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With the launch of o3-pro, let’s talk about what AI “reasoning” actually does
New studies reveal pattern-matching reality behind the AI industry’s reasoning claims.
Recent studies on Math Olympiad problems reveal that SR models still function as sophisticated pattern-matching machines—they cannot catch their own mistakes or adjust failing approaches, often producing confidently incorrect solutions without any "awareness" of errors. But as we consider the industry's stated trajectory toward artificial general intelligence and even superintelligence, the evidence so far suggests that simply scaling up current approaches or adding more "thinking" tokens may not bridge the gap between statistical pattern recognition and what might be called generalist algorithmic reasoning. Tool augmentation represents another useful direction already used by o3-pro and other ChatGPT models—by connecting LLMs to calculators, symbolic math engines, or formal verification systems, researchers can compensate for some of the models' computational weaknesses.
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