activity
20232025
most citedA Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation

17 citations · 22 across the 10 of their papers we have counts for

collaborators

10 papers

cs.CV2025

Activation Steering Meets Preference Optimization: Defense Against Jailbreaks in Vision Language Models

Sihao Wu, Gaojie Jin, Wei Huang +2

Vision Language Models (VLMs) have demonstrated impressive capabilities in integrating visual and textual information for understanding and reasoning, but remain highly vulnerable…

cs.CV2025★ 1 cited

Preference Alignment on Diffusion Model: A Comprehensive Survey for Image Generation and Editing

Sihao Wu, Xiaonan Si, Chi Xing +5

The integration of preference alignment with diffusion models (DMs) has emerged as a transformative approach to enhance image generation and editing capabilities. Although integrat…

cs.LG2025

Enhancing Robust Fairness via Confusional Spectral Regularization

Gaojie Jin, Sihao Wu, Jiaxu Liu +2

Recent research has highlighted a critical issue known as ``robust fairness", where robust accuracy varies significantly across different classes, undermining the reliability of de…

cs.CV2024★ 1 cited

Integrating Object Detection Modality into Visual Language Model for Enhanced Autonomous Driving Agent

Linfeng He, Yiming Sun, Sihao Wu +2

In this paper, we propose a novel framework for enhancing visual comprehension in autonomous driving systems by integrating visual language models (VLMs) with additional visual per…

cs.RO2024

Robust RL with LLM-Driven Data Synthesis and Policy Adaptation for Autonomous Driving

Sihao Wu, Jiaxu Liu, Xiangyu Yin +5

The integration of Large Language Models (LLMs) into autonomous driving systems demonstrates strong common sense and reasoning abilities, effectively addressing the pitfalls of pur…

cs.LG2024★ 1 cited

Data Augmentation for Continual RL via Adversarial Gradient Episodic Memory

Sihao Wu, Xingyu Zhao, Xiaowei Huang

Data efficiency of learning, which plays a key role in the Reinforcement Learning (RL) training process, becomes even more important in continual RL with sequential environments. I…

cs.CL2024

Tiny Refinements Elicit Resilience: Toward Efficient Prefix-Model Against LLM Red-Teaming

Jiaxu Liu, Xiangyu Yin, Sihao Wu +4

With the proliferation of red-teaming strategies for Large Language Models (LLMs), the deficiency in the literature about improving the safety and robustness of LLM defense strateg…

cs.LG2024

Continuous Geometry-Aware Graph Diffusion via Hyperbolic Neural PDE

Jiaxu Liu, Xinping Yi, Sihao Wu +4

While Hyperbolic Graph Neural Network (HGNN) has recently emerged as a powerful tool dealing with hierarchical graph data, the limitations of scalability and efficiency hinder itse…

cs.LG2023★ 2 cited

ReRoGCRL: Representation-based Robustness in Goal-Conditioned Reinforcement Learning

Xiangyu Yin, Sihao Wu, Jiaxu Liu +4

While Goal-Conditioned Reinforcement Learning (GCRL) has gained attention, its algorithmic robustness against adversarial perturbations remains unexplored. The attacks and robust r…

cs.AI2023★ 17 cited

A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation

Xiaowei Huang, Wenjie Ruan, Wei Huang +14

Large Language Models (LLMs) have exploded a new heatwave of AI for their ability to engage end-users in human-level conversations with detailed and articulate answers across many…