Core Viewpoint - The article discusses the evolution of artificial intelligence (AI) from a text-based model to a multimodal large model (MLLM) capable of perceiving and interacting with the physical world through vision and language [1][2]. Group 1: MLLM Architecture - The MLLM architecture is described as a "trinity" system consisting of three core components: the visual encoder (Vision Transformer), the language model (LLM), and the connector [3][5]. - The visual encoder transforms images into mathematical representations, enabling AI to "see" and understand visual information [5][17]. - The LLM serves as the cognitive center, integrating visual features with textual instructions to generate coherent responses [17][20]. - The connector acts as a bridge, translating visual features into a format that the LLM can understand, thus facilitating seamless communication between the two modalities [32][33]. Group 2: Vision Transformer (ViT) - ViT revolutionizes image processing by treating images as sequences of patches, allowing the model to leverage transformer architecture for visual understanding [7][9]. - The process involves several steps: image patching, flattening and linear projection, adding positional information, and processing through a transformer encoder [9][10][15]. - ViT's ability to encode spatial relationships using rotary position embedding enhances its understanding of image context [13][14]. Group 3: Language Model (LLM) - The LLM processes a combined sequence of visual and textual information, allowing for a richer context in generating responses [20][31]. - It employs a multi-head attention mechanism to capture relationships between visual tokens and textual tokens, enhancing its ability to understand complex queries [19][24]. - The LLM's architecture is evolving towards a mixture of experts (MoE) model, which allows for more efficient processing by activating only a subset of parameters during inference [28][31]. Group 4: Connector Mechanism - The connector's role is crucial in aligning the visual and textual modalities, ensuring that the LLM can effectively interpret the visual features provided by the ViT [32][34]. - There are two main design philosophies for connectors: the minimalist approach exemplified by LLaVA, which relies on a simple linear transformation, and the more sophisticated Q-Former, which actively extracts key information from visual features [36][38]. - Q-Former utilizes learnable queries and cross-attention mechanisms to distill essential information from the visual input, reducing the cognitive load on the LLM [42][45]. Group 5: Challenges and Future Directions - The article highlights the challenge of processing high-resolution images without overwhelming the LLM's computational capacity, leading to the exploration of different architectural solutions [54]. - The need for models to efficiently handle complex visual data while maintaining performance is a key focus for future developments in MLLM technology [54].

作者 | 我要吃鸡腿 编辑 | 大模型之心Tech 原文链接： https://zhuanlan.zhihu.com/p/1963658684765833212 本文只做学术分享，已获转载授权 ，欢迎添加小助理微信AIDriver004做进一步咨询 点击下方 卡片 ，关注" 大模型之心Tech "公众号 戳我-> 领取大模型巨卷干货 引言：当 AI 睁开双眼，我们看到了一个怎样的未来？ 曾几何时，我们对人工智能的印象还停留在那个聪慧但略显"盲目"的"数字大脑"上——它能写诗、能编程、能回答深奥的哲学问题，但这一切都局限 于冰冷的文本世界。然而，就在最近两年，一场深刻的变革正在悄然发生。 您或许已经惊叹于 GPT-5 那般流畅自如的实时图片对话，它能"看到"您房间的布局并给出整理建议；又或者，您可能对 Qwen3-VL 直接"注视"着手 机屏幕、精准地点击按钮、操作应用程序的能力感到不可思议。AI 不再仅仅是一个"只会读书"的语言模型，它正在进化成一个能听、会看、可交互 的"智能体"，真正地睁开了双眼，开始感知和理解我们所处的这个五彩斑斓的物理世界。 这场从"符号"到"感知"的飞跃，背后究竟隐藏着怎样的技术密码 ...