Wired for Overconfidence: A Mechanistic Perspective on Inflated Verbalized Confidence in LLMs

• URL: http://arxiv.org/abs/2604.01457v1
• Date: Wed, 01 Apr 2026 23:06:58 GMT
• Title: Wired for Overconfidence: A Mechanistic Perspective on Inflated Verbalized Confidence in LLMs
• Abstract summary: We show that verbalized overconfidence in large language models is driven by identifiable internal circuits.<n>Our results suggest that verbalized overconfidence in LLMs is driven by identifiable internal circuits and can be mitigated through targeted intervention.
• Score: 31.99173240336646
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models are often not just wrong, but \emph{confidently wrong}: when they produce factually incorrect answers, they tend to verbalize overly high confidence rather than signal uncertainty. Such verbalized overconfidence can mislead users and weaken confidence scores as a reliable uncertainty signal, yet its internal mechanisms remain poorly understood. We present a circuit-level mechanistic analysis of this inflated verbalized confidence in LLMs, organized around three axes: capturing verbalized confidence as a differentiable internal signal, identifying the circuits that causally inflate it, and leveraging these insights for targeted inference-time recalibration. Across two instruction-tuned LLMs on three datasets, we find that a compact set of MLP blocks and attention heads, concentrated in middle-to-late layers, consistently writes the confidence-inflation signal at the final token position. We further show that targeted inference-time interventions on these circuits substantially improve calibration. Together, our results suggest that verbalized overconfidence in LLMs is driven by identifiable internal circuits and can be mitigated through targeted intervention.

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