[Submitted on 3 Nov 2026]
Title:RAGCompass: Generalizable Retriever Routing via Reranking
View a PDF of the paper titled RAGCompass: Generalizable Retriever Routing via Reranking, by Valentin Six and 5 other authors
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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.
Subjects:
Computation and Language (cs.CL); Information Retrieval (cs.IR)
Cite as: arXiv:2611.05596 [cs.CL]
(or arXiv:2611.05596v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2611.05596
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Valentin Six [view email] [v1] Tue, 3 Nov 2026 20:36:24 UTC (420 KB)
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