arXiv AI July 1, 2026 By Lingyu Xiao, Zexin Feng, Xintao Yan

Long-term Traffic Simulation via Structured Autoregressive Modeling

arXiv:2606. 31209v1 Announce Type: new Abstract: Interactive traffic simulation is a vital world model for autonomous driving.

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

arXiv AI

Jul 1

Bridging Local Observation and Global Simulation in Closed-Loop Traffic Modeling

arXiv:2606. 31844v1 Announce Type: cross Abstract: A local-to-global context mismatch arises when autoregressive traffic simulators trained on ego-centric driving logs are deployed in globally observable closed-loop environments.

By Ziyan Wang, Tan Xiang, Peng Chen, Xintao Yan

agents safety

arXiv AI

Jul 28

AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models

arXiv:2603. 28963v2 Announce Type: replace-cross Abstract: Simulation with realistic traffic agents is essential for validating autonomous driving systems.

By Mozhgan Pourkeshavarz, Tianran Liu, Nicholas Rhinehart

agents diffusion robotics

arXiv AI

Jun 2

From Segments to Scenes: Temporal Understanding in Autonomous Driving via Vision-Language Model

arXiv:2512. 05277v3 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) are increasingly deployed as the perception and reasoning backbone of autonomous agents acting in the wild, with autonomous driving (AD) being one of the most safety-critical instances.

By Kevin Cannons, Saeed Ranjbar Alvar, Mohammad Asiful Hossain, Ahmad Rezaei, Mohsen Gholami, Alireza Heidarikhazaei, Zhou Weimin, Yong Zhang, Mohammad Akbari

llms agents robotics multimodal benchmarks safety

arXiv AI

Jun 4

From Segments to Scenes: Temporal Understanding for Agentic Autonomous Driving via Vision-Language Models

arXiv:2512. 05277v4 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) are increasingly deployed as the perception and reasoning backbone of autonomous agents acting in the wild, with autonomous driving (AD) being one of the most safety-critical instances.

By Kevin Cannons, Saeed Ranjbar Alvar, Mohammad Asiful Hossain, Ahmad Rezaei, Mohsen Gholami, Alireza Heidarikhazaei, Zhou Weimin, Yong Zhang, Mohammad Akbari

llms agents robotics multimodal benchmarks safety

arXiv AI

Jun 9

SpatialWorld: Benchmarking Interactive Spatial Reasoning of Multimodal Agents in Real-World Tasks

arXiv:2606. 09669v1 Announce Type: new Abstract: Spatial reasoning is a foundational capability for multimodal large language models (MLLMs) to perceive and operate within the physical world.

By Hongcheng Gao, Hailong Qu, Jingyi Tang, Jiahao Wang, Zihao Huang, Hengkang Qiao, Shihong Huang, Junming Yang, Yi Li, Hongyixuan Yuan, Wenjie Li, Bohan Zeng, Wenbo Li, Bo Wang, Jianhui Liu, Olive Huang, Haoyang Huang, Wentao Zhang, Guoqing Huang, Nan Duan, Yinpeng Dong

llms agents multimodal benchmarks

arXiv AI

Jul 21

Masked Diffusion Language Models are Strong and Steerable Text-Based World Models for Agentic RL

arXiv:2607. 16204v1 Announce Type: new Abstract: Recent growth in reinforcement learning (RL) has surfaced a need for diverse, specialized training environments.

By Darshan Deshpande

llms agents diffusion reinforcement-learning fine-tuning efficiency safety

Related stories

Bridging Local Observation and Global Simulation in Closed-Loop Traffic Modeling

AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models

From Segments to Scenes: Temporal Understanding in Autonomous Driving via Vision-Language Model

From Segments to Scenes: Temporal Understanding for Agentic Autonomous Driving via Vision-Language Models

SpatialWorld: Benchmarking Interactive Spatial Reasoning of Multimodal Agents in Real-World Tasks

Masked Diffusion Language Models are Strong and Steerable Text-Based World Models for Agentic RL