Research

PhysOpsAI's work is built on foundational research in geometric representation learning. Specifically, how AI systems can be designed to learn genuinely invariant relationships rather than environment-specific shortcuts, and how that property can be measured and verified.

The question that organizes the program: when does a learned system's good behavior survive deployment into a new environment, and what evidence would prove it? Research papers are under peer review in 2026.

Publications and preprints will be listed here as they become available. US provisional patent filed 2026; patent pending.