From Structure to Synergy: A Survey of Vision-Language Perception Paradigm Evolution in Multimodal Large Language Models

2026-06-24

AI Summary

Existing surveys often treat vision and language modalities in isolation, lacking a unified perspective on the evolution of perceptual capabilities in Multimodal Large Language Models (MLLMs). This work presents the first systematic review framed around human-like innate visio-linguistic joint perception, introducing a five-stage taxonomy that captures paradigmatic shifts and emphasizes cross-modal synergy over modality separation. Through comprehensive literature synthesis, paradigm categorization, and cross-modal analysis, the study systematically examines representative architectures, training strategies, and key milestones, tracing the trajectory of multimodal perception from structural fusion toward collaborative understanding. It further identifies current challenges and outlines a clear research roadmap toward Artificial General Intelligence.

Abstract

Multimodal Large Language Models (MLLMs) have recently made remarkable progress in unifying vision-language understanding and reasoning, especially following the introduction of models such as OpenAI's O-series and DeepSeek's R-series, which have driven a paradigm shift toward perception-centric intelligence. However, there remains a lack of systematic surveys that examine perception from a truly unified vision-language perspective -- one that treats vision and language as an inseparable modality. Existing reviews are often fragmented, focusing separately on either vision or language, and thus rarely capture the cross-modal evolution of perception as an integrated capability. To bridge this gap, we present the first systematic survey of unified vision-language perception in MLLMs. Specifically, we (1) formalize MLLM perception as an intrinsic, unified vision-language capability analogous to human innate perception, (2) introduce a five-stage taxonomy tracing the paradigm evolution of MLLM perception and survey representative methods and milestones at each phase, and (3) identify open challenges and outline promising research directions toward truly general, unified multimodal intelligence. We hope our study will provide both a foundational understanding and an actionable roadmap to foster further innovation on the path toward artificial general intelligence (AGI).

Problem

• vision-language perception
• multimodal large language models
• unified modality
• cross-modal evolution
• systematic survey

Innovation

• vision-language perception
• multimodal large language models
• unified modality
• perception taxonomy
• multimodal intelligence