Learning to Explore: Policy-Guided Outlier Synthesis for Graph Out-of-Distribution Detection

Researchers propose PGOS (Policy-Guided Outlier Synthesis), a new framework that uses reinforcement learning to improve Graph Neural Network safety by better detecting out-of-distribution graphs. The system replaces static sampling methods with a learned exploration strategy that navigates low-density regions to generate pseudo-OOD graphs for enhanced detector training.