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

• URL: http://arxiv.org/abs/2603.02748v1
• Date: Tue, 03 Mar 2026 08:49:41 GMT
• Title: iGVLM: Dynamic Instruction-Guided Vision Encoding for Question-Aware Multimodal Understanding
• Authors: HanZpeng Liu, Yaqian Li, Zidan Wang, Shuoxi Zhang, Zihao Bo, Rinyoichi Takezoe, Kaiwen Long, Kun He,
• Abstract summary: iGVLM is a framework for instruction-guided visual modulation.<n>We show that iGVLM consistently enhances instruction sensitivity across diverse language backbones.
• Score: 15.347813563177253
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite the success of Large Vision--Language Models (LVLMs), most existing architectures suffer from a representation bottleneck: they rely on static, instruction-agnostic vision encoders whose visual representations are utilized in an invariant manner across different textual tasks. This rigidity hinders fine-grained reasoning where task-specific visual cues are critical. To address this issue, we propose iGVLM, a general framework for instruction-guided visual modulation. iGVLM introduces a decoupled dual-branch architecture: a frozen representation branch that preserves task-agnostic visual representations learned during pre-training, and a dynamic conditioning branch that performs affine feature modulation via Adaptive Layer Normalization (AdaLN). This design enables a smooth transition from general-purpose perception to instruction-aware reasoning while maintaining the structural integrity and stability of pre-trained visual priors. Beyond standard benchmarks, we introduce MM4, a controlled diagnostic probe for quantifying logical consistency under multi-query, multi-instruction settings. Extensive results show that iGVLM consistently enhances instruction sensitivity across diverse language backbones, offering a plug-and-play paradigm for bridging passive perception and active reasoning.

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