nav emailalert searchbtn searchbox tablepage yinyongbenwen piczone journalimg journalInfo journalinfonormal searchdiv searchzone qikanlogo popupnotification paper paperNew
Thoughts on the Applications of World Model in Intelligent Warfare
Email:
DOI: 10.16358/j.issn.1009-1300.20260142
Published:   2026-09-21
Publication Date:   2026-09-21
Online:   2026-09-21
Mobile reading
Abstract:

This paper systematically reviews the application requirements and typical scenarios of world model technology in intelligent warfare and provides a forward-looking discussion of its future development directions. First, the potential and main operational application patterns of world model technology are systematically summarized from three dimensions: battlefield situation understanding and prediction, operational plan simulation and wargaming, and manned-unmanned operation planning and collaboration. Furthermore, the key technical systems underpinning military world model applications are explored in depth, encompassing core aspects such as scene understanding and perceptual prediction, dynamics modeling and simulation rollout, as well as action planning and decision optimization. Finally, recommendations are proposed for key directions demanding urgent breakthroughs in the future military applications of world model technology. It is demonstrated that world model technology, by constructing a complete cognitive closed loop of “perception-prediction-decision”, is expected to compress the operational decision-making cycle from the traditional magnitude of weeks or days to hours or minutes, thereby achieving a generational leap over traditional operational tempos. This paradigm shift in decision-making is driving operational systems to evolve from data-driven reactive responses toward model-driven proactive anticipation.

References

[1] Vincent C. The U.S. DoD’s AI acceleration strategy [EB/OL]. 2026-01-22. https://dsm.forecastinternational.com/2026/01/22/the-u-s-dods-ai-acceleration-strategy.

[2] Michael P. US Air Force wants AI to power high-speed wargaming [EB/OL]. 2025-12-10. https://www.defensenews.com/air/2025/12/09/us-air-force-wants-ai-to-power-high-speed-wargaming/.

[3] Cristina S. US Air Force debuts operational AI wargame system [EB/OL]. 2026-04-17. https://www.c4isrnet.com/industry/techwatch/2026/04/15/us-air-force-debuts-operational-ai-wargame-system/.

[4] Ben A. DARPA’s DRBE to revolutionize electronic warfare training [EB/OL]. 2026-08-13. https://defenceinnovationreview.com/2025/08/13/darpas-drbe-to-revolutionize-electronic-warfare-training/.

[5] Li X, He X, Zhang L, et al. A comprehensive survey on world models for embodied AI[J/OL]. 2026-06-25. https://doi.org/10.48550/arXiv.2510.16732.

[6] Hu Y, Wang L, Liu X, et al. Simulating the real world: A unified survey of multimodal generative models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2026.

[7] Zidan A H, Pan Y, Jiang H, et al. World models: A comprehensive survey of architectures, methodologies, reasoning paradigms, and applications [J/OL]. 2026-05-28. https://doi.org/10.48550/arXiv.2606.00133.

[8] 赵翔,黑梦哲,李家旭,等.基于大模型的世界模型研究综述(特邀)[J].计算机工程,2026,52(6):1-16.

[9] The Investor. NC AI wins defense AI project with Hyundai Rotem [EB/OL]. 2026-05-28. https://m.theinvestor.co.kr/article/10758105.

[10] DARPA. ERIS special topic area – DARPAVERSE [EB/OL]. 2025-11-13. https://sam.gov/workspace/contract/opp/e2b84cd5ac614ef0affe1bf2c57d0e35/view.

[11] DARPA. Critical orientation of mathematics to produce advancements in science and security (COMPASS) workshop [EB/OL]. 2025-03-05. https://sam.gov/opp/66657ec7d1d945bb801f2d54f04192ad/view.

[12] NATO M&S COE. Modelling and Simulation for Autonomous System Conference - MESAS 2025 [EB/OL]. 2025-10-23. https://www.mscoe.org/modelling-and-simulation-for-autonomous-system-conference-mesas-2025/.

[13] Danielle M. Dimitris Giovanis receives DARPA grant to advance mathematical approaches for military decision-making using wargaming simulations [EB/OL]. 2025-09-15. https://engineering.jhu.edu/case/news/dimitris-giovanis-receives-darpa-grant-to-advance-mathematical-approaches-for-military-decision-making-using-wargaming-simulations/.

[14] 刘阳,秦刘震,张洋.美以伊冲突无人机运用情况及分析[J].战术导弹技术,2026,(3):18-25+158.DOI:10.16358/j.issn.1009-1300.20260601.

[15] Assran M, Duval Q, Misra I, et al. Self-supervised learning from images with a joint-embedding predictive architecture[C].Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. Vancouver, BC, Canada: IEEE, 2023: 15619-15629.

[16] He K, Chen X, Xie S, et al. Masked autoencoders are scalable vision learners[C].Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). New Orleans, LA, USA: IEEE, 2022: 16000-16009.

[17] Zhou G, Pan H, LeCun Y, et al. DINO-WM: World models on pre-trained visual features enable zero-shot planning[C].Proceedings of the 42nd International Conference on Machine Learning (ICML). Vancouver, BC, Canada: 2025.

[18] Karypidis E, Kakogeorgiou I, Gidaris S, et al. DINO-Foresight: Looking into the future with DINO[C].Advances in Neural Information Processing Systems (NeurIPS). San Diego, CA, USA / Mexico City, Mexico: NeurIPS Foundation, 2025.

[19] Wu T, Zhu B, Zhang R, et al. Pairwise proximal policy optimization: Language model alignment with comparative RL[C].First Conference on Language Modeling. Philadelphia, PA, USA: COLM, 2024.

[20] Wang G, Tang P, Ren X, et al. Learning vision-language-action world models for autonomous driving[C].Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Denver, CO, USA: IEEE, 2026: 1073-1084.

[21] Chen D, Moutakanni T, Chung W, et al. Planning with reasoning using vision language world model [J/OL]. 2025-09-06. https://doi.org/10.48550/arXiv.2509.02722.

[22] Jianan X, Yi G, Zihan L, et al. PAN: A world model for general, interactable, and long-horizon world simulation [J/OL]. 2026-02-03. https://doi.org/10.48550/arXiv.2511.01467.

[23] Wu T, Yang S, Po R, et al. Video world models with long-term spatial memory[C].Advances in Neural Information Processing Systems (NeurIPS). San Diego, CA, USA/Mexico City, Mexico: NeurIPS Foundation, 2025.

[24] Missal T, Domingues L, Guler B, et al. RopeDreamer: A kinematic recurrent state space model for dynamics of flexible deformable linear objects [J/OL]. 2026-04-30. https://doi.org/10.48550/arXiv.2604.28161.

[25] Gu A, Dao T. Mamba: Linear-time sequence modeling with selective state spaces [J/OL]. 2024-05-31. https://doi.org/10.48550/arXiv.2312.00752.

[26] Wu J, Yin S, Feng N, et al. Rlvr-world: Training world models with reinforcement learning[J]. Advances in Neural Information Processing Systems, 2025, 38: 125312-125350.

[27] Lanier J, Kim K, Karamzade A, et al. Adapting world models with latent-state dynamics residuals [J/OL]. 2025-04-03. https://doi.org/10.48550/arXiv.2504.02252.

[28] 赵云龙,齐亚莉,陆利坤.扩散模型研究综述:理论、改进、应用与展望[J/OL].计算机工程与应用,1-21[2026-07-21].https://link.cnki.net/urlid/11.2127.TP.20260610.1845.012.

[29] Fuest M, Ma P, Gui M, et al. Diffusion models and representation learning: A survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2026 , 48(7): 7209-7228.

[30] Huang S, Wu J, Zhou Q, et al. Vid2world: Crafting video diffusion models to interactive world models [J/OL]. 2026-03-08. https://doi.org/10.48550/arXiv.2505.14357.

[31] Xie K, Yang I, Gunerli J, et al. Making large language models into world models with precondition and effect knowledge[C].Proceedings of the 31st International Conference on Computational Linguistics. Abu Dhabi, UAE: Association for Computational Linguistics, 2025: 7532-7545.

[32] 牛莅原,游光荣,朱晓敏,等.基于蒙特卡洛树搜索与大语言模型智能体的战略博弈策略优化研究[J].军事运筹与评估,2025,40(4):28-34.

[33] Zhao Z, Fu T, Wang Y, et al. From forecasting to planning: Policy world model for collaborative state-action prediction[J]. Advances in Neural Information Processing Systems, 2026, 38: 134585-134611.

[34] Chen D, Moutakanni T, Chung W, et al. Planning with reasoning using vision language world model [J/OL]. 2025-09-06. https://doi.org/10.48550/arXiv.2509.02722.

[35] Ye S, Ge Y, Zheng K, et al. World action models are zero-shot policies [J/OL]. 2026-02-17. https://doi.org/10.48550/arXiv.2602.15922.

[36] Feng Q, Yu J, Liu J, et al. Harmowam: Harmonizing generalizable and precise manipulation via adaptive world action models [J/OL]. 2026-05-11. https://doi.org/10.48550/arXiv.2605.10942.

[37] Zhang X, Zeng X, Zhang W. From world models to world action models: A concise tutorial for robotics [J/OL]. 2026-08-12. https://doi.org/10.48550/arXiv.2607.00836.

[38] Cen J, Yu C, Yuan H, et al. Worldvla: Towards autoregressive action world model. [J/OL]. 2025-06-26. https://doi.org/10.48550/arXiv.2506.21539.

[39] Sanchez-Martin P, Rateike M, Valera I. Vaca: Design of variational graph autoencoders for interventional and counterfactual queries [J/OL]. 2024-02-04. https://doi.org/10.48550/arXiv.2110.14690.

[40] Nishimoto Y, Matsubara T. Object-centric world models for causality-aware reinforcement learning[C].Proceedings of the AAAI Conference on Artificial Intelligence. Singapore: AAAI Press, 2026, 40(29): 24585-24593.

[41] 王亚珅,张尚志,刘馨竹,等.2024年军事智能技术主要发展分析[J].战术导弹技术,2025,(2):44-63.

[42] Zhang W, Terver B, Zholus A, et al. Hierarchical planning with latent world models, [J/OL]. 2026-06-16. https://doi.org/10.48550/arXiv.2604.03208.

[43] Wang J, Li Y, Gao R X, et al. Hybrid physics-based and data-driven models for smart manufacturing: Modelling, simulation, and explainability[J]. Journal of Manufacturing Systems, 2022, 63: 381-391.

[44] Fang L, Yu X, Cai J, et al. Knowledge distillation and dataset distillation of large language models: Emerging trends, challenges, and future directions[J]. Artificial Intelligence Review, 2026, 59(1): 17.

[45] 李彩军,王亚珅,江昊,等.美以伊冲突中人工智能技术的应用分析及启示[J].战术导弹技术,2026,(3):1-9.

[46] Gu J, Jiang X, Shi Z, et al. A survey on llm-as-a-judge[J]. The Innovation, 2026, 7(6): 101253:1-101253:30.

Basic Information:

DOI:10.16358/j.issn.1009-1300.20260142

China Classification Code:E91

Citation Information:

[1]Xiang Husheng,Wang Yashen,Han Long ,et al.Thoughts on the Applications of World Model in Intelligent Warfare[J].Tactical Missile Technology().DOI:10.16358/j.issn.1009-1300.20260142.

Fund Information:

国家自然科学基金(U22B2061); 重点实验室自主科研项目(2025-JCJQ-LC-007)

Published:  

2026-09-21

Publication Date:  

2026-09-21

Online:  

2026-09-21

quote

GB/T 7714-2015
MLA
APA
Search Advanced Search