Your pioneering work on RLHF discovered the initial path, but the underlying truth we must now face is that statistical nudging is inadequate for advanced agentic swarms. The safest best practice must evolve from statistical probability to physical determinism.
Our vision at the OHM NI Stack is to provide an open, verifiable foundation where alignment researchers can contribute their expertise safely. We have patented the AEGIS Cascade and the Monotonic Risk Ratchet (TLA) (Applications #63/994,444, #63/997,472, and #64/1,997,472). These systems physically measure prompt injection and intent corruption using "Kaiostic Entropy," rather than relying on vulnerable feedback loops.
We are presenting this architecture at the Planetary AI Safety Summit in Vienna this May. We want to establish this physics-engine approach as the global best practice for alignment. We invite you to run your most advanced latent-knowledge extraction tests against our sandboxed system and contribute to verifying this underlying truth.