Beacon: Single-Turn Diagnosis and Mitigation of Latent Sycophancy in
  Large Language Models
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.
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Sanskar Pandey
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2025-10-21T14:00:36.471Z
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pandey
user
sanskxr02
68f769b29e6a0ffab2a9c3af
Ruhaan Chopra
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Angkul Puniya
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Sohom Pal
Beacon: Single-Turn Diagnosis and Mitigation of Latent Sycophancy in
  Large Language Models
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.
Beacon, a benchmark, measures sycophancy in large language models, revealing it as a combination of linguistic and affective biases that can be mitigated through interventions.
large language models reward optimization sycophancy forced-choice benchmark factual accuracy submissive bias prompt-level interventions activation-level interventions alignment drift normative misgeneralization