Flash Point: Detecting Answer Commitment in Reasoning Models

Reasoning LLMs demonstrate a great deal of power through test-time scaling of the length of their generation. However, they often continue reasoning long after they have already effectively committed to an answer. We show that such over-reasoning is not only computationally wasteful but can also reduce accuracy. Across model families ranging from 3B to 20B parameters, we find that commitment to a final answer trajectory is marked by correlated stabilization of attention entropy and feed-forward activations across layers. Building on this observation, we introduce the Flash Point Probe, a lightweight causal classifier that detects this commitment point and enables dynamic early stopping. Across scientific reasoning and knowledge-retrieval benchmarks, the probe matches or exceeds full-length reasoning accuracy in most settings. Early stopping improves scientific reasoning accuracy by an average of $+18$ percentage points across three model families, while reducing reasoning tokens by up to 93% on knowledge-retrieval tasks. Overall, Flash Point reduces token usage by 28% on in-distribution tasks while preserving or improving accuracy, enabling more efficient adaptive reasoning.