Christian A. Naesseth

Nested sequential Monte Carlo methods can dramatically improve inference-time control in text generation, outperforming traditional techniques in steering towards desired outcomes.

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.