Vision AI models have a flaw. When shown a medical scan, they might correctly diagnose a condition while citing anatomically impossible reasons. Or they might solve a geometry problem with the right answer but skip essential theorems and rely on made-up ones instead. These models reach correct conclusions through reasoning that makes no sense.
This hints at a deeper problem. Current models don’t really think through visual problems – they pattern match their way to answers. The LlamaV-o1 team discovered this by doing something simple: they forced their model to show its work. The results revealed that most visual reasoning errors don’t come from failing to see what’s in an image. They come from skipping key logical steps between seeing and concluding.
This gap between seeing and reasoning matters. A model that gets the right answer through wrong reasoning is like a student who memorizes solutions without understanding the principles. It will fail unpredictably when faced with new problems.
The solution turns out to require rethinking how we train these models. Today’s standard approach gives a model an image and question, then trains it to predict the correct answer. This works well enough to pass many benchmarks. But it’s like teaching a student to recognize answer patterns without understanding the underlying concepts, like training to answer physics problems by memorizing flash cards with the problem on the front and a single number, the answer, on the back.
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