CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting | Paper Citation Record

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This paper cites Cosmos 3: Omnimodal World Models for Physical AI.

CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting Cosmos 3: Omnimodal World Models for Physical AI

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting Cosmos World Foundation Model Platform for Physical AI

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting World Simulation with Video Foundation Models for Physical AI

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting Revisiting Feature Prediction for Learning Visual Representations from Video

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This paper cites In: Forty-first International Conference on Machine Learning (2024).

CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting In: Forty-first International Conference on Machine Learning (2024)

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting In: 2025 IEEE/CVF International Conference on Computer Vision (ICCV)

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting Unresolved cited work

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting In: Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition (CVPR) Workshops (2024)

Reference 10

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting In: Scott, D., Bel, N., Zong, C

Reference 12

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting Advances in Neural Information Processing Systems37, 91560–91596 (2024)

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This paper cites World Models.

CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting World Models

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting Dream to Control: Learning Behaviors by Latent Imagination

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting In: International conference on machine learning

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting CLIPScore: A Reference-free Evaluation Metric for Image Captioning

Reference 18

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting In: NeurIPS (2022)

Reference 20

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting GAIA-1: A Generative World Model for Autonomous Driving

Reference 21

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting In: ICLR (2022)

Reference 22

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting DrivingWorld: Constructing World Model for Autonomous Driving via Video GPT

Reference 23

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops (2025)

Reference 24

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting In: ECCV

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting In: Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics: HLT- NAACL 2004

Reference 26

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting 2, 2022-06-27

Reference 27

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting In: International Conference on Machine Learning (ICML) (2023) CosmosAlign for Generative Traffic Video Forecasting 17

Reference 28

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

CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting A Comprehensive Survey on World Models for Embodied AI

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting In: Advances in Neural Information Processing Systems (NeurIPS) (2023)

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting In: Forty-first International Conference on Machine Learning (2024)

Reference 31

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting In: Proceedings of the IEEE conference on computer vision and pattern recognition

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting Dolphins: Multimodal Language Model for Driving

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting In: 2025 IEEE International Conference on Robotics and Automation (ICRA)

Reference 34

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting Advances in Neural Information Processing Systems37, 121038–121072 (2024)

Reference 35

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting Advances in Neural Information Processing Systems 37, 42292–42310 (2024)

Reference 36

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting A Survey on Future Frame Synthesis: Bridging Deterministic and Generative Approaches

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting Advances in Neural Information Processing Systems38, 4741–4770 (2026)

Reference 38

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting In: Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

Reference 39

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting Beyond Low-rank Decomposition: A Shortcut Approach for Efficient On-Device Learning

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting In: Proceedings of the Computer Vision and Pattern Recognition Conference

Reference 41

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting IEEE Transactions on Pattern Analysis and Machine Intelligence44(6), 2806–2826 (2020)

Reference 42

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting In: ICML

Reference 43

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting GAIA-2: A Controllable Multi-View Generative World Model for Autonomous Driving

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting In: Proceedings of the 18 Q

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting In: Conference on Robot Learning

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting In: European Conference on Computer Vision (ECCV) (2024)

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting In: ECCV Workshops

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting RAFT: Recurrent All-Pairs Field Transforms for Optical Flow

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting In: International Conference on Learning Representations Workshop (2019)

Reference 50

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting Wan: Open and Advanced Large-Scale Video Generative Models

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting Efficient Reinforcement Learning for Autonomous Driving with Parameterized Skills and Priors

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting In: European conference on computer vision

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting Self-Consistency Improves Chain of Thought Reasoning in Language Models

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CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting In: Proceed- ings of the 2022 Conference on Empirical Methods in Natural Language Processing

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This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.

CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-11T00:28:52.334845Z

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Unavailable: canonical work link unavailable.

Observation a478f21f-5dca-4d8c-ae85-f7da1984b8a2 · outbound

This paper cites On the Road with GPT-4V(ision): Early Explorations of Visual-Language Model on Autonomous Driving.

CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting On the Road with GPT-4V(ision): Early Explorations of Visual-Language Model on Autonomous Driving

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-11T00:28:52.337702Z

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Unavailable: canonical work link unavailable.

Observation f21a5695-d1a8-440a-9ee7-e6e0bf231239 · outbound

This paper cites Improvisation through Physical Understanding: Using Novel Objects as Tools with Visual Foresight.

CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting Improvisation through Physical Understanding: Using Novel Objects as Tools with Visual Foresight

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-11T00:28:52.340700Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 274845e0-296f-44ad-b264-6d3bcb410945 · outbound

This paper cites IEEE Robotics and Automation Letters (2024).

CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting IEEE Robotics and Automation Letters (2024)

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:28:52.826024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8b7bae63-a6b0-43ed-a3de-53aaf89f7947 · outbound

This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2020).

CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2020)

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-11T00:28:52.346757Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81b5d2f5-b83c-4b33-9d95-ac2a4a3e4a15 · outbound

This paper cites In: CVPR.

CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting In: CVPR

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-11T00:28:52.349991Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fee7f6a9-c78a-4306-9866-cd4ec206b9a6 · outbound

This paper cites In: Proceedings of the AAAI Conference on Artificial Intelligence.

CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting In: Proceedings of the AAAI Conference on Artificial Intelligence

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-11T00:28:52.353642Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c7e0ba6-6cc0-43e6-9ce4-ff760042fb4c · outbound

This paper cites GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection.

CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-11T00:28:52.357358Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a29efc2-28f9-4242-99b3-cb9e9c52650e · outbound

This paper cites In: Thirty-seventh Conference on Neural Information Processing Systems (2023),https://openreview.net/forum? id=hrkmlPhp1u.

CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting In: Thirty-seventh Conference on Neural Information Processing Systems (2023),https://openreview.net/forum? id=hrkmlPhp1u

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:28:52.793138Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 22940223-ebd4-4e5d-8134-1b14e5ad0f27 · outbound

This paper cites Transactions of the Association for Com- putational Linguistics12, 525–542 (2024).

CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting Transactions of the Association for Com- putational Linguistics12, 525–542 (2024)

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:28:52.781146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 78ac1796-b5cc-442b-92bd-182e28ec1f49 · outbound

This paper cites arXiv preprint arXiv:2601.01528 (2026).

CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting arXiv preprint arXiv:2601.01528 (2026)

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-11T00:28:52.368199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Outbound references

Pith citing papers