arXiv Machine Learning
Jun 9

GraphER: An Efficient Graph-Based Enrichment and Reranking Method for Retrieval-Augmented Generation

arXiv:2603. 24925v2 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) systems that rely on semantic search often fail to retrieve the complete set of evidence for complex queries, particularly when information is distributed across multiple sources.

By Ruizhong Miao, Yuying Wang, Rongguang Wang, Chenyang Li, Tao Sheng, Sujith Ravi, Dan Roth
arXiv AI
Jun 18

RankGraph-2: Lifecycle Co-Design for Billion-Node Graph Learning in Recommendation

arXiv:2606. 18379v1 Announce Type: cross Abstract: Graph-based retrieval at billion-node scale requires jointly solving three tightly coupled problems -- graph construction, representation learning, and real-time serving -- yet existing work addresses each in isolation.

By Renzhi Wu, Zikun Cui, Junjie Yang, Tai Guo, Hong Li, Xian Chen, Li Yu, Ke Pan, Sri Reddy, Mahesh Srinivasan, Nipun Mathur, Haomin Yu, Hong Yan
arXiv Computation and Language
Aug 27

E2Rank: Unifying Text Embedding and Listwise Reranking for Effective and Efficient Search

E2Rank (Efficient Embedding-based Ranking) is a unified framework that extends a single text embedding model to perform both retrieval and listwise reranking. By treating the listwise prompt—constructed from the query and its top‑K candidates—as a pseudo‑relevance feedback query, E2Rank reranks via cosine similarity against precomputed document embeddings, avoiding costly autoregressive decoding. The approach achieves state‑of‑the‑art results on BEIR, competitive performance on the reasoning‑intensive BRIGHT benchmark, lower latency than existing LLM‑based rerankers, and improved embedding performance on MTEB—all within a single model.

By Qi Liu, Yanzhao Zhang, Mingxin Li, Dingkun Long, Pengjun Xie, Jiaxin Mao