Apple researchers show how popular AI models ‘collapse’ at complex problems

By | June 10, 2025
Apple researchers analyzing model collapse in AI systems

June 10, 2025 — Apple’s machine learning team has uncovered a significant problem in today’s most popular artificial intelligence models. Their study shows that many models collapse under complex reasoning tasks, despite producing fluent and confident answers.

This discovery could reshape how AI models are developed. It also raises concerns for companies relying heavily on large-scale models like OpenAI’s GPT-4, Google’s Gemini, and Meta’s LLaMA.

What Is ‘Model Collapse’?

Apple defines model collapse as a situation where AI models give convincing but flawed responses when solving complex problems. These issues occur in tasks that require logic, multiple reasoning steps, or structured problem-solving.

Even though these models excel at writing text and answering simple questions, they often fail in deeper reasoning. The problem becomes worse as models scale up, despite their improved fluency and memory.

Key Findings From Apple’s Study

Apple’s researchers tested a range of AI models using a custom benchmark. They focused on areas like:

  • Math and logic problems
  • Multi-step reasoning tasks
  • Symbolic thinking
  • Chain-of-thought challenges

The results showed that models often gave confident but incorrect answers. Many users might miss these errors because the responses sound polished.

Even highly regarded models, such as GPT-4 and Claude, showed weaknesses. Their performance dropped significantly when tasks demanded step-by-step reasoning or logical consistency.

Why Are Models Failing?

The study lists several reasons behind these failures:

  1. Focus on Language, Not Logic: Most models are trained to predict the next word, not to reason logically. This makes them sound intelligent, but they struggle with actual problem-solving.
  2. Overfitting Patterns: Large models memorize patterns in training data. This weakens their ability to generalize to new, unseen problems.
  3. Repetitive Training Data: Models trained on similar content tend to reinforce surface-level responses instead of learning deep reasoning.

A Warning to the AI Industry

Apple’s findings challenge a core belief in AI research — that bigger models are better. The study suggests that size and data volume alone cannot fix reasoning weaknesses.

While LLMs like GPT-4 can pass exams or write stories, they often lack true thinking skills. Apple’s research warns that unless developers rethink how these models are built, the progress in AI might hit a ceiling.

This comes at a time when major tech companies are racing to develop even larger and more powerful models. Apple’s research offers a timely reminder: intelligence is more than fluency.

Apple’s AI Strategy Becomes Clearer

Apple has stayed quiet in the AI race — until now. The company hasn’t released a major AI model, but it’s been publishing research on efficient training, memory handling, and now, reasoning.

These efforts point to a thoughtful approach. Apple seems to be focusing on models that are not only powerful but also safe, efficient, and logical. With iOS 18 expected to include several AI-powered features, Apple is slowly moving toward serious AI deployment — on its own terms.

Smarter Tests for Smarter AI

The Apple team also introduced a new benchmark. This test moves beyond typical datasets and challenges models with problems that require:

  • Logical thinking
  • Multiple reasoning steps
  • Pattern detection
  • Error identification

This benchmark pushes models beyond surface-level tasks and measures their real reasoning ability. It’s designed to separate models that sound smart from those that think smart.

Conclusion

Apple’s new research is a reality check for the AI industry. Models may impress with fluent answers, but many still fail at thinking logically under pressure.

For AI to move forward, it must go beyond language fluency. It needs a stronger foundation in reasoning. Apple’s work suggests that solving this will require smarter training, better evaluation, and a focus on logic — not just language.

As AI evolves, Apple’s insights could lead the way to safer, smarter, and more reliable systems.