5 papers
Agentic Routing: The Harness-Native Data Flywheel
Xinchen Liu, Hang Zhou, Yingjie Zong +12
The paper introduces a step‑level routing framework for large language model agents that selects the most suitable model(s) based on the full execution harness state, using logged…
Granite Embedding Multilingual R2 Models
Parul Awasthy, Aashka Trivedi, Yushu Yang +14
We introduce the multilingual Granite Embedding R2 models, a family of encoder-based embedding models for enterprise-scale dense retrieval across 200+ languages. Extending our Engl…
Contrastive Retrieval Heads Improve Attention-Based Re-Ranking
Linh Tran, Yulong Li, Radu Florian +1
The strong zero-shot and long-context capabilities of recent Large Language Models (LLMs) have paved the way for highly effective re-ranking systems. Attention-based re-rankers lev…
Granite Embedding R2 Models
Parul Awasthy, Aashka Trivedi, Yulong Li +17
We introduce the Granite Embedding R2 models, a comprehensive family of high-performance English encoder-based embedding models engineered for enterprise-scale dense retrieval appl…
Granite Embedding Models
Parul Awasthy, Aashka Trivedi, Yulong Li +19
We introduce the Granite Embedding models, a family of encoder-based embedding models designed for retrieval tasks, spanning dense-retrieval and sparse retrieval architectures, wit…