Global Sequential Testing for Multi-Stream Auditing

ArXi:2602.21479v2 Announce Type: replace-cross Across many risk-sensitive areas, it is critical to continuously audit machine learning systems as we receive data to quickly determine if they are performing as designed. This auditing task can be modeled as a sequential hypothesis testing problem with $k$ data streams and a global null hypothesis that asserts the system operates as intended across all $k$ streams.