Creator of M-COP 2.0 — a framework for verifiable, self-auditing AI cognition. I build production-grade systems where reasoning is traceable, energy is conserved, and the seam between mind and machine is engineered, not assumed.
Cognition should be auditable. Energy should be conserved. Causality should be respected. Everything I work on lives at the intersection of those three constraints — and the surprising shapes that emerge when you take them seriously.
Adaptive Optimization Framework 2.0 — recursive meta-cognitive optimization across domains. Source-available under BUSL 1.1; converts to MIT on 2030-04-26.
Interpreting the cyber-physical. A prototype for grounding model claims in thermodynamic constraint — what a system can know is what its energy budget can carry.
Proposal for a new subfield. The study of energy-conserving, information-preserving, causality-respecting propagation of sensory data through distributed media — governed by universal physical limits.
M-COP 2.0 surfaces a continuous trace of its own reasoning steps, energetic cost, and constraint violations. The terminal below is a sample window into that stream — sanitized, slowed, and stitched from real runs.
A timeline of the work, not a CV. The questions are what continued.
I'm reachable for research collaboration, framework integration, and unreasonably ambitious projects at the seam of physics, computation, and cognition.