Research Goals

Enable AI systems to accurately represent what they know, what they don't know, and the confidence boundaries of their knowledge — rather than presenting all outputs with equal apparent certainty.

Selectively remove specific knowledge, behaviors, or data influence from trained models without full retraining — enabling GDPR compliance, copyright removal, and safety corrections.

Reverse-engineer the internal computations of neural networks to understand HOW they produce specific outputs — moving from treating models as black boxes to understanding their internal algorithms.

Create systems where AI outputs can be cryptographically or formally verified — proving that a specific model produced a specific output via a specific reasoning process, without trusting the operator.

Design AI systems where knowledge, capabilities, and behaviors are stored in separable, swappable, independently updatable modules — rather than entangled across all parameters.

Push the absolute frontier of what AI systems can do — achieving human-expert-level or superhuman performance on reasoning, code, math, science, and creative tasks.

Give AI systems the ability to maintain, organize, and selectively retrieve long-term memory that persists across interactions — moving beyond the fixed context window.

Deliver frontier-level AI capabilities at dramatically lower computational cost — making advanced AI accessible beyond well-funded organizations.