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Apple’s machine-learning group set off a rhetorical firestorm earlier this month with its launch of “The Phantasm of Considering,” a 53-page analysis paper arguing that so-called massive reasoning fashions (LRMs) or reasoning massive language fashions (reasoning LLMs) comparable to OpenAI’s “o” collection and Google’s Gemini-2.5 Professional and Flash Considering don’t really interact in impartial “pondering” or “reasoning” from generalized first rules realized from their coaching information.
As an alternative, the authors contend, these reasoning LLMs are literally performing a form of “sample matching” and their obvious reasoning skill appears to collapse as soon as a activity turns into too complicated, suggesting that their structure and efficiency isn’t a viable path to bettering generative AI to the purpose that it’s synthetic generalized intelligence (AGI), which OpenAI defines as a mannequin that outperforms people at most economically useful work, or superintelligence, AI even smarter than human beings can comprehend.
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Unsurprisingly, the paper instantly circulated extensively among the many machine studying group on X and plenty of readers’ preliminary reactions have been to declare that Apple had successfully disproven a lot of the hype round this class of AI: “Apple simply proved AI ‘reasoning’ fashions like Claude, DeepSeek-R1, and o3-mini don’t really purpose in any respect,” declared Ruben Hassid, creator of EasyGen, an LLM-driven LinkedIn put up auto writing device. “They only memorize patterns rather well.”
However now in the present day, a brand new paper has emerged, the cheekily titled “The Phantasm of The Phantasm of Considering” — importantly, co-authored by a reasoning LLM itself, Claude Opus 4 and Alex Lawsen, a human being and impartial AI researcher and technical author — that features many criticisms from the bigger ML group in regards to the paper and successfully argues that the methodologies and experimental designs the Apple Analysis staff used of their preliminary work are basically flawed.
Whereas we right here at VentureBeat usually are not ML researchers ourselves and never ready to say the Apple Researchers are unsuitable, the talk has definitely been a energetic one and the difficulty in regards to the capabilities of LRMs or reasoner LLMs in comparison with human pondering appears removed from settled.
How the Apple Analysis research was designed — and what it discovered
Utilizing 4 basic planning issues — Tower of Hanoi, Blocks World, River Crossing and Checkers Leaping — Apple’s researchers designed a battery of duties that compelled reasoning fashions to plan a number of strikes forward and generate full options.
These video games have been chosen for his or her lengthy historical past in cognitive science and AI analysis and their skill to scale in complexity as extra steps or constraints are added. Every puzzle required the fashions to not simply produce an accurate ultimate reply, however to elucidate their pondering alongside the way in which utilizing chain-of-thought prompting.
Because the puzzles elevated in problem, the researchers noticed a constant drop in accuracy throughout a number of main reasoning fashions. In essentially the most complicated duties, efficiency plunged to zero. Notably, the size of the fashions’ inside reasoning traces—measured by the variety of tokens spent pondering by means of the issue—additionally started to shrink. Apple’s researchers interpreted this as an indication that the fashions have been abandoning problem-solving altogether as soon as the duties turned too arduous, primarily “giving up.”
The timing of the paper’s launch, simply forward of Apple’s annual Worldwide Builders Convention (WWDC), added to the affect. It rapidly went viral throughout X, the place many interpreted the findings as a high-profile admission that current-generation LLMs are nonetheless glorified autocomplete engines, not general-purpose thinkers. This framing, whereas controversial, drove a lot of the preliminary dialogue and debate that adopted.
Critics take intention on X
Among the many most vocal critics of the Apple paper was ML researcher and X person @scaling01 (aka “Lisan al Gaib”), who posted a number of threads dissecting the methodology.
In one extensively shared put up, Lisan argued that the Apple staff conflated token funds failures with reasoning failures, noting that “all fashions may have 0 accuracy with greater than 13 disks just because they can not output that a lot!”
For puzzles like Tower of Hanoi, he emphasised, the output measurement grows exponentially, whereas the LLM context home windows stay fastened, writing “simply because Tower of Hanoi requires exponentially extra steps than the opposite ones, that solely require quadratically or linearly extra steps, doesn’t imply Tower of Hanoi is tougher” and convincingly confirmed that fashions like Claude 3 Sonnet and DeepSeek-R1 typically produced algorithmically right methods in plain textual content or code—but have been nonetheless marked unsuitable.
One other put up highlighted that even breaking the duty down into smaller, decomposed steps worsened mannequin efficiency—not as a result of the fashions failed to grasp, however as a result of they lacked reminiscence of earlier strikes and technique.
“The LLM wants the historical past and a grand technique,” he wrote, suggesting the true drawback was context-window measurement quite than reasoning.
I raised one other necessary grain of salt myself on X: Apple by no means benchmarked the mannequin efficiency towards human efficiency on the identical duties. “Am I lacking it, or did you not examine LRMs to human perf[ormance] on [the] similar duties?? If not, how have you learnt this similar drop-off in perf doesn’t occur to individuals, too?” I requested the researchers straight in a thread tagging the paper’s authors. I additionally emailed them about this and plenty of different questions, however they’ve but to reply.
Others echoed that sentiment, noting that human drawback solvers additionally falter on lengthy, multistep logic puzzles, particularly with out pen-and-paper instruments or reminiscence aids. With out that baseline, Apple’s declare of a basic “reasoning collapse” feels ungrounded.
A number of researchers additionally questioned the binary framing of the paper’s title and thesis—drawing a tough line between “sample matching” and “reasoning.”
Alexander Doria aka Pierre-Carl Langlais, an LLM coach at power environment friendly French AI startup Pleias, stated the framing misses the nuance, arguing that fashions is perhaps studying partial heuristics quite than merely matching patterns.
Ethan Mollick, the AI targeted professor at College of Pennsylvania’s Wharton College of Enterprise, known as the concept that LLMs are “hitting a wall” untimely, likening it to related claims about “mannequin collapse” that didn’t pan out.
In the meantime, critics like @arithmoquine have been extra cynical, suggesting that Apple—behind the curve on LLMs in comparison with rivals like OpenAI and Google—is perhaps attempting to decrease expectations,” developing with analysis on “the way it’s all faux and homosexual and doesn’t matter anyway” they quipped, declaring Apple’s popularity with now poorly performing AI merchandise like Siri.
In brief, whereas Apple’s research triggered a significant dialog about analysis rigor, it additionally uncovered a deep rift over how a lot belief to put in metrics when the take a look at itself is perhaps flawed.
A measurement artifact, or a ceiling?
In different phrases, the fashions might have understood the puzzles however ran out of “paper” to jot down the complete resolution.
“Token limits, not logic, froze the fashions,” wrote Carnegie Mellon researcher Rohan Paul in a extensively shared thread summarizing the follow-up exams.
But not everybody is able to clear LRMs of the cost. Some observers level out that Apple’s research nonetheless revealed three efficiency regimes — easy duties the place added reasoning hurts, mid-range puzzles the place it helps, and high-complexity instances the place each customary and “pondering” fashions crater.
Others view the talk as company positioning, noting that Apple’s personal on-device “Apple Intelligence” fashions path rivals on many public leaderboards.
The rebuttal: “The Phantasm of the Phantasm of Considering”
In response to Apple’s claims, a brand new paper titled “The Phantasm of the Phantasm of Considering” was launched on arXiv by impartial researcher and technical author Alex Lawsen of the nonprofit Open Philanthropy, in collaboration with Anthropic’s Claude Opus 4.
The paper straight challenges the unique research’s conclusion that LLMs fail because of an inherent lack of ability to purpose at scale. As an alternative, the rebuttal presents proof that the noticed efficiency collapse was largely a by-product of the take a look at setup—not a real restrict of reasoning functionality.
Lawsen and Claude show that most of the failures within the Apple research stem from token limitations. For instance, in duties like Tower of Hanoi, the fashions should print exponentially many steps — over 32,000 strikes for simply 15 disks — main them to hit output ceilings.
The rebuttal factors out that Apple’s analysis script penalized these token-overflow outputs as incorrect, even when the fashions adopted an accurate resolution technique internally.
The authors additionally spotlight a number of questionable activity constructions within the Apple benchmarks. Among the River Crossing puzzles, they word, are mathematically unsolvable as posed, and but mannequin outputs for these instances have been nonetheless scored. This additional calls into query the conclusion that accuracy failures characterize cognitive limits quite than structural flaws within the experiments.
To check their principle, Lawsen and Claude ran new experiments permitting fashions to provide compressed, programmatic solutions. When requested to output a Lua operate that would generate the Tower of Hanoi resolution—quite than writing each step line-by-line—fashions out of the blue succeeded on much more complicated issues. This shift in format eradicated the collapse totally, suggesting that the fashions didn’t fail to purpose. They merely failed to adapt to a man-made and overly strict rubric.
Why it issues for enterprise decision-makers
The back-and-forth underscores a rising consensus: analysis design is now as necessary as mannequin design.
Requiring LRMs to enumerate each step might take a look at their printers greater than their planners, whereas compressed codecs, programmatic solutions or exterior scratchpads give a cleaner learn on precise reasoning skill.
The episode additionally highlights sensible limits builders face as they ship agentic programs—context home windows, output budgets and activity formulation could make or break user-visible efficiency.
For enterprise technical resolution makers constructing functions atop reasoning LLMs, this debate is greater than tutorial. It raises important questions on the place, when, and learn how to belief these fashions in manufacturing workflows—particularly when duties contain lengthy planning chains or require exact step-by-step output.
If a mannequin seems to “fail” on a fancy immediate, the issue might not lie in its reasoning skill, however in how the duty is framed, how a lot output is required, or how a lot reminiscence the mannequin has entry to. That is notably related for industries constructing instruments like copilots, autonomous brokers, or decision-support programs, the place each interpretability and activity complexity may be excessive.
Understanding the constraints of context home windows, token budgets, and the scoring rubrics utilized in analysis is crucial for dependable system design. Builders might have to think about hybrid options that externalize reminiscence, chunk reasoning steps, or use compressed outputs like capabilities or code as an alternative of full verbal explanations.
Most significantly, the paper’s controversy is a reminder that benchmarking and real-world software usually are not the identical. Enterprise groups needs to be cautious of over-relying on artificial benchmarks that don’t mirror sensible use instances—or that inadvertently constrain the mannequin’s skill to show what it is aware of.
In the end, the massive takeaway for ML researchers is that earlier than proclaiming an AI milestone—or obituary—be sure the take a look at itself isn’t placing the system in a field too small to assume inside.
