Papers by Lizhu Zhang
TARo: Token-level Adaptive Routing for LLM Test-time Alignment (2026.findings-acl)
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Arushi Rai, Qiang Zhang, Hanqing Zeng, Yunkai Zhang, Dipesh Tamboli, Xiangjun Fan, Zhuokai Zhao, Lizhu Zhang
| Challenge: | Large language models (LLMs) exhibit strong reasoning capabilities but typically require expensive post-training to reach high performance. |
| Approach: | They propose to use token-level Adaptive Routing to steer frozen LLMs toward structured reasoning entirely at inference time. |
| Outcome: | Extensive experiments show that TARo significantly improves reasoning performance by up to +22.4% over base model and +8.4% . |
Mixture-of-Minds: Multi-Agent Reinforcement Learning for Table Understanding (2026.acl-long)
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Yuhang Zhou, Mingrui Zhang, Ke Li, Mingyi Wang, Qiao Liu, Qifei Wang, Jiayi Liu, Fei Liu, Serena Li, Weiwei LI, Mingze Gao, Abhishek Kumar, Xiangjun Fan, Zhuokai Zhao, Lizhu Zhang
| Challenge: | Large language models (LLMs) have shown promise on understanding and reasoning over tables, but current approaches remain limited. |
| Approach: | They propose a multi-agent framework that decomposes table reasoning into three specialized roles: planning, coding, and answering. |
| Outcome: | The proposed framework decomposes table reasoning into three specialized roles: planning, coding, and answering. |