Is AI Reasoning Right for the Wrong Reasons? | Quanta Magazine

I’ll just say it: What the hell is going on with AI “reasoning”? Sorry for the air quotes. That punctuational side-eye was more common in 2024, when the specially trained cousins of LLMs now known as “large reasoning models,” or LRMs, were still new. Nowadays it may seem downright churlish, though, given that a “general-purpose reasoning model” from OpenAI solved a famous open mathematical research problem in one shot in May 2026. Still, I’m not sure how else to acknowledge my intellectual whiplash over the scientific interpretation of what these AI systems are actually doing. Reasoning comes in many technically defined forms, but the basic procedure is easily recognizable: arriving at a sound conclusion by linking together intermediate steps that logically follow from each other. We do this with thoughts; LRMs use so-called chains of thought, a term of art for the streams of synthetic text that the models emit before arriving at an answer to a complex query. One minute, the idea that AI could reason via these chains was being prominently and credibly critiqued (by a team of researchers from Apple) as an “Illusion of Thinking” subject to “complete accuracy collapse” under surprisingly simple conditions. The next minute, LRMs were bagging gold medals at the International Mathematical Olympiad, a feat so challenging that “even very successful mathematicians and scientists may well highlight [it] on their CVs all their lives,” as the scientist and AI critic Gary Marcus and Ernest Davis wrote in 2025. If that’s not a sign of “real” reasoning, what is? But wait — soon after, more research, from the Santa Fe Institute, showed that LRMs can crush even carefully designed benchmarks for reasoning (like a collection of analogy-like visual puzzles) using mere “surface-level ‘shortcuts.’” What they were doing looked less like generalizable reasoning than just gaming the system. Then, as if on cue, another “hold my beer” moment: Google DeepMind and the mathematician Terence Tao (the GOAT!) used AI to rediscover or improve the solutions to 67 problems “spanning mathematical analysis, combinatorics, geometry, and number theory.” Deal with it, haters! What about additional evidence that LRMs can’t reason reliably, even when they possess the necessary algorithm and computational budget to do so, and suffer from a list of scientifically documented failure states long enough to use as a Slip ’N Slide? Whatever — I guess that’s just “jagged intelligence” for you (AI-speak for “when it works, it works”). And so it went from late 2025 into 2026. I’ve been a science journalist for 20 years and an AI journalist for half of that, so I know better than to expect tidy consistency out of rapidly advancing research. But even for me, this back-and-forth has been a bit much. To quote Al Pacino in The Insider, “I’m getting two things: pissed off, and curious.” I don’t believe there’s fraud to be found here. I just want to know which way is up. Can AI reasoning somehow be both BS and not at the same time? And if so, how on Earth does that work? I knew just who to call first. Melanie Mitchell’s career in AI stretches back to the 1980s, but lately she’s earned a reputation as an au courant AI truth teller, penning lucid explainers for Science and her widely read newsletter, as well as conducting research at the Santa Fe Institute. (The study about “surface-level ‘shortcuts’” is hers.) When I asked her what we actually know about AI reasoning, her answer was brief enough to fit on an index card. “Number one: It works. It improves things,” she said, referring to LRMs’ superior accuracy on reasoning tasks compared to LLMs. “Number two: The actual text that’s generated” — i.e., the chain of thought that every LRM is trained to produce to improve its performance — “isn’t necessarily faithful to what’s going on [inside the model]. And number three: A lot of that text isn’t even useful. You can actually take it out.” Let’s unpack numbers two and three, because that’s where the superposition of “BS and not” actually lives. Chains of thought were half-discovered, half-devised in 2022 as a prompting hack for LLMs: Provide them with examples of written-out reasoning (or, famously, just ask them to “think step by step”), and they’ll suddenly give less boneheaded answers to simple logic and math problems. LRMs, starting with OpenAI’s o1 model in 2024, are trained to automate this trick by generating such prompts — also called reasoning traces or thinking tokens — and then feeding them back to themselves. Because LRMs are essentially just language models, those extra bits of text create what looks convincingly like a paper trail of the model’s “thought process.” Except it’s not that simple. A growing body of academic and industry research has cast doubt on whether these “intermediate tokens” are a faithful representation of an LRM’s inner workings. Instead of being auditable receipts or accurate reports, they can appear more like what the Arizona State University researcher Subbarao Kambhampati calls “mumblings” — bits of language, yes, but ones whose meaning may be entirely incidental to any reasoning that might have occurred. Kambhampati’s lab showed in 2025 that fully replacing a model’s correct “traces” with incorrect or irrelevant ones didn’t degrade its performance on a formal reasoning task. Meanwhile, training the model only on correct trace data still led it to occasionally generate invalid records of its reasoning — even when it produced a correct solution to the original problem it was given. A 2024 paper from researchers at New York University showed that “meaningless filler tokens” — literally, strings of dots — could function effectively in place of a human-readable “chain of thought.” William Merrill, one of the authors on that paper and currently a professor at the Toyota Technological Institute at Chicago, put the matter plainly: “There’s no guarantee the chain of thought has to be meaningful in any sense.” Pavel Izmailov, a researcher at NYU who also works for Anthropic (and was part of its original reasoning-model team), said he doubts that reinforcement learning — a typical training method for LRMs — even incentivizes models to produce faithful chains of thought in the first place. “I mean, maybe it will,” he told me. “But I would say the chances are not very high.” 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. “Many ideas that have been proposed [about] the sources of strength [of these models] have been misunderstood or mischaracterized,” he said. “There’s this general mindset that says, ‘Let’s go ahead and claim certain abilities, because eventually that might become true anyway.’ And my sense is: That’s not science. That is investment.” 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.) Here’s the thing: Nobody denies that AI reasoning models can, indeed, produce significant and accurate results. Furthermore, every researcher I spoke to acknowledged that negative findings about the models’ capabilities on certain reasoning tasks (especially those of smaller, open-source LRMs) may not always generalize to the latest-and-greatest AI products. Their inner workings remain trade secrets. But if we’re disinclined (as I am) to simply dismiss contradictory evidence about the mechanisms driving AI reasoning, the question remains: How do we account for it?