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cs.LG2026
Theoretical and Practical Advances in Hierarchical Bayesian Deep Learning Methodology
Neil K. Chada, Simon Cotter, Kody J. H. Law +1
Reliable uncertainty quantification remains an important challenge in AI. Hierarchical Bayesian methods provide a principled framework for this by representing unknown quantities t…
cs.LG2026
Information-Theoretic Foundations of Online Temperature Sampling
Jin Sima, Nikolaos Papagiannis, Ananth Grama +1
Repetitive, degenerate generation is a persistent failure of LLM decoding, and the standard remedies (temperature, nucleus, and top- sampling) are static heuristics. By Kac's le…