Papers by Hong Jiao
SeNsER: Learning Cross-Building Sensor Metadata Tagger (2020.findings-emnlp)
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| Challenge: | Sensor metadata tagging is a key component of smart building applications. |
| Approach: | They propose a framework that learns a sensor metadata tagger for a new building based on its raw metadata and some existing fully annotated building. |
| Outcome: | The proposed framework learns a sensor metadata tagger for a new building based on its raw metadata and some existing fully annotated building. |
Curing Miracle Steps in LLM Mathematical Reasoning with Rubric Rewards (2026.acl-long)
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Youliang Yuan, Qiuyang Mang, Jingbang Chen, Hong Wan, Xiaoyuan Liu, Junjielong Xu, Jen-tse Huang, Wenxuan Wang, Wenxiang Jiao, Pinjia He
| Challenge: | Existing models are susceptible to reward hacking, leading to a substantial overestimation of a model's reasoning ability. |
| Approach: | They propose a Rubric Reward Model that rewards the entire reasoning trajectory against problem-specific rubrics. |
| Outcome: | The proposed model outperforms outcome-only supervision on four math benchmarks and boosts Verified Pass@1024 from 26.7% to 62.6% and reduces the incidence of Miracle Steps by 71%. |
Understanding the Thinking Process of Reasoning Models: A Perspective from Schoenfeld’s Episode Theory (2025.emnlp-main)
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Ming Li, Nan Zhang, Chenrui Fan, Hong Jiao, Yanbin Fu, Sydney Peters, Qingshu Xu, Robert Lissitz, Tianyi Zhou
| Challenge: | Large Reasoning Models (LRMs) generate extensive chain-of-thought reasoning, but we lack a principled framework for understanding how these thoughts are structured. |
| Approach: | They propose a method to analyze the reasoning traces of Large Reasoning Models using Schoenfeld’s Episode Theory. |
| Outcome: | The proposed framework provides a theoretically grounded methodology for interpreting LRM cognition and enables future work on more controllable and transparent reasoning systems. |
Can LLMs Estimate Student Struggles? Human-AI Difficulty Alignment with Proficiency Simulation for Item Difficulty Prediction (2026.findings-acl)
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| Challenge: | Accurate estimation of item (question or task) difficulty suffers from the cold start problem. |
| Approach: | They propose to use large-scale empirical analysis to examine human-AI Difficulty Alignment . they find that models struggle to simulate the capability limitations of students . |
| Outcome: | The proposed model size is not reliably helpful for human-AI alignment . high performance often impedes accurate difficulty estimation, the authors say . |