With WWDC on deck, Apple says “reasoning” AI models collapse with complexity
Apple tested state-of-the-art “chain of thought” models and found that they aren’t “reasoning,” but merely pattern matching, calling into question the direction the industry is taking.
Apple’s troubled AI rollout was plagued by a series of remarkable feature failures and product delays.
What was supposed to be the year of “Apple Intelligence” has failed to deliver an AI-enhanced Siri on par with voice assistants from competitors like Google, OpenAI, and Meta. This week, all eyes are on Apple as it holds its Worldwide Developers Conference (WWDC) to see what it’s planning to get back in the AI race.
But behind the scenes, researchers at Apple have been digging into the competition’s latest and greatest “reasoning” models to see how they respond to tricky challenges as they scale in complexity.
In a new paper, Apple’s researchers found that the leading state-of-the-art “chain of thought” models “face a complete accuracy collapse” when they dialed up the complexity of puzzle-based tests. The spectacular failures of the models led the researchers to question their “reasoning” label, calling it instead “the illusion of thinking.”
The suite of tests included puzzles like “Tower of Hanoi,” in which the player must stack a series of disks of various sizes from one post to another, one disk at a time, only moving the top disk, and always placing smaller disks on larger ones.
While the models could solve the simplest versions of the puzzles, they fell on their face once things got more complex. The research tested reasoning models DeepSeek-R1, OpenAI’s o3-mini, and Anthropic’s Claude 3.7 Sonnet Thinking.
Chain of “thought”
After hitting performance plateaus from the “more data, more compute” approach, the industry followed OpenAI’s o1 release and started to build “chain of thought” reasoning models, which showed their “thought” processes.
This technique did boost the performance of large language models to new levels, offering a promising new pathway out of what looked to be a computational dead end. While they required vastly higher computation resources and time, the approach seemed to be the way forward.
Apple’s research seems to show that rather than reasoning, these models are merely displaying sophisticated pattern matching.
Apple researchers also examined the “thought” processes behind each solution to the puzzle, to better understand exactly how the models approached solutions.
The fact of the matter is that very little is known about how these recent models actually work. It remains to be seen if Apple has been cooking up an alternate approach, but reports indicate an AI-enhanced Siri isn’t likely to make a debut at this week’s WWDC.
