activity
20242026
collaborators

7 papers

cs.IR2026

Think When Needed: Model-Aware Reasoning Routing for LLM-based Ranking

Huizhong Guo, Tianjun Wei, Dongxia Wang +4

Large language models (LLMs) are increasingly applied to ranking tasks in retrieval and recommendation. Although reasoning prompting can enhance ranking utility, our preliminary ex…

cs.MA2025

MAS: Self-Generative, Self-Configuring, Self-Rectifying Multi-Agent Systems

Kun Wang, Guibin Zhang, ManKit Ye +6

The past two years have witnessed the meteoric rise of Large Language Model (LLM)-powered multi-agent systems (MAS), which harness collective intelligence and exhibit a remarkable…

cs.CR2025

RAS-Eval: A Comprehensive Benchmark for Security Evaluation of LLM Agents in Real-World Environments

Yuchuan Fu, Xiaohan Yuan, Dongxia Wang

The rapid deployment of Large language model (LLM) agents in critical domains like healthcare and finance necessitates robust security frameworks. To address the absence of standar…

cs.AI2025

Fair-PP: A Synthetic Dataset for Aligning LLM with Personalized Preferences of Social Equity

Qi Zhou, Jie Zhang, Dongxia Wang +5

Human preference plays a crucial role in the refinement of large language models (LLMs). However, collecting human preference feedback is costly and most existing datasets neglect…

cs.CR2025

A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment

Kun Wang, Guibin Zhang, Zhenhong Zhou +100

The remarkable success of Large Language Models (LLMs) has illuminated a promising pathway toward achieving Artificial General Intelligence for both academic and industrial communi…

cs.CV2025

TruePose: Human-Parsing-guided Attention Diffusion for Full-ID Preserving Pose Transfer

Zhihong Xu, Dongxia Wang, Peng Du +2

Pose-Guided Person Image Synthesis (PGPIS) generates images that maintain a subject's identity from a source image while adopting a specified target pose (e.g., skeleton). While di…

cs.CV2024

Defending LVLMs Against Vision Attacks through Partial-Perception Supervision

Qi Zhou, Tianlin Li, Qing Guo +4

Recent studies have raised significant concerns regarding the vulnerability of Large Vision Language Models (LVLMs) to maliciously injected or perturbed input images, which can mis…