Test Case Prioritization for DNNs via Neural Collapse Instability
A Neural-Collapse-Inspired Prioritization (NCIP) framework that replaces absolute confidence with cross-checkpoint prediction variability in the terminal training regime, where model geometry becomes highly structured and achieves strong performance in early fault discovery compared with competitive baselines.