From Structure to Synergy: A Survey of Vision-Language Perception Paradigm Evolution in Multimodal Large Language Models
Quick Answer
This paper shows that This survey presents a systematic examination of Multimodal Large Language Models (MLLMs), emphasizing a unified vision-language perception framework.
Quick Take
It introduces a five-stage taxonomy of MLLM perception evolution and highlights open challenges and future research directions to advance toward artificial general intelligence (AGI).
Key Points
- Formalizes MLLM perception as a unified capability akin to human perception.
- Introduces a five-stage taxonomy tracing MLLM perception evolution.
- Surveys key methods and milestones in vision-language integration.
- Identifies open challenges in achieving unified multimodal intelligence.
- Aims to provide a roadmap for advancing artificial general intelligence.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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).
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Multimedia (cs.MM) |
| Cite as: | arXiv:2606.26196 [cs.CL] |
| (or arXiv:2606.26196v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.26196 arXiv-issued DOI via DataCite (pending registration) |
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| Related DOI: | https://doi.org/10.1016/j.inffus.2026.104285
DOI(s) linking to related resources |
Submission history
From: Haoxiang Sun [view email]
[v1]
Wed, 24 Jun 2026 15:20:32 UTC (7,821 KB)
— Originally published at arxiv.org
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