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Apple’s Bombshell AI Study: Why ChatGPT and Claude Can’t Actually Reason (And What This Means for the Future)

Curtis Pyke by Curtis Pyke
June 9, 2025
in Blog
Reading Time: 21 mins read
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TLDR;

The paper “The Illusion of Thinking: Why Language Models Cannot Reason” critically examines the widely held belief that large language models (LLMs) truly “think” or engage in genuine reasoning. It introduces a comparative framework—juxtaposing standard LLMs with so‑called Large Reasoning Models (LRMs)—and categorizes reasoning tasks into low, medium, and high complexity regimes.

Through systematic experiments using controlled puzzle environments (for example, the Tower of Hanoi and River Crossing puzzles), the study reveals that both LLMs and LRMs exhibit clear deficiencies in reasoning. Notably, while LRMs display improved performance on medium‑complexity tasks by engaging in explicit intermediate steps, both model types struggle significantly when faced with high‑complexity problems.

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The analysis shows that as task complexity increases, the models’ reasoning traces (evidenced by token usage) unexpectedly diminish, suggesting that the chain‑of‑thought outputs may be more theatrical than substantive. These results call into question current approaches to enhancing language model reasoning and advocate for rethinking AI strategies.

In particular, the paper emphasizes that hybrid systems—integrating neural and symbolic methodologies—may offer a path forward for achieving more authentic reasoning capabilities. For further commentary on this study, see Apple Research and related discussions on SimplyMac.

The Illusion of Thinking: Why Language Models Cannot Reason

Introduction

The paper opens by challenging the prevailing narrative that modern language models possess an inherent capacity for reasoning. Despite an abundance of impressive performances on various benchmarks, the authors contend that much of what appears to be “thinking” is, in reality, a sophisticated form of pattern matching rather than genuine reasoning.

The study sets out to debunk the illusion of thought by exploring the limitations of LLMs and contrasting them with models specifically designed to perform explicit reasoning steps—referred to as Large Reasoning Models (LRMs).

A key motivation behind this work is the observation that popular evaluation metrics and benchmarks, such as MATH or GSM8K, may inadvertently contaminate the results due to overexposure and fine‑tuning to these very tasks. As such, the paper proposes the use of controlled environments—like logic puzzles that are less susceptible to memorization—to investigate the true nature of reasoning abilities.

For instance, puzzles such as the Tower of Hanoi and various river crossing challenges are chosen because they require explicit and systematic planning, thus providing a clearer window into whether a model can genuinely work through a multi‑step problem.

In setting the stage, the authors underline that while language models have made tremendous strides—achieving state‑of‑the‑art performance in a range of natural language tasks—their purported reasoning capabilities remain superficial. Brief overviews of chain‑of‑thought reasoning and self‑prompting techniques are discussed, only to be later critiqued as “performance theater,” offering a façade of deep thought without the underlying algorithmic consistency that genuine reasoning would demand.

This introduction thus poses a central question: Do LLMs simply simulate reasoning by regurgitating learned patterns, or do they possess an intrinsic, scalable capacity to think through complex problems?

The discussion in this section is grounded by links to supplementary resources. For example, detailed commentary on the limitations of scaling parameter counts and token budgets can be found on Apple Research, while broader critiques of chain‑of‑thought approaches can be seen on SimplyMac. This baseline framing is crucial for understanding the experimental approach and the subsequent analysis presented in the paper.


Problem Complexity and Reasoning Models

Moving into the core analysis, the paper organizes reasoning tasks into three discrete complexity regimes: low, medium, and high. These regimes are designed to uncover how models perform as problem difficulty escalates, and they provide a framework for comparing LLMs with LRMs.

The authors argue that while LLMs may perform adequately on simpler tasks by virtue of being optimized for a vast array of language tasks, they begin to show limitations when required to undertake tasks that demand explicit sequential reasoning.

In the low‐complexity regime, the models perform well enough that there is little need for extended planning. Yet, paradoxically, the authors note that LRMs, which are explicitly designed to articulate intermediate steps, can sometimes “overthink” these tasks. Overthinking here refers to the scenario where the reasoning process becomes unnecessarily verbose or circuitous, leading to avoidable errors.

This phenomenon underscores the idea that even when stepping through intermediate steps is possible, it must be executed in a streamlined fashion to be truly beneficial.

For medium‑complexity tasks, however, the balance shifts. LRMs demonstrate a relative advantage over LLMs thanks to their capacity to generate explicit intermediate reasoning steps. The controlled experiments show that when the problem requires a multi‑step approach—such as breaking down the Tower of Hanoi puzzle into a clear sequence of moves—the explicit reasoning logs help LRMs arrive at correct solutions more reliably than their standard counterparts.

Detailed analysis of these experiments, including the tracking of token generation and reasoning trace lengths, reveals that models benefit from having a more granular thought process in problems that straddle the perfect balance between trivial and highly complex.

When tasks cross into the high‑complexity regime, a striking failure reveals itself: both LLMs and LRMs begin to “give up” on sustained reasoning. The paper shows that as complexity increases, the traces of reasoning become unnaturally short, indicating that the models halt their intermediate analysis well before reaching a correct solution.

This counterintuitive scaling limit—in which an increase in problem difficulty results in a decrease in expressed reasoning effort—is a central empirical observation that challenges assumptions about model scalability. For extended perspectives on this phenomenon, see reports on DeepNewz and discussions on Sean Goedecke’s blog.

By categorizing tasks along a complexity axis and comparing the performance of LLMs with LRMs, the authors not only highlight the inadequacies of current language model reasoning but also set the stage for a deeper inquiry into the mechanisms that underpin these failures. The analysis posits that the inability of these models to maintain robust reasoning traces across increasing complexity calls into question the effectiveness of chain‑of‑thought techniques in furnishing genuine reasoning skills.


Methodology

The heart of the paper lies in its rigorous experimental design. Rather than relying on well‑trodden benchmarks that may have been “gamed” by prior overexposure, the study employs controlled puzzle environments that force the models to engage with tasks requiring genuine sequential planning. Two notable examples are the Tower of Hanoi puzzle and a set of river crossing puzzles.

The Tower of Hanoi, a classic problem in the realm of algorithmic puzzles, requires a strict sequence of moves that a willful agent must deduce step by step. In the experiments, both LLMs and LRMs are tasked with parsing the rules of the puzzle and then producing a valid sequence of moves. The controlled setting ensures that the expected output is unambiguous, allowing the researchers to measure the intermediate reasoning tokens generated by the models.

The final accuracy of the move sequence is tracked alongside the length and quality of the generated chain‑of‑thought. Results indicate complex interactions between task difficulty and reasoning effort, with LRMs outperforming LLMs in medium‑complexity configurations.

In addition to the Tower of Hanoi challenges, river crossing puzzles are selected for their ability to require non‑linear planning and the management of multiple constraints simultaneously. Such puzzles often present scenarios where a set of characters or items must be transported across a river with restrictions on which items can be left together. These puzzles demand that the model consider multiple interdependent variables—a test that exposes the boundaries of pattern matching versus actual reasoning.

Evaluation metrics in this methodology emphasize not only the final correctness of the solution, but also the internal “reasoning trace” that the models produce in the process. The researchers record the number of tokens generated during intermediate steps, as well as their fidelity to the logically sound progression required by the puzzle.

This dual focus—both outcome and process—allows for a clear assessment of whether the models merely produce the correct answer by chance or whether they demonstrate a coherent step‑by‑step problem solving approach.

Furthermore, the methodology includes analyses that account for why existing metrics (as seen in prior benchmarks) might overestimate a model’s reasoning abilities. By carefully managing task exposure and controlling for memorized patterns, the experiments reveal that what appears as reasoning in simpler tasks dissipates when models are confronted with unanticipated complexity. For readers interested in a technical deep‑dive, extended details on this methodology can be found at Apple Research and through discussions referenced in Sean Goedecke’s overview.

This rigorous experimental framework thus sets a clear standard for evaluating reasoning ability—not just by the correctness of the answer but by the robustness of the reasoning process itself. The experiment design underscores that if a model’s reasoning effort (as measured by token usage and intermediary steps) diminishes when it is most needed, then the chain‑of‑thought outputs cannot be taken as a reliable sign of authentic cognitive processing.


Key Experiments and Results

The experimental section of the paper is extensive, detailing several controlled tasks that probe the limits of both LLMs and LRMs. The experiments are divided according to the three complexity regimes outlined earlier (low, medium, high) and include various puzzles and logical tests that mimic real‑world problem solving.

In the low‑complexity regime, the experiments confirm an interesting paradox. LLMs, which are optimized for rapid prediction based on broad datasets, often produce correct answers without necessarily articulating elaborate intermediate steps. Conversely, LRMs, which are explicitly engineered to “think out loud,” tend to overcomplicate the process.

For example, when solving a simple Tower of Hanoi setup with a small number of disks, an LLM might present the solution with minimal explanation, while an LRM may present a lengthy, redundant chain‑of‑thought that introduces opportunities for error. This “overthinking” manifests as extra tokens that do not necessarily contribute to a correct solution and may even confuse the final answer generation.

When tasks are upgraded to medium complexity, the performance margin shifts in favor of LRMs. Detailed experiments show that for problems requiring several sequential steps with clear intermediate goals, the deliberate articulation of reasoning can help the model stay on track. In one instance, an LRM is observed to generate a comprehensive list of sub‐steps necessary for correctly solving the Tower of Hanoi problem, moving the disks systematically and obeying the puzzle’s constraints.

In these cases, the explicit chain‑of‑thought not only aids in reaching the correct solution but also serves as a diagnostic tool to explain model behavior—a factor that becomes invaluable for troubleshooting model missteps.

However, the most striking results emerge in the high‑complexity regime. As the difficulty of a puzzle increases—either by adding more disks to the Tower of Hanoi or by increasing the number of constraints in a river crossing puzzle—both models exhibit a collapse in reasoning ability. The experiments reveal that instead of generating longer or more detailed chains‑of‑thought to accommodate the increased complexity, the models abruptly shorten their reasoning traces.

This phenomenon is counterintuitive: one would expect that more complex tasks would trigger a more elaborate reasoning process. Instead, the token analysis shows that the models “give up” on providing extensive reasoning once a certain complexity threshold is met.

This finding is illustrated by a set of experiments where both LLMs and LRMs are forced to handle puzzles with rapidly growing solution spaces. As the internal metrics (such as token count per reasoning step) drop sharply with complexity, the final answers are more error‑prone, hinting at a disconnect between the appearance of reasoning and the true underlying computation. The authors term this a “scaling limit” of current reasoning architectures.

For further insights into these scaling limits and the performance drop‑offs, readers may refer to discussions on DeepNewz.

What is particularly notable is that even when models are provided with explicit algorithmic procedures (for instance, a step‑by‑step guide to solving a Tower of Hanoi puzzle), the actual generation of coherent intermediate reasoning steps falls short. This failure is not due to a lack of memorization or training data, but rather hints at an inherent architectural limitation: the inability to engage in genuine, flexible reasoning when confronted with the demands of high‑complexity tasks.

Overall, the key experiments serve to dismantle the assumption that modern language models, when prompted with chain‑of‑thought instructions, truly “understand” or “reason” in the human sense. Instead, the controlled experiments reveal that the performance gains in structured reasoning contexts are partial and fragile.


Analysis of Reasoning Traces

A central aspect of the paper is its deep dive into the notion of “reasoning traces.” In contrast to simply evaluating the correctness of model outputs, the study investigates the intermediate tokens generated as the models articulate their thought process—providing a window into what might be happening “behind the scenes.”

The analysis begins with the hypothesis that a true reasoning process would manifest as a steadily increasing chain‑of‑thought that correlates positively with task complexity. However, the data reveal a peculiar pattern: as the complexity of the puzzle increases, the quantity and quality of the intermediate reasoning tokens diminish.

This “reasoning collapse” indicates that the models, when faced with insurmountable complexity, do not simply produce a longer, more detailed output. Instead, they truncate the chain‑of‑thought, effectively “giving up” on the full computation.

This behavior is quantified by carefully measuring the token usage at different stages of reasoning. In controlled experiments, the token count initially rises with task complexity and peaks at a medium level, only to fall sharply for problems that fall into the high‑complexity regime. The result is not only a reduction in the length of the chain‑of‑thought but also a noticeable drop in its logical coherence.

For instance, in one case with a high‑complexity Tower of Hanoi puzzle, the model attempts to enumerate the plan only to revert rapidly to a final move count without the necessary transition steps.

These findings are crucial because they support the central thesis that current language models, even when equipped with explicit reasoning prompts, are not truly “thinking” in a human‑like way. Rather, the chain‑of‑thought outputs appear to be a kind of “performance theater”—a scripted regurgitation of learned patterns rather than evidence of an underlying problem‑solving capacity. The paper draws on these observations to argue that the superficial nature of these internal tokens undermines claims of genuine reasoning capabilities.

A further layer of analysis is provided by comparing the reasoning traces of LRMs against those of traditional LLMs. While LRMs are designed to produce a more deliberate and structured chain‑of‑thought, the experiments reveal that such explicit reasoning only offers an advantage within a narrow band of task complexity. Once the task becomes too intricate, even the detailed reasoning of the LRMs falls apart.

This suggests that the architectural modifications intended to enhance reasoning do not scale appropriately with task difficulty.

The implications of these results are far‑reaching. They question whether current designs—dominated by autoregressive mechanisms that rely on learned token distributions—are fundamentally incapable of supporting true algorithmic reasoning. As such, the study calls into question the widespread reliance on chain‑of‑thought as a proxy for deep cognitive processing in LLMs. For more empirical discussions of this phenomenon, see Sean Goedecke’s analysis and related evaluations on platforms like SimplyMac.


Implications for AI Research

The insights derived from this study have profound implications for the future of AI research and development. One of the primary messages of the paper is that the prevailing reliance on chain‑of‑thought prompting and scaling up language models may be reaching a point of diminishing returns.

The observed “scaling limit” in reasoning traces suggests that simply increasing model size or token budgets does not guarantee better reasoning performance; in fact, it may lead to more pronounced breakdowns in complex problem-solving scenarios.

A further implication is that current language models are overly dependent on pattern matching—an ability honed by extensive exposure to human‑generated text—rather than on the enactment of algorithmic reasoning. This foundational critique challenges the assumption that increased data and parameters will inevitably lead to the emergence of true intelligence. Instead, the paper argues in favor of rethinking the underlying architecture.

One promising avenue is the development of hybrid models that combine neural networks with symbolic reasoning components. Such hybrid systems could leverage the strengths of both paradigms—the flexibility and adaptability of neural architectures together with the precision and rule‑based clarity of symbolic logic.

The discussion in this section is illuminated by external commentary. For instance, the analysis found on AppleInsider emphasizes that the new evidence challenges conventional wisdom about the efficacy of LLM‑based reasoning systems. Similarly, discussions on DeepNewz point to the need for re‑evaluating the benchmarks and methodologies used to certify model capabilities.

Another notable implication is the potential for these findings to shape future research directions in the field of explainable AI (XAI). If chain‑of‑thought outputs do not accurately reflect genuine reasoning, then the interpretability of language model decisions may be more limited than previously assumed.

This raises important questions about how much trust can be placed in these models, and what additional safeguards or complementary methodologies might be necessary to ensure that AI systems do not overpromise and underdeliver in critical applications.

On a strategic level, the paper advocates for a paradigm shift in both how researchers assess AI capabilities and how they design systems to perform complex reasoning tasks. It suggests that the community should invest more heavily in alternative strategies—potentially drawing on insights from cognitive science, neuroscience, and formal logic—to create models that can truly reason, rather than simply simulate reasoning.

This cross‑disciplinary approach may be essential for overcoming the current limitations of autoregressive language models.

In summary, the implications for AI research extend well beyond the technical nuances of token counts or puzzle‑solving. They strike at the heart of how AI is conceptualized and developed—a reminder that true cognitive abilities may require fundamentally different architectures than those that have been pursued over the past decade. For researchers and practitioners alike, these insights should prompt a re‑evaluation of both the promise and the limitations of current LLM‑based systems.


Conclusion

In the final analysis, “The Illusion of Thinking: Why Language Models Cannot Reason” provides a sobering assessment of current AI architectures and their ability to perform genuine reasoning. Despite the impressive performance of large language models on many tasks, the paper demonstrates that the apparent reasoning exhibited through chain‑of‑thought techniques is largely illusory. Instead of revealing a deep, scalable capacity for logical thought, the models produce reasoning traces that diminish rapidly under increased complexity. This “scaling limit” is a key finding, one that underscores the fundamental gap between pattern matching and authentic algorithmic reasoning.

The experiments across various controlled puzzles show that while LRMs may have an edge over standard LLMs on medium‑complexity tasks, neither architecture suffices when faced with high‑complexity challenges. The failure to generate robust and coherent reasoning traces, even when provided with explicit algorithmic procedures, indicates that the current reliance on autoregressive, token‑predictive models is reaching its natural limits when it comes to true reasoning.

These results call for a rethinking of how reasoning is approached in AI research. The paper advocates for a shift toward hybrid systems that combine the statistical strengths of neural networks with the explicit, rule‑based precision of symbolic systems. Such models might be more capable of supporting the kind of flexible, context‑sensitive reasoning that is required for complex problem solving.

Moreover, the methodological critique presented in the paper is itself a call to arms: current evaluation benchmarks may not be sufficient for discerning genuine reasoning from clever pattern reproduction. As discussed throughout the paper, the use of controlled puzzle environments—free from the confounds of overexposure and memorization—offers a promising route for future research.

The need for robust evaluation metrics that capture both output quality and the internal reasoning process is imperative if the field is to overcome the significant challenges posed by high‑complexity tasks.

The broader implication is a message of caution: while scaling up models and data can yield impressive results on many fronts, there remains a fundamental, unresolved challenge in achieving true cognitive reasoning. For AI researchers, developers, and end‑users alike, the study serves as a reminder that the illusions of “thinking” must not be conflated with genuine understanding or reasoning capability.

In closing, the paper reinforces the idea that the field of AI has reached a critical juncture. The limitations exposed by the study point to the necessity of new paradigms and cross‑disciplinary approaches in the quest for models that can truly reason. For additional perspectives and ongoing discussions about these challenges, see the in‑depth analyses available on AppleInsider and SimplyMac.


Final Thoughts

The study “The Illusion of Thinking: Why Language Models Cannot Reason” is a crucial milestone in the ongoing quest to understand and improve AI reasoning capabilities. By dissecting the reasoning process and exposing the shortcomings of both LLMs and LRMs, the paper offers a nuanced critique of current methodologies.

It challenges researchers to look beyond surface‑level performance metrics and to develop systems that can truly articulate and maintain robust thought processes across a range of complexity levels.

In an era where AI is increasingly deployed in decision‑making and critical applications, understanding the true limits of model reasoning is more than an academic exercise—it is essential for ensuring the safe and effective deployment of these technologies. This paper, with its rigorous experiments and thoughtful analysis, lays the groundwork for future innovations that may finally bridge the gap between impressive linguistic imitation and genuine reasoning capability.

As the field moves forward, the lessons gleaned from this study will need to be integrated into both the design of new AI architectures and the strategies by which model performance is evaluated. Whether through hybrid approaches, more robust evaluation metrics, or novel architectures informed by human cognition, the challenge remains clear: true reasoning in AI is a goal that will require fresh thinking, bold experimentation, and, above all, a willingness to look past the illusions of current state‑of‑the‑art systems.

For further reading and discussion on these topics, readers are encouraged to explore additional resources available on Apple Research, as well as the commentary provided by thought leaders on DeepNewz and Sean Goedecke’s blog.

Curtis Pyke

Curtis Pyke

A.I. enthusiast with multiple certificates and accreditations from Deep Learning AI, Coursera, and more. I am interested in machine learning, LLM's, and all things AI.

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