Multimodal Large Language Models with Context-Aware Fusion Mechanisms

Multimodal reasoning tasks, which require integrating and processing diverse modalities such as vision and language, are critical for developing intelligent systems. In this paper, we propose AMCI-MLLM (Adaptive Multimodal Context Integration for Multimodal Large Language Models), a novel generative model that dynamically adjusts the contributions of different modalities based on task-specific queries. The core innovation of our method lies in a context-aware gating mechanism integrated within cross-modal attention layers, enabling fine-grained multimodal reasoning. To optimize learning, we introduce a two-stage training strategy: task-specific pretraining and adaptive fine-tuning with curriculum learning. Our experiments show that AMCI-MLLM achieves state-of-the-art performance on benchmarks such as VQAv2, TextVQA, and COCO Captions, outperforming existing models in accuracy, relevance, and fluency. Extensive analyses further highlight its scalability, robustness to noisy inputs, and enhanced interpretability. These findings showcase the potential of AMCI-MLLM to address key challenges in multimodal reasoning tasks and provide a robust framework for future research in this domain.