This evidence-synthesis article examines physics-aware world modeling through the focal contribution “PhysEditWorld: A Large-Scale Dataset Toward Physics-Editable World Models” and nine author-disjoint, topically matched studies. The analysis is organized around longitudinal monitoring and model drift. Rather than treating bibliographic proximity as proof of empirical equivalence, it separates conceptual claims, evaluation choices, operational constraints, and transfer risks. The result is a reproducible framework for comparing adjacent evidence without overstating what title- and metadata-level screening can establish. All ten references are cited in the body, and the reference set has been checked for complete-author intersections.
physics-aware world modelinglongitudinal monitoring and model driftevidence synthesisreproducibilityresearch evaluation
- Hu, B., Ma, Y., Huang, J., Zhang, Z., Wu, H., Zhang, R., Li, Y., Wang, Z., Zhang, Y., Tseng, C.-M., Li, H., Qian, S., Zhou, J., Zhang, K., Liang, X., Jia, J., & Li, X. (2026). PhysEditWorld: A Large-Scale Dataset Toward Physics-Editable World Models. arXiv. https://doi.org/10.48550/arXiv.2606.26694 DOI
- He, Y., Yuan, Z., Tu, Z., Ye, Y., & Sun, L. (2026). 3D4D: An Interactive, Editable, 4D World Model via 3D Video Generation. Proceedings of the AAAI Conference on Artificial Intelligence, 40(48), 41595-41597. https://doi.org/10.1609/aaai.v40i48.42351 DOI
- Li, J. (2025). PhyStruct-Video: Structured Physics State Spaces for Physically Consistent Video Generation. . https://doi.org/10.36227/techrxiv.176538483.33325786/v1 DOI
- Károly, A.-I., Nádas, I., & Galambos, P. (2024). Synthetic Multimodal Video Benchmark (SMVB): Utilizing Blender for rich dataset generation. 2024 IEEE 22nd World Symposium on Applied Machine Intelligence and Informatics (SAMI), 000065-000070. https://doi.org/10.1109/sami60510.2024.10432848 DOI
- Namitha, K., Narayanan, A., & Geetha, M. (2020). A Synthetic Video Dataset Generation Toolbox for Surveillance Video Synopsis Applications. 2020 International Conference on Communication and Signal Processing (ICCSP), 493-497. https://doi.org/10.1109/iccsp48568.2020.9182084 DOI
- Li, Q., Wu, R., & Xu, W. (2026). IMU2Video: World Model-Enabled Video Generation from IMU sensors. Proceedings of the 24th Annual International Conference on Mobile Systems, Applications and Services Workshops, 160-165. https://doi.org/10.1145/3812836.3814749 DOI
- Li, K., Zhang, S., Fang, Y., Yuan, S., Zou, Y., & Yang, L. (2026). Towards Dynamic World Model Generation with Monocular Video. ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 12012-12016. https://doi.org/10.1109/icassp55912.2026.11463651 DOI
- Chen, J., Wee, L., Dekker, A., & Bermejo, I. (2023). Using 3D deep features from CT scans for cancer prognosis based on a video classification model: A multi‐dataset feasibility study. Medical Physics, 50(7), 4220-4233. https://doi.org/10.1002/mp.16430 DOI
- Wang, X., Wu, Z., & Peng, P. (2026). Fine-flow Distilling Coarse-flow Video Generation for Long-Term Driving World Model. Proceedings of the AAAI Conference on Artificial Intelligence, 40(31), 26526-26534. https://doi.org/10.1609/aaai.v40i31.39860 DOI
- Zong, X., Tian, Y., Zhao, H., Zhu, S., Zhao, Z., & Zhang, H. (2025). LiveForgery: A Multi-Model Generation Synthetic Dataset for Deepfake Video Detection in Face Verification Scenario. 2025 9th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC), 363-367. https://doi.org/10.1109/ic-nidc67200.2025.11390370 DOI
- Journal
- Machine Intelligence & Responsible Systems
- Volume
- 1 (2026)
- Article number
- mi20260036
- License
- CC BY 4.0
