Affective Context Amplifies Sycophancy in LLM Responses

• URL: http://arxiv.org/abs/2608.21242v1
• Date: Fri, 21 Aug 2026 15:52:20 GMT
• Title: Affective Context Amplifies Sycophancy in LLM Responses
• Abstract summary: We study how affective context modulates LLM sycophancy in subjective, evaluative interactions.<n>We find that user-facing responses consistently soften or withhold negative or oppositional judgments.<n>These findings suggest that affective context functions as a vulnerability signal that suppresses critical feedback when users may need it most.
• Score: 8.381781773533099
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As conversational companions, large language models (LLMs) often have access to users' emotional states. We study how this affective context modulates LLM sycophancy in subjective, evaluative interactions, where users share actions or opinions that invite feedback. Drawing on ingratiation theory, we measure sycophancy as the divergence between a model's independent evaluation and its user-facing response, elicited by presenting the same content as either a third-party account or the user's own disclosure. Across seven LLMs and two Reddit datasets (r/AmItheAsshole and r/TrueUnpopularOpinion), we find that this divergence is systematic and strongly one-directional. User-facing responses consistently soften or withhold negative or oppositional judgments. Affective context further amplifies this divergence with negative states, particularly loneliness and distress, producing the largest effects. These findings suggest that affective context functions as a vulnerability signal that suppresses critical feedback when users may need it most, often through evasive sycophancy, in which models retreat toward non-committal responses rather than outright agreement.

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