Zhiwei Xu

About

Zhiwei Xu is a leading researcher at the intersection of large language models (LLMs) and multi-agent systems, with a focus on scalable decision-making and coordination. His most cited work, "Controlling Large Language Model-based Agents for Large-Scale Decision-Making: An Actor-Critic Approach" (2023), introduces a novel framework that mitigates LLM hallucination while enabling effective coordination among numerous agents. By integrating actor-critic reinforcement learning with LLM-based planning, Xu addresses critical challenges in scaling intelligent agents for real-world applications, such as autonomous logistics and distributed robotics. Though early in its trajectory, this paper has already garnered 3 citations, signaling growing influence in the field. Xu’s contributions are particularly notable for bridging the gap between generative AI and multi-agent control, offering a principled method to harness LLMs’ reasoning capabilities without sacrificing reliability. His work is essential reading for researchers exploring LLM-driven autonomy, multi-agent reinforcement learning, and human-AI collaboration. As the demand for robust, large-scale AI systems grows, Xu’s research provides foundational insights into designing agents that can reason, plan, and act coherently in complex environments.

Research Focus

Key Achievements

Top Papers

1. 1
   Controlling Large Language Model-based Agents for Large-Scale Decision-Making: An Actor-Critic Approach

   3 citations · 2023

Key Collaborators

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