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ORDER: Task-Conditioned Routing for Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) pipelines typically rely on a fixed indexing and retrieval configuration determined at preprocessing time. This one-size-fits-all design is ill-suited to domain-expert settings, where heterogeneous queries require different chunking granularities, metadata constraints, and source-selection strategies. As a result, configurations that are effective for one family of queries often perform poorly for others. In this paper, we introduce ORDER (Optimal Routing for Dynamic Evidence Retrieval), a query-conditioned RAG framework that jointly adapts indexing and ret
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- arXiv · AI, language, vision and robotics · 2026-09-15T11:23:16.000Z
First collected: 2026-09-20T08:40:59.508Z. This is not the publication date.