Christian A. Naesseth

Uncertainty scores derived from molecular diffusion models can effectively predict sample quality, enabling targeted filtering that boosts model performance.

Confidence-based remasking in dLLMs may not deliver the expected improvements and can actually worsen diversity issues in certain decoding settings.