TTE-Flash: Accelerating Multimodal Embeddings with Latent Think Tokens
A new AI research paper proposes TTE-Flash, a method to accelerate reasoning-based multimodal representations by replacing explicit Chain-of-Thought (CoT) traces with latent think tokens. The approach optimizes think tokens using CoT generation loss and embedding tokens via contrastive loss, achieving high-performance reasoning-aware representations at constant inference cost. The study investigates architectural designs for extracting think and embedding tokens from the same model. The paper is published on arXiv under ID 2605.16638.
Key facts
- arXiv paper ID 2605.16638
- Proposes TTE-Flash method
- Replaces explicit CoT with latent think tokens
- Optimizes think tokens via CoT generation loss
- Optimizes embedding tokens via contrastive loss
- Achieves constant inference cost
- Investigates two key architectural designs
- Focuses on Universal Multimodal Embedding (UME)
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- arXiv