arXiv:2603.13356v1 Announce Type: new
Abstract: Standard approaches to Robust Reinforcement Learning assume that feedback sources are either globally trustworthy or globally adversarial. In this paper, we challenge this assumption and we identify a more subtle failure mode. We term this mode as Contextual Sycophancy, where evaluators are truthful in benign contexts but strategically biased in critical ones. We prove that standard robust methods fail in this setting, suffering from Contextual Objective Decoupling. To address this, we propose CESA-LinUCB, which learns a high-dimensional Trust Boundary for each evaluator. We prove that CESA-LinUCB achieves sublinear regret $tildeO(sqrtT)$ against contextual adversaries, recovering the ground truth even when no evaluator is globally reliable.
Dissociable contributions of cortical thickness and surface area to cognitive ageing: evidence from multiple longitudinal cohorts.
Cortical volume, a widely-used marker of brain ageing, is the product of two genetically and developmentally dissociable morphometric features: thickness and area. However, it remains