Papers by Xiangwen Kong
PaCoRe: Learning to Scale Test-Time Compute with Parallel Coordinated Reasoning (2026.acl-long)
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Jingcheng Hu, Yinmin Zhang, Shijie Shang, Xiaobo Yang, Yue Peng, Zhewei Huang, Hebin Zhou, Xin Wu, Jie Cheng, Fanqi Wan, Xiangwen Kong, Chengyuan Yao, Kaiwen Yan, Ailin Huang, Hongyu Zhou, Qi Han, Zheng Ge, Xiangyu Zhang, Heung-Yeung Shum
| Challenge: | Parallel Coordinated Reasoning (PaCoRe) overcomes a central limitation of contemporary language models: their inability to scale test-time compute (TTC) far beyond sequential reasoning under a fixed context window. |
| Approach: | They propose a training-and-inference framework to overcome a central limitation of language models: their inability to scale test-time compute (TTC) under a fixed context window. |
| Outcome: | The proposed model scales to multi-million-token effective TTC without exceeding context limits. |
SenseJudge: Human-Centric Preference-Driven Judgment Framework (2026.findings-acl)
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| Challenge: | Existing judgment approaches rely on trained judgers using fixed preference data . existing judgment approaches neglect diverse user preferences and struggle to adapt to real-world human-AI dialogue scenarios. |
| Approach: | They propose a customizable judgment framework driven by human preferences and a diverse instruction following benchmark derived from real-world multi-turn interactions. |
| Outcome: | The proposed framework surpasses other judgment methods and models in two tasks, and achieves model ranking that aligns with real human sense. |