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Apple researchers recently published a study titled "The Illusion of Thinking," suggesting that advanced language models (LLMs) struggle with true reasoning, relying instead on pattern matching. The study presented findings based on tasks like the Tower of Hanoi puzzle, where models purportedly failed when complexity increased, leading to the conclusion that these models possess limited problem-solving abilities. However, these conclusions are now under scrutiny, with critics arguing the experiments were not fairly designed.
Alex Lawsen of Open Philanthropy has published a counter-study challenging the foundations of Apple's claims. Lawsen argues that models like Claude, Gemini, and OpenAI's latest systems weren't failing due to cognitive limits, but rather because the evaluation methods didn't account for key technical constraints. One issue raised was that models were often cut off from providing full answers because they neared their maximum token limit, a built-in cap on output text, which Apple's evaluation counted as a reasoning failure rather than a practical limitation.
Another point of contention involved the River Crossing test, where models faced unsolvable problem setups. When the models correctly identified the tasks as impossible and refused to attempt them, they were still marked wrong. Furthermore, the evaluation system strictly judged outputs against exhaustive solutions, failing to credit models for partial but correct answers, pattern recognition, or strategic shortcuts. To illustrate, Lawsen demonstrated that when models were instructed to write a program to solve the Hanoi puzzle, they delivered accurate, scalable solutions even with 15 disks, contradicting Apple's assertion of limitations.
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References :
Alex Lawsen of Open Philanthropy has published a counter-study challenging the foundations of Apple's claims. Lawsen argues that models like Claude, Gemini, and OpenAI's latest systems weren't failing due to cognitive limits, but rather because the evaluation methods didn't account for key technical constraints. One issue raised was that models were often cut off from providing full answers because they neared their maximum token limit, a built-in cap on output text, which Apple's evaluation counted as a reasoning failure rather than a practical limitation.
Another point of contention involved the River Crossing test, where models faced unsolvable problem setups. When the models correctly identified the tasks as impossible and refused to attempt them, they were still marked wrong. Furthermore, the evaluation system strictly judged outputs against exhaustive solutions, failing to credit models for partial but correct answers, pattern recognition, or strategic shortcuts. To illustrate, Lawsen demonstrated that when models were instructed to write a program to solve the Hanoi puzzle, they delivered accurate, scalable solutions even with 15 disks, contradicting Apple's assertion of limitations.
ImgSrc: chatgptiseating
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References :
- chatgptiseatingtheworld.com: Research: Did Apple researchers overstate “The Illusion of Thinking†in reasoning models. Opus, Lawsen think so.
- Digital Information World: Apple’s AI Critique Faces Pushback Over Flawed Testing Methods
- NextBigFuture.com: Apple Researcher Claims Illusion of AI Thinking Versus OpenAI Solving Ten Disk Puzzle
- Bernard Marr: Beyond The Hype: What Apple's AI Warning Means For Business Leaders
Classification:
- HashTags: #AI #LLM #Reasoning
- Company: Apple
- Target: LLM developers
- Attacker: Researchers
- Product: LLMs
- Feature: AI Reasoning
- Type: Research
- Severity: Informative