Papers by Bowen Fang
Reasoning as Gradient: Scaling MLE Agents Beyond Tree Search (2026.findings-acl)
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Yifei Zhang, Xu Yang, Xiao Yang, Bowen Xian, Qizheng Li, Shikai Fang, Jingyuan Li, Jian Wang, Minrui Xu, Yuge Zhang, Weiqing Liu, Jiang Bian
| Challenge: | LLM-based agents for machine learning engineering rely on tree search to rank candidates. |
| Approach: | They propose an LLM-based agent that operationalizes gradient-based optimization. |
| Outcome: | The proposed agent achieves a state-of-the-art 35.1% any-medal rate on MLE-Bench with a limited budget on a single GPU. |
KELE: Residual Knowledge Erasure for Enhanced Multi-hop Reasoning in Knowledge Editing (2025.findings-emnlp)
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| Challenge: | Existing knowledge editing techniques show limitations when applied to multi-hop reasoning . residual single-hop knowledge causes edited models to revert to original answers . |
| Approach: | They propose a knowledge editing method that incorporates a Knowledge Erasure mechanism for Large language model Editing (KELE) they propose an erasure function for residual knowledge and an injection function for new knowledge . |
| Outcome: | The proposed method significantly improves multi-hop reasoning capability of edited models. |
GenPilot: A Multi-Agent System for Test-Time Prompt Optimization in Image Generation (2025.findings-emnlp)
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Wen Ye, Zhaocheng Liu, Gui Yuwei, Tingyu Yuan, Yunyue Su, Bowen Fang, Chaoyang Zhao, Qiang Liu, Liang Wang
| Challenge: | Existing methods for text-to-image synthesis lack systematic error analysis and refinement strategies, resulting in limited reliability and effectiveness. |
| Approach: | They propose a plug-and-play multi-agent system called GenPilot that integrates error analysis, clustering-based adaptive exploration, fine-grained verification and a memory module for iterative optimization. |
| Outcome: | The proposed method improves text consistency and structural coherence on images with a plug-and-play system. |
Toolscaler: Scalable Generative Tool Calling via Structure-Aware Semantic Tokenization (2025.findings-emnlp)
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Yunyue Su, Zhang Jinshuai, Bowen Fang, Wen Ye, Jinghao Zhang, Bowen Song, Weiqiang Wang, Qiang Liu, Liang Wang
| Challenge: | Extensive experiments demonstrate the effectiveness of SGTC across various tasks. |
| Approach: | They propose a generative tool invocation framework that introduces structure-aware semantic tokenization to encode tools as discrete code sequences. |
| Outcome: | The proposed framework reduces the size of the representation space and underutilizes collaborative signals among tools in downstream tasks. |
Universal Information Extraction with Meta-Pretrained Self-Retrieval (2023.findings-acl)
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Xin Cong, Bowen Yu, Mengcheng Fang, Tingwen Liu, Haiyang Yu, Zhongkai Hu, Fei Huang, Yongbin Li, Bin Wang
| Challenge: | Existing methods for IE are task-specific, resulting in specialized and isolated approaches for different tasks. |
| Approach: | They propose a method to retrieve task-specific knowledge from pretrained language models to enhance universal IE by using a Meta-Pretraining Algorithm. |
| Outcome: | The proposed method achieves the new state-of-the-art on 4 IE tasks, 12 datasets under fully-supervised, low-resource and few-shot scenarios. |