3 papers
cs.LG2026
HoReN: Normalized Hopfield Retrieval for Large-Scale Sequential Model Editing
Yuan Fang, Yi Xie, Xuming Ran
Large language models encode vast factual knowledge that can become outdated or incorrect after deployment, yet retraining is prohibitively costly. This motivates lifelong model ed…
cs.AI2026
Retrieval is Cheap, Show Me the Code: Executable Multi-Hop Reasoning for Retrieval-Augmented Generation
Jiashuo Sun, Jimeng Shi, Yixuan Xie +10
Retrieval-Augmented Generation (RAG) has become a standard approach for knowledge-intensive question answering, but existing systems remain brittle on multi-hop questions, where so…
cs.CL2026
TaSR-RAG: Taxonomy-guided Structured Reasoning for Retrieval-Augmented Generation
Jiashuo Sun, Yixuan Xie, Jimeng Shi +2
Retrieval-Augmented Generation (RAG) helps large language models (LLMs) answer knowledge-intensive and time-sensitive questions by conditioning generation on external evidence. How…