From the article:

OK, so the linguistic content of reasoning traces may be dubious. But surely the tokens themselves must play a role in producing the model’s outputs? (Think of a pinball machine: It runs on coins, not the words “In God We Trust.”)

Not so fast. A 2025 paper from Northeastern University and the University of California, Berkeley on frontier open-source LRMs showed that between 30% and 60% of their “thinking steps” had “minimal causal impact” on the answers the models produced to benchmark math questions. Chop half of them out, and a model’s performance barely suffers. “We want to be careful when we review these chain-of-thought prompts because they may not be linked to the final output,” said Weiyan Shi, one of the study’s authors.

So reasoning traces, the very things that supposedly distinguish LRMs from the mere next-word-predicting LLMs, are not necessarily either meaningful or causal to a model’s … reasoning? I’m no philosopher, but this seems to stretch the meaning of “reasoning” beyond its tensile strength. Kambhampati’s research group sounded frankly fed up in the title of their position paper on the subject (presented at the 2026 International Conference on Machine Learning, one of the field’s most prestigious academic gatherings): “Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!”

To be clear, Kambhampati, a former president of the Association for the Advancement of Artificial Intelligence, with a background in AI planning algorithms, doesn’t deny that LRMs can work (when they work). “We are in wondrous times,” he told me, when I asked what he thought of OpenAI’s 2026 victory in solving the famous unit distance problem in math. If he has a bone to pick, it’s with what he sees as a rush in both academia and industry to embrace overly convenient explanations.

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On the other side of the AI-reasoning fence, the disdain seems to be mutual. “These ‘scientific’ papers from last summer — I would put this in big, big air quotes,” said Sébastien Bubeck, a member of OpenAI’s technical staff (and a prominent evangelist for the company’s reasoning models among scientists and mathematicians). He called earlier Apple results critiquing AI reasoning “wrong,” claiming that they were due to a training quirk in models that are now obsolete. “Modern models starting with GPT-5.5 do not suffer from this issue,” he said. “It would be interesting to revisit those results.” (Apple did not make its researchers available for interviews.)

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Kambhampati compared this process to mumbling words to yourself to jog your memory: It barely matters what the words are (though related ones may help), as long as they knock loose something useful. An LRM’s vast “memory” includes all the call-and-response-like examples of written reasoning it was trained on, mulched into numerical “embeddings” that encode their similarities and differences (plus other inscrutable associations) as geometric relationships in a high-dimensional space. Probabilistically arriving at an answer within that space may involve intermediate tokens whose embeddings map to coherent-looking “thoughts” in plain English, but not necessarily. They could be bits of other languages. They could be fake exclamations like “aha.” Under the right conditions, they could just be dots.

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“Whether the [embedding] actually corresponds to a single word or not” — much less a faithful reasoning process — “is beside the point,” Kambhampati said.

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Kambhampati hardly considers his case closed, and neither do I. But it’s a start — and one I find plausible, given that other researchers have also used similar “it’s the training, stupid” approaches to demystify AI behavior. Still, there was an elephant left in the room: How much does it matter whether or not we can accurately observe, characterize, and validate the processes at work inside large reasoning models?

The honest answer, according to Mitchell, is that it depends. “Think of AlphaFold,” she said, referring to Google’s AI tool for predicting protein structures. “It’s doing some kind of incredibly complex statistical associations. We don’t know what they are, but they seem to work. These things are [already] black boxes, even without a ‘reasoning trace.’” If LRMs can supercharge mathematics research the way AlphaFold did for computational biology, this line of thinking goes, why not embrace them, idiosyncrasies and all, and just verify the results? “My perspective is: We’re trying to be useful. We’re trying to build these models so that they can solve problems that matter, so that we actually accelerate scientific research,” said Bubeck. “It’s more interesting and more productive to talk about what they can do, rather than, ‘Oh, but they can only do that because of X [reasons].’”

But as Mitchell also points out, the possibility that an LRM could be “right for the wrong reasons” has an obvious relevance to the future of doing research. “You want the right answer for the right reason, so you can trust these things,” she said, and not just in verifiable domains.

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In summer 2024, just months before the first LRM appeared, Mitchell turned me on to a concept that I keep returning to in my AI reporting: “wishful mnemonics.” The phrase was first used all the way back in 1976 by the computer scientist Drew McDermott, in a paper with the epically grouchy title “Artificial Intelligence Meets Natural Stupidity.” I’ll quote the same passage Mitchell did:

A major source of simple-mindedness in AI programs is the use of mnemonics like “UNDERSTAND” or “GOAL” to refer to programs and data structures. … If a researcher … calls the main loop of his program “UNDERSTAND,” he is (until proven innocent) merely begging the question. He may mislead a lot of people, most prominently himself. … What he should do instead is refer to this main loop as “G0034,” and see if he can convince himself or anyone else that G0034 implements some part of understanding. … Many instructive examples of wishful mnemonics by AI researchers come to mind once you see the point.

This is how I make sense of AI reasoning. LRMs, chains of thought, thinking tokens: It’s wishful mnemonics all the way down — a heady mix of shorthand and suspended disbelief, like Oprah-style “manifesting” with a computer science spin. This isn’t necessarily a dig; all novel research likely requires some version of this mindset just to get off the ground. It certainly doesn’t mean AI reasoning can’t or doesn’t work. But the “wishful” part seems to be as powerful as ever.