activity
20242026
most citedMembership Inference on Text-to-Image Diffusion Models via Conditional Likelihood Discrepancy

2 citations · 2 across the 16 of their papers we have counts for

collaborators
Showing 2026Show all

6 papers · 1 filter

cs.CL2026

Membership Inference in Fine-tuned Diffusion Language Models via Token-level Memorization Asymmetry

Shengfang Zhai, Leo Marchyok, Yuling Shi +4

Diffusion language models (DLMs) have recently emerged as an alternative modeling paradigm to autoregressive LMs, offering advantages such as parallel generation and bidirectional…

cs.CV2026

UniNDM: A Unified Noise-driven Detection and Mitigation Framework Against Sexual Content in Text-to-Image Generation

Yao Huang, Yitong Sun, Huanran Chen +8

Despite the impressive generative capabilities of text-to-image diffusion models, they remain vulnerable to implicit sexual prompts, where subtle cues disguised as benign terms or…

cs.CL2026

Reinforcement Learning from Denoising Feedback

Qi He, Huan Chen, Ya Guo +3

Policy loss estimation remains a fundamental and long-standing challenge in reinforcement learning (RL) for diffusion language models (DLMs). We introduce Reinforcement Learning fr…

cs.LG2026

Nexus: Same Pretraining Loss, Better Downstream Generalization via Common Minima

Huanran Chen, Huaqing Zhang, Xiao Li +3

The foundational capabilities of large language models are acquired during pretraining on internet-scale, highly heterogeneous data mixtures. In this work, we investigate an intere…

cs.LG2026

Alignment Dynamics in LLM Fine-Tuning

Yuhan Huang, Huanran Chen, Yinpeng Dong

Although Large Language Models (LLMs) achieve strong alignment through supervised fine-tuning and reinforcement learning from human feedback, the alignment is often fragile under s…

cs.CR2026

AgentWorm: Self-Propagating Attacks Across LLM Agent Ecosystems

Yihao Zhang, Zeming Wei, Xiaokun Luan +7

Autonomous LLM-based agents increasingly operate as long-running processes forming densely interconnected multi-agent ecosystems, whose security properties remain largely unexplore…