The Cost of Context: Mitigating Textual Bias in Multimodal Retrieval-Augmented Generation

While Multimodal Large Language Models (MLLMs) are increasingly integrated with Retrieval-Augmented Generation (RAG) to mitigate hallucinations, retrieved documents can conceal severe instance-level failures. We formalize *recorruption*, in which even perfectly accurate "oracle" context turns an initially correct prediction into an incorrect one. Our attention analysis associates recorruption with lower visual attention mass ($M_{\mathrm{vis}}$) and sharpness ($S_{\mathrm{vis}}$), as well as attention concentrated at textual boundaries. This reveals an *Illusion of Success*. Successful RAG outputs and recorruption failures have similar measured attention profiles, while accuracy varies with the position of relevant evidence. This suggests that some correct outputs may benefit from favorable evidence placement, even when measured visual attention remains diffuse. To address these vulnerabilities, we propose **Bottleneck Attention Intervention for Recovery (BAIR)**, a training-free, inference-time framework that restores visual attention mass and sharpness while applying position-aware penalties to textual distractors. Across medical factuality, social fairness, and geospatial benchmarks, BAIR improves performance without model retraining or fine-tuning.