2 citations · 3 across the 11 of their papers we have counts for
13 papers
Towards Evolving Context Parameterization for Large Language Models
Xiaobing Shi, Zherui Li, Yiming Jiang +2
Context parameterization enables large language models (LLMs) to internalize contexts into reusable model parameters, avoiding repeated processing across subsequent queries. Howeve…
In-Place Instruction Following in Diffusion Language Models
Zheng Nie, Zherui Li, Jiaming Zhang +3
Diffusion Large Language Models (dLLMs) generate text via bidirectional iterative denoising, naturally supporting user-specified constraints anchored at arbitrary output positions,…
Adversarial Attacks for Good: A Survey of Proactive Protection across the Visual Content Lifecycle
Jiaming Zhang, Boyang Chen, Zherui Li +14
Once visual content enters an AI pipeline, its owner often retains little technical control over how it is used. Legal and regulatory remedies can address misuse, but many technica…
Omni-Safety under Cross-Modality Conflict: Vulnerabilities, Dynamics Mechanisms and Efficient Alignment
Kun Wang, Zherui Li, Zhenhong Zhou +8
Omni-modal Large Language Models (OLLMs) greatly expand LLMs' multimodal capabilities but also introduce cross-modal safety risks. However, a systematic understanding of vulnerabil…
Lookahead-then-Verify: Reliable Constrained Decoding for Diffusion LLMs under Context-Free Grammars
Yitong Zhang, Yongmin Li, Yuetong Liu +4
Diffusion Large Language Models (dLLMs) have demonstrated promising generative capabilities and are increasingly used to produce formal languages defined by context-free grammars,…
LatentEvolve: Self-Evolving Test-Time Scaling in Latent Space
Guibin Zhang, Fanci Meng, Guancheng Wan +5
Test-time Scaling (TTS) has been demonstrated to significantly enhance the reasoning capabilities of Large Language Models (LLMs) during the inference phase without altering model…
DiffuGuard: How Intrinsic Safety is Lost and Found in Diffusion Large Language Models
Zherui Li, Zheng Nie, Zhenhong Zhou +7
The rapid advancement of Diffusion Large Language Models (dLLMs) introduces unprecedented vulnerabilities that are fundamentally distinct from Autoregressive LLMs, stemming from th…
Jailbreaking Large Language Diffusion Models: Revealing Hidden Safety Flaws in Diffusion-Based Text Generation
Yuanhe Zhang, Fangzhou Xie, Zhenhong Zhou +4
Large Language Diffusion Models (LLDMs) exhibit comparable performance to LLMs while offering distinct advantages in inference speed and mathematical reasoning tasks.The precise an…
Goal-Aware Identification and Rectification of Misinformation in Multi-Agent Systems
Zherui Li, Yan Mi, Zhenhong Zhou +4
Large Language Model-based Multi-Agent Systems (MASs) have demonstrated strong advantages in addressing complex real-world tasks. However, due to the introduction of additional att…
CORBA: Contagious Recursive Blocking Attacks on Multi-Agent Systems Based on Large Language Models
Zhenhong Zhou, Zherui Li, Jie Zhang +4
Large Language Model-based Multi-Agent Systems (LLM-MASs) have demonstrated remarkable real-world capabilities, effectively collaborating to complete complex tasks. While these sys…
Reinforced Lifelong Editing for Language Models
Zherui Li, Houcheng Jiang, Hao Chen +5
Large language models (LLMs) acquire information from pre-training corpora, but their stored knowledge can become inaccurate or outdated over time. Model editing addresses this cha…
Speak Out of Turn: Safety Vulnerability of Large Language Models in Multi-turn Dialogue
Zhenhong Zhou, Jiuyang Xiang, Haopeng Chen +3
Large Language Models (LLMs) have been demonstrated to generate illegal or unethical responses, particularly when subjected to "jailbreak." Research on jailbreak has highlighted th…
PoseAction: Action Recognition for Patients in the Ward using Deep Learning Approaches
Zherui Li, Raye Chen-Hua Yeow
Real-time intelligent detection and prediction of subjects' behavior particularly their movements or actions is critical in the ward. This approach offers the advantage of reducing…