TTE-Flash: Accelerating Reasoning-based Multimodal Representations via Think-Then-Embed Tokens
The paper titled 'TTE-Flash: Accelerating Reasoning-based Multimodal Representations via Think-Then-Embed Tokens' presents a new approach to enhance Universal Multimodal Embedding. The authors propose using latent think tokens to replace explicit Chain-of-Thought reasoning, aiming to reduce computational overhead while maintaining performance. Their model, TTE-Flash-2B, demonstrates superior results on the MMEB-v2 benchmark and shows promising scaling behavior in zero-shot evaluations across multiple video datasets.
- ▪The study introduces a model called TTE-Flash-2B that outperforms traditional explicit-CoT models.
- ▪Latent think tokens are used to optimize the generation of reasoning traces without incurring high computational costs.
- ▪The research investigates the extraction and training of think and embedding tokens from the same LLM backbone.
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Computer Science > Artificial Intelligence arXiv:2605.16638 (cs) [Submitted on 15 May 2026] Title:TTE-Flash: Accelerating Reasoning-based Multimodal Representations via Think-Then-Embed Tokens Authors:Jianpeng Cheng, Xian Wu, Jiangfan Zhang, Wentao Bao, Chaitanya Ahuja, Shlok Kumar Mishra, Hanchao Yu, Yang Gao, Fan Xia, Qi Guo, Shaodan Zhai, Xiangjun Fan, Jun Xiao View a PDF of the paper titled TTE-Flash: Accelerating Reasoning-based Multimodal Representations via Think-Then-Embed Tokens, by Jianpeng Cheng and 12 other authors View PDF HTML (experimental) Abstract:Recent research has demonstrated that Universal Multimodal Embedding (UME) benefits significantly from Chain-of-Thought (CoT) reasoning.
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