Papers by Chenxu Zhao
Lil: Less is Less When Applying Post-Training Sparse-Attention Algorithms in Long-Decode Stage (2026.findings-acl)
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Junhao Hu, Fangze Li, Mingtao Xu, Feifan Meng, Shiju Zhao, Tiancheng Hu, Ting Peng, Anmin Liu, Wenrui Huang, Chenxu Liu, Ziyue Hua, Tao Xie
| Challenge: | Prior work typically decomposes inference into prefill and decode stages, with the decode stage dominating total latency. |
| Approach: | They propose an algorithm that detects threshold where information loss exceeds information gain during sparse decoding to reduce token consumption by up to 90% and a marginal accuracy degradation of less than 2%. |
| Outcome: | The proposed algorithm reduces token consumption by 90% with a marginal accuracy degradation of less than 2% across reasoning-intensive benchmarks. |
Quantifying and Understanding Uncertainty in Large Reasoning Models (2026.acl-long)
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| Challenge: | Existing methods for estimating generation uncertainty do not provide finite-sample guarantees for reasoning-answer generation. |
| Approach: | They propose a method that provides the uncertainty of the reasoning-answer structure with statistical guarantees. |
| Outcome: | The proposed method disentangles reasoning quality from answer correctness while establishing theoretical guarantees for efficient explanation methods. |