3 papers
cs.AI2026
A Theoretical Analysis of Why Masked Diffusion Models Mitigate the Reversal Curse
Moongyu Jeon, Sangwoo Shin, BumJun Kim +2
Autoregressive language models (ARMs) suffer from the reversal curse: after learning '' is ,'' they often fail on the reverse query '' is .'' Masked diffusion language…
cs.CL2026
A2D: Any-Order, Any-Step Safety Alignment for Diffusion Language Models
Wonje Jeung, Sangyeon Yoon, Yoonjun Cho +4
Diffusion large language models (dLLMs) enable any-order generation, but this flexibility enlarges the attack surface: harmful spans may appear at arbitrary positions, and template…
cs.LG2025
Information-Theoretic Discrete Diffusion
Moongyu Jeon, Sangwoo Shin, Dongjae Jeon +1
We present an information-theoretic framework for discrete diffusion models that yields principled estimators of log-likelihood using score-matching losses. Inspired by the I-MMSE…