5 papers
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…
Pailitao-MMSearch: Building Native E-Commerce Multimodal Search Foundation
Xiaohan Ye, Xu Chen, Zihan Gong +15
The evolution of e-commerce has fundamentally transformed how users search for products, shifting from simple text-based keyword queries to complex multimodal interactions that sea…
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…
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…
AgentCourt: Simulating Court with Adversarial Evolvable Lawyer Agents
Guhong Chen, Liyang Fan, Zihan Gong +8
Current research in LLM-based simulation systems lacks comprehensive solutions for modeling real-world court proceedings, while existing legal language models struggle with dynamic…