Author

Mingkun Yang

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#artificial intelligence Preprint Oct 2026

PerceptRL: Making Visual Perception Rewardable in MLLMs from Answers to Interactions

PerceptRL is presented, which strengthens perception at two complementary levels in multimodal large language models by using a multi-agent pipeline to extract verified errors from dense image captions and convert the corresponding visual facts into QA pairs with fixed answer labels and rule-based rewards.

Tong-Kun Guan, Zhi-Bo Yang, Ming-Kun Yang et al. · 0 citations

Preprint Aug 2026

Qwen-Drive-1.0: An Initial Step towards a Vision-Language Foundation Model for Autonomous Driving

Experiments demonstrate strong 3D perception and driving scene understanding while largely preserving general vision-language capability and comprehensive evaluations across open-loop, pseudo-closed-loop, and closed-loop settings further show highly competitive motion-planning performance.

Xin Zhou, Zongchuang Zhao, Zhibo Yang et al. · 9 citations · ⚡2