1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.AI2026
CuraWeb: Joint Optimization of Quality, Redundancy, and Diversity for Web-Scale Pretraining Data
Peiguang Li, Yongwei Zhou, Juncheng Diao +12
Open-web corpora curated via highly selective filters, such as FineWeb-Edu and DCLM, constitute the core of LLM pretraining data and have significantly advanced LLM performance. Ho…
cs.IR2026
Doctor-RAG: A Failure-Aware Repair Framework for Agentic Retrieval-Augmented Generation
Shuguang Jiao, Chengkai Huang, Shuhan Qi +6
Agentic Retrieval-Augmented Generation interleaves retrieval and reasoning for multi-hop QA and complex knowledge tasks. As reasoning trajectories lengthen, failures become more fr…
cs.IR2026★ 1 cited
PruneRAG: Confidence-Guided Query Decomposition Trees for Efficient Retrieval-Augmented Generation
Shuguang Jiao, Xinyu Xiao, Yunfan Wei +4
Retrieval-augmented generation (RAG) has become a powerful framework for enhancing large language models in knowledge-intensive and reasoning tasks. However, as reasoning chains de…