activity
20242026
most citedReinforcement Learning Enhanced LLMs: A Survey

7 citations · 7 across the 4 of their papers we have counts for

collaborators

15 papers

cs.CR2026

DECEIVE-AFC: Adversarial Claim Attacks against Search-Enabled LLM-based Fact-Checking Systems

Haoran Ou, Kangjie Chen, Gelei Deng +4

Fact-checking systems with search-enabled large language models (LLMs) have shown strong potential for verifying claims by dynamically retrieving external evidence. However, the ro…

cs.CV2026

Unifying Watermarking via Dimension-Aware Mapping

Jiale Meng, Runyi Hu, Jie Zhang +3

Deep watermarking methods often share similar encoder-decoder architectures, yet differ substantially in their functional behaviors. We propose DiM, a new multi-dimensional waterma…

cs.CL2026

Inference-time Alignment via Sparse Junction Steering

Runyi Hu, Jie Zhang, Shiqian Zhao +7

Token-level steering has emerged as a pivotal approach for inference-time alignment, enabling fine grained control over large language models by modulating their output distributio…

cs.CR2025

When Search Goes Wrong: Red-Teaming Web-Augmented Large Language Models

Haoran Ou, Kangjie Chen, Xingshuo Han +4

Large Language Models (LLMs) have been augmented with web search to overcome the limitations of the static knowledge boundary by accessing up-to-date information from the open Inte…

cs.CR2025

Towards Effective Prompt Stealing Attack against Text-to-Image Diffusion Models

Shiqian Zhao, Chong Wang, Yiming Li +7

Text-to-Image (T2I) models, represented by DALLE and Midjourney, have gained huge popularity for creating realistic images. The quality of these images relies on the careful…

cs.AI2025

Beyond Retrieval: Improving Evidence Quality for LLM-based Multimodal Fact-Checking

Haoran Ou, Gelei Deng, Xingshuo Han +4

The increasing multimodal disinformation, where deceptive claims are reinforced through coordinated text and visual content, poses significant challenges to automated fact-checking…