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arXiv · 2611.05596

RAGCompass: Generalizable Retriever Routing via Reranking

Valentin Six·Mufei Li·Dongqi Fu·Yinglong Xia·Hong Li·Pan Li
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Abstract

Different retrievers exist for retrieval-augmented generation, yet none performs best for all queries. Therefore, the key question is: given a query and retrieved passages from different retrievers, which one is most suited for that query? Existing routers usually make predictions from query-specific features and a fixed pool of retrievers; such routers have no output for an unseen retriever and are incapable by design on a new corpus. To fill these gaps, RAGCompass reads the retrieved passage sets themselves: passages from an unfamiliar retriever or corpus stay directly comparable to other passages, so routing stays well designed where a query-only map would fail. We contrastively fine-tune a reranker against the answer each evidence set produces, which accounts for most of the gain; a small comparison head adds a moderate improvement, and neither component has retriever-specific parameters. RAGCompass improves answer F1 by 28% relative to the best fixed retriever, and outperforms fusion baselines, three query-only routers, and a prompted 8B language model. Generalization experiments show that RAGCompass keeps most of its gain under an unseen corpus, retriever, or reader, while query-only baselines fall to the best fixed retriever or below.

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Valentin Six, Mufei Li, Dongqi Fu, Yinglong Xia, Hong Li, Pan Li. 2026-11-03. RAGCompass: Generalizable Retriever Routing via Reranking. https://arxiv.org/abs/2611.05596

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