![[2602.13704] Pailitao-VL: Unified Embedding and Reranker for Real-Time Multi-Modal Industrial Search](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.13704] Pailitao-VL: Unified Embedding and Reranker for Real-Time Multi-Modal Industrial Search
Summary
The paper presents Pailitao-VL, a multi-modal retrieval system designed for real-time industrial search, addressing key challenges in retrieval granularity, environmental noise, and efficiency-performance gaps.
Why It Matters
Pailitao-VL represents a significant advancement in multi-modal search technology, crucial for industries relying on high-precision retrieval systems. By overcoming existing limitations, it enhances the efficiency and effectiveness of search operations in large-scale environments, which is vital for competitive business performance.
Key Takeaways
- Pailitao-VL shifts from traditional contrastive learning to absolute ID-recognition for improved embedding.
- The system employs a novel listwise policy for generative reranking, enhancing relevance scoring.
- Extensive testing shows Pailitao-VL achieves state-of-the-art performance in real-world applications.
Computer Science > Information Retrieval arXiv:2602.13704 (cs) [Submitted on 14 Feb 2026] Title:Pailitao-VL: Unified Embedding and Reranker for Real-Time Multi-Modal Industrial Search Authors:Lei Chen, Chen Ju, Xu Chen, Zhicheng Wang, Yuheng Jiao, Hongfeng Zhan, Zhaoyang Li, Shihao Xu, Zhixiang Zhao, Tong Jia, Jinsong Lan, Xiaoyong Zhu, Bo Zheng View a PDF of the paper titled Pailitao-VL: Unified Embedding and Reranker for Real-Time Multi-Modal Industrial Search, by Lei Chen and 12 other authors View PDF HTML (experimental) Abstract:In this work, we presented Pailitao-VL, a comprehensive multi-modal retrieval system engineered for high-precision, real-time industrial search. We here address three critical challenges in the current SOTA solution: insufficient retrieval granularity, vulnerability to environmental noise, and prohibitive efficiency-performance gap. Our primary contribution lies in two fundamental paradigm shifts. First, we transitioned the embedding paradigm from traditional contrastive learning to an absolute ID-recognition task. Through anchoring instances to a globally consistent latent space defined by billions of semantic prototypes, we successfully overcome the stochasticity and granularity bottlenecks inherent in existing embedding solutions. Second, we evolved the generative reranker from isolated pointwise evaluation to the compare-and-calibrate listwise policy. By synergizing chunk-based comparative reasoning with calibrated absolute relevance scorin...
