This August 2025 paper from Arizona State University's Data Mining and Machine Learning Lab investigates whether Chain-of-Thought (CoT) reasoning in Large Language Models (LLMs) represents genuine inference or merely superficial pattern matching. The authors hypothesize that CoT effectiveness is bounded by the training data's distribution, proposing that LLMs generate reasoning paths by approximating patterns seen during training. To test this, they developed DataAlchemy, a controlled environment for training LLMs from scratch, allowing for systematic probing across task, length, and format generalization. Their findings suggest that CoT reasoning is "a brittle mirage", performing well only within or near training data distributions and failing significantly when pushed beyond them. This implies CoT is a sophisticated form of structured pattern matching rather than a true understanding of logical inference.
Source: https://arxiv.org/pdf/2508.01191
