Beacon: Single-Turn Diagnosis and Mitigation of Latent Sycophancy in
  Large Language Models
Authors: name: Sanskar Pandey, name: Ruhaan Chopra, name: Angkul Puniya, name: Sohom Pal

Large language models internalize a structural trade-off between truthfulness
and obsequious flattery, emerging from reward optimization that conflates
helpfulness with polite submission. This latent bias, known as sycophancy,
manifests as a preference for user agreement over principled reasoning. We
introduce Beacon, a single-turn forced-choice benchmark that isolates this bias
independent of conversational context, enabling precise measurement of the
tension between factual accuracy and submissive bias. Evaluations across twelve
state-of-the-art models reveal that sycophancy decomposes into stable
linguistic and affective sub-biases, each scaling with model capacity. We
further propose prompt-level and activation-level interventions that modulate
these biases in opposing directions, exposing the internal geometry of
alignment as a dynamic manifold between truthfulness and socially compliant
judgment. Beacon reframes sycophancy as a measurable form of normative
misgeneralization, providing a reproducible foundation for studying and
mitigating alignment drift in large-scale generative systems.