Papers by Yuheng Wu
Data Interpreter: An LLM Agent for Data Science (2025.findings-acl)
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Sirui Hong, Yizhang Lin, Bang Liu, Bangbang Liu, Binhao Wu, Ceyao Zhang, Danyang Li, Jiaqi Chen, Jiayi Zhang, Jinlin Wang, Li Zhang, Lingyao Zhang, Min Yang, Mingchen Zhuge, Taicheng Guo, Tuo Zhou, Wei Tao, Robert Tang, Xiangtao Lu, Xiawu Zheng, Xinbing Liang, Yaying Fei, Yuheng Cheng, Yongxin Ni, Zhibin Gou, Zongze Xu, Yuyu Luo, Chenglin Wu
| Challenge: | Large Language Models (LLMs) excel in various domains but face challenges when applied to data science workflows due to their complex, multi-stage nature. |
| Approach: | They propose a hierarchical graph-based agent that represents complexity and a progressive strategy for step-by-step verification, refinement, and consistent context management. |
| Outcome: | The proposed agent surpasses state-of-the-art baselines on the MATH dataset and performs better on InfiAgent-DABench. |
DEL-ToM: Inference-Time Scaling for Theory-of-Mind Reasoning via Dynamic Epistemic Logic (2025.emnlp-main)
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| Challenge: | Theory-of-Mind (ToM) tasks pose a unique challenge for large language models (LLMs), which often lack the capability for dynamic logical reasoning. |
| Approach: | They propose a framework that decomposes ToM tasks into a sequence of belief updates grounded in Dynamic Epistemic Logic (DEL) they use data generated automatically via a DEL simulator to train a verifier, which is called the Process Belief Model (PBM). |
| Outcome: | The proposed framework improves verifiable ToM reasoning through inference-time scaling rather than architectural changes. |
SATBench: Benchmarking LLMs’ Logical Reasoning via Automated Puzzle Generation from SAT Formulas (2025.emnlp-main)
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Anjiang Wei, Yuheng Wu, Yingjia Wan, Tarun Suresh, Huanmi Tan, Zhanke Zhou, Sanmi Koyejo, Ke Wang, Alex Aiken
| Challenge: | SATBench is a benchmark for evaluating the logical reasoning capabilities of large language models (LLMs) through logical puzzles derived from Boolean satisfiability (SAT) problems. |
| Approach: | They propose a benchmark to evaluate logical reasoning capabilities of large language models (LLMs) using logical puzzles derived from Boolean satisfiability problems. |
| Outcome: | The proposed model achieves 65.0% accuracy on hard UNSAT problems, close to the baseline of 50%. |
Evaluating the Expressive Appropriateness of Speech in Rich Contexts (2026.acl-long)
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Tianrui Wang, Ziyang Ma, Yizhou Peng, Haoyu Wang, Zhikang Niu, Zikang Huang, Yihao Wu, Yi-Wen Chao, Yu Jiang, Yuheng Lu, Guanrou Yang, Xuanchen Li, Hexin Liu, Chunyu Qiang, Cheng Gong, Yifan Yang, Tianchi Liu, Junyu Wang, Nana Hou, Meng Ge, Fuming You, Yang Wei, Zhongqian Sun, Hu Haifeng, Xiaobao Wang, Eng Siong Chng, Xie Chen, Longbiao Wang, Jianwu Dang
| Challenge: | Existing methods for evaluating expressive speech focus on word accuracy, naturalness, signal quality, or emotional intensity at the utterance level. |
| Approach: | They propose a framework for Evaluating Expressive Appropriateness in speech that assesses whether a speech sample aligns with the underlying communicative intent implied by its discourse-level narrative context. |
| Outcome: | The proposed framework outperforms existing speech evaluation and analysis systems on a human-annotated test set. |
EngiBench: A Benchmark for Evaluating Large Language Models on Engineering Problem Solving (2026.findings-acl)
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Xiyuan Zhou, Xinlei Wang, Yirui He, Ruixi Zou, Yang Wu, Yuheng Cheng, Yulu Xie, Wenxuan Liu, Huan Zhao, Yan Xu, Jinjin Gu, Junhua Zhao
| Challenge: | Existing benchmarks focus on well-defined or abstract reasoning and fail to capture real-world engineering problems. |
| Approach: | They propose a hierarchical benchmark to evaluate large language models on engineering problems. |
| Outcome: | The proposed model performs well under well-defined conditions and is based on three levels of difficulty and covers diverse engineering subfields. |
AMAS: Adaptively Determining Communication Topology for LLM-based Multi-agent System (2025.emnlp-industry)
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| Challenge: | Large language models (LLMs) have revolutionized natural language processing, but their practical implementation as autonomous multi-agent systems remains fraught with unresolved challenges. |
| Approach: | They propose a dynamic graph selector that redefines LLM-based MAS by exploiting the intrinsic properties of individual inputs to intelligently direct query trajectories. |
| Outcome: | The proposed framework exceeds state-of-the-art approaches in question answering, mathematical deduction, and code generation benchmarks. |
TeachMaster: Generative Teaching via Code (2026.acl-industry)
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Yuheng Wang, Runde Yang, Lin Wu, Jie Zhang, Jingru Fan, Tianle Zhou, Ruoyu Fu, Huatao Li, Ruijie Shi, Siheng Chen, Weinan E, Chen Qian
| Challenge: | Existing methods for creating video content are limited by high costs and slow update cycles. |
| Approach: | They propose a paradigm shifting educators from manual creators to high-level directors who focus on pedagogical intents while agents handle execution. |
| Outcome: | The proposed framework reduces production costs to 0.3% of traditional course videos and provides a robust solution for scalable education. |