Papers by Yutong Bai
AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time (2025.emnlp-main)
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Junyu Zhang, Runpei Dong, Han Wang, Xuying Ning, Haoran Geng, Peihao Li, Xialin He, Yutong Bai, Jitendra Malik, Saurabh Gupta, Huan Zhang
| Challenge: | Existing monotonic scaling methods for large reasoning models are not reliable. |
| Approach: | They propose a universal framework for modulating reasoning progress in large reasoning models at test time. |
| Outcome: | The proposed framework unifies and generalizes existing monotonic scaling methods and enables flexible and dense slow-to-fast reasoning modulation. |
Probing Audio-Visual Reasoning in Multimodal Language Models through the Lens of Audio (2026.acl-long)
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Kaixiong Gong, Kaituo Feng, Bohao Li, Yibing Wang, Mofan Cheng, Shijia Yang, Jiaming Han, Benyou Wang, Yutong Bai, Zhuoran Yang, Xiangyu Yue
| Challenge: | Recent multimodal large language models lack robust audio-visual integration ability and performance on DeafTest is highly correlated with AV-Odyssey accuracy. |
| Approach: | They propose a benchmarking tool that integrates audio-visual reasoning with audio-video cues to infer solutions. |
| Outcome: | The proposed model performs well on DeafTest, but lacks audio perception in simple audio tasks. |
Learning Dynamic Multi-attribute Interest for Personalized Product Search (2024.findings-emnlp)
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| Challenge: | Existing methods to capture valuable features for Personalized product search ignore that the user’s attention varies on product attributes. |
| Approach: | They propose a dynamic multi-attribute interest learning model to tackle the influences from attributes to user interests. |
| Outcome: | The proposed model significantly improves existing methods on large-scale datasets. |