arXiv:2609.15169v1 Announce Type: new
Abstract: Driving vision-language-action (VLA) models increasingly reason before acting, but their intermediate reasoning is often weakly grounded in physical sc...
By Xiao Liu, Haoyu Li, Jianghao Leng, Lin Wang, Chao Sun
The paper surveys the transition from textual chain-of-thought reasoning to action-grounded reasoning in autonomous driving, highlighting that driving decisions require continuous actions that mirror the spatiotemporal structure of the physical world. It reviews 171 papers, categorizing 130 methods into four main types—language-based, visual-spatial, latent-dynamic, and externalized reasoning—along with 13 subtypes linked to specific regions of interest. The authors argue that the future of driving agent reasoning lies in intermediate representations that are grounded in reality, linked to real-time actions, and verifiable within safety-critical systems.
By Zhengxu Tang, Xiaozhou Zhang, Guofeng Cui, Ziyu Gong, Zi Wang, Yunfei Shi, Ruifeng Deng, Chengzhi Qi, Ke Chen, Sachin Patil, Tianjun Xiao, Langechuan Liu, Pichao Wang
arXiv:2609. 28366v1 Announce Type: cross Abstract: Vision-language models (VLMs) offer a promising approach to long-tail autonomous driving, but existing driving datasets provide limited supervision for connecting decision-critical visual evidence with reasoning and planning.
By Zhipeng Bao, Wenjie Zhao, Tianle Zhu, Haohua Que, Chence Yang, Geng Yuan, Qianwen Li
arXiv:2610.00601v1 Announce Type: cross
Abstract: Reasoning-enabled VLA policies expose chain-of-thought (CoT) traces that appear to explain and guide their actions, creating a potential interface fo...
By Sathwik Karnik, Joseph JR. Lee, Aryaman Gupta, Somil Bansal
arXiv:2606. 27926v1 Announce Type: new Abstract: Geometry Problem Solving have increasingly adopt the neuro-symbolic paradigm, combining neural intuition with symbolic rigor.
By Can Li, Ting Zhang, Junbo Zhao, Hua Huang
arXiv:2607. 04681v1 Announce Type: cross Abstract: Embodied Chain-of-Thought has emerged as a promising mechanism to enhance robot decision-making and interpretability in black-box Vision-Language Action (VLA) models.
By Matthew Foutter, Matteo Cercola, Lena Wild, Yunshan Wang, Michelle Li, Daniele Gammelli, Marco Pavone