activity
20242026
collaborators

7 papers

cs.CR2026

RMCW: A Deletion-Robust Watermark Based on Reed--Muller Codes for Language Models

Yi Wang, Baicheng Chen, Yu Wang +3

Large Language Model (LLM) watermarking provides a lightweight mechanism for identifying text generated by a specific model, but its robustness remains fragile under post-processin…

cs.AI2026

Step-Level Preference Learning for Generative Agents in Social Simulations

Wenchang Gao, Pingyue Sheng, Lanlan Qiu +7

Large language model (LLM)-based generative agents simulate human behavior through long-horizon decision-making processes that comprise intermediate steps such as planning, memory…

cs.CR2026

CREBench: Evaluating Large Language Models in Cryptographic Binary Reverse Engineering

Baicheng Chen, Yu Wang, Ziheng Zhou +4

Reverse engineering (RE) is central to software security, particularly for cryptographic programs that handle sensitive data and are highly prone to vulnerabilities. It supports cr…

cs.CR2026

VirtualCrime: Evaluating the Criminal Potential and Agentic Behaviors of Large Language Models via Sandbox Simulation

Yilin Tang, Yu Wang, Lanlan Qiu +4

Large language models (LLMs) have shown strong capabilities in multi-step decision-making, and are increasingly integrated into real-world agentic applications. While existing safe…

cs.LG2025

FusionGen: Feature Fusion-Based Few-Shot EEG Data Generation

Yuheng Chen, Dingkun Liu, Xinyao Yang +3

Brain-computer interfaces (BCIs) provide potential for applications ranging from medical rehabilitation to cognitive state assessment by establishing direct communication pathways…

cs.LG2025

The 1st International Workshop on Disentangled Representation Learning for Controllable Generation (DRL4Real): Methods and Results

Qiuyu Chen, Xin Jin, Yue Song +45

This paper reviews the 1st International Workshop on Disentangled Representation Learning for Controllable Generation (DRL4Real), held in conjunction with ICCV 2025. The workshop a…

cs.CR2024

VGMShield: Mitigating Misuse of Video Generative Models

Yan Pang, Baicheng Chen, Yang Zhang +1

With the rapid advancement in video generation, people can conveniently use video generation models to create videos tailored to their specific desires. As a result, there are also…