The Secret Circuits of Machine Reasoning: Why Transformers Don’t Just Predict — They Decide…

Author(s): R. Thompson (PhD)

Originally published on Towards AI.

What if artificial intelligence didn’t just autocomplete your sentences or solve equations, but actually reasoned? Not like a mere pattern-matching machine, but like a thinking entity with logic steps, memory, inference — and its own peculiar style of cognition.🧠

This isn’t science fiction anymore. In 2025, AI is learning to reason, and it’s doing so in ways that reflect our minds yet sometimes transcend them.

Let’s explore how today’s advanced models (like GPT-4 and OpenAI’s o1) are pushing past mimicry into the realm of machine reasoning, often faster, deeper, and stranger than we imagined.

The black box is cracking open.

Mechanistic Interpretability (MI) has emerged as a deep-dive methodology into how neural networks operate beneath the surface. Rather than just interpreting inputs and outputs, MI reverse-engineers model internals — decoding features, circuits, and decision chains embedded within.

Take GPT-4, for instance. A 2023 Microsoft study revealed that it could solve multi-step function problems like f(f(f(6))) = 4 using internal logic circuits formed during training. These aren’t hardcoded rules; they’re emergent behaviors, surfacing during late-phase training in a phenomenon called grokking.

The bigger the model, the more intricate the circuits. And yet, understanding these trillion-parameter behemoths remains a massive technical challenge.

AI’s reasoning isn’t a carbon copy of… Read the full blog for free on Medium.

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Published via Towards AI