Fox Framework Reduces Hallucinations in Vision-Language Models

Large Vision-Language Models (LVLMs) are powerful but often suffer from "object hallucination," where they describe objects not present in an image. This issue is not merely due to attention intensity but stems from a deeper structural misalignment: specific attention heads can decouple from visual evidence, relying instead on language priors, creating a "pathological shortcut." Researchers have developed Fox (Faithfulness and Observational-flow via eXpression-rectification), a novel framework designed to dismantle these shortcuts during inference without requiring retraining. Fox first diagnoses structural misalignment by using a visual attention entropy probe to identify these "risky mediators" in an unsupervised manner. Once identified, Fox performs a targeted causal intervention by numerically saturating logits, effectively severing the shortcut path. A conflict-gated cooperative decoding strategy then reconciles the improved faithfulness with the model's natural fluency. Extensive experiments show Fox significantly outperforms existing methods, improving faithfulness by 29.1% while maintaining linguistic quality.