arXiv:2608. 05642v1 Announce Type: new Abstract: This paper proposes the Bayesian Expected Uncertainty Reduction (B-EUR) model, which formalizes the value of trying a candidate design action as its expected reduction of epistemic uncertainty about action--outcome relations.
By Shimon Honda, Takuma Miyaguchi, Koji Koizumi, Takanori Sano, Tristan Briard, Hideyoshi Yanagisawa
arXiv:2606. 19353v1 Announce Type: cross Abstract: In-Context Learning (ICL) allows LLMs to adapt to new tasks from a few demonstrations, but its reliability remains a concern: predictions are highly sensitive to both prompt design and the model's ability to understand the context, obscuring whether failures arise from data properties or model limitations.
By Jinseok Chung, Minkyoung Song, Hyunji Jung, Namhoon Lee
arXiv:2606. 19328v1 Announce Type: cross Abstract: Preference-based RL provides an approach to learning reward models from pairwise comparisons of behaviors, bypassing the need for explicit reward design.
By Mohamed Nabail, Leo Cheng, Jingmin Wang, Nicholas Rhinehart
arXiv:2607. 14817v1 Announce Type: cross Abstract: Current evaluation of epistemic uncertainty relies on tasks such as out-ofdistribution detection and active learning.
By Jakub Paplh\'am, Willem Waegeman, Eyke H\"ullermeier, Vojt\v{e}ch Franc
arXiv:2608. 16428v1 Announce Type: cross Abstract: Artificial intelligence systems often disclose uncertainty, yet they rarely make clear what response that uncertainty should trigger.
By Chisom Anyabolu, Akshat Dubey, Georges Hattab
arXiv:2607. 28894v1 Announce Type: new Abstract: Computational cognitive modeling seeks to infer latent cognitive mechanisms underlying observed behavior.
By Manisha Dubey, Rimvydas Rubavicius, N. Siddharth, Subramanian Ramamoorthy