collaborators

7 papers

cs.RO2026

Cross-Stage Sensorimotor Perception Scheduling and Sparse Map Encoding for Efficient Edge Embodied Navigation

Yaotian Liu, Sri Sai Rakesh Nakkilla, Xiangyu Zhou +2

Embodied agents must close a perception-to-action loop on embedded hardware under tight latency, memory, and energy budgets, making deployment a system-level co-design problem rath…

cs.CV2026

WalkGPT: Grounded Vision-Language Conversation with Depth-Aware Segmentation for Pedestrian Navigation

Rafi Ibn Sultan, Hui Zhu, Xiangyu Zhou +4

Ensuring accessible pedestrian navigation requires reasoning about both semantic and spatial aspects of complex urban scenes, a challenge that existing Large Vision-Language Models…

cs.LG2026

Attention Smoothing Is All You Need For Unlearning

Saleh Zare Zade, Xiangyu Zhou, Sijia Liu +1

Large Language Models are prone to memorizing sensitive, copyrighted, or hazardous content, posing significant privacy and legal concerns. Retraining from scratch is computationall…

cs.LG2025

Not All Tokens Are Meant to Be Forgotten

Xiangyu Zhou, Yao Qiang, Saleh Zare Zade +3

Large Language Models (LLMs), pre-trained on massive text corpora, exhibit remarkable human-level language understanding, reasoning, and decision-making abilities. However, they te…

cs.LG2025

Learning to Poison Large Language Models for Downstream Manipulation

Xiangyu Zhou, Yao Qiang, Saleh Zare Zade +4

The advent of Large Language Models (LLMs) has marked significant achievements in language processing and reasoning capabilities. Despite their advancements, LLMs face vulnerabilit…

cs.LG2025

Hijacking Large Language Models via Adversarial In-Context Learning

Xiangyu Zhou, Yao Qiang, Saleh Zare Zade +2

In-context learning (ICL) has emerged as a powerful paradigm leveraging LLMs for specific downstream tasks by utilizing labeled examples as demonstrations (demos) in the preconditi…