When Models Stop Listening: How Feature Collapse Quietly Erodes Machine Learning Systems

Recent research highlights that machine learning models can fail silently through a phenomenon called feature collapse, where they excessively narrow their focus to a limited set of features, leading to fragility and degraded performance. This subtle form of failure occurs without explicit noise or errors, undermining model robustness and emphasizing the need for techniques that promote diverse feature utilization to maintain system stability.