mlsanityData to 14 Oct 2026

ORI: O Routing Intelligence

arXiv 2502.10051 ↗First posted 14 Feb 2025cs.CLcited by 2

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Abstract (arXiv)

Single large language models (LLMs) often fall short when faced with the ever-growing range of tasks, making a single-model approach insufficient. We address this challenge by proposing ORI (O Routing Intelligence), a dynamic framework that leverages a set of LLMs. By intelligently routing incoming queries to the most suitable model, ORI not only improves task-specific accuracy, but also maintains efficiency. Comprehensive evaluations across diverse benchmarks demonstrate consistent accuracy gains while controlling computational overhead. By intelligently routing queries, ORI outperforms the strongest individual models by up to 2.7 points on MMLU and 1.8 points on MuSR, ties the top performance on ARC, and on BBH. These results underscore the benefits of a multi-model strategy and demonstrate how ORI's adaptive architecture can more effectively handle diverse tasks, offering a scalable, high-performance solution for a system of multiple large language models.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Feb 2025.

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Citing papers per year: 2025: 1; 2026: 1.

2 papers.