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A new study from Arizona State University researchers suggests that the celebrated “Chain-of-Thought” (CoT) reasoning in Large Language Models (LLMs) may be more of a “brittle mirage” than genuine intelligence. The research builds on a growing body of work questioning the depth of LLM reasoning, but it takes a unique “data distribution” lens to test where and why CoT breaks down systematically.
Crucially for application builders, the paper goes beyond critique to offer clear, practical guidance on how to account for these limitations when developing LLM-powered applications, from testing strategies to the role of fine-tuning.
The promise and problem of Chain-of-Thought
CoT prompting, which asks an LLM to “think step by step,” has shown impressive results on complex tasks, leading to the perception that models are engaging in human-like inferential processes. However, a closer inspection often reveals logical inconsistencies that challenge this view.
Various studies show that LLMs frequently rely on surface-level semantics and clues rather than logical procedures. The models generate plausible-sounding logic by repeating token patterns they have seen during training. Still, this approach often fails on tasks that deviate from familiar templates or when irrelevant information is introduced.
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Despite these observations, the researchers of the new study argue that “a systematic understanding of why and when CoT reasoning fails is still a mystery,” which their study aims to address. Previous work has already shown that LLMs struggle to generalize their reasoning abilities. As the paper notes, “theoretical and empirical evidence shows that CoT generalizes well only when test inputs share latent structures with training data; otherwise, performance declines sharply.”
A new lens on LLM reasoning
The ASU researchers propose a new lens to view this problem: CoT isn’t an act of reasoning but a sophisticated form of pattern matching, fundamentally bound by the statistical patterns in its training data. They posit that “CoT’s success stems not from a model’s inherent r …