Papers by Kuikun Liu
SciExplore: Evaluating Autonomous Agents from Scientific Navigation to Information Integration (2026.findings-acl)
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Yinhao Tang, Youqing Fang, Yanan Sun, Wenran Liu, Weiming Zhang, Bin Liu, Kuikun Liu, Wenwei Zhang, Kai Chen
| Challenge: | Existing benchmarks emphasize general-domain retrieval or static scientific question answering . SciExplore focuses on scientific database navigation, ambiguous literature retrieval, missing reference completion, and cross-source structured knowledge synthesis tasks. |
| Approach: | They propose a benchmark to evaluate scientific information-seeking and reasoning capabilities of LLMs and agents. |
| Outcome: | The new benchmark assesses the capabilities of state-of-the-art LLMs and agents in scientific research workflows. |
Are Your LLMs Capable of Stable Reasoning? (2025.findings-acl)
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Junnan Liu, Hongwei Liu, Linchen Xiao, Ziyi Wang, Kuikun Liu, Songyang Gao, Wenwei Zhang, Songyang Zhang, Kai Chen
| Challenge: | Existing evaluation protocols and metrics do not capture the full spectrum of LLM capabilities, especially in complex reasoning tasks. |
| Approach: | They propose a new evaluation metric that continuously assesses model performance across multiple sampling attempts, quantifying both the model’s potential capabilities and operational consistency. |
| Outcome: | The proposed evaluation metric measures model performance across multiple sampling attempts and provides comprehensive insights into their potential capabilities and operational consistency. |
Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models (2024.findings-acl)
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| Challenge: | Existing studies focus on prompt engineering or framework scheduling of one/multiple LLMs. |
| Approach: | They propose to integrate LLMs as agents into their training corpus by decomposition and redesigning the training corpu . they propose to use LLM-FLAN to effectively fine-tune LANguage models for Agents by reducing hallucinations. |
| Outcome: | The proposed model outperforms prior best models by 3.5% across agent evaluation datasets. |
T-Eval: Evaluating the Tool Utilization Capability of Large Language Models Step by Step (2024.acl-long)
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Zehui Chen, Weihua Du, Wenwei Zhang, Kuikun Liu, Jiangning Liu, Miao Zheng, Jingming Zhuo, Songyang Zhang, Dahua Lin, Kai Chen, Feng Zhao
| Challenge: | Existing studies evaluate the tool utilization ability of large language models based on the final output or only consider the single-step tool calling. |
| Approach: | They propose a new approach to evaluate the tool utilization capability of large language models (LLMs) they decompose the tool usage into multiple sub-processes, including instruction following, planning, reasoning, retrieval, understanding, and review. |
| Outcome: | The proposed model exhibits consistency with the outcome-oriented evaluation and provides a more fine-grained analysis of the capabilities of LLMs. |