8 papers
Beyond Recall Saturation: Scaling Hard-Negative Reranker Training for Industrial Legal Retrieval
Yunhan Li, Mingjie Xie, Zeyang Shi +2
Industrial retrieval systems often improve quality by enlarging embedding candidate pools or adopting complex training pipelines. We show that, under a sufficiently large but pract…
LegalCiteTrust: Benchmarking Citation Trustworthiness in Chinese Long-Form Legal Research Reports
Yunhan Li, Mingjie Xie, Zeyang Shi +2
Long-form legal research reports increasingly rely on LLMs and agentic research systems, but their reliability depends not only on answering the task, but also on whether cited leg…
GenFacet: End-to-End Generative Faceted Search via Multi-Task Preference Alignment in E-Commerce
Zhouwei Zhai, Min Yang, Jin Li
Faceted search acts as a critical bridge for navigating massive ecommerce catalogs, yet traditional systems rely on static rule-based extraction or statistical ranking, struggling…
CogSearch: A Cognitive-Aligned Multi-Agent Framework for Proactive Decision Support in E-Commerce Search
Zhouwei Zhai, Mengxiang Chen, Haoyun Xia +3
Modern e-commerce search engines, largely rooted in passive retrieval-and-ranking models, frequently fail to support complex decision-making, leaving users overwhelmed by cognitive…
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…
Beyond Retrieval-Ranking: A Multi-Agent Cognitive Decision Framework for E-Commerce Search
Zhouwei Zhai, Mengxiang Chen, Haoyun Xia +3
The retrieval-ranking paradigm has long dominated e-commerce search, but its reliance on query-item matching fundamentally misaligns with multi-stage cognitive decision processes o…
PEToolLLM: Towards Personalized Tool Learning in Large Language Models
Qiancheng Xu, Yongqi Li, Heming Xia +3
Tool learning has emerged as a promising direction by extending Large Language Models' (LLMs) capabilities with external tools. Existing tool learning studies primarily focus on th…