当前的视觉-语言模型（VLM）通常通过多阶段对齐将独立的图像编码器与语言解码器拼接起来，这种模块化框架不可避免地导致帧间像素级信号的割裂以及早期像素-文字交互的分散。与此同时，尽管原生VLM在单图像任务上展现出优异性能，其在多图像、视频理解及空间智能等方向仍基本未被探索。为此，我们提出NEO-ov——一种原生基础模型，能够端到端地学习跨帧及像素-文字对应关系，无需任何外部编码器、辅助适配器或后验融合机制。通过彻底消除模块边界，NEO-ov使细粒度且统一的时空建模能力得以在模型内部原生涌现。值得注意的是，NEO-ov在大幅缩小与模块化模型性能差距的同时，在细粒度视觉感知任务上表现更优，验证了原生“单视觉”架构不仅可行，而且在大规模场景下具备竞争力。除实证性能外，我们还揭示了系统的架构分析与详尽的训练方案，以促进后续原生多模态建模研究。代码与模型已开源：https://github.com/EvolvingLMMs-Lab/NEO。

Current vision-language models (VLMs) typically stitch together separate image encoders and language decoders via multi-stage alignment, a modular framework that inevitably fragments pixel-level signals across frames and scatters early pixel-word interactions. In parallel, native VLMs, despite impressive performance on single images, remain largely unexplored in multi-image, video understanding, and spatial intelligence. Hence, we introduce NEO-ov, a native foundation model that learns cross-frame and pixel-word correspondence end-to-end, without any external encoders, auxiliary adapters, or post-hoc fusion. By eliminating module boundaries entirely, NEO-ov enables fine-grained and unified spatiotemporal modeling to emerge natively inside the model. Notably, NEO-ov largely narrows the gap to modular counterparts while excelling at fine-grained visual perception, validating that native "one-vision" architectures are not only feasible but competitive at scale. Beyond empirical performance, we unveil systematic architectural analyses and detailed training recipes to facilitate subsequent native multimodal modeling. Our code and models are publicly available at: https://github.com/EvolvingLMMs-Lab/NEO.