activity
20212026
most citedLearning Fair Representations for Recommendation: A Graph-based Perspective

1 citations · 2 across the 15 of their papers we have counts for

collaborators

15 papers

cs.CL2026

Beyond Cross-Lingual Transfer: Benchmarking Propagation Boundaries in Multilingual LLM Unlearning

Pengyang Shao, Chuanpeng Lu, Wei Qin +5

Large Language Model (LLM) unlearning aims to suppress target knowledge while preserving general capabilities. In multilingual settings, unlearning must additionally propagate with…

cs.AI2026

Who Bridges Safety? Identifying and Targeting Cross-Lingual Shared Safety Pathways

Shuyi Miao, Wangjie Qiu, Pengyang Shao +4

Uncovering the internal mechanisms underlying the safety capabilities of large language models (LLMs) is crucial for developing trustworthy artificial intelligence. Currently, mech…

cs.CE2026

CoLAS: Multimodal Corroboration of Latent Asset Signals for Financial Trading

Yanzheng Jin, Pengyang Shao, Xiaohao Liu +3

Financial trading relies on extracting reliable signals from heterogeneous market modalities such as price series, breaking news, and investor sentiment. Existing multimodal method…

cs.LG2026

Sharpness-Aware Poisoning: Enhancing Transferability of Injective Attacks on Recommender Systems

Junsong Xie, Yonghui Yang, Pengyang Shao +1

Recommender Systems~(RS) have been shown to be vulnerable to injective attacks, where attackers inject limited fake user profiles to promote the exposure of target items to real us…

cs.SE2026

StressWeb: A Diagnostic Benchmark for Web Agent Robustness under Realistic Interaction Variability

Haoyue Bai, Dong Wang, Long Chen +5

Large language model-based web agents have demonstrated strong performance on realistic web interaction tasks. However, existing evaluations are predominantly conducted under relat…

cs.CV2026

MURE: Hierarchical Multi-Resolution Encoding via Vision-Language Models for Visual Document Retrieval

Fengbin Zhu, Zijing Cai, Yuzhe Wang +5

Visual Document Retrieval (VDR) requires representations that capture both fine-grained visual details and global document structure to ensure retrieval efficacy while maintaining…