Papers by Chengyou Jia
PhysReason: A Comprehensive Benchmark towards Physics-Based Reasoning (2025.acl-long)
Copied to clipboard
Xinyu Zhang, Yuxuan Dong, Yanrui Wu, Jiaxing Huang, Chengyou Jia, Basura Fernando, Mike Zheng Shou, Lingling Zhang, Jun Liu
| Challenge: | Large language models demonstrate remarkable capabilities across various domains, including mathematics and logic reasoning. |
| Approach: | They propose a physics-based reasoning benchmark that includes physics theorems and constraints and a Physics Solution Auto Scoring Framework to evaluate physics based reasoning in large language models. |
| Outcome: | The proposed framework enables models to achieve less than 60% on answer-level evaluation, with performance dropping from knowledge questions (75.11%) to hard problems (31.99%). |
OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis (2025.acl-long)
Copied to clipboard
Qiushi Sun, Kanzhi Cheng, Zichen Ding, Chuanyang Jin, Yian Wang, Fangzhi Xu, Zhenyu Wu, Chengyou Jia, Liheng Chen, Zhoumianze Liu, Ben Kao, Guohao Li, Junxian He, Yu Qiao, Zhiyong Wu
| Challenge: | Graphical User Interface (GUI) agents powered by Vision-Language Models (VLMs) have demonstrated human-like computer control capability. |
| Approach: | They propose a GUI data synthesis pipeline that reverse engineers GUI trajectory construction process by executing pre-defined tasks. |
| Outcome: | The proposed GUI data synthesis pipeline overcomes the bottlenecks of previous methods that rely on pre-defined tasks and limited data diversity. |
AgentStore: Scalable Integration of Heterogeneous Agents As Specialized Generalist Computer Assistant (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing agents lack generalization and specialization capabilities for open-ended tasks . specialized generalists are often underdeveloped in real-world environments . |
| Approach: | They propose a platform to dynamically integrate heterogeneous agents for automating computer tasks . they propose specialized generalist agent MetaAgent with the AgentToken strategy . |
| Outcome: | The proposed platform expands capabilities of existing agents in generalization and specialization . it can be used to automate open-ended tasks in real-world environments . |