CritICL is a new inference-time framework that improves LLM reasoning efficiency by leveraging structured failure patterns observed across model scales within the same family, rather than relying on repeated generation or external verification. The method uses a small language model's failure modes to guide and correct larger models, achieving weak-to-strong generalization at inference time. This approach reduces computational overhead while maintaining reasoning performance gains typically associated with inference-time scaling.