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This technical report introduces Douyin Multimodal Embedding (DME), a two-stage trained model that combines contrastive pre-training with evidence-grounded latent reasoning and cross-conditional reconstruction, achieving state-of-the-art results on MMEB-v2 and deployment in Douyin search.
The paper introduces TTE-Flash, a method that replaces explicit chain-of-thought reasoning with latent think tokens to generate reasoning-aware multimodal representations at constant inference cost, outperforming explicit CoT baselines on the MMEB-v2 benchmark.