collaborators

14 papers

cs.CR2026

Measuring the Security of Mobile LLM Agents under Adversarial Prompts from Untrusted Third-Party Channels

Chenghao Du, Quanfeng Huang, Tingxuan Tang +3

Large Language Models (LLMs) have transformed software development, enabling AI-powered applications known as LLM-based agents that promise to automate tasks across diverse apps an…

cs.CV2026

Hierarchical Concept-to-Appearance Guidance for Multi-Subject Image Generation

Yijia Xu, Zihao Wang, Haokun Gui +1

Multi-subject image generation aims to synthesize images that faithfully preserve the identities of multiple reference subjects while following textual instructions. However, exist…

cs.SE2026

Knowledge-Graph-Driven Data Synthesis for Low-Resource Software Development: A HarmonyOS Case Study

Mingwei Liu, Zheng Pei, Yanlin Wang +5

In low-resource framework development (e.g., HarmonyOS), large language models (LLMs) often lack sufficient pre-training exposure, resulting in poor code generation performance. Al…

cs.CL2026

DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence

DeepSeek-AI, Anyi Xu, Bangcai Lin +315

We present a preview version of DeepSeek-V4 series, including two strong Mixture-of-Experts (MoE) language models -- DeepSeek-V4-Pro with 1.6T parameters (49B activated) and DeepSe…

cs.CR2026

Activation-Guided Local Editing for Jailbreaking Attacks

Jiecong Wang, Haoran Li, Hao Peng +4

Jailbreaking is an essential adversarial technique for red-teaming these models to uncover and patch security flaws. However, existing jailbreak methods face significant drawbacks.…

cs.MM2026

A User-Friendly Framework for Generating Model-Preferred Prompts in Text-to-Image Synthesis

Nailei Hei, Qianyu Guo, Zihao Wang +3

Well-designed prompts have demonstrated the potential to guide text-to-image models in generating amazing images. Although existing prompt engineering methods can provide high-leve…