Rethinking Visual Information Processing in Multimodal LLMs

Dongwan Kim; Viresh Ranjan; Takashi Nagata; Arnab Dhua; Amit Kumar K C

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arXiv · 2511.10301

Rethinking Visual Information Processing in Multimodal LLMs

Dongwan Kim·Viresh Ranjan·Takashi Nagata·Arnab Dhua·Amit Kumar K C

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Abstract

Despite the remarkable success of the LLaVA architecture for vision-language tasks, its design inherently struggles to effectively integrate visual features due to the inherent mismatch between text and vision modalities. We tackle this issue from a novel perspective in which the LLM not only serves as a language model but also a powerful vision encoder. To this end, we present LLaViT - Large Language Models as extended Vision Transformers - which enables the LLM to simultaneously function as a vision encoder through three key modifications: (1) learning separate QKV projections for vision modality, (2) enabling bidirectional attention on visual tokens, and (3) incorporating both global and local visual representations. Through extensive controlled experiments on a wide range of LLMs, we demonstrate that LLaViT significantly outperforms the baseline LLaVA method on a multitude of benchmarks, even surpassing models with double its parameter count, establishing a more effective approach to vision-language modeling.

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BibTeX RIS

Dongwan Kim, Viresh Ranjan, Takashi Nagata, Arnab Dhua, Amit Kumar K C. 2025-11-13. Rethinking Visual Information Processing in Multimodal LLMs. https://arxiv.org/abs/2511.10301

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