A new study by researchers at Apple challenges prevailing assumptions about the capabilities of frontier AI models designed for reasoning. The paper, “The Illusion of Thinking,” reveals that Large Reasoning Models (LRMs)—advanced variants of large language models (LLMs) trained to mimic structured “thinking”—fail to scale effectively when faced with complex tasks, despite their impressive performance on standard benchmarks.

Researchers Parshin Shojaee, Iman Mirzadeh, and colleagues found that while LRMs such as Claude 3.7 Sonnet Thinking, DeepSeek-R1, and OpenAI’s o3-mini show early advantages in medium-difficulty reasoning tasks, their performance collapses entirely as complexity increases.

The authors write that they observed a complete breakdown in reasoning accuracy once tasks surpass a certain threshold. Unlike traditional evaluations focused on math or coding problems, this study used controlled puzzle environments—like Tower of Hanoi and River Crossing—to precisely measure how reasoning scales.

The results identify three distinct “regimes” of reasoning: low-complexity tasks where standard LLMs outperform, medium-complexity tasks where LRMs show an edge, and high-complexity tasks where both models fail. More surprisingly, LRMs reduce their own reasoning effort (measured by token usage) as problem difficulty rises—despite having sufficient resources—suggesting an inherent scaling flaw.

In-depth analysis of the models’ reasoning traces revealed patterns of “overthinking,” where models explore incorrect paths long after finding a solution. For more complex tasks, LRMs often fail to find any valid solution at all. Even when explicitly provided with step-by-step algorithms, models like Claude 3.7 Sonnet still struggled to execute them reliably.

By exposing these cognitive limitations, the study signals the need for rethinking how AI reasoning is evaluated—and raises important questions about whether current models are truly equipped for the demands of real-world problem-solving.

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