collaborators

20 papers

cs.LG2026

GDformer: Going Beyond Subsequence Isolation for Multivariate Time Series Anomaly Detection

Qingxiang Liu, Xiaoliang Luo, Chenghao Liu +5

Unsupervised anomaly detection of multivariate time series is a challenging task, given the requirements of deriving a compact detection criterion without accessing the anomaly poi…

cs.CL2026

ZoFia: Zero-Shot Fake News Detection with Entity-Guided Retrieval and Multi-LLM Interaction

Lvhua Wu, Xuefeng Jiang, Sheng Sun +4

The rapid spread of fake news threatens social stability and public trust, highlighting the urgent need for its effective detection. Although large language models (LLMs) show pote…

cs.LG2026

Discrete Prototypical Memories for Federated Time Series Foundation Models

Liwei Deng, Qingxiang Liu, Xinhe Niu +5

Leveraging Large Language Models (LLMs) as federated learning (FL)-based time series foundation models offers a promising way to transfer the generalization capabilities of LLMs to…

cs.CL2026

from Benign import Toxic: Jailbreaking the Language Model via Adversarial Metaphors

Yu Yan, Sheng Sun, Zenghao Duan +5

Current studies have exposed the risk of Large Language Models (LLMs) generating harmful content by jailbreak attacks. However, they overlook that the direct generation of harmful…

cs.CR2026

Red-teaming the Multimodal Reasoning: Jailbreaking Vision-Language Models via Cross-modal Entanglement Attacks

Yu Yan, Sheng Sun, Shengjia Cheng +3

Vision-Language Models (VLMs) with multimodal reasoning capabilities are high-value attack targets, given their potential for handling complex multimodal harmful tasks. Mainstream…

cs.CL2026

SearchAttack: Red-Teaming LLMs against Knowledge-to-Action Threats under Online Web Search

Yu Yan, Sheng Sun, Mingfeng Li +6

Recently, people have suffered from LLM hallucination and have become increasingly aware of the reliability gap of LLMs in open and knowledge-intensive tasks. As a result, they hav…