Baiyang Song — accepted AI-conference papers

2026

Grounded Chain-of-Thought for Multimodal Large Language Models

Qiong Wu, Xiangcong Yang, Yiyi Zhou, Chenxin Fang, Baiyang Song, Xiaoshuai Sun +1

CVPR 2026

Despite great progress, existing multimodal large language models (MLLMs) are still inferior in visual-spatial reasoning, which greatly impedes their trustworthy applications in scenarios such as Embodied AI. To facilitate the research, we propose a new MLLM task in this paper, called Grounded Chain

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2026

KTV: Keyframes and Key Tokens Selection for Efficient Training-Free Video LLMs

Baiyang Song, Jun Peng, Yuxin Zhang, Guangyao Chen, Feidiao Yang, Jianyuan Guo

AAAI 2026technical

Training-free video understanding methods leverage the strong image comprehension capabilities of pre-trained vision language models (VLMs) by treating videos as a sequences of static frames, thus obviating the need for costly video-specific training. However, this paradigm often suffers from severe

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