Can AI reason without words? A small model puts the idea to the test

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. In training, AI models are often shown a few examples before they try a new problem. Many keep those examples in front of them as they work.

But on reasoning tests, BDH-CQ (DH is short for Dragon Hatchling) instead uses each example to update a fixed-size memory, which stays the same size as it takes in more examples, so it does not have to keep going back through everything it has already seen. Stamirowska says the model works through a problem internally without turning each step into words. Stamirowska and colleagues said the model solved nearly three in 10 puzzles on the public ARC-AGI-1 evaluation set when given two attempts.

The result showed that a small model could solve some new reasoning problems without spelling out its intermediate steps. The benchmark tests ask AI systems to infer visual rules from a few examples and apply them to new puzzles. The tests are like human student exams.

The model did better on some puzzles than others. For example, it handled some tasks involving turning or moving shapes but struggled more with color changes and some combinations of rules. On harder ordering and nesting puzzles, the model did better after seeing an example of similar difficulty.