From the 1 of 12 linked papers with an AI index.
12 papers
CMT-RAG: Complementary Memory Traces for Multi-turn Multi-hop RAG
Lang Zhou, Yingjian Chen, Shuxuan Li +2
The paper proposes CMT-RAG, a framework that stores structured sub-question reasoning traces as memory to improve multi-turn, multi-hop retrieval‑augmented generation, and introduc…
Omanic: Towards Step-wise Evaluation of Multi-hop Reasoning in Large Language Models
Xiaojie Gu, Sherry T. Tong, Aosong Feng +8
Evaluating the reasoning abilities of large language models (LLMs) solely from final answers can obscure failures in intermediate steps, especially in multi-hop QA benchmarks witho…
Position: The Hidden Costs and Measurement Gaps of Reinforcement Learning with Verifiable Rewards
Fang Wu, Aaron Tu, Weihao Xuan +21
Reinforcement learning with verifiable rewards (RLVR) is a practical, scalable way to improve large language models on math, code, and other structured tasks. However, we argue tha…
From Chains to Graphs: Self-Structured Reasoning for General-Domain LLMs
Yingjian Chen, Haoran Liu, Yinhong Liu +7
Large Language Models (LLMs) show strong reasoning ability in open-domain question answering, yet their reasoning processes are typically linear and often logically inconsistent. I…
Toward Global Large Language Models in Medicine
Rui Yang, Huitao Li, Weihao Xuan +47
Despite continuous advances in medical technology, the global distribution of health care resources remains uneven. The development of large language models (LLMs) has transformed…
Retrieval-Augmented Generation in Biomedicine: A Survey of Technologies, Datasets, and Clinical Applications
Jiawei He, Boya Zhang, Hossein Rouhizadeh +6
Large language models (LLMs) in biomedicine face a fundamental conflict between static parameter knowledge and the dynamic nature of clinical evidence. Retrieval-Augmented Generati…