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Paper Citation Record · LEDGER

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

As of 9 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 4 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 63 of 63 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 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T09:12:51.088632Z

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=sha256:68b9721708fd0cdfae9e2e7bc3c0230bdc38a5ba47e4ea26cb8d48dd49a610fe

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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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=2026-08-07T11:46:59.004195Z digest=sha256:82adc0e1f81c5a1f82bb0aae9709492ba22bcde1e1ef397d87082a74732659ea

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=sha256:3af59b6277582c364fbfbd78337f55af9e87fc5636dcbf8425fb0d0b3d94005b

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:f6b907384c03bc695fa1a44730db8b71952f52028bfb9afdaa236c240c91f7db

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=sha256:699daac4a959020ad6a658dcce0afb6ca16dfe69eda25321adf1a6754149c37e

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=sha256:981a77293f5b002b33a6a738e810e743f6f213418adb5700da4bcb4fcf69f3ec

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=sha256:01a18e2d58cf23069ce9299a11285695e2682379e5345670f8e8509264a0a48b

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:b6528a5d1313772a3c7c59c34d9e571e42bb70abe868fde6a67f2c02d4568959

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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source=pdf_text observed=2026-08-07T11:46:59.260528Z digest=sha256:5be4bac99eeb4d0761a1083e696574a0c46dc338113e019ee92efff436c6500c

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=sha256:6a68fe94fe9e433286fff6359bbd38199661d6e0ad6c61cf037e0d7753b98757

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:c51feb879670ef4481544602a6c20955ae77ec0c3fbd7318d7a6aa31c4fd8f7a

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:cbb9835153e04d37db1b927ea63616050d024a5ec6df61743851dc93544db090

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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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=2026-08-07T11:46:59.417902Z digest=sha256:8cba16f3339508bd0c6b095bc98b816027e0314bfb0d528e2611e2ff0de42f45

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:f03545000e278e0e7344ac425365135d86790b1dba4d41dc05d976aa8056f028

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=sha256:20b70e6eb6fea6b10d6cab2aa0c6860e225d9d453e1a37c891b0a4de315ac718

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=sha256:69f9e63987cf3346bd5fb901e2404a751bc420c23b37408bb5b1bec5d8fef697

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:af81a41dde764bebd63475166115e35bc94f7bf6c4fe9489675a3adba851873d

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:b2e7cc698811366c725149fc72a48029752e10fb0420a9929d6c5515cc20aece

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=sha256:0fa5ae82db98fa44150c9b828c0dd421f2ffbeebbbee22319823b4712a26a257

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:aba10d26265e756f82b5d7bf950bb24e3f783e384ec05aaf22b55076ff33cb33

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:edc4cf5bea596823e9d60015b4dd5a623e108c2f3e4fac2c07f5216f1557f9b7

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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source=pdf_text observed=2026-08-07T11:46:59.771650Z digest=sha256:7e51d2983a3aa91c930cfd920193319b772416e5137c1308dc358920c49fc7ae

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:a76e1195d6e16e065157dc9cac64418f8e818c52348391725448a1d580771e0a

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:a0ffb443a76d7b91b091f077cdf2af8c0001304ca68dbbef986022c66b8a5286

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=sha256:1f7ea46a14c3411a1a93cb87c08e6a3a762af1f3de8c5df745e27984c4e6e389

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T11:46:59.945219Z digest=sha256:fb63a958dd988d84e347fb5dd16bf6437c943e294b1f403eb79775fabe80d277

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=sha256:42869c5cad2b1b1d11c3f2c631ec98f28586637840587ff7915c5f955a037ded

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:e1c4a407c71e6ab8e1859d10d04045a0aa48d5145fea17a4ca55cf9ffe297ad6

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=pdf_text observed=2026-08-07T11:47:00.052139Z digest=sha256:fdc0164834374f058c5070680bdbe0d0362e5890444379c66e49f9b278439c71

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=sha256:559568c9af3b70a47d7be6815511ade86f155a1a73a46af92c2d86f1ac98d1eb

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T11:47:00.130517Z digest=sha256:3bf0ff501477404e25a1fe1a0ee7f65e4d3ad487b9909b7b87fc5195e6d12e5c

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=sha256:9f84c0776b05aa3b65836a549b6402efbcb91bf32961a724c836a73d62f4bdcf

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=sha256:5ec9a57c93d1ac75123cf545642539dae87d76744292b04344fb766507288321

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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no resolver link, observed 2026-08-07T11:47:00.245613Z

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source=pdf_text observed=2026-08-07T11:47:00.245613Z digest=sha256:a582847b114b435ade1fd7e2af366d55afbac678a210ae23b68d86b8c00239f1

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:e967d82c232275e96b6c5214b2b557e8ca9ef4884106b4e8ab2951ab677ffac3

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=sha256:2f37920d3ef9da337deb2e0950ab7cd9d0c3f4f7018438610fc014358e5900ea

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=sha256:10d0c695c10baaeae31fe661ceb805f09fef0d468b61c808fbc38a852b604529

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:b0ca1a7eb5c74bee075bc7cfb76398162cf35df26542a64408d8c8aa07066c51

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:b13fdd0293d12d7a8f4d09deb730545d6894e8d0ae861b0ed7f907fcfc3409de

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=sha256:295610530e3f3155f5c7925ffeea8a544d2c1de1b2369d8b017e265159cfe31b

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T11:47:00.538906Z digest=sha256:2dfdb0e0c369135f84a53a875b16813ac6d32a77382c1f6dab5cbcfd5c2e4008

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T11:47:00.583405Z digest=sha256:cd022c64d16d25fe983dadc105722a9eaf5422eaccbed3b0febce31a49099e99

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:c7865304d907f4126f330f6142d9045cd58d7004c161a62f01b8cf906e52d577

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=sha256:1442a6078c9a74b8ec3da9522e2e8d717afd935ef15949e2af54b941b7e4e432

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=sha256:0989a596984e95bd949127d6c105987ed96ed70f27dcf6f8774fae9c3a02fe17

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:c99d2e918ca36fe01a7f4df030beeeeff0ba3b49371b951d1399a28f9ce2d8b2

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=sha256:6211a2564a2fb9586e626c8f0780e594f5feeeb07dcb8fb84cefbfc7c1d2d16e

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:bb82b80ba0bfbd2b95d58656622aa5f9e5d9fd8fa415bb523b15480fe5aee2ae

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=sha256:8406104259f596f1e6922265e28519fd65ce071f002d2b07e00184a8a75afe12

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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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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T11:47:49.640169Z digest=sha256:6878fa2af2190f94132a1c7fb439980b463f9411f4bcb34cf0bf8725a6403ea5

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:c2941794ae86f5a7a0c56fcd61a8db946ebae449e3ecc46b2887fd4e04c0e4f7

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:fae5cdb824f506a9f783ec9e3a2499762080d8629199bd3cdd17e23e96a173b2

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T11:47:49.810987Z digest=sha256:1605ceb010ae7eece70356bbf4102e1c54171aadd695b1da59ff888965436fad

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:b2e9458cfa70ef187c48222e159e05dcfd45a2ea24210c5bfb648e3e282161a9

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:df0ea42dcab45e3d2be1f574620bac0ed9c4abc4b32e8c0db808cd30109530ab

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=sha256:3bc578e10f4b17c19155dd4be442ffcaa95934449a40b3d306b7e6d1ccdf5c35

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T11:47:50.069067Z digest=sha256:8ae0fdb9fbb2b14df654bbea9fdd7ce04b83cf686ba5eb5d67cf955ddc445d6a

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T11:47:50.104111Z digest=sha256:91c97408c788055d53ffdaf6971b2f0a40a72dfa6b5e7b67981c87d6559fc04a

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T11:47:50.172713Z digest=sha256:5c3396fe1014eecca4d5edd285138b865678fb3d30ac79ab0a9a50a09e5b7b18

Pith citing papers

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=sha256:3f31427c6e8c5eb2933899f23e62fec96f5f0dddbc8fe21e9bec2c896776932c

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:bed2570496ed534586c09266d049c9854b04a9f77b396acd4eec31df7e005311

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

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

source=pdf_text observed=2026-05-10T06:23:22.058330Z digest=sha256:c6bb07c388bb4b7e1a28dd7e59c028304ee9a5e672105343c66591648550c4db

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:f1e6ee624ecbf2f21396f691044f643d40b586392b5e3f5f653e053ed59b755c