Does the Model Know Which Steps Matter? Probing Causal Importance in Chain-of-Thought Reasoning

Chain-of-thought (CoT) reasoning drives modern large language models but carries substantial computational cost, motivating methods that identify which reasoning steps are truly load-bearing. Recent work quantifies this through resampling-based importance metrics, but computing them requires hundreds of rollouts per prompt, making the tool meant to streamline CoT itself too expensive to deploy at scale. We address this bottleneck by providing evidence that sentence-level importance can be predicted from a model's internal activations: lightweight probes trained on hidden representations can identify important reasoning steps with reliable accuracy. Not only does this reduce inference cost by many orders of magnitude, but it also carries a deeper implication: the decodability of importance from internal activations suggests that these metrics reflect genuine properties of the model's computation rather than artifacts of the measurement procedure, opening the door to CoT interpretability at the scale that resampling cannot reach, including real-time monitoring, adaptive compute allocation, and large-scale mechanistic analysis.