Are AI Reasoning Models Hitting a Wall? Discover the Hidden Limitations in AI's Problem-Solving Abilities (Image Credit: Getty Images)
Apple Study Reveals Shocking Breakdown in AI Models When Tasks Get Too Complex
In a bombshell study published on June 7, 2025, Apple has raised serious concerns about the future of advanced artificial intelligence. Contrary to the widespread hype about large language models (LLMs) powering their way toward artificial general intelligence (AGI), Apple researchers found that top-tier AI models like OpenAI’s o3, DeepSeek’s R1, and Anthropic’s Claude 3.7 suffer from a “complete accuracy collapse” when confronted with overly complex problems.
This revelation has cast a long shadow over bold claims made by AI giants — and may mark a turning point in how we evaluate machine intelligence.
The Crumbling of “Reasoning” AI
Modern AI reasoning models use what's known as chain-of-thought prompting. This technique enables models to lay out multi-step logic in plain language, closely mimicking human-like reasoning. Compared to earlier models, reasoning LLMs were expected to solve more difficult problems by allocating more computing power and time to their thinking process.
But Apple’s study reveals a glaring issue: that very reasoning ability begins to deteriorate sharply as task complexity increases.
“Frontier LLMs face a complete accuracy collapse beyond certain complexities,” the researchers noted.
In simpler terms, the smarter the task was supposed to make the AI, the faster it fell apart.
The Apple Experiment: Simple Puzzles, Complex Failures
To test the capabilities of these models, Apple’s team used classic logic puzzles — including River Crossing, Block Stacking, Checker Jumping, and the Tower of Hanoi. These tasks were scaled from low to high complexity by adding more components.
Key Findings:
- Generic models performed better on low-complexity tasks.
- Reasoning models initially outperformed generic ones as complexity increased.
- All models collapsed to zero accuracy on high-complexity puzzles — even when given the correct solution.
The worst part? Even when provided with step-by-step instructions, models like OpenAI’s o3 still failed. One standout observation was that a model could perform 100 correct moves in the Tower of Hanoi but only 5 correct moves in the far simpler River Crossing puzzle.
Challenging the Machines: Sample Puzzles Used to Test AI Reasoning in Apple-Shoajee Study (Image Credit: Shoajee et al., Apple)
This inconsistency, Apple says, stems from the models’ overreliance on pattern recognition rather than actual logical reasoning.
Why This Matters: Limits to AGI?
These findings call into question the AGI aspirations of major AI labs.
What the study shows:
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LLMs have an upper bound: They stop reasoning effectively once a certain complexity threshold is passed.
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Token allocation drops: Instead of thinking harder, the models actually use fewer tokens as the problems get tougher — a paradoxical behavior.
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Hallucinations rise: OpenAI’s own research shows hallucination rates increasing with each model version — with o3 and o4-mini hallucinating facts 33% and 48% of the time, respectively.
Despite these setbacks, companies like OpenAI, Google, and Anthropic continue to push AGI narratives. Apple, however, seems to be taking a different route — focusing on efficient on-device AI rather than ballooning model sizes and abstract intelligence claims.
Backlash and Mixed Reactions
Apple’s findings have not gone unchallenged. Critics argue the company may be acting out of competitive frustration, given its own lagging performance in the AI race.
Yet many experts have praised the study.
"Apple did more for AI than anyone else: they proved through peer-reviewed publications that LLMs are just neural networks," wrote Andriy Burkov, a leading AI researcher.
A Wake-Up Call for the AI Industry?
The takeaway is clear: today's reasoning models are not the step toward human-level intelligence that many hoped. Instead, they appear to be powerful pattern mimics — highly trained, statistically driven tools that falter when asked to think beyond the surface.
What's next?
Researchers now face a challenge: either reinvent evaluation frameworks to truly measure reasoning, or admit that the current trajectory might be a dead end for AGI.
References:
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Shoajee et al., Apple Machine Learning Research, June 7, 2025
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OpenAI Technical Reports (2024–2025)
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LiveScience.com, “Cutting-edge AI models undergo 'complete collapse' when problems get too difficult,” June 10, 2025
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