The Unseen Bias: How Norm Discrepancy in Pre-Norm MLLMs Leads to Visual Information Loss

This article likely discusses a technical issue within Multimodal Large Language Models (MLLMs), specifically focusing on how discrepancies in the normalization process (pre-norm) can lead to a loss of visual information. The title suggests an investigation into a subtle bias that affects the model's ability to process and retain visual data effectively. The source, ArXiv, indicates this is a research paper.