September 20, 2025

Words Over Pixels? Rethinking Vision in Multimodal Large Language Models

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MLLMs struggle to effectively use visual information, often relying on textual cues, impairing their performance.
Key challenges include weak spatial reasoning and poor fine-grained visual perception in multimodal large language models.
Current vision encoders have limitations affecting downstream tasks, highlighting the need for improvement in MLLMs.
Research opportunities are critical for enhancing cross-modal alignment and developing robust multimodal models.

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Survey analyzes vision challenges in multimodal large language models, highlighting the need for better integration and understanding.

Jain et al. (2025) studied this question.

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