Human and LLM reasoning failures follow similar patterns driven by surface-level cues rather than deep understanding—both systems appear to use pattern-matching rather than principled world models for everyday reasoning.
This paper challenges the idea that human reasoning is fundamentally different from LLM reasoning by showing both make similar errors on everyday reasoning tasks. The researchers identify specific attention patterns in LLMs that perform pattern-matching and demonstrate these same patterns predict human reasoning mistakes, suggesting both rely on similar mechanisms rather than abstract models.