想搞懂多模态模型怎么从分开看图文进化成统一感知？这篇综述用五阶段框架讲清了O-series和R-series带来的转变，比碎片化教程系统得多。
这篇来自 arXiv 的论文系统梳理了多模态大语言模型（MLLM）中视觉-语言感知的演变，首次将其视为统一的跨模态能力。论文提出了五阶段分类法，涵盖从早期方法到 OpenAI O-series、DeepSeek R-series 等最新模型带来的感知中心范式转变。它总结了每个阶段的代表性方法，并指出了开放挑战与通向通用智能的研究方向。该综述为 MLLM 感知研究提供了结构化理解与可操作的路线图。
From Structure to Synergy: A Survey of Vision-Language Perception Paradigm Evolution in Multimodal Large Language Models
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).