How Do LLMs Reason? A Look Inside the ‘Thinking’ Mind of AI

Author(s): Abhishek Gautam

Originally published on Towards AI.

How Do LLMs Reason? A Look Inside the ‘Thinking’ Mind of AI

It’s the question at the heart of the AI revolution. When you prompt a Large Language Model (LLM) and it lays out a step-by-step plan, solves a complex problem, or generates a creative strategy, is it actually thinking? Are we witnessing a genuine spark of digital consciousness, or are we being captivated by an incredibly sophisticated illusion?

This article explores the reasoning capabilities of Large Language Models (LLMs) and introduces Large Reasoning Models (LRMs) that enhance reasoning through structured processes known as “Chains of Thought.” Researchers have developed a “cognitive gym” of complex logic puzzles to study these models systematically, revealing significant strengths and weaknesses in how they tackle varying difficulties. Findings indicate that while LRMs perform well on medium complexities, they struggle with high complexities, illustrating a pattern-matching failure that leads to reasoning breakdowns. Overall, this relationship highlights both the real capabilities and limits of AI reasoning when applied in practical scenarios.

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Published via Towards AI