3 citations · 4 across the 6 of their papers we have counts for
7 papers
DeepCode: Open Agentic Coding
Zongwei Li, Zhonghang Li, Zirui Guo +2
Recent advances in large language models (LLMs) have given rise to powerful coding agents, making it possible for code assistants to evolve into code engineers. However, existing m…
RAG-Anything: All-in-One RAG Framework
Zirui Guo, Xubin Ren, Lingrui Xu +2
Retrieval-Augmented Generation (RAG) has emerged as a fundamental paradigm for expanding Large Language Models beyond their static training limitations. However, a critical misalig…
RecGPT: A Foundation Model for Sequential Recommendation
Yangqin Jiang, Xubin Ren, Lianghao Xia +3
This work addresses a fundamental barrier in recommender systems: the inability to generalize across domains without extensive retraining. Traditional ID-based approaches fail enti…
VideoRAG: Retrieval-Augmented Generation with Extreme Long-Context Videos
Xubin Ren, Lingrui Xu, Long Xia +3
Retrieval-Augmented Generation (RAG) has demonstrated remarkable success in enhancing Large Language Models (LLMs) through external knowledge integration, yet its application has p…
RecLM: Recommendation Instruction Tuning
Yangqin Jiang, Yuhao Yang, Lianghao Xia +3
Modern recommender systems aim to deeply understand users' complex preferences through their past interactions. While deep collaborative filtering approaches using Graph Neural Net…
EasyRec: Simple yet Effective Language Models for Recommendation
Xubin Ren, Chao Huang
Deep neural networks have emerged as a powerful technique for learning representations from user-item interaction data in collaborative filtering (CF) for recommender systems. Howe…
XRec: Large Language Models for Explainable Recommendation
Qiyao Ma, Xubin Ren, Chao Huang
Recommender systems help users navigate information overload by providing personalized recommendations aligned with their preferences. Collaborative Filtering (CF) is a widely adop…