8 papers
Perceive Before Reasoning: A Pre-Reasoning Perception Framework for Efficient and Reliable Proactive Mobile Agents
Zhijie Ding, Weinan Hong, Zicheng Zhu +6
Multimodal large language models (MLLMs) have substantially advanced mobile agents, yet proactive mobile assistance remains challenging because agents must decide \emph{when} to in…
CRAB-Bench: Evaluating LLM Agents under Complex Task Dependencies and Human-aligned User Simulation
Danqing Wang, Akshay Sivaraman, Lei Li
Evaluating LLM agents in realistic service scenarios requires complex task dependencies, imperfect user behavior, and an evaluation that accommodates multiple valid solutions. We i…
Is Vibe Coding Safe? Benchmarking Vulnerability of Agent-Generated Code in Real-World Tasks
Songwen Zhao, Danqing Wang, Kexun Zhang +3
Vibe coding is a new software development paradigm in which human engineers prompt a large language model (LLM) agent to complete complex coding tasks with little supervision. Alth…
Strategic Planning and Rationalizing on Trees Make LLMs Better Debaters
Danqing Wang, Zhuorui Ye, Xinran Zhao +2
Winning competitive debates requires sophisticated reasoning and argument skills. There are unique challenges in the competitive debate: (1) The time constraints force debaters to…
TypedThinker: Diversify Large Language Model Reasoning with Typed Thinking
Danqing Wang, Jianxin Ma, Fei Fang +1
Large Language Models (LLMs) have demonstrated strong reasoning capabilities in solving complex problems. However, current approaches primarily enhance reasoning through the elabor…
Scaling LLM Inference with Optimized Sample Compute Allocation
Kexun Zhang, Shang Zhou, Danqing Wang +2
Sampling is a basic operation in many inference-time algorithms of large language models (LLMs). To scale up inference efficiently with a limited compute, it is crucial to find an…