Si Qin


2025

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UFO: A UI-Focused Agent for Windows OS Interaction
Chaoyun Zhang | Liqun Li | Shilin He | Xu Zhang | Bo Qiao | Si Qin | Minghua Ma | Yu Kang | Qingwei Lin | Saravan Rajmohan | Dongmei Zhang | Qi Zhang
Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

We introduce UFO, a UI-Fcused agent designed to fulfill user requests tailored to Windows OS applications by observing and analyzing the GUI and control information of these applications. UFO utilizes a hierarchical dual-agent framework that decomposes user requests using a divide-and-conquer approach, enabling seamless navigation and addressing sub-tasks across multiple applications. It also incorporates a control interaction module tailored for Windows OS, which detects control elements effectively and allows for fully automated execution. As a result, UFO simplifies complex and time-consuming processes into tasks that can be completed with natural language commands.We conducted testing of UFO across 9 popular Windows applications, encompassing a variety of scenarios. The results derived from both quantitative metrics and real-case studies, underscore the superior effectiveness of UFOin fulfilling user requests. To the best of our knowledge, UFO stands as the first UI agent specifically tailored for task completion within the Windows OS.

2024

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Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation
Ruomeng Ding | Chaoyun Zhang | Lu Wang | Yong Xu | Minghua Ma | Wei Zhang | Si Qin | Saravan Rajmohan | Qingwei Lin | Dongmei Zhang
Findings of the Association for Computational Linguistics: ACL 2024

This paper introduce a novel thought prompting approach called ”Everything of Thoughts” (XoT) for Large Language Models (LLMs) to defy the law of ”Penrose triangle” of existing thought paradigms, to achieve three key perspectives in thought generation simultaneously: performance, efficiency, and flexibility. XoT leverages pretrained reinforcement learning and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge and planning capability into thoughts, thereby enhancing LLMs’ decision-making capabilities. Through the MCTS-LLM collaborative thought revision framework, XoT autonomously produces high-quality comprehensive cognitive mappings with minimal LLM interactions. Additionally, XoT empowers LLMs to utilize flexible cognitive mappings for solving problems with multiple solutions.We evaluate XoT on several challenging problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our results demonstrate that XoT significantly outperforms existing approaches in various dimensions, showcasing its remarkable proficiency in addressing complex problems across diverse domains. The data and code are available at https://github.com/microsoft/Everything-of-Thoughts-XoT.