New Framework for Automatic Stability and Recovery in Neural Network Training

A research paper introduces a supervisory runtime stability framework for neural network training that enables automatic detection and recovery from destabilizing updates. The framework treats optimization as a controlled stochastic process, isolating an innovation signal from secondary measurements like validation probes. It provides theoretical runtime safety guarantees for bounded degradation and recovery, without modifying the underlying optimizer. The implementation incurs minimal overhead and is compatible with memory-constrained settings. The paper is available on arXiv under computer science and machine learning.