Expired90% confidenceFactExact time
A native multimodal architecture refers to a model that simultaneously learns data from multiple modalities such as text, images, audio, and video during the pre-training stage, rather than attaching a visual encoder or audio encoder on top of a text-only large language model.
7
Sources
90%
Confidence
Medium-term (~90 days)
Relevance
7/2/2026
First Seen
This claim has expired. It is kept for the record and no longer describes the current state.
Valid until: 9/30/2026(expired)
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Related Claims
UnverifiedThe difference between native multimodality and pipeline multimodality is that native multimodality simultaneously learns text, image, audio, and video representations during the pre-training phase, whereas pipeline multimodality (such as early GPT-4V) first uses dedicated encoders to convert images or audio into textual descriptions before feeding them into the language model.78% similarUnverifiedQwen3.5-Omni采用原生多模态预训练方式，从数据构建阶段就将图像、音频、视频与文本混合编排，与早期'感知模块+语言模型'串联方案有本质区别78% similarUnverifiedMultimodal processing refers to a model's ability to simultaneously understand and generate multiple data types such as text, images, and code, relying on deep integration of a visual encoder and a language model backbone.78% similarVerifiedThe technical foundation of multimodal large models is the joint training of a visual encoder (such as ViT) and a language model, enabling the model to map visual information from pixel space into a semantic space shared with text78% similarUnverified原生多模态架构的核心突破在于构建统一语义表示空间，在预训练阶段通过统一编码器将文本、图像、音频、视频映射到同一高维向量空间77% similar
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