How Transparent is DiffusionGemma?

As AI systems take on more consequential roles, understanding how they reason has become as important as what they produce. Diffusion-based language models like DiffusionGemma represent a departure from traditional autoregressive generation, performing much of their computation in a continuous latent space rather than producing tokens step by step. This raises a pressing question: does that shift come at the cost of interpretability? This paper tackles that question systematically, developing tools to peer inside DiffusionGemma's reasoning process. Applications range from AI safety auditing and model debugging to regulatory compliance, where stakeholders need assurance that model behavior can be monitored and explained.