I am the creator and project lead of Corridor, an independent AI diagnostics and assurance project focused on structural failure detection, evidence quality, and long-horizon evaluation of advanced AI systems. I have directed Corridor’s architecture, experimental design, validation strategy, and technical development using AI-assisted engineering tools, GitHub, Replit, Python-based testing, and structured evidence pipelines.

My research includes the SSRN paper “The Local Illusion vs The Structural Reality: Structural False Relief in Agentic Systems,” which introduces Structural False Relief—the idea that apparent local recovery in an agent can mask accumulating structural degradation over longer execution horizons. That work developed directly from Corridor experiments.

I am especially interested in trustworthy AI, cognitive architectures, machine consciousness research, and developing better methods for independently observing what advanced AI systems are actually doing beneath surface-level outputs.