Yangyi Chen
Papers
1
Total Citations
9
H-Index
1
About
Yangyi Chen is a rising star in artificial intelligence, whose research centers on advancing large language model (LLM) agents and their ability to interact with the real world. His most-cited work, "Executable Code Actions Elicit Better LLM Agents" (2024), makes a pivotal contribution by demonstrating that LLM agents perform more effectively when prompted to generate executable code actions rather than traditional JSON or text formats. This insight fundamentally improves how agents invoke tools, control robots, and tackle complex tasks, offering a more flexible and powerful paradigm for agent design. With 9 citations in just its first year, the paper signals Chen’s growing influence in the field. His work bridges the gap between language understanding and practical action, positioning him as a key voice in the next wave of autonomous systems. For students and researchers, Chen’s research offers a clear path forward: by rethinking how we structure agent outputs, we can unlock more robust, adaptable, and capable AI systems.
Research Focus
Key Achievements
Top Papers
- 1Executable Code Actions Elicit Better LLM Agents9 citations · 2024