Coherence, Not Cognition: Rethinking How LLMs Help Us Think
LLMs don’t recall facts—they reconstruct meaning. This blog introduces “coherence reconstruction,” a practical theory explaining why LLMs feel useful even when wrong—by simulating thought-like patterns that amplify human reasoning and creativity.
We’ve all heard it:
“LLMs are just stochastic parrots.”
“They don’t know anything.”
“They hallucinate—they can’t be trusted.”
And yet…
They help us write.
They help us think.
They show us things we didn’t quite see before.
So what’s actually going on?
I recently wrote a white paper that offers a practical theory for why LLMs feel so useful—even when they’re wrong. I call it coherence reconstruction.
The basic idea is this:
LLMs don’t store knowledge the way we do.
They rebuild meaning on demand—by lighting up patterns across a massive internal space.
They don’t recall facts. They simulate fragments of thought.
And that’s what makes them powerful collaborators.
They don’t form a mind. But they simulate fragments of one.
And when activated well, those fragments amplify human reasoning, augment effort, and catalyze insight.
📄 Read the full paper:
👉 AI Coherence: A Theory of Utility in Large Language Models (PDF)
This piece digs into:
- Why prompts act like force vectors through latent meaning
- Why hallucinations are signals—not just noise
- How conceptual clouds emerge as you interact
- And why we need to stop treating LLMs like oracles—and start treating them like instruments
If you’re working on GenAI, trying to frame hallucinations, or just want a fresh theory to build around—this might help.