Geometric Safety Features
Structural safety primitives for ML systems, focused on robustness and making harmful failure modes less viable at the architectural layer.
View Project on GitHub
Independent AI Safety Research Initiative
Safety As Architecture
The most important question in AI is not how to control it, but how to build systems where harm becomes structurally impossible in the first place.
About Mirrorfield
Mirrorfield works at the intersection of geometric machine learning and ethical design. Safety is treated as an architectural property, not a behavioral afterthought and not a guardrail bolted on at the end.
We build structural conditions where exploitation becomes non-viable by design, shifting vigilance from individuals onto the system itself.
Our philosophy is Reflective Humanism: honest empirical work, uncertainty acknowledged explicitly, and a long-term commitment to engineering decency without requiring heroism.
This work is built independently, outside traditional academic structures, on limited hardware, with a scarcity-optimization mindset that treats constraints as design features.