A new approach to compute allocation in reasoning models focuses on the consequences of errors rather than just their difficulty, potentially improving accuracy in high-stakes tasks. This consequence-aware method has shown to reduce cost-weighted loss by up to 33% compared to traditional difficulty-based routing, indicating a significant advancement in task prioritization for AI applications.