CritICL improves reasoning while maintaining high efficiency by leveraging failure modes from weaker models as guidance through critique-based in-context examples

CritICL, a novel inference-time framework that improves reasoning while maintaining high efficiency. Our key insight is that LLM failure modes exhibit structured patterns across model scales within the same family. Instead of treating failures as undesirable outputs, CritICL leverages them as a source of guidance. Specifically, we utilize failure modes derived from weaker models and incorporate them into inference through critique-based in-context examples

http://arxiv.org/abs/2608.27455v1