What it is
The user defines training intent as a set of structured constraints — priorities, limits, and safety requirements that the training process must respect.
During training, the system continuously collects structured telemetry and feeds it into a constrained, language-model-based controller. The controller selects safe training actions from a finite, predefined action set, keeping every adjustment interpretable and reversible.
How it works
- 01User-defined training intent
- 02Structured training telemetry
- 03Constrained AI controller
- 04Filter synthesis
- 05Temporary training branches
- 06Parameter freezing
- 07Rollback and audit logs
What sets it apart
Feature 01
Human-guided optimization
Feature 02
Safety-aware training
Feature 03
Explainable decisions
Feature 04
Reversible control actions
Feature 05
Full auditability
Where it applies
From concept to clinic
Controller simulation
Telemetry interface
Controlled training sandbox
Safety evaluation
Production-ready research framework