Jiawei Chen
Papers
1
Total Citations
14
H-Index
1
About
Jiawei Chen is an emerging researcher at the intersection of large language models (LLMs), embodied intelligence, and AI security. His work focuses on understanding the vulnerabilities and robustness of decision-making systems that integrate LLMs with embodied agents — AI systems designed to perceive and interact with real-world environments. His most notable contribution, "Exploring the Robustness of Decision-Level Through Adversarial Attacks on LLM-Based Embodied Models" (2024), has already garnered 14 citations, a remarkable achievement for such a recently published work. This paper investigates how adversarial attacks can compromise the decision-making pipelines of LLM-powered embodied agents, a critical concern as these systems are increasingly deployed in real-world applications. By probing the weaknesses of language-guided planning under adversarial conditions, Chen's research highlights essential safety considerations that the broader AI community must address before widespread deployment of such systems. His work bridges the gap between natural language processing and robotics safety, making meaningful contributions to the growing field of trustworthy AI. Chen's research is particularly timely, given the rapid proliferation of LLM-based autonomous systems across industries.
Research Focus
Key Achievements
Top Papers
- 1