Confidence is Not Competence

Debdeep Sanyal, Manya Pandey, Dhruv Kumar, Saurabh Deshpande, Murari Mandal

Keywords: Large Language Models, Mechanistic Interpretability, Representation Engineering, Reasoning, Metacognition

TL;DR: We show that large language models separate confidence from competence through a “two-brain” architecture.

Abstract: Large language models (LLMs) often exhibit a puzzling disconnect between their asserted confidence and actual problem-solving competence. We offer a mechanistic account of this decoupling by analyzing the geometry of internal states across two phases: pre-generative assessment and solution execution. A simple linear probe decodes the internal \enquote{solvability belief} of a model, revealing a well-ordered belief axis that generalizes across model families and across math, code, planning, and logic tasks. Yet, the geometries diverge; although belief is linearly decodable, the assessment manifold has high linear effective dimensionality as measured from the principal components, while the subsequent reasoning trace evolves on a much lower-dimensional manifold. This sharp reduction in geometric complexity from thought to action mechanistically explains the confidence–competence gap. Causal interventions that steer representations along the belief axis leave final solutions unchanged, indicating that linear nudges in the complex assessment space do not control the constrained dynamics of execution. We thus uncover a two-system architecture: a geometrically complex assessor feeding a geometrically simple executor. These results challenge the assumption that decodable beliefs are actionable levers, instead arguing for interventions that target the procedural dynamics of execution rather than the high-level geometry of assessment.

Primary Area: interpretability and explainable AI

Submission Number: 14272