From Vision to Reasoning: Leveraging Deep Learning for Enhancing Large Language Models in Multimodal Understanding

Abstract

In recent years, the integration of deep learning and large language models (LLMs) has become a transformative force in artificial intelligence, driving advances in multimodal understanding, reasoning, and human-computer interaction. While LLMs exhibit strong linguistic and reasoning capabilities, their perception of non-textual modalities such as images, videos, and signals remains limited. This paper proposes a unified framework named DeepVision-Reasoner, which leverages deep neural architectures to enhance the multimodal reasoning capacity of LLMs. The framework integrates a vision encoder based on convolutional and transformer-based representations with a large language decoder, enabling the model to learn from both visual and textual sources in an end-to-end manner. The proposed method introduces a dual-stage alignment process that harmonizes visual embeddings with linguistic tokens through a shared latent space and an adaptive cross-attention mechanism. Extensive experiments across visual question answering, caption generation, and image-grounded reasoning demonstrate that the proposed model significantly outperforms baseline multimodal LLMs in accuracy, coherence, and semantic grounding. Moreover, the model exhibits robust generalization under zero-shot settings, highlighting the synergy between deep learning feature extraction and large-scale generative reasoning. This study contributes to the ongoing convergence between perceptual deep networks and cognitive-level language models, paving the way for more unified, human-like artificial intelligence systems.