About Me
Welcome!
I’m Wenyue Hua, senior researcher at Microsoft Research, AI Frontiers. I was a postdoctoral researcher at University of California, Santa Barbara, working with Prof. William Yang Wang (2024 - 2025). I obtained my Ph.D. degree from Rutgers University, New Brunswick (2020 - 2024). I’m honored to be advised by Prof. Yongfeng Zhang. I received MA in Linguistics at Rutgers in 2020 (proudly advised by Prof. Adam Jardine) and BA in Linguistics and Philosophy and BS in Mathematics at UCLA in 2018 (proudly advised by Prof. Edward Keenan).
My research interests lie in Large Language Models and its various application, such as LLM-based agent, multi-agent system, generative recommender system, LLM reasoning. I care about the decision-making ability, safety, and efficiency of LLM-based agents.
Collaboration & Mentoring
I welcome discussions about AI agents and am open to collaborations with researchers and industry professionals. I also enjoy mentoring students at various stages of their academic journey.
Feel free to schedule a conversation here to explore potential partnerships or discuss recent developments in the field.
- Large language models
- LLM-based agent
- Trustworthy AI
- Efficient AI
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Postdoctoral Research in Computer Science, 2024-2025
Computer Science Department, University of California, Santa Barbara
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Ph.D. in Computer Science, 2020-2024
Computer Science Department, Rutgers University, New Brunswick
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Master of Arts in Linguistics (Ph.D. track transfer out), 2018-2020
Department of Linguistics, Rutgers University, New Brunswick
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B.S. in Mathematics, General & B.A. in Linguistics&Philosophy with Specialization in Computing, 2014-2018
UCLA
News
Experience
- Advisor: Prof. William Yang Wang
- Dissertation: Trustworthy Large Language Model
- Advisor: Prof. Yongfeng Zhang
- Thesis: Learning Underlying Representations and Input-Strictly-Local Functions
- Advisor: Prof. Adam Jardine
- Thesis: Boolean-Algebraic Representation of Possible Worlds
- Advisor: Prof. Edward Keenan
Featured Publications
Large language models (LLMs) have shown remarkable improvements in reasoning and many existing benchmarks have been addressed by models such as o1 and o3 either fully or partially. However, a majority of these benchmarks emphasize deductive reasoning, including mathematical and coding tasks in which rules such as mathematical axioms or programming syntax are clearly defined, based on which LLMs can plan and apply these rules to arrive at a solution. In contrast, inductive reasoning, where one infers the underlying rules from observed data, remains less explored. Such inductive processes lie at the heart of scientific discovery, as they enable researchers to extract general principles from empirical observations. To assess whether LLMs possess this capacity, we introduce InductionBench, a new benchmark designed to evaluate the inductive reasoning ability of LLMs. Our experimental findings reveal that even the most advanced models available struggle to master the simplest complexity classes within the subregular hierarchy of functions, highlighting a notable deficiency in current LLMs’ inductive reasoning capabilities. Coda and data are available this url.
This paper investigates the rationality of large language models (LLMs) in strategic decision-making contexts, specifically within the framework of game theory. We evaluate several state-of-the-art LLMs across a spectrum of complete-information and incomplete-information games. Our findings reveal that LLMs frequently deviate from rational strategies, particularly as the complexity of the game increases with larger payoff matrices or deeper sequential trees. To address these limitations, we design multiple game-theoretic workflows that guide the reasoning and decision-making processes of LLMs. These workflows aim to enhance the models’ ability to compute Nash Equilibria and make rational choices, even under conditions of uncertainty and incomplete information. Experimental results demonstrate that the adoption of these workflows significantly improves the rationality and robustness of LLMs in game-theoretic tasks. Specifically, with the workflow, LLMs exhibit marked improvements in identifying optimal strategies, achieving near-optimal allocations in negotiation scenarios, and reducing susceptibility to exploitation during negotiations. Furthermore, we explore the meta-strategic considerations of whether it is rational for agents to adopt such workflows, recognizing that the decision to use or forgo the workflow constitutes a game-theoretic issue in itself. Our research contributes to a deeper understanding of LLMs’ decision-making capabilities in strategic contexts and provides insights into enhancing their rationality through structured workflows. The findings have implications for the development of more robust and strategically sound AI agents capable of navigating complex interactive environments.Coda and data are available this url.
LLM-based intelligent agents face significant deployment challenges, particularly related to resource management. Allowing unrestricted access to LLM or tool resources can lead to inefficient or even potentially harmful resource allocation and utilization for agents. Furthermore, the absence of proper scheduling and resource management mechanisms in current agent designs hinders concurrent processing and limits overall system efficiency. As the diversity and complexity of agents continue to grow, addressing these resource management issues becomes increasingly critical to LLM-based agent systems. To address these challenges, this paper proposes the architecture of AIOS (LLM-based AI Agent Operating System) under the context of managing LLM-based agents. It introduces a novel architecture for serving LLM-based agents by isolating resources and LLM-specific services from agent applications into an AIOS kernel. This AIOS kernel provides fundamental services (e.g., scheduling, context management, memory management, storage management, access control) and efficient management of resources (e.g., LLM and external tools) for runtime agents. To enhance usability, AIOS also includes an AIOS-Agent SDK, a comprehensive suite of APIs designed for utilizing functionalities provided by the AIOS kernel. Experimental results demonstrate that using AIOS can achieve up to 2.1x faster execution for serving agents built by various agent frameworks. The source code is available at this url.