Papers by Mingchen Zhuge
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. |
Beyond Outlining: Heterogeneous Recursive Planning for Adaptive Long-form Writing with Language Models (2025.emnlp-main)
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| Challenge: | Current writing agents rely on predefined workflows and rigid thinking patterns to generate outlines before writing . authors propose a framework for long-form writing agents built on heterogeneous recursive planning . |
| Approach: | They propose a general agent framework that achieves human-like adaptive writing . they propose recursive task decomposition and dynamic integration of task types . |
| Outcome: | The proposed framework outperforms state-of-the-art approaches on both fiction and technical report generation. |