Safety & Alignment.
Ensuring narrow ASI systems reliably pursue human-beneficial goals through mathematical frameworks and measurable safety metrics.
Active Frameworks
We take a multi-layered approach: mathematical value learning, scalable oversight, and formal verification bounds.
Value Learning Frameworks
Mathematical formalization of beneficial AI. Using inverse reinforcement learning to extrapolate latent human preferences across distribution shifts.
Constitutional Integration
Scalable oversight protocols. Implementing recursive reward modeling and principle-based training to reduce reliance on direct human labels.
Behavioral Bounds
Hard constraints on action spaces. Utilizing mathematical proofs to guarantee system behavior remains within safety envelopes during runtime.
Emergency Response
Fail-safe architectures. Developing tripwire mechanisms and rapid-shutdown protocols for detecting sudden alignment failures.
Technical Theses
Formal publications detailing our progress in alignment theory and architectural safety.
Solving alignment
together.
We welcome collaboration with academic institutions and safety researchers to advance shared goals.