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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.
Visa’s $3.5B Bet on AI
The promise and problem of Chain-of-Thought
