{"ID":2878258,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.21085","arxiv_id":"2508.21085","title":"Granite Embedding R2 Models","abstract":"We introduce the Granite Embedding R2 models, a comprehensive family of high-performance English encoder-based embedding models engineered for enterprise-scale dense retrieval applications. Building upon our first-generation release, these models deliver substantial improvements, including 16x expanded context length (8,192 tokens), state-of-the-art performance across diverse retrieval domains - text, code, long-document search, multi-turn conversational, and tabular data - and measurable speed advantages of 19-44\\% over leading competitors while maintaining superior accuracy. Our release encompasses both bi-encoder and cross-encoder architectures, featuring a highly effective 22-layer retriever model and its efficient 12-layer counterpart, alongside a high-quality reranker model, all trained exclusively on enterprise-appropriate data with comprehensive governance oversight. The models demonstrate exceptional versatility across standard benchmarks, IBM-developed evaluation suites, and real-world enterprise use cases, establishing new performance standards for open-source embedding models. In an era where retrieval speed and accuracy are paramount for competitive advantage, the Granite R2 models deliver a compelling combination of cutting-edge performance, enterprise-ready licensing, and transparent data provenance that organizations require for mission-critical deployments. All models are publicly available under the Apache 2.0 license at https://huggingface.co/collections/ibm-granite, enabling unrestricted research and commercial use.","short_abstract":"We introduce the Granite Embedding R2 models, a comprehensive family of high-performance English encoder-based embedding models engineered for enterprise-scale dense retrieval applications. Building upon our first-generation release, these models deliver substantial improvements, including 16x expanded context length (...","url_abs":"https://arxiv.org/abs/2508.21085","url_pdf":"https://arxiv.org/pdf/2508.21085v1","authors":"[\"Parul Awasthy\",\"Aashka Trivedi\",\"Yulong Li\",\"Meet Doshi\",\"Riyaz Bhat\",\"Vignesh P\",\"Vishwajeet Kumar\",\"Yushu Yang\",\"Bhavani Iyer\",\"Abraham Daniels\",\"Rudra Murthy\",\"Ken Barker\",\"Martin Franz\",\"Madison Lee\",\"Todd Ward\",\"Salim Roukos\",\"David Cox\",\"Luis Lastras\",\"Jaydeep Sen\",\"Radu Florian\"]","published":"2025-08-26T19:06:29Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.IR\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}