JUMP: Single-Pass Membership Inference on Fine-Tuned Diffusion Language Models

arXiv:2511.14763v3 Announce Type: replace-cross Abstract: Membership Inference Attack (MIA) aims to determine whether a specific data sample was included in the training dataset

arXiv:2604.19561v1 Announce Type: new Abstract: Large Language Models (LLMs) utilize large amounts of data for their training, some of which may come from copyrighted sources.

arXiv:2604.08557v1 Announce Type: cross Abstract: Diffusion-based language models (dLLMs) generate text by iteratively denoising masked token sequences. We show that their safety

arXiv:2610.24643v1 Announce Type: new Abstract: A widely repeated account of membership inference holds that models lean on fine-grained detail for samples they have memorized, so

arXiv:2608.23807v1 Announce Type: new Abstract: Masked diffusion language models (dLLMs) can in principle generate text faster than autoregressive (AR) models, since they denoise

arXiv:2604.13206v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly integrated into agentic workflows, their unpredictability stemming from numerical