AI Models Collapse in Face of Complex Problems

Just days ahead of the much-anticipated Worldwide Developer Conference (WWDC), Apple has released a study titled "The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity", which saw researchers testing 'reasoning' AI models such as Anthropic's Claude, OpenAI's models, DeepSeek RL, and Google's Thinking models to see how far they can scale to replicate human reasoning.

- Vishal Mathur

NEW DELHI: Just days ahead of the much-anticipated Worldwide Developer Conference (WWDC), Apple has released a study titled "The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity," which saw researchers testing 'reasoning' AI models such as Anthropic's Claude, OpenAI's models, DeepSeek RL, and Google's Thinking models to see how far they can scale to replicate human reasoning. Spoiler alert—not as much, as the entire AI marketing pitch would have you believe. Could this signal what may be in store for Apple's AI conversation ahead of the keynote?

The study questions the current standard evaluation of Large Reasoning Models (LRMs) using established mathematical and coding benchmarks, arguing they suffer from data contamination and don't reveal insights into reasoning trace structure and quality. Instead, it proposes a controlled experimental test-bed using algorithmic puzzle environments. The limitations of AI benchmarking, and need to evolve, is something we had written about earlier.