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cs.IR2026
Reasoning Quality Matters: Combating Reasoning Collapse in LLM-based Embedding Learning
Zihan Gong, Xiaohan Ye, Jiangchao Yao +3
Large Language Models (LLMs) have recently shown strong potential for producing context-rich text embeddings for retrieval. Most existing methods either treat embedding learning as…
cs.IR2026
Pailitao-VL: Unified Embedding and Reranker for Real-Time Multi-Modal Industrial Search
Lei Chen, Chen Ju, Xu Chen +13
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 chal…