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We identify a more subtle failure mode that escapes this dichotomy, which we call \\emph{Contextual Sycophancy}. In this failure, evaluators are truthful in benign contexts but systematically biased in critical ones, so that no single evaluator is reliable everywhere and the corrupt evaluators may form a \\emph{majority} in the contexts that matter. Our first result is an information-theoretic lower bound. We exhibit two problem instances that induce \\emph{identical} social-feedback distributions yet have disjoint optimal actions, proving that \\emph{any} algorithm relying on social feedback alone (including any robust aggregator, regardless of breakdown point) incurs $\\Omega(T)$ latent regret. This shows that breaking contextual sycophancy is impossible without having some information. We then","title":"Learning When to Trust in Contextual Social Bandits","url":"https://arxiv.org/abs/2603.13356","vendor":"arxiv_cs_ai"},"summary":"arXiv:2603.13356v2 Announce Type: replace \nAbstract: Robust reinforcement learning typically assumes that feedback sources are either globally trustworthy or corrupted within a fixed global budget. We identify a more subtle failure mode that escapes this dichotomy, which we call \\emph{Contextual Sycophancy}. In this failure, evaluators are truthful in benign contexts but systematically biased in critical ones, so that no single evaluator is reliable everywhere and the corrupt evaluators may form a \\emph{majority} in the contexts that matter. Our first result is an information-theoretic lower bound. We exhibit two problem instances that induce \\emph{identical} social-feedback distributions yet have disjoint optimal actions, proving that \\emph{any} algorithm relying on social feedback alone (including any robust aggregator, regardless of breakdown point) incurs $\\Omega(T)$ latent regret. This shows that breaking contextual sycophancy is impossible without having some information. 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