SOURCE-LINKED INTELLIGENCE
WeMM-Embedding: WeChat Multi-Modal Embedding Technical Report
Universal multimodal embeddings are becoming a core component of modern AI systems, enabling heterogeneous content to be represented in a shared space for applications such as retrieval, recommendation, classification, and agentic systems. In this report, we present WeMM-Embedding, a family of universal multimodal embedding models supporting text, images, videos, visual documents, and arbitrarily interleaved multimodal inputs with flexible output dimensions. The family comprises 2B, 4B, and 9B variants and is trained in two stages: a large-scale multimodal alignment stage, followed by a refine
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-25T04:23:03.000Z
First collected: 2026-09-21T10:22:00.206Z. This is not the publication date.