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Agent evolution under the VLA architecture: From mechanistic construction to application expansion
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Hui ZHANG1, Dongjin XIE2, Shutong LIANG1, Mingxuan LI1, Xiaofeng JIA3, *, Yonglin TIAN4, Siji MA5, Haoran LI4, Yidong LI1
Science & Technology Review | 2025, 43(20) : 48 - 61
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Science & Technology Review | 2025, 43(20): 48-61
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Agent evolution under the VLA architecture: From mechanistic construction to application expansion
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Hui ZHANG1, Dongjin XIE2, Shutong LIANG1, Mingxuan LI1, Xiaofeng JIA3, *, Yonglin TIAN4, Siji MA5, Haoran LI4, Yidong LI1
Affiliations
  • 1School of Computer Science and Technology, Beijing Jiaotong University, Beijing 100044, China
  • 2School of Software, Xinjiang University, Urumqi 830046, China
  • 3Beijing Big Data Centre, Beijing 101117, China
  • 4Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
  • 5Faculty of Innovation Engineering, Macau University of Science and Technology, Macau 999078, China
Published: 2025-10-28 doi: 10.3981/j.issn.1000-7857.2025.10.00077
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Embodied intelligence represents a new stage in the evolution of artificial intelligence, marking a transition from "perception−cognition" to an integrated paradigm of "perception−cognition−action." The Vision−Language−Action (VLA) model provides a critical technological pathway for enabling autonomous agent operation in the real world by unifying visual perception, language understanding, and action generation. This paper systematically reviews the development trajectory and representative achievements of VLA technologies, and summarizes their architectural paradigm, which includes multi−modal perception, semantic fusion mechanisms, reinforcement and imitation learning, world models, and hierarchical action output. By considering application scenarios such as autonomous driving, human–computer interaction, and industrial equipment, we further analyze the core challenges faced by VLA development, including the scarcity of data resources, limited generalization and transferability, insufficient interpretability, and increasing computational demands, and we outline the future development trends.

vision−language−action model  /  multi−modal learning  /  embodied intelligence  /  large language model
Hui ZHANG, Dongjin XIE, Shutong LIANG, Mingxuan LI, Xiaofeng JIA, Yonglin TIAN, Siji MA, Haoran LI, Yidong LI. Agent evolution under the VLA architecture: From mechanistic construction to application expansion[J]. Science & Technology Review, 2025 , 43 (20) : 48 -61 . DOI: 10.3981/j.issn.1000-7857.2025.10.00077
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doi: 10.3981/j.issn.1000-7857.2025.10.00077
  • Receive Date：2025-09-11
  • Online Date：2025-12-29
  • Published：2025-10-28
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  • Received：2025-09-11
  • Revised：2025-10-18
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Affiliations
    1School of Computer Science and Technology, Beijing Jiaotong University, Beijing 100044, China
    2School of Software, Xinjiang University, Urumqi 830046, China
    3Beijing Big Data Centre, Beijing 101117, China
    4Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
    5Faculty of Innovation Engineering, Macau University of Science and Technology, Macau 999078, China
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