[2605.05594] The Cost of Context: Mitigating Textual Bias in Multimodal Retrieval-Augmented Generation
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Computer Science > Computation and Language
arXiv:2605.05594 (cs)
[Submitted on 7 May 2026]
Title:The Cost of Context: Mitigating Textual Bias in Multimodal Retrieval-Augmented Generation
Authors:Hoin Jung, Xiaoqian Wang
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Abstract:While Multimodal Large Language Models (MLLMs) are increasingly integrated with Retrieval-Augmented Generation (RAG) to mitigate hallucinations, the introduction of external documents can conceal severe failure modes at the instance level. We identify and formalize the phenomenon of recorruption, where the introduction of even perfectly accurate "oracle" context causes a capable model to abandon an initially correct prediction. Through a mechanistic diagnosis of internal attention matrices, we show that recorruption is driven by a two-fold attentional collapse: (1) visual blindness, characterized by the systemic suppression of visual attention mass ($M_{vis}$) and sharpness ($S_{vis}$), and (2) a structural positional bias that forces the model to prioritize boundary tokens over semantic relevance. Our analysis reveals an Illusion of Success, demonstrating that many seemingly correct RAG outcomes are merely positional coincidences where the model's textual copying bias happens to align with the ground-truth location. To address these vulnerabilities, we propose Bottleneck Attention Intervention for Recovery (BAIR), a parameter-free, inference-time framework that restores visual saliency and applies position-aware penalties to textual distractors. Across medical factuality, social fairness, and geospatial benchmarks, BAIR successfully restores multimodal grounding and improves diagnostic reliability without requiring model retraining or fine-tuning.
Subjects:
Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2605.05594 [cs.CL]
(or arXiv:2605.05594v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.05594
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arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Hoin Jung [view email] [v1] Thu, 7 May 2026 02:27:04 UTC (5,545 KB)
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