Papers by Tianyu Fan
AutoAgent: A Fully-Automated and Zero-Code Framework for LLM Agents (2026.findings-acl)
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| Challenge: | Large Language Model (LLM) agents have demonstrated remarkable capabilities in task automation and intelligent decision-making. |
| Approach: | They propose a Fully-Automated and highly Self-Developing framework that enables users to create and deploy LLM agents using natural language alone. |
| Outcome: | AutoAgent is a fully-automated and highly self-developing framework that enables users to create and deploy LLM agents using natural language alone. |
Training Verifier to Assessing Complex Real-World Tool-Use Trajectories (2026.findings-acl)
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Linzhuang Sun, Mingyang Chen, Hao Liang, Tianpeng Li, Zhou Yijie, Chenzheng Zhu, Tianyu Guo, Huanyao Zhang, Jingxuan Wei, Bihui Yu, Fan Yang, Wentao Zhang
| Challenge: | Existing methods for training effective AI agents often resort to synthetic data generation. |
| Approach: | They propose a plug-and-play framework for data quality control in tool-use scenarios . they construct a tool-verify dataset and release a benchmark to assess its performance . |
| Outcome: | The proposed framework surpasses Qwen2.5-72B-Instruct on Tool-V-Bench and the previous APIGen-MT dataset. |
MiniRAG: A Lightweight RAG system with Small Language Models (2026.acl-long)
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| Challenge: | Existing RAG frameworks rely on Large Language Models (LLMs) for all stages of the process, resulting in high computational costs and resource demands. |
| Approach: | They propose a semantic-aware heterogeneous graph indexing mechanism that combines text chunks and named entities in a unified structure and a lightweight topology-enhanced retrieval approach that leverages graph structures for efficient knowledge discovery without requiring advanced language capabilities. |
| Outcome: | The proposed system achieves comparable performance to LLM-based methods while requiring only 25% of the storage space. |
Well Begun is Half Done: Low-resource Preference Alignment by Weak-to-Strong Decoding (2025.findings-acl)
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| Challenge: | Low-resource methods for LLM alignment have been popular, but still face challenges in obtaining high-quality and aligned content. |
| Approach: | They propose a framework to enhance alignment ability of base models by the guidance of a small aligned model. |
| Outcome: | The proposed framework outperforms baseline methods while avoiding degradation on downstream tasks. |
HSS-Synth: Humanities and Social Sciences Data Synthesis for LLMs (2026.findings-acl)
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Ru Peng, Tianyu Zhao, Xijun Gu, Zhiting Fan, Haokai Xu, Jinyang Zhang, Yawen Zeng, Yihong Zhuang, Kexin Yang, Junyang Lin, Dayiheng Liu, Junbo Zhao
| Challenge: | High-quality, diverse data are vital for large language models (LLMs) but remain scarce and costly. |
| Approach: | They define the first HSS domain system covering 14 mainstream fields and introduce HSS-Synth. |
| Outcome: | the proposed pipeline outperforms 14 leading baselines on 16 benchmarks. |