3 papers
cs.AI2026
How Uncertainty Estimation Scales with Sampling in Reasoning Models
Maksym Del, Markus Kängsepp, Marharyta Domnich +4
Uncertainty estimation is critical for deploying reasoning language models, yet remains poorly understood under extended chain-of-thought reasoning. We study parallel sampling as a…
cs.LG2025
Aligning the Evaluation of Probabilistic Predictions with Downstream Value
Novin Shahroudi, Viacheslav Komisarenko, Meelis Kull
Every prediction is ultimately used in a downstream task. Consequently, evaluating prediction quality is more meaningful when considered in the context of its downstream use. Metri…
cs.LG2025
Probability Density from Latent Diffusion Models for Out-of-Distribution Detection
Joonas Järve, Karl Kaspar Haavel, Meelis Kull
Despite rapid advances in AI, safety remains the main bottleneck to deploying machine-learning systems. A critical safety component is out-of-distribution detection: given an input…