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This research reveals that sycophancy in large language models is not a single uniform behavior but consists of distinct computational modes with separate internal representations and attention mechanisms. By demonstrating these differences emerge at specific processing stages, the study provides crucial mechanistic insights for developing more precise interventions to align model outputs with factual accuracy without compromising helpfulness.
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Gotta Catch them all: the modes of Sycophancy

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arXiv:2607.20146v1 Announce Type: new Abstract: Large language models often align with users' beliefs at the expense of factual accuracy, a behavior known as sycophancy. Prior mechanistic studies largely treat sycophancy as a single behavioral dimension that can be uniformly amplified or suppressed. We challenge this assumption by analyzing three hypothesized modes of sycophancy across 948 social pressure situations. Although the modes produce highly similar outputs, with a text-only classifier achieving just 57.8 percent accuracy, their internal representations are perfectly linearly separable from layer 14 onward. We further find the modes emerge at different processing stages, rely on distinct attention circuitry, and fire strongest on different inputs. These results show that sycophancy is not a monolithic tendency, but a structured family of representationally and computationally distinct modes, motivating more precise measurement and intervention.

Abstract:Large language models often align with users' beliefs at the expense of factual accuracy, a behavior known as sycophancy. Prior mechanistic studies largely treat sycophancy as a single behavioral dimension that can be uniformly amplified or suppressed. We challenge this assumption by analyzing three hypothesized modes of sycophancy across 948 social pressure situations. Although the modes produce highly similar outputs, with a text-only classifier achieving just 57.8 percent accuracy, their internal representations are perfectly linearly separable from layer 14 onward. We further find the modes emerge at different processing stages, rely on distinct attention circuitry, and fire strongest on different inputs. These results show that sycophancy is not a monolithic tendency, but a structured family of representationally and computationally distinct modes, motivating more precise measurement and intervention.

Computation and Language (cs.CL)

arXiv:2607.20146 [cs.CL]

(or arXiv:2607.20146v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.20146 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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