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cs.CV2026

SpatioLM: Towards General Physical Spatial Intelligence in Vision-Language Models

Jing Wu, Jianhua Wu, Jiayi Guan +5

Vision-Language Models (VLMs) perform well on commonsense reasoning tasks but struggle with visual spatial reasoning. Most existing solutions introduce extra 3D prior inputs or ext…

cs.CV2026

Resonant Minds: Closed-Loop Social Avatars with Theory of Mind

Jianxu Shangguan, Jing Xu, Hang Ye +4

Creating lifelike digital humans with genuine social intelligence requires unifying cognitive reasoning and multimodal generation within a coherent framework. Current approaches tr…

cs.CV2026

DriveReward: A Comprehensive Dataset and Generative Vision-Language Reward Model for Autonomous Driving

Qimao Chen, Fang Li, Yuechen Luo +11

Reward models play a pivotal role in reinforcement learning (RL) and multi-modal trajectory selection for autonomous driving. However, acquiring such rewards typically relies on ha…

cs.CV2026

LVDrive: Latent Visual Representation Enhanced Vision-Language-Action Autonomous Driving Model

Xiaodong Mei, Diankun Zhang, Hongwei Xie +3

Vision-Language-Action (VLA) models have emerged as a promising framework for end-to-end autonomous driving. However, existing VLAs typically rely on sparse action supervision, whi…

cs.CV2026

SimScale: Learning to Drive via Real-World Simulation at Scale

Haochen Tian, Tianyu Li, Haochen Liu +11

Achieving fully autonomous driving systems requires learning rational decisions in a wide span of scenarios, including safety-critical and out-of-distribution ones. However, such c…

cs.CV2026

MeanFuser: Fast One-Step Multi-Modal Trajectory Generation and Adaptive Reconstruction via MeanFlow for End-to-End Autonomous Driving

Junli Wang, Yinan Zheng, Xueyi Liu +9

Generative models have shown great potential in trajectory planning. Recent studies demonstrate that anchor-guided generative models are effective in modeling the uncertainty of dr…