with Diffusion Models

Reference 25

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source=pdf_text observed=2026-08-07T11:46:59.897735Z digest=sha256:247b674f2213f2e852bfc6233f4e7477c365180122892b63bb8e2779834ef9d3

Observation 63fcdda7-734a-44ed-954a-62575f05908f · outbound

This paper cites Nuwa-infinity: Autoregressive over autoregressive generation for infinite visual synthesis.Advances in Neural Information Processing Systems, 35:15420–15432, 2022.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model Nuwa-infinity: Autoregressive over autoregressive generation for infinite visual synthesis.Advances in Neural Information Processing Systems, 35:15420–15432, 2022

Reference 26

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raw_fallback, observed 2026-08-07T11:47:52.212908Z

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.

source=pdf_text observed=2026-08-07T11:46:59.945219Z digest=sha256:be08bba19bb7432614d1b72f350f10a23f24538e754a0d3acc1a5cfe05449471

Observation 6459270e-51ee-4474-8542-3aacf5de1ab0 · outbound

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

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model StreamingT2V: Consistent, Dynamic, and Extendable Long Video Generation from Text

Reference 27

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source=pdf_text observed=2026-08-07T11:46:59.981165Z digest=sha256:518d1ed652d98645d9bd006caabb99eeaf529213b37e0047545ea5e57353bb72

Observation eb4072ee-63d5-456c-998d-175c62ed402c · outbound

This paper cites ADriver-I: A General World Model for Autonomous Driving.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model ADriver-I: A General World Model for Autonomous Driving

Reference 28

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source=pdf_text observed=2026-08-07T11:47:00.017376Z digest=sha256:33a01f534ffad4a238d0383b3fd6f66768052dd7625ab0416fe26aac41f8ef95

Observation ef6585e4-e1d8-421c-99ff-842c66a39ef9 · outbound

This paper cites ProphetDWM: A Driving World Model for Rolling Out Future Actions and Videos.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model ProphetDWM: A Driving World Model for Rolling Out Future Actions and Videos

Reference 29

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T11:47:00.052139Z digest=sha256:159abbf4595b0e0fd3a670c84101f7f54df68c1d196c1a59952eb5539f8d938c

Observation 3c2884b7-b4ad-4d31-af54-5f45afcac20f · outbound

This paper cites MagicDrive: Street View Generation with Diverse 3D Geometry Control.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model MagicDrive: Street View Generation with Diverse 3D Geometry Control

Reference 30

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source=pdf_text observed=2026-08-07T11:47:00.094030Z digest=sha256:a5e27992742900606bf945ee86c6020f2166f3b5a3a2e003e7ee7695adceb7b6

Observation 4e83aba9-8ccd-495b-b019-96e31b54da03 · outbound

This paper cites Occworld: Learning a 3d occupancy world model for autonomous driving.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model Occworld: Learning a 3d occupancy world model for autonomous driving

Reference 31

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verified fuzzy
raw_fallback, observed 2026-08-07T11:47:52.122447Z

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.

source=pdf_text observed=2026-08-07T11:47:00.130517Z digest=sha256:a2f3df1dc3633b05351f5cbb1248f08a9765b330a3b616944f38aaa603f4487d

Observation bcbec392-857c-45cf-865d-7dbdf3e4b07e · outbound

This paper cites Temporal triplane transformers as occupancy world models.arXiv preprint arXiv:2503.07338, 2025.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model Temporal triplane transformers as occupancy world models.arXiv preprint arXiv:2503.07338, 2025

Reference 32

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source=pdf_text observed=2026-08-07T11:47:00.165155Z digest=sha256:3f91f11e132bf651b0e1e8ee3dfa1b1c361fd116e257c1fb03eacd02f29e2faf

Observation 3a925439-815a-4d93-b68e-de76a092771b · outbound

This paper cites Doe-1: Closed-Loop Autonomous Driving with Large World Model.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model Doe-1: Closed-Loop Autonomous Driving with Large World Model

Reference 33

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source=pdf_text observed=2026-08-07T11:47:00.200689Z digest=sha256:cec5a11844aef9a61eb9dc3ac76e59ac3384f63b712a3b17e2350c772076131f

Observation 7aeffd4b-36ad-490b-ae0f-7214df40eafb · outbound

This paper cites HoloDrive: Holistic 2D-3D Multi-Modal Street Scene Generation for Autonomous Driving.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model HoloDrive: Holistic 2D-3D Multi-Modal Street Scene Generation for Autonomous Driving

Reference 34

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source=pdf_text observed=2026-08-07T11:47:00.245613Z digest=sha256:b2cfcbf0e0f85aeedfcfab12a08993fba1d57cb1f8ba3dbd4d2118a67da4dbb4

Observation 007e9952-2bf1-4772-bf7c-b500332258fe · outbound

This paper cites MagicDrive-V2: High-Resolution Long Video Generation for Autonomous Driving with Adaptive Control.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model MagicDrive-V2: High-Resolution Long Video Generation for Autonomous Driving with Adaptive Control

Reference 35

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source=pdf_text observed=2026-08-07T11:47:00.281052Z digest=sha256:8f9de00acf2302cf39b13ec7c839ef7cafa2c78cd6be8d5ebc225a4023734274

Observation 02fcb41a-391c-4ce6-bb7f-b8c9cec9012a · outbound

This paper cites DiVE: DiT-based Video Generation with Enhanced Control.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model DiVE: DiT-based Video Generation with Enhanced Control

Reference 36

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source=pdf_text observed=2026-08-07T11:47:00.324816Z digest=sha256:2e812238aa892078a58c05717f42adf1a2f69754f36bf6fb43095af498682fa2

Observation adc644dc-6d24-46e9-b438-898f25b8336c · outbound

This paper cites DiST-4D: Disentangled Spatiotemporal Diffusion with Metric Depth for 4D Driving Scene Generation.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model DiST-4D: Disentangled Spatiotemporal Diffusion with Metric Depth for 4D Driving Scene Generation

Reference 37

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source=pdf_text observed=2026-08-07T11:47:00.358770Z digest=sha256:ba04b9261cc14017d6bca57a73c350aecd3978f7253d4a34a4ba0f466ad3840d

Observation 005692a3-595a-4c1c-a42e-76386b3d6628 · outbound

This paper cites UniScene: Unified Occupancy-centric Driving Scene Generation.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model UniScene: Unified Occupancy-centric Driving Scene Generation

Reference 38

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source=pdf_text observed=2026-08-07T11:47:00.395079Z digest=sha256:27b2fd7999aaad265b18a37568fca5dc3daf755ad8ab8f6b6f9c80e40cd4d7f2

Observation ce621a10-0bee-4d83-ba34-400b5821924f · outbound

This paper cites Llava-next: A strong zero-shot video understanding model, April 2024.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model Llava-next: A strong zero-shot video understanding model, April 2024

Reference 39

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source=pdf_text observed=2026-08-07T11:47:00.438509Z digest=sha256:ac66b582a14f0e18a7b0136899d6a704c3b89fd6747c27a2fc79ac16ccbfe9f2

Observation b8c04efd-ffff-4db0-a5c7-97488a70ea09 · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.Journal of machine learning research, 21(140):1–67, 2020.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model Exploring the limits of transfer learning with a unified text-to-text transformer.Journal of machine learning research, 21(140):1–67, 2020

Reference 40

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source=pdf_text observed=2026-08-07T11:47:00.483953Z digest=sha256:0c32d2b9b258941364b03397bb6d6df972b6de232fb8330617538be1becc4e5c

Observation 83a86584-f442-448f-af81-02981b6f85bb · outbound

This paper cites Learning 3d photography videos via self-supervised diffusion on single images.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model Learning 3d photography videos via self-supervised diffusion on single images

Reference 41

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raw_fallback, observed 2026-08-07T11:47:52.006400Z

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.

source=pdf_text observed=2026-08-07T11:47:00.538906Z digest=sha256:a4436b85b981e210e7701933e6dbc60d513501fcdab43eb0a8755cd29f9d720a

Observation 7f779168-7ec7-49d2-9386-59f619d294b7 · outbound

This paper cites One-step diffusion with distribution matching distillation.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model One-step diffusion with distribution matching distillation

Reference 42

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raw_fallback, observed 2026-08-07T11:47:51.919388Z

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.

source=pdf_text observed=2026-08-07T11:47:00.583405Z digest=sha256:5476eb7670989be5f8c88ab67ddea075390213c7b93e988d8ab04f61599ad2d3

Observation 2d1fbaaa-5fd2-4a6d-8102-c4606583364d · outbound

This paper cites Improved Distribution Matching Distillation for Fast Image Synthesis.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model Improved Distribution Matching Distillation for Fast Image Synthesis

Reference 43

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source=pdf_text observed=2026-08-07T11:47:00.618254Z digest=sha256:1d5274251822425d36bcc049618ca26139d5784a364b1d66b15f0ee880a46c46

Observation fd3db0a8-f58e-49ca-b7b8-33dce26d642d · outbound

This paper cites From slow bidirectional to fast causal video generators.arXiv preprint arXiv:2412.07772, 2024.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model From slow bidirectional to fast causal video generators.arXiv preprint arXiv:2412.07772, 2024

Reference 44

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source=pdf_text observed=2026-08-07T11:47:00.651537Z digest=sha256:7b28a3f411c3fd6fa91cbee5910348f044f78981d3e11b4b3d2c25596ddd2b7c

Observation 3baeb08e-587f-41f5-aaa2-0c1bc40d61e2 · outbound

This paper cites CameraCtrl: Enabling Camera Control for Text-to-Video Generation.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model CameraCtrl: Enabling Camera Control for Text-to-Video Generation

Reference 45

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source=pdf_text observed=2026-08-07T11:47:00.685250Z digest=sha256:5f5fd4a6e4d41b49cad6326ce7c8bce4e9c0df7c0b1859a69e7f49d96d93ddc4

Observation bdbfa3f0-12a2-4962-8bd7-cb5a1b32900a · outbound

This paper cites VD3D: Taming Large Video Diffusion Transformers for 3D Camera Control.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model VD3D: Taming Large Video Diffusion Transformers for 3D Camera Control

Reference 46

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source=pdf_text observed=2026-08-07T11:47:00.718882Z digest=sha256:383596b039f2d2eb8a41d6fef0ad26c36a0fdefcf7adad0f363ffb47b6d1cf19

Observation 51a552b4-2e8c-45a3-8c8e-f03fa6567f5c · outbound

This paper cites Training-free Camera Control for Video Generation.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model Training-free Camera Control for Video Generation

Reference 47

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source=pdf_text observed=2026-08-07T11:47:00.763720Z digest=sha256:ebf37db0492b04b7445a953e26e2db1e716d9fbbc902a5e77483cf7cb7d65cec

Observation bcb3a2dc-f5c6-4b1b-8e26-99e76102c154 · outbound

This paper cites GS-DiT: Advancing Video Generation with Pseudo 4D Gaussian Fields through Efficient Dense 3D Point Tracking.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model GS-DiT: Advancing Video Generation with Pseudo 4D Gaussian Fields through Efficient Dense 3D Point Tracking

Reference 48

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source=pdf_text observed=2026-08-07T11:47:00.816915Z digest=sha256:dece6d9257476e176a489c96368046892d8dd7b322de13bfb4c45b105931bb9f

Observation 3953cf5f-77ff-42a5-8524-be049147a8d7 · outbound

This paper cites Drivegan: Towards a controllable high-quality neural simulation.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model Drivegan: Towards a controllable high-quality neural simulation

Reference 49

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source=pdf_text observed=2026-08-07T11:47:49.549503Z digest=sha256:2c20ffe7ab4c2b3e1d8e3052c388bb8a25aeeb3c028da4556320330eb08f3325

Observation 4ed420eb-de9f-4a15-9246-b9ab784b77b0 · outbound

This paper cites Wovogen: World volume-aware diffusion for controllable multi-camera driving scene generation.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model Wovogen: World volume-aware diffusion for controllable multi-camera driving scene generation

Reference 50

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verified fuzzy
raw_fallback, observed 2026-08-07T11:47:51.837062Z

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.

source=pdf_text observed=2026-08-07T11:47:49.640169Z digest=sha256:a0959551465dc5100127c5b61c0530bd9293d98b23cda6af776331f0a1c3357d

Observation a09e1d22-a9c9-435b-b143-bbd08b3fc1aa · outbound

This paper cites Generalized predictive model for autonomous driving.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model Generalized predictive model for autonomous driving

Reference 51

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source=pdf_text observed=2026-08-07T11:47:49.692063Z digest=sha256:1c09d62037dc0ee8046761faa694a446cda0ca0ba71bca6e97d2c972cfd6de57

Observation ae6a38c9-b824-4347-88a9-d7fdfbb12d6a · outbound

This paper cites UniMLVG: Unified Framework for Multi-view Long Video Generation with Comprehensive Control Capabilities for Autonomous Driving.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model UniMLVG: Unified Framework for Multi-view Long Video Generation with Comprehensive Control Capabilities for Autonomous Driving

Reference 52

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source=pdf_text observed=2026-08-07T11:47:49.751538Z digest=sha256:9727b6cbffba7e7d9ce2d4c0f53fe1b258b502018aade5d72c45031d8dfb30cd

Observation 79604640-358a-466b-8da1-42f6ef6efcfa · outbound

This paper cites Dynamicrafter: Animating open-domain images with video diffusion priors.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model Dynamicrafter: Animating open-domain images with video diffusion priors

Reference 53

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raw_fallback, observed 2026-08-07T11:47:51.766742Z

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.

source=pdf_text observed=2026-08-07T11:47:49.810987Z digest=sha256:7a417e8da85ee3cd9f52e7c43aec825ba4146c3b30059e7a587b51c9e42a8fa3

Observation 0ef387e5-42b5-4bad-9192-7fffcdce9be6 · outbound

This paper cites I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Models.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Models

Reference 54

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source=pdf_text observed=2026-08-07T11:47:49.885230Z digest=sha256:4333d80be8db465f238f3c2e14deb50c3d6092f7519079ca914629e0b08dc3c5

Observation dbd51a92-8c7a-4fc9-9837-16594c524ffd · outbound

This paper cites Vbench: Comprehensive benchmark suite for video generative models.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model Vbench: Comprehensive benchmark suite for video generative models

Reference 55

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source=pdf_text observed=2026-08-07T11:47:49.940367Z digest=sha256:41e1d9f81c503c9e2dd7523122a495a335a79ec400418305a8988d6413a128b6

Observation ec6931a9-5da1-4ddf-91ca-4e0100403e97 · outbound

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

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model Seine: Short-to-long video diffusion model for generative transition and prediction

Reference 56

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source=pdf_text observed=2026-08-07T11:47:50.011620Z digest=sha256:71c18ecc07e4f7159be6f0a9a6e1647c8af127cb1d3150ecb79bf00eb933b7fc

Observation 29567794-8c8e-4222-a573-e33c0103bc65 · outbound

This paper cites Framer: Interactive frame interpolation.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model Framer: Interactive frame interpolation

Reference 57

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verified fuzzy
raw_fallback, observed 2026-08-07T11:47:51.572889Z

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.

source=pdf_text observed=2026-08-07T11:47:50.069067Z digest=sha256:74e9aa0b8d02453f4352e5c841479516a318462b24244ccbd684e50847fbe689

Observation 12b02a85-8d27-40b9-90ea-d338a9aca5ec · outbound

This paper cites Navier-stokes, fluid dynamics, and image and video inpainting.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model Navier-stokes, fluid dynamics, and image and video inpainting

Reference 58

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verified fuzzy
raw_fallback, observed 2026-08-07T11:47:51.387477Z

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.

source=pdf_text observed=2026-08-07T11:47:50.104111Z digest=sha256:1e745ddd6d9a9b34202397cadf82dc558b54960f8ba7f5f9fbbd735f7384f285

Observation b9ef6034-e5fa-43cf-903f-ce737c2b9ca4 · outbound

This paper cites Distillation.After obtaining the well-trained Coarse DiT and Fine DiT, we establish the distillation training.

LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model Distillation.After obtaining the well-trained Coarse DiT and Fine DiT, we establish the distillation training

Reference 59

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raw_fallback, observed 2026-08-07T11:47:51.170442Z

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.

source=pdf_text observed=2026-08-07T11:47:50.172713Z digest=sha256:75d0816549f6901a73a918ae3c1c32a62cd1091b9ee192a50f409514bf15ba86

Pith citing papers

Observation ff06596d-6830-4d77-a90f-7b7725653de6 · inbound

A Survey of World Models for Autonomous Driving cites this paper.

A Survey of World Models for Autonomous Driving LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model

Reference 194

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source=pdf_text observed=2026-08-10T18:31:52.876814Z digest=sha256:2ab29772a8233acad57590d409797ed02f25298d3db660f3efd6f34c7ef1d590

Observation 3cd088f7-07b6-4aba-8155-ff5b73c71209 · inbound

A Comprehensive Survey on World Models for Embodied AI cites this paper.

A Comprehensive Survey on World Models for Embodied AI LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model

Reference 188

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source=pdf_text observed=2026-08-04T09:12:51.088632Z digest=sha256:2b31a521432178976b4a47c662bceeeb57fa0650c887397e7b118b4e8948c3c9

Observation 420e0359-8b98-448d-93e6-fd24cf7d6f3f · inbound

WorldLens: Full-Spectrum Evaluations of Driving World Models in Real World cites this paper.

WorldLens: Full-Spectrum Evaluations of Driving World Models in Real World LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model

Reference 104

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source=pdf_text observed=2026-08-03T17:02:40.451968Z digest=sha256:0b3b70adcb157fb6296e85c3c7f4be9b7dc5f80cf4c126c83e08c10b4379a7d5

Observation bf26ed9c-ebd7-4eb2-9fd1-e1e75653ebd1 · inbound

ScenarioControl: Vision-Language Controllable Vectorized Latent Scenario Generation cites this paper.

ScenarioControl: Vision-Language Controllable Vectorized Latent Scenario Generation LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model

Reference 50

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arxiv_id, observed 2026-05-10T06:26:27.483110Z

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.

source=pdf_text observed=2026-05-10T06:23:22.058330Z digest=sha256:12c22d9286a1e7c20c2ec5548b5517efb180be597d70410afdbb4dcce0ae3554

Observation f92c35dd-22c3-4efa-b365-00ae323d9a11 · inbound

OpenLongTail: Generative Scaling of Long-Tail Driving Data cites this paper.

OpenLongTail: Generative Scaling of Long-Tail Driving Data LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model

Reference 29

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source=pdf_text observed=2026-07-13T01:26:27.220907Z digest=sha256:ca7754ffaad497b4d9c09fd9132d78b09978cd702c86727cef3b72ecbc49d3b7