Emilian Radoi

Induction in Both Directions: A Mechanistic Analysis of In-Context Learning in Masked Diffusion Language Models

This work studies how diffusion language models implement induction, a mechanism behind in-context learning in which the model finds a repeated context and copies the token that followed it, and compares attention-only AR models and absorbing-mask DLMs with matched architectures.