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This paper proposes a quantile Bayesian risk-aware MDP framework for online RL that adaptively balances robustness and exploration over time, providing theoretical regret bounds and demonstrating strong empirical performance.
This paper investigates Neutrosophic Logic as a framework for modeling epistemic states in Large Language Models, demonstrating that it can capture 'hyper-truth' states beyond traditional probability constraints, leading to more transparent and ethically aware AI systems.
This paper develops a PAC-Bayesian framework for test-time adaptation that uses MMD-balls as credal sets, providing formal generalization bounds and separating epistemic from aleatoric uncertainty under distribution shift.
This paper introduces GOEN, a pipeline combining multi-scale features, L2 normalization, and Mahalanobis distance for OOD detection, and finds that CenterLoss regularization actually degrades OOD performance despite improving classification accuracy.
MIT researchers developed a new method for identifying overconfident LLMs by measuring cross-model disagreement across similar models, rather than relying solely on self-consistency metrics. This approach better captures epistemic uncertainty and more accurately identifies unreliable predictions in high-stakes applications.