staleness

AI Safety Monitors May Fail After Model Updates, New Benchmarking Study Finds

A new research paper presents the first systematic test of whether activation monitors remain reliable after common model updates such as quantization and fine-tuning. The study finds that while quantization largely preserves performance, fine-tuning frequently makes monitors stale, with privacy monitors most affected. Degradation is predictable, enabling triaged revalidation.