2 citations · 3 across the 19 of their papers we have counts for
20 papers
SCOUT: Synergizing Reasoning and Tool-Use for Computer-Use Safety
Jianxing Chen, Xiao Yu, Shipra Agrawal +1
Computer-use agents (CUAs), while capable of completing computer tasks in everyday and professional workflows, can cause unintended harm even under benign instructions and environm…
APPSim-Bench: Bridging Real-world Apps and Reproducible Evaluation for Mobile GUI Agents
Jintian Feng, Long Chen, Xiao Yu +7
Mobile GUI agents can execute tasks from natural-language instructions, but their evaluation remains difficult to make both realistic and reproducible. Existing benchmarks typicall…
How do World Models and Policies Compose in LLM Agents? A Joint Spectral and Behavioral Account
Ruize Xu, Xiao Yu, Yujin Tang +2
How do LLM agents come to both understand environments they act in and master tasks set within them? Through controlled experiments combining world-model training (next-state predi…
OpenForgeRL: Train Harness-native Agents in Any Environment
Xiao Yu, Baolin Peng, Ruize Xu +7
Modern AI agents rely on elaborate inference harnesses such as Claude Code, Codex, and OpenClaw to drive multi-turn reasoning, tool use, and access to external systems. While power…
OSWorld 2.0: Benchmarking Computer Use Agents on Long-Horizon Real-World Tasks
Mengqi Yuan, Zilong Zhou, Xinzhuang Xiong +33
Existing computer-use benchmarks fail to capture the realism, complexity, and long-horizon demands of real-world computer use, limiting their ability to reveal the limitations of f…
ConFit v3: Improving Resume-Job Matching with LLM-based Re-Ranking
Xiao Yu, Ruize Xu, Chengyuan Xue +6
A reliable resume-job matching system helps a company find suitable candidates from a pool of resumes and helps a job seeker find relevant jobs from a list of job posts. While rece…
Orchard: An Open-Source Agentic Modeling Framework
Baolin Peng, Wenlin Yao, Qianhui Wu +11
Agentic modeling aims to transform LLMs into autonomous agents capable of solving complex tasks through planning, reasoning, tool use, and multi-turn interaction with external envi…
Reinforcement World Model Learning for LLM-based Agents
Xiao Yu, Baolin Peng, Ruize Xu +6
Large language models (LLMs) have achieved strong performance in language-centric tasks. However, in agentic settings, LLMs often struggle to anticipate action consequences and ada…
Dyna-Mind: Learning to Simulate from Experience for Better AI Agents
Xiao Yu, Baolin Peng, Michel Galley +6
Reasoning models have recently shown remarkable progress in domains such as math and coding. However, their expert-level abilities in math and coding contrast sharply with their pe…
AI Agents for Web Testing: A Case Study in the Wild
Naimeng Ye, Xiao Yu, Ruize Xu +2
Automated web testing plays a critical role in ensuring high-quality user experiences and delivering business value. Traditional approaches primarily focus on code coverage and loa…
Dyna-Think: Synergizing Reasoning, Acting, and World Model Simulation in AI Agents
Xiao Yu, Baolin Peng, Ruize Xu +5
Recent progress in reasoning with large language models (LLMs), such as DeepSeek-R1, demonstrates impressive capabilities in domains like mathematics and coding, by exhibiting comp…
Strategize Globally, Adapt Locally: A Multi-Turn Red Teaming Agent with Dual-Level Learning
Si Chen, Xiao Yu, Ninareh Mehrabi +3
The exploitation of large language models (LLMs) for malicious purposes poses significant security risks as these models become more powerful and widespread. While most existing re…
ConFit v2: Improving Resume-Job Matching using Hypothetical Resume Embedding and Runner-Up Hard-Negative Mining
Xiao Yu, Ruize Xu, Chengyuan Xue +3
A reliable resume-job matching system helps a company recommend suitable candidates from a pool of resumes and helps a job seeker find relevant jobs from a list of job posts. Howev…
Solving the Content Gap in Roblox Game Recommendations: LLM-Based Profile Generation and Reranking
Chen Wang, Xiaokai Wei, Yexi Jiang +7
With the vast and dynamic user-generated content on Roblox, creating effective game recommendations requires a deep understanding of game content. Traditional recommendation models…
FocalPO: Enhancing Preference Optimizing by Focusing on Correct Preference Rankings
Tong Liu, Xiao Yu, Wenxuan Zhou +2
Efficient preference optimization algorithms such as Direct Preference Optimization (DPO) have become a popular approach in aligning large language models (LLMs) with human prefere…
ExACT: Teaching AI Agents to Explore with Reflective-MCTS and Exploratory Learning
Xiao Yu, Baolin Peng, Vineeth Vajipey +4
Autonomous agents have demonstrated significant potential in automating complex multistep decision-making tasks. However, even state-of-the-art vision-language models (VLMs), such…
LIONs: An Empirically Optimized Approach to Align Language Models
Xiao Yu, Qingyang Wu, Yu Li +1
Alignment is a crucial step to enhance the instruction-following and conversational abilities of language models. Despite many recent work proposing new algorithms, datasets, and t…
LocalRQA: From Generating Data to Locally Training, Testing, and Deploying Retrieval-Augmented QA Systems
Xiao Yu, Yunan Lu, Zhou Yu
Retrieval-augmented question-answering systems combine retrieval techniques with large language models to provide answers that are more accurate and informative. Many existing tool…
ConFit: Improving Resume-Job Matching using Data Augmentation and Contrastive Learning
Xiao Yu, Jinzhong Zhang, Zhou Yu
A reliable resume-job matching system helps a company find suitable candidates from a pool of resumes, and helps a job seeker find relevant jobs from a list of job posts. However,…
Teaching Language Models to Self-Improve through Interactive Demonstrations
Xiao Yu, Baolin Peng, Michel Galley +2
The self-improving ability of large language models (LLMs), enabled by prompting them to analyze and revise their own outputs, has garnered significant interest in recent research.…