NeuroGenesysFrontier science.
Human guardrails.
We work on the two most consequential technologies imaginable — machines that think and interfaces to the human mind. Both demand the same discipline: capability never ships ahead of oversight, and people always come before results.
Mental privacy.
Model containment.
Neural data and frontier model weights are the two most sensitive assets in science. Ours are governed by the same principle: strict isolation, informed consent, independent oversight, and no exceptions for expedience — not for a deadline, not for a demo, not for a headline.
Neural Data Privacy
Mental privacy is a right. Any neural data we ever touch will be collected under informed consent, de-identified at acquisition, and never used outside its study.
Secured Frontier Weights
Our commitment: frontier weights belong in hardware-isolated enclaves under strict access control. Capability stays locked until it clears the safety bar.
Contained Evaluation
Our standard: dangerous-capability evaluations run in contained sandboxes before any external exposure. No model ships ahead of its oversight.
Clinical Governance
We run no interface trials today. Any future clinical work will run under proper regulatory protocols with independent ethics oversight — participant wellbeing before publishable results.
Mental privacy,
by design.
Thoughts are not telemetry. Our design rule for any future study: neural signals de-identified at the electrode, encrypted end-to-end, and readable only inside the study they belong to — with participants free to withdraw themselves and their data at any time.
Oversight that scales
with capability.
Our bar for the Genesys program: interpretability that tells us what a model has learned, not just what it says. Scalable oversight and contained evaluations gate every capability increase — understanding first, deployment second.
Adversarial
by profession.
Falsification is our method. Every claim ships with a pre-registered kill criterion, and we attack our own results before anyone else can — then publish when the attack wins. External replication and challenge are always welcome.
Built in the open.
Verified by everyone.
Safety claims you can't inspect are just marketing. Our research runs on the open stack the community shares, our interpretability tools and benchmark datasets are published, and our safety framework is documented publicly — so external researchers can check our work, not take our word for it.
PyTorch
Real-checkpoint validation for our published studies.
NumPy
Run-once, seeded falsification experiments.
Python
Cost-model campaigns for analog and optical compute.
pytest
Automated verification suites behind every claim.
Markdown + KaTeX
Open papers, readable by humans and machines.
TypeScript
Engineering across the AW3 venture portfolio.
