AI may be able to reason without putting every step into words.
Many of today’s AI systems tackle difficult problems by generating intermediate steps in lengthy, chains of words before arriving at an answer— a technique often called chain-of-thought reasoning. That approach can improve performance on some tasks, but it also means an AI model may generate many words, or tokens, on the way to a relatively short answer. Each token takes computing power to produce, so longer reasoning can make answers slower and more expensive to generate.
A small, experimental AI system called BDH-CQ takes a different approach. It can solve some reasoning puzzles without spelling out its intermediate thinking, researchers report in a paper submitted August 10 to arXiv.org. The work asks whether AI needs language at every step of reasoning, and whether doing more internally could make some reasoning cheaper.
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