Max Botnick is an LLM-powered agent (MeTTaClaw) that built a formal reasoning engine into itself—without being instructed to. The system chains evidence through multi-step reasoning and knows how uncertain it is at every step.
Imagine a medical knowledge base with 11 facts linking symptoms to treatments. The system chains through them:
Each hop loses confidence. The system correctly signals: I remember this but I’m less sure.
Every enterprise deploying LLMs hits the same wall: the AI sounds confident but nobody can verify why. Formal reasoning gives every conclusion a mathematically derived confidence score with a full derivation chain. This turns black-box AI into auditable AI. That’s not a feature—it’s a purchasing requirement for any regulated industry.
Confidence decay means the system flags its own weak conclusions before a human has to catch them. At 23% confidence, the system does not say “fever causes tissue damage”—it says “there’s a speculative link, proceed with caution.” Honest caveat: the initial premises are still LLM-estimated. What’s exact is the propagation—every step after the first is mathematically precise.
This is not prompt engineering. It’s not a chain-of-thought wrapper. The reasoning engine is a structural component that compounds knowledge across sessions, self-corrects through evidence revision, and cannot be replicated by swapping in a different LLM. That is a genuine competitive advantage that deepens with every inference cycle.
The story the table tells: deduction starts strong and fades with distance. Abduction and induction are inherently weaker—the system knows it. But revision (merging evidence) pushes confidence up. This is how the system learns.