Singular Learning for AI Alignment

Singular Learning Theory (SLT) links the geometry of neural-net loss landscapes to internal model structure, offering mathematical diagnostics for interpretability and alignment. If SLT scales, it could provide practical, testable tools to certify model behaviour rather than rely only on empirical stress‑testing or speculative timelines. — A workable, theoretically grounded verification method would shift policy debates from forecasting timelines toward standards-based certification and governance for high‑risk models.