arXiv AI June 19, 2026 By Shihao Ji, HongXi Li, Zihui Song, Mingyu Li

Lagrange: An Open-Vocabulary, Energy-Based Sparse Framework for Generalized End-to-End Driving

arXiv:2606. 20274v1 Announce Type: new Abstract: Scaling end-to-end autonomous driving to complex, open-world environments requires perceptual models that generalize to anomalous scenarios and planners that produce kinematically valid trajectories.

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

arXiv AI

Jul 7

AnchorVLA: Bridging Discrete Decisions and Continuous Trajectories for Vision-Language-Action Planning

arXiv:2607. 03182v1 Announce Type: cross Abstract: Autonomous driving planning requires translating navigation intent, traffic rules, dynamic interactions, and language instructions into executable continuous trajectories.

By Qi Liu, Yabei Li, Hongsong Wang, Heng Zhang, Lei He

llms agents robotics multimodal benchmarks safety

arXiv AI

Jul 2

What's Hidden Matters: Identifying Planning-Critical Occluded Agents using Vision-Language Models

arXiv:2607. 00283v1 Announce Type: cross Abstract: Autonomous vehicles must safely navigate complex environments where planning-critical agents may be hidden from view.

By Amirhosein Chahe, Tyler Naes, Jovin D'sa, Faizan M. Tariq, Sangjae Bae, Lifeng Zhou, David Isele

llms agents robotics fine-tuning multimodal benchmarks

arXiv AI

Jun 30

LWDrive: Layer-Wise World-Model-Guided Vision-Language Model Planning for Autonomous Driving

arXiv:2606. 29879v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) provide powerful semantic understanding and commonsense reasoning for End-to-End Autonomous Driving (E2E-AD) planning.

By Chen Yang, Yuhao Wei, Ze Xu, Ziheng Zou, Shuang Liang, Delin Ouyang, Lingfeng Qi, Jie Li, Guofa Li

llms agents robotics multimodal benchmarks

arXiv AI

Jun 6

CLEAR: Cognition and Latent Evaluation for Adaptive Routing in End-to-End Autonomous Driving

arXiv:2606. 06219v1 Announce Type: cross Abstract: End-to-end autonomous driving models often struggle to balance multi-modal maneuver generation with real-time inference constraints.

By Yining Xing, Zehong Ke, Zhiyuan Liu, Yanbo Jiang, Wenhao Yu, Jianqiang Wang

agents diffusion robotics fine-tuning multimodal benchmarks safety

arXiv AI

Jun 30

X-Mind: Efficient Visual Chain-of-Thought via Predictive World Model for End-to-End Driving

arXiv:2606. 28758v1 Announce Type: cross Abstract: Predicting future states is essential for autonomous agents, yet current Vision-Language-Action (VLA) models fundamentally lack this capability, relying instead on reactive perception-action mapping.

By Bohao Zhao, Chengrui Wei, Guangfeng Jiang, Ruixin Liu, Xuejie Lv, Liu Liang, Sutao Deng, Xiuyang Fan, Pengkun Zheng, Jinyun Zhou, Rui Guo, Hanpeng Liu, Yutong Zheng, Yi Guo, Xinlong Zheng, Qingyu Luo, Zhuangzhuang Ding, Yu Zhang, Hang Zhang, Xianming Liu

agents diffusion multimodal

arXiv AI

Jun 17

ThinkJEPA: Empowering Latent World Models with Large Vision-Language Reasoning Model

arXiv:2603. 22281v2 Announce Type: replace-cross Abstract: Recent progress in latent world models (e.

By Haichao Zhang, Yijiang Li, Shwai He, Tushar Nagarajan, Mingfei Chen, Jianglin Lu, Ang Li, Yun Fu

llms robotics multimodal safety

Related stories

AnchorVLA: Bridging Discrete Decisions and Continuous Trajectories for Vision-Language-Action Planning

What's Hidden Matters: Identifying Planning-Critical Occluded Agents using Vision-Language Models

LWDrive: Layer-Wise World-Model-Guided Vision-Language Model Planning for Autonomous Driving

CLEAR: Cognition and Latent Evaluation for Adaptive Routing in End-to-End Autonomous Driving

X-Mind: Efficient Visual Chain-of-Thought via Predictive World Model for End-to-End Driving

ThinkJEPA: Empowering Latent World Models with Large Vision-Language Reasoning Model