Papers by Jiayu Chen
AgentSense: Benchmarking Social Intelligence of Language Agents through Interactive Scenarios (2025.naacl-long)
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Xinyi Mou, Jingcong Liang, Jiayu Lin, Xinnong Zhang, Xiawei Liu, Shiyue Yang, Rong Ye, Lei Chen, Haoyu Kuang, Xuanjing Huang, Zhongyu Wei
| Challenge: | Large language models are increasingly employed to empower autonomous agents to simulate human behavior. |
| Approach: | They propose to evaluate LLM-driven agents through multi-turn interactions using a bottom-up approach to create diverse social scenarios constructed from extensive scripts. |
| Outcome: | The proposed model evaluates LLM-driven agents through multi-turn interactions emphasizing goal completion and implicit reasoning. |
BoundRL: Efficient Token-level Structured Text Segmentation through Reinforced Boundary Generation (2026.findings-acl)
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Haoyuan Li, Zhengyuan Shen, Sullam Jeoung, Yueyan Chen, Jiayu Li, Qi Zhu, Shuai Wang, Vassilis N. Ioannidis, Huzefa Rangwala
| Challenge: | Structured texts often contain elements beyond plain language, such as code snippets, which conventional sentence-level segmentation methods cannot handle effectively. |
| Approach: | They propose a token-level approach that performs efficient token-based text segmentation and label prediction for long structured texts. |
| Outcome: | The proposed approach outperforms existing models on short-shot prompts and SFT and standard RLVR models on complex LLM prompts. |
SPD-Faith Bench: Diagnosing and Improving Faithfulness in Chain-of-Thought for Multimodal Large Language Models (2026.findings-acl)
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| Challenge: | Existing studies on multimodal faithfulness have focused on perceptual hallucinations, raising concerns about the validity of reasoning traces. |
| Approach: | They propose a diagnostic benchmark that enforces explicit visual comparison to assess faithfulness of reasoning traces. |
| Outcome: | The proposed framework improves visual routing and aligns reasoning with perception. |
VERITAS: Leveraging Vision Priors and Expert Fusion to Improve Multimodal Data (2025.emnlp-main)
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| Challenge: | supervised fine-tuning (SFT) data is critical for large multimodal models . current methods suffer from factual errors and hallucinations due to inadequate visual perception . |
| Approach: | They propose a pipeline that integrates vision priors and state-of-the-art LMMs with statistical methods to enhance SFT data quality. |
| Outcome: | The proposed pipeline outperforms existing models in text-rich reasoning tasks while enhancing reasoning efficiency. |
JUREX-4E: Juridical Expert-Annotated Four-Element Knowledge Base for Legal Reasoning (2025.emnlp-main)
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| Challenge: | Recent studies have introduced legal theories into LLM workflows to improve their understanding of legal texts and reasoning accuracy. |
| Approach: | They evaluate an expert-annotated four-element knowledge base covering 155 criminal charges. |
| Outcome: | The proposed model can be used to analyze criminal charges and retrieve them in legal cases. |