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iGVLM: Dynamic Instruction-Guided Vision Encoding for Question-Aware Multimodal Understanding

arXiv – CS AI|HanZpeng Liu, Yaqian Li, Zidan Wang, Shuoxi Zhang, Zihao Bo, Rinyoichi Takezoe, Kaiwen Long, Kun He|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers propose iGVLM, a new framework that addresses limitations in Large Vision-Language Models by introducing dynamic instruction-guided visual encoding. The system uses a dual-branch architecture to enable task-specific visual reasoning while preserving pre-trained visual knowledge.

Key Takeaways

→iGVLM introduces a dual-branch architecture with frozen representation and dynamic conditioning branches for improved multimodal understanding.
→The framework addresses the representation bottleneck in existing LVLMs that rely on static, instruction-agnostic vision encoders.
→Adaptive Layer Normalization (AdaLN) enables affine feature modulation for task-specific visual processing.
→MM4 diagnostic probe was introduced to measure logical consistency in multi-query, multi-instruction settings.
→The system provides a plug-and-play solution that enhances instruction sensitivity across diverse language backbones.

#large-vision-language-models #multimodal-ai #computer-vision #machine-learning #adaptive-layer-normalization #instruction-guided #vision-encoding #arxiv #research

Read Original →via arXiv – CS AI

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