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Inside the Black Box: How Large Language Models "Think" — And Why It Matters

Introduction: Do Neural Networks Actually Think? Almost two years have passed since ChatGPT became a household name. And yet, AI researchers are still debating the big question: are large language models (LLMs) genuinely capable of thinking — or are they just glorified parrots, mimicking patterns without true understanding? This article takes you deep into the heart of the issue: how scientists approach the challenge of interpreting what LLMs are doing internally, why it’s so hard, and what it means for the future of AI and humanity. Spoiler: the answer may not be found in the model’s outputs — but rather in how it gets there. Arithmetic as a Window into AI Reasoning Let’s start with something simple: basic math. Ask a language model “what’s 2+3?”, and it answers “5” without hesitation. That’s not surprising — this exact question has probably appeared thousands of times in its training data. But what happens when you ask it to add two  40-digit numbers , randomly generated and pr...

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