Towards LLM-Centric Multimodal Fusion: A Survey on Integration Strategies and Techniques

2025-06-05

AI Summary

Existing research lacks a systematic analysis of modality–language backbone integration mechanisms in multimodal large language models (MLLMs). Method: We systematically survey 125 MLLMs published between 2021 and 2025, proposing the first large-language-model-centric three-dimensional taxonomy—architectural integration, representation learning, and training paradigms—to unify cross-modal alignment pathways. Leveraging bibliometric analysis, architectural decoupling, and fine-grained modeling of embeddings and loss functions, we characterize evolutionary patterns in fusion granularity, joint representation design, and objective function development. Contribution/Results: This work fills a critical theoretical gap in structured analysis of MLLM fusion mechanisms, identifies shared bottlenecks—including misaligned modality-specific feature hierarchies and suboptimal cross-modal optimization objectives—and delivers a reusable theoretical framework and practical guidelines for developing robust, scalable multimodal foundation models.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Computer Vision: Multi-modal Vision

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

The rapid progress of Multimodal Large Language Models(MLLMs) has transformed the AI landscape. These models combine pre-trained LLMs with various modality encoders. This integration requires a systematic understanding of how different modalities connect to the language backbone. Our survey presents an LLM-centric analysis of current approaches. We examine methods for transforming and aligning diverse modal inputs into the language embedding space. This addresses a significant gap in existing literature. We propose a classification framework for MLLMs based on three key dimensions. First, we examine architectural strategies for modality integration. This includes both the specific integration mechanisms and the fusion level. Second, we categorize representation learning techniques as either joint or coordinate representations. Third, we analyze training paradigms, including training strategies and objective functions. By examining 125 MLLMs developed between 2021 and 2025, we identify emerging patterns in the field. Our taxonomy provides researchers with a structured overview of current integration techniques. These insights aim to guide the development of more robust multimodal integration strategies for future models built on pre-trained foundations.

Problem

• Systematic understanding of multimodal integration with LLMs
• Classification framework for MLLMs based on key dimensions
• Analysis of 125 MLLMs to identify emerging patterns

Innovation

• LLM-centric multimodal fusion analysis
• Classification framework for MLLMs
• Training paradigms and objective functions