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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
Optimizing Retrieval Components for a Shared Backbone via Component-Wise Multi-Stage Training
Yunhan Li, Mingjie Xie, Zihan Gong +3
Recent advances in embedding-based retrieval have enabled dense retrievers to serve as core infrastructure in many industrial systems, where a single retrieval backbone is often sh…
cs.IR2026
LegalMALR:Multi-Agent Query Understanding and LLM-Based Reranking for Chinese Statute Retrieval
Yunhan Li, Mingjie Xie, Gaoli Kang +3
Statute retrieval is essential for legal assistance and judicial decision support, yet real-world legal queries are often implicit, multi-issue, and expressed in colloquial or unde…