ARC’s Matching Sampling Pipeline: From Structure Detection to Alignment Guarantees
ARC now centers its research on the Matching Sampling Principle, building a pipeline that monitors model training, extracts internal structure, and uses mechanistic estimators to predict rare catastrophic failures without needing sampled failures. If successful, this could let us flag deceptive alignment or reward‑hacking early and steer powerful AI toward safe behavior.