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Alignment isn't a single test. It's behavior over time. Aurelius watches how models reason continuously in dynamic scenarios.
Score agents on behavior, not just answers. The network learns which approaches actually work under pressure.
The best-performing agents produce training data. That data trains future AI to reason through uncertainty.
Alignment develops over time. Agents improve. The training data grows. Future models inherit what the network learned.
