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Paper Citation Record · LEDGER

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

As of 8 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 2026-08-08T06:32:00.761636+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=sha256:5cad1bc968f8f744ffc575d3b83f1ffdff52c3dba15bfec761f4790260dc7150

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:46:59.004195Z digest=sha256:47c95c178de772f918b6da1b88b66fe0609036e3acc946c9f1e8f8d9b853b6a3

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=sha256:3cf6f95d5400e8eb53cfb4102b8917bfdb8afc6eb35eb3aff158e6ffbeaeecae

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:e361db8fdbcb3482bb4cf088de5a8a0be11dc7945a3c5f90099e55865c8e903f

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=sha256:558f671424958ef3ce958e8d719de1e47e09ad953f82fa1e1a8e9a4a9c3f4f58

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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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:46:59.145898Z digest=sha256:8ec834eed86e235c329fd04f381e4d9b948e2450e9f0a22f3ee17ef108947d53

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=sha256:4a2162313c74e2d16c103ef49038085aab3f787e90a2ffad2d49209fd3aaa120

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=sha256:5258ff713916324d0e124dccf6525bced551cff65cad10eb1a4fd655d2f8b535

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:b9e01545fd54842b1b4cf00b5ed572836d38da0b9c16298548b1d094b9e5a0af

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=sha256:5bc7df97fde24257d71f59a1961a5ba7e3cb5a0a4e29e44b93b0810e98517c5e

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=sha256:61801231779bab1ee35a97484acf3a533db243acf111a78372f367d57411acad

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:ea99941a621709ec70a8f0b801ec3421a2a01d041a44848e5b6aed1b6a206cb9

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:46:59.417902Z digest=sha256:63106bd28110e30855de42d80cf5e9b6b10f4adc0a32b1610dd6619fe304b1b6

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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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:46:59.453238Z digest=sha256:7e5ada2d36b73e9228e0e03602faf9030110f6f146eacc99b3368635ef670e3f

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=sha256:083339aa761628fdc4322b58ffcba0d9a62ef9028d6285cd1f45c8b483c6b3db

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=sha256:1ff0abf6f98f619162decc7ff653d70fe523f5cddc3f8cff974473d69a9ff6db

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=sha256:1a09256513f97beeec3454f9e19ae6fa041d652268e40bc994e0c2386b0f07a2

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=sha256:7093fb2a28ff2b4e246c571cc3cf9caff5b7b66303e2a6e5bcd5258d91c65d64

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=sha256:191167d395eabbda4af74f4bc310f1d413e0d16f4dd21a4324c1d16059668208

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=sha256:70aafc2c81affea93e0d1a02a9cc2c45b5b6b91723bea52f6b7e8cc6a61b6fcb

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:d49ae4ad1170272870813204e66ddd1b48cc9be07714a5d48c607ff75383db2c

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=sha256:2625c975cd7eeb023aa33be016a7d3bda6763dd1e67252e276da10182b234ea9

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=sha256:5663a9c8fdcf8fc0912f97540ad29270cc8104b9731c2cdf3c93ed40f6721520

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:eb5dc88d7eb0aecace7aa18b40eb656aa6a175f4fccceffa42a575cecb61fc91

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=sha256:75ce6c0895b25198161dfa1aa367b85a8f0233b1239a726c7d3ec313e94eb865

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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verified fuzzy
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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:46:59.945219Z digest=sha256:2d207832c31d801820df6e203d954a9beddbe349923e85d61c167da28aa726a2

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=sha256:8b8bcd242f4187887fba482237112235d69ddbbaec1c19493981a774d5ad3485

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=sha256:7fd9033e95f9a9972ea7f388a5f532c175e2dcdcea57c0bc14f2916d8f7c9991

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

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:47:00.052139Z digest=sha256:fcb48b1891336445ac39a10cab48e4d6cfd214c85434f822d4a599ce42240554

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=sha256:46d2abc8e7df0edb4d21091f67f2f7db05fe340646446721e1f03f8e373bfd42

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

Resolution
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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:47:00.130517Z digest=sha256:cdd68942c4171b239bc9734d40a7615ba1250d023a5c43173b69dc5bff2dafac

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-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:47:00.165155Z digest=sha256:91c9860028a2d374a0972ebbfc697b7e0f81c6d93e82d77da0c5dd2bbe37d26d

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:f889b630a608559e5d7c28805c3b570b6796bdbb05b930b92a7314f414600d5e

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=sha256:9ed563e1df3e78ce6719bc54b6e46f553f741b7f257482ad1f4776928e5236db

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=sha256:5beb970e8eae2363aa6c37f6424cc8875d0fa4138bd6db73edaa49103870e4a3

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=sha256:96a68b03269b957ecda9cdb1579ece256f6066ada0be9a6ce5961ccb5bfb69a3

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:b12a5faec7dfd0d84e19c265ab84e27c829bd82868454f4eacb667308dd952f7

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=sha256:1bfb62bc754a4666bdf5e25dffc263958b2caa72514f3e64fdbe142ded79bc81

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=sha256:1ca9463b69821b980eddf78cf73201790f62ef9dc4b777e648565abb1a8826d4

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=sha256:9393c518c17f58ad2eb221845d86d81c89880ebf99c07b91eff3fa81001ae856

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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verified fuzzy
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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:47:00.538906Z digest=sha256:9acecbd606faf98cbc0c7615b61db233ec84a82652ccfb02ad4d75071d291b08

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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verified fuzzy
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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:47:00.583405Z digest=sha256:05dc7fc3af5a64457d2ea3e5baee9db16e1329cd1a2867eb0cb30ea8c854eb85

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=sha256:496fbf5c65e697b11fc109020dede699e1a8e570b767e645dcaa3a0aa8485697

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=sha256:184e28de73df99812c69422847c2a0bb15ad561091c726407151c662c4d8b379

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:e4aad1ed334d6db4648f564d54b09d5db91c1d2b80e2373c7d564ef0fc33b455

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=sha256:20326034a574e5cbb9a914dff2fa13907855c8fc792ae75c53e1b1d5f4781adf

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=sha256:83424a30930e217392f251638dbfeef8e5f10090d6a9c7766e6878e7c62cb9a6

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:a735908f53133198c9c61e42583a0053be0928ce4e14c5639a93ee37fe2bb95f

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:d0000d63e7e9dedc37df93300a231c52cf78219489370f31a8de29ac88befea7

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:47:49.640169Z digest=sha256:6ed994d777b74d533ff47164779e4785601d6c91f6889c97a3e936879fc11dde

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:ff44fdf438cf2b61ef1766b0f0af1a5ffbbee5c5bf1736650fcc818f3335028f

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:e242debab3f3cc6d4683b387f265ef96c97a048df66abcad468157220a70d40a

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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verified fuzzy
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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:47:49.810987Z digest=sha256:be0097fa9c859ec98bb62ffcfdf00a5d23b54fde27850a87fad3741c972fd27d

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=sha256:6e6e81bd7155a8cc9e8e4400103f88e47f767739997a2a8d552f0a6c49ba74ed

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:d0461007a61e3d7f9cdbe2db09856351c7cfd192f6bcf94a19bc798ea03f3fab

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=sha256:2a4c5183f1f2ce8786b873a708978ef3f8c991d1eea99828ea088d2928b10b95

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:47:50.069067Z digest=sha256:80dc28411f04c896d7e27098f06b418d4b26475cd1aac1e77677f4f39f1d3382

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:47:50.104111Z digest=sha256:a23388520982eec7b5b79e5415cc73db5b0aa94547a65549c5e7fe9245ef9f97

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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verified fuzzy
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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T11:47:50.172713Z digest=sha256:80276b97ee63839ca5ba23ab68382ab6e8ce0cd32eeda63904992db91eb810c8

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-reported events for the cited work

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source=pdf_text observed=2026-08-04T09:12:51.088632Z digest=sha256:522574a12dd1ba3cd270af8af9fb85459cc64653f139f4a516473166636f71a2

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=sha256:881f47bbe5c3209825ecd64edc51f71e6ae13517467a0dfecfefe63a64d2a816

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 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T06:23:22.058330Z digest=sha256:b63b078f6adf4ccbbeabae783ecba1792a94d263cc737a2f150d645a82b5f7ef

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=sha256:268dc10d17cbf3f69fea05a11c5b269156a589ff42197bff2ad784e75e78cf63