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.
“Once the query arrives, the model doesn’t write out its thinking in words at all,” says complexity scientist Zuzanna Stamirowska, CEO of AI company Pathway. Stamirowska says the model works through a problem internally without turning each step into words. “Nothing in between ever converts into language,” she says.
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.
The process with fewer words may help keep computing costs down, Stamirowska and colleagues say. They estimate that each puzzle query costs about $0.00070 to run, about one-eleventh as much as GPT-5.6 Luna on the same benchmark test, though the two costs were calculated differently. The study has not been peer-reviewed.
The finding is “an interesting efficiency result,” says computer scientist Yuntian Deng of the University of Waterloo in Canada. But he says it does not show that BDH-CQ’s underlying architecture is better than other approaches.
“Specialized small models [like BDH-CQ] are a promising route to efficient reasoning,” Deng says. But more testing is needed to separate the effects of the model’s design from how it was trained, he says. Without written-out reasoning, the model is harder to inspect, Deng says. But even a written chain of thought may not faithfully show how the model reached its answer.
The BDH-CQ model was built specifically for ARC-style problems, which makes it hard to compare directly with general-purpose AI systems, says machine learning researcher Jonas Geiping of the ELLIS Institute Tübingen and the Max Planck Institute for Intelligent Systems in Germany. But he called the approach “neat” and noted that it can tackle test problems without retraining.
For Deng, the study raises a broader question: Does AI need to put all of its reasoning into words?. “Human language is useful for communicating reasoning, but it need not be the most efficient representation for every intermediate computation,” Deng says.
