In the latest developments in the field of artificial intelligence, Ilya Sutskever's recent statement has triggered a major shift. He stated that the era of simply scaling up model size has come to an end, and future breakthroughs will stem from more intelligent architectural designs. The entire AI community has felt a profound transformation, as the development path of recent years seemed to have fallen into the "scale worship" of data and parameters, but this approach is now facing diminishing returns.

Against this backdrop, the open-source native multimodal architecture NEO, developed by a Chinese research team, has emerged. Unlike previous mainstream multimodal models such as GPT-4V and Claude 3.5, which used a concatenation approach, NEO fundamentally redefines the relationship between vision and language. Traditional multimodal models typically separate the visual encoder from the language model, forcibly concatenating them at the data level, leading to inefficient information transmission. In contrast, NEO creates a unified model, integrating vision and language from the beginning, as if they were deeply connected.

NEO's core innovation lies in three technological breakthroughs. First, it introduces native token embedding technology, allowing AI to build high-fidelity visual representations directly from pixels, enhancing the ability to capture image details. Second, NEO develops native three-dimensional rotation position encoding, using different combinations of high-frequency and low-frequency components to accurately handle positional relationships in images and text, forming an intelligent spatiotemporal coordinate system. Finally, the native multi-head attention mechanism enables visual and linguistic information to communicate within the same framework, significantly enhancing the model's ability to understand complex semantics.

Surprisingly, NEO has matched or even surpassed many flagship competitors with only one-tenth of the training data used by traditional models. This achievement not only proves the effectiveness of the native architecture but also marks a new direction in the development of AI models.