BEACON: Behavioral Entropy Aggregation for Cross-Model Hallucination Detection in Large Language Models

The paper introduces BEACON, a black-box framework for detecting hallucination in large language models by analyzing model outputs without internal access or external knowledge. It extracts a 31-dimensional feature vector and a gradient-boosted classifier achieves 0.8123 AUROC, outperforming existing baselines.