arXiv AI August 18, 2026 By Jiawei Liu, Jiacheng Guo, Tian Zhang, Yiwei Xu, Juan Wang, Jinlin Fan, Bowen Xiao, Chi Guo, Keyan Guo, Hongxin Hu

When State Becomes an Attack Surface: State-Semantic Injection in LLM-Driven Embodied Agents

Read the original on arXiv AI →

arXiv:2608. 16806v1 Announce Type: cross Abstract: Large Language Models (LLMs) have demonstrated capabilities in in-context learning, task decomposition, step-by-step reasoning, and code generation, driving their gradual evolution from text generation models into the core of agents capable of perceiving environments, invoking tools, and executing tasks.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 30

Demonstration-Free Robotic Control via LLM Agents

arXiv:2601. 20334v2 Announce Type: replace-cross Abstract: Robotic manipulation has increasingly adopted vision-language-action (VLA) models, which achieve strong performance but typically require task-specific demonstrations and fine-tuning, and often generalize poorly under domain shift.

By Brian Y. Tsui, Alan Y. Fang, Tiffany J. Hwu

llms agents robotics fine-tuning multimodal benchmarks

arXiv AI

Jun 17

MagicSim: A Unified Infrastructure for Executable Embodied Interaction

arXiv:2606. 17511v1 Announce Type: cross Abstract: Robot learning and embodied agents now require simulation to serve as a shared execution substrate linking control, skills, and planning, not only as a renderer, controller testbed, or fixed task environment.

By Haoran Lu, Songling Liu, Yue Chen, Guo Ye, Mutian Shen, Shuyang Yu, Yu Xiao, Jihai Zhao, Shang Wu, Jianshu Zhang, Xiangtian Gui, Chuye Hong, Yuran Wang, Maojiang Su, Jiayi Wang, Ruihai Wu, Zhaoran Wang, Han Liu

agents reinforcement-learning robotics multimodal benchmarks

arXiv AI

Aug 10

Capek 0.5: An Execution-Centric Vision-Language Model for Embodied Intelligence

arXiv:2608. 06756v1 Announce Type: new Abstract: Vision-language models are increasingly serving as the reasoning core of embodied agents.

By Ying Chen, Weizhen Li, Zhe Hu, Zhenjiang Li, Rui Jiang, Zhifeng Gu, Lihuang Fang, Jiangping Liu, Lei Yi, Jie Chen

llms agents reinforcement-learning robotics efficiency multimodal benchmarks

arXiv AI

Jul 14

VIA: Visual Interface Agent for Robot Control

arXiv:2607. 11119v1 Announce Type: cross Abstract: Robot manipulation is a complex task that requires visual understanding, physical reasoning, planning, and closed-loop control.

By Hengyuan Hu, Priya Sundaresan, Jensen Gao, Dorsa Sadigh

llms agents robotics fine-tuning multimodal

arXiv AI

Jul 17

MEMORA: Embodied Action Memory from Egocentric Videos for Reasoning and Planning

arXiv:2607. 14252v1 Announce Type: cross Abstract: Long-horizon robot planning requires more than predicting what actions will do next; it also requires memory of the embodied experience that makes future goals interpretable.

By Zihao Yu, Xiu Yuan, Chongjie Zhang

llms robotics

arXiv AI

Jun 6

World-Language-Action Model for Unified World Modeling, Language Reasoning, and Action Synthesis

arXiv:2606. 05979v1 Announce Type: cross Abstract: We propose world-language-action (WLA) models as a new class of embodied foundation models.

By Yi Yang, Zhihong Liu, Siqi Kou, Yiyang Chen, Yanzhe Hu, Jianbo Zhou, Boyuan Zhao, Zhijie Wei, Xiao Xia, Xueqi Li, Pengfei Liu, Zhijie Deng

llms diffusion robotics multimodal benchmarks

Related stories

Demonstration-Free Robotic Control via LLM Agents

MagicSim: A Unified Infrastructure for Executable Embodied Interaction

Capek 0.5: An Execution-Centric Vision-Language Model for Embodied Intelligence

VIA: Visual Interface Agent for Robot Control

MEMORA: Embodied Action Memory from Egocentric Videos for Reasoning and Planning

World-Language-Action Model for Unified World Modeling, Language Reasoning, and Action Synthesis