4 citations · 10 across the 21 of their papers we have counts for
7 papers · 1 filter
EvolvAttack: Backdooring self Evolving VLM Agents
Chuan Zou, Yang Xiang, Zexin Li +2
Self-evolving mechanisms let vision-language model (VLM) agents continually adapt through evolving memory, but they also allow malicious behavior to emerge only after deployment. W…
Boosting Transferable Adversarial Attacks against Deep Reinforcement Learning
Zexin Li, Ruili Yao, Yiming Zeng +1
Most adversarial attacks on deep reinforcement learning (DRL) assume white-box access to the victim policy, which rarely holds in practice. This paper studies transfer-based black-…
TetraRL: A Self-Adaptive Runtime for On-Device Deep Reinforcement Learning Systems
Zexin Li, Soheil Shirvani, Cong Liu
Autonomous robotic systems, including autonomous vehicles, drones, and mobile robots, increasingly rely on on-device Deep Reinforcement Learning (DRL) to adapt to dynamic environme…
Energy-Efficient Multimodal Inference Serving with Tri-serve
Ziyang Jia, Sara Rashidi Golrouye, Laxmi Bhuyan +5
Multimodal model inference creates substantial energy demand with growing performance requirements. Within GPUs, power is autonomously managed by an on-board power management unit…
Orion: Enabling Self-adaptive Memory Management for On-device Online Continual Learning
Zexin Li, Nikil Dutt, Cong Liu
Online continual learning (OCL) enables real-time adaptation to new data, making it crucial for dynamic robotic applications. However, its practical deployment is hindered by memor…
RED: Adaptive Real-Time DAG Scheduling for Robotic Inference under Environmental Dynamics
Zexin Li, Tao Ren, Johnathan Liu +2
Robots deployed in dynamic environments must contend with environment-driven changes that reshape computation at runtime: new tasks may appear, precedence relations can shift, and…
PIMbot: A Self-Adaptive Attack Framework for Adversarial Manipulation of Multi-Robot Reinforcement Learning
Zexin Li, Ziliang Zhang, Hyoseung Kim +1
Recent research has demonstrated the potential of reinforcement learning in effective multi-robot collaboration, particularly in social dilemmas where robots face a trade-off betwe…