Apple Study Reveals Hard Limits in AI Reasoning Capabilities
In a recent research paper, Apple scientists put advanced AI models — known as Large Reasoning Models (LRMs) — to the test by evaluating their performance in controlled reasoning tasks. The findings suggest that while these models outperform traditional Large Language Models (LLMs) on moderately complex problems, both ultimately fail when task complexity increases.
The researchers focused on two of the most advanced LRMs available: Claude 3.7 Sonnet Thinking and DeepSeek-R1 . Rather than relying on standard benchmarks like math or coding tests, they designed custom puzzle-based environments , including classic logic challenges such as the Tower of Hanoi and River Crossing . These allowed them to systematically increase difficulty and observe how well the models could reason through each step.
Their goal wasn’t just to measure whether the AI reached the correct final answer — but to analyze how it arrived there. By comparing LRMs with standard LLMs under equal computational conditions, the team aimed to assess the true depth of AI reasoning and its potential for human-like generalization .
Ultimately, the results highlighted a key limitation: current AI systems, including so-called “reasoning” models, struggle to replicate the flexible, adaptive thinking that comes naturally to humans. Apple’s researchers argue that this points to fundamental gaps in how today’s models understand and solve problems — even those marketed as next-generation reasoning engines.
This study adds to the growing debate over what modern AI can actually achieve, and whether we’re truly building systems that think — or simply better mimicking thought.
Apple Study Reveals AI Reasoning Models Hit a Hard Ceiling
Apple researchers have uncovered critical limitations in how Large Reasoning Models (LRMs) perform under varying levels of task complexity. Their findings show that the effectiveness of these models is highly dependent on the difficulty of the problem — and at a certain point, even the most advanced systems completely break down.
In testing environments like Tower of Hanoi and River Crossing , standard Large Language Models (LLMs) — which lack structured reasoning mechanisms — actually outperformed LRMs when dealing with simple tasks . These traditional models were not only more accurate but also more resource-efficient, achieving better results using fewer computational resources.
As problems grew moderately complex, LRMs enhanced with Chain-of-Thought prompting began to show their advantage, surpassing regular LLMs by leveraging structured reasoning techniques. However, this edge didn’t last long.
When the puzzles became highly complex , both types of models failed entirely — no matter how much computing power was available. Accuracy across the board dropped to zero , suggesting that current AI systems still struggle with true generalization and deep reasoning .
What Went Wrong? A Closer Look at AI’s Thought Process
A deeper analysis of the models’ internal reasoning processes revealed some surprising inefficiencies:
- Initially, LRMs responded to increasing complexity by generating longer chains of thought , which seemed logical. But as they approached the failure threshold, they suddenly shortened their reasoning paths , even when they had unused compute capacity left.
- Even when given explicit algorithms or step-by-step instructions , the models often failed to follow them correctly — highlighting serious gaps in their ability to execute logical sequences .
- The study also showed that model performance varied widely between familiar and unfamiliar puzzles , indicating that success was largely tied to prior exposure in training data , rather than generalizable reasoning skills .
These findings suggest that while LRMs may appear smarter in controlled conditions, they are still far from replicating human-like reasoning — especially when faced with novel, complex challenges.