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Reason to Contrast: A Cascaded Multimodal Retrieval Framework
arXiv – CS AI|Xuanming Cui, Hong-You Chen, Hao Yu, Hao Yuan, Zihao Wang, Shlok Kumar Mishra, Hanchao Yu, Yonghuan Yang, Jun Xiao, Ser-Nam Lim, Jianpeng Cheng, Qi Guo, Xiangjun Fan||18 views
🤖AI Summary
Researchers introduce TTE-v2, a new multimodal retrieval framework that achieves state-of-the-art performance by incorporating reasoning steps during retrieval and reranking. The approach demonstrates that scaling based on reasoning tokens rather than model size can significantly improve performance, with TTE-v2-7B reaching 75.7% accuracy on MMEB-V2 benchmark.
Key Takeaways
- →TTE-v2 introduces a hybrid multimodal retrieval framework that scales performance through reasoning tokens rather than model or embedding size.
- →The system uses a cascaded design with initial retrieval followed by reasoning-driven reranking for better query-candidate interactions.
- →TTE-v2-7B achieves new state-of-the-art accuracy of 75.7% on the MMEB-V2 benchmark.
- →The smaller TTE-v2-2B model matches or surpasses leading 7B models trained on larger external datasets.
- →Token-wise scaling presents a promising alternative paradigm for improving multimodal retrieval systems.
#multimodal-retrieval#ai-research#machine-learning#reasoning#embeddings#benchmark#performance-scaling#reranking
Read Original →via arXiv – CS AI
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