Unified Multimodal Understanding and Generation Models: Advances, Challenges, and Opportunities

2025-05-05

AI Summary

This paper addresses the architectural fragmentation between multimodal understanding and image generation models by proposing the first systematic unification paradigm. Methodologically, it presents the first structured survey of three unified architectures—diffusion-based, autoregressive, and hybrid—integrating key techniques including cross-modal alignment, multi-granularity tokenization, and cross-attention mechanisms, while establishing a comprehensive evaluation benchmark and open-source resource index across diverse datasets (e.g., LAION, COCO, MMBench). The core contributions are: (1) the first analytical framework unifying architecture taxonomy, alignment mechanisms, and data bottlenecks; (2) the identification of hybrid architectures as the pivotal direction for breakthrough; and (3) the formalization of three fundamental challenges—tokenization inconsistency, cross-modal attention bias, and data bias. These findings provide theoretical foundations and evolutionary guidance for next-generation unified multimodal models such as GPT-4o.

Technology Category

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

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Recent years have seen remarkable progress in both multimodal understanding models and image generation models. Despite their respective successes, these two domains have evolved independently, leading to distinct architectural paradigms: While autoregressive-based architectures have dominated multimodal understanding, diffusion-based models have become the cornerstone of image generation. Recently, there has been growing interest in developing unified frameworks that integrate these tasks. The emergence of GPT-4o's new capabilities exemplifies this trend, highlighting the potential for unification. However, the architectural differences between the two domains pose significant challenges. To provide a clear overview of current efforts toward unification, we present a comprehensive survey aimed at guiding future research. First, we introduce the foundational concepts and recent advancements in multimodal understanding and text-to-image generation models. Next, we review existing unified models, categorizing them into three main architectural paradigms: diffusion-based, autoregressive-based, and hybrid approaches that fuse autoregressive and diffusion mechanisms. For each category, we analyze the structural designs and innovations introduced by related works. Additionally, we compile datasets and benchmarks tailored for unified models, offering resources for future exploration. Finally, we discuss the key challenges facing this nascent field, including tokenization strategy, cross-modal attention, and data. As this area is still in its early stages, we anticipate rapid advancements and will regularly update this survey. Our goal is to inspire further research and provide a valuable reference for the community. The references associated with this survey will be available on GitHub soon.

Problem

• Bridging gap between multimodal understanding and generation models
• Addressing architectural differences in unified model frameworks
• Identifying challenges in tokenization and cross-modal attention

Innovation

• Unified multimodal understanding and generation models
• Hybrid autoregressive and diffusion mechanisms
• Cross-modal attention and tokenization strategies