Measuring Mechanistic Uncertainty in Large Language Models via Causal Robustness

Mechanistic uncertainty quantifies model reliability by measuring the stability of internal activations through the causal robustness margin. Using Jacobian Lens concept steering vectors, the method identifies that incorrect responses require smaller internal interventions to deviate, providing a metric that complements traditional predictive uncertainty across multiple open-source LLMs.