2 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.IR2025
Fishing for Answers: Exploring One-shot vs. Iterative Retrieval Strategies for Retrieval Augmented Generation
Huifeng Lin, Gang Su, Jintao Liang +3
Retrieval-Augmented Generation (RAG) based on Large Language Models (LLMs) is a powerful solution to understand and query the industry's closed-source documents. However, basic RAG…
cs.AI2025★ 2 cited
Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges
Jintao Liang, Gang Su, Huifeng Lin +3
Retrieval-Augmented Generation (RAG) has emerged as a powerful framework to overcome the knowledge limitations of Large Language Models (LLMs) by integrating external retrieval wit…
cs.MS2023★ 1 cited
MindOpt Tuner: Boost the Performance of Numerical Software by Automatic Parameter Tuning
Mengyuan Zhang, Wotao Yin, Mengchang Wang +7
Numerical software is usually shipped with built-in hyperparameters. By carefully tuning those hyperparameters, significant performance enhancements can be achieved for specific ap…