Reasoning models reason well, until they don't

LLMs falter because likelihood-driven pattern completion doesn’t enforce coherence across uncertainty (probability), representation (geometry), composition (category), and search (reasoning). To get robust reasoning, we need these layers to be explicit, typed, and mutually constraining—with verification and calibrated belief updates in the loop.

I was interviewed about this recently, and mentioned the great work of a professor of CS and Law who has been building the foundations for this approach. My own article about it was recently un-linked due to a Notion mishap (but available if anyone is interested - I have to publish it again)

https://www.forbes.com/sites/hessiejones/2025/09/30/llms-are...