TTE-Flash: Accelerating Reasoning-based Multimodal Representations via Think-Then-Embed Tokens
Recent research shows Universal Multimodal Embedding (UME) benefits from Chain-of-Thought (CoT) reasoning, but explicit CoT traces are computationally expensive. This paper proposes replacing explicit CoT with latent think tokens, which serve as latent variables that can produce explicit CoT traces as observed variables. By optimizing think tokens using CoT generation loss and embedding tokens using contrastive loss, the model achieves high-performance, reasoning-aware representations at constant inference cost. The introduced TTE-Flash-2B model outperforms its explicit-CoT counterpart on the MMEB-v2 benchmark, with interpretable think tokens. Zero-shot evaluation across 15 video datasets shows scaling behavior with more think tokens, motivating adaptive think budget allocation.
[2605.16638] TTE-Flash: Accelerating Reasoning-based Multimodal Representations via Think-Then-Embed Tokens
[Submitted on 15 May 2026]
Title:TTE-Flash: Accelerating Reasoning-based Multimodal Representations via Think-Then-Embed Tokens
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Abstract:Recent research has demonstrated that Universal Multimodal Embedding (UME) benefits significantly from Chain-of-Thought (CoT) reasoning. In this paradigm, a generative model produces explicit reasoning traces for a multimodal query, with the final representation extracted from an embedding token attending to both the query and the reasoning. Despite its effectiveness, the computational overhead of generating explicit CoT traces is often prohibitive. In this work, we propose replacing explicit CoT with latent think tokens, which are interpreted as latent variables that can produce explicit CoT traces as observed variables. By optimizing think tokens using CoT generation loss and subsequent embedding tokens using contrastive loss, we produce high-performance, reasoning-aware representations at a constant inference cost. Our study investigates two key architectural designs: 1) how think and embeddings tokens should be extracted from the same LLM backbone. 2) how the tokens should be trained as two dependent tasks. We introduce TTE-Flash-2B, a reasoning-aware multimodal representation model that outperforms its explicit-CoT counterpart on the MMEB-v2 benchmark, while producing latent think tokens that are interpretable both textually and visually. Furthermore, zero-shot evaluation across 15 video datasets reveals scaling behavior as the number of think tokens increases, and motivating a pilot study of adaptive think budget allocation based on task requirements.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.16638 [cs.AI]
(or arXiv:2605.16638v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.16638
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Xian Wu [view email] [v1] Fri, 15 May 2026 21:10:56 UTC (42,978 KB)
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