This work compares matched query-only and query+retrieval routers while holding the optional actions, router family, training procedure, and evaluation fixed and suggests retrieval-state features should not be credited with routing value unless they improve over a matched query-only control.
Abstract
Adaptive RAG often uses retrieval-time signals to decide whether another retrieval, reranking, or multimodal step should run. We ask whether these signals add routing value once the query itself is already known. Across document, audio, and video RAG, we compare matched query-only and query+retrieval routers while holding the optional actions, router family, training procedure, and evaluation fixed. On the held-out final evaluation, adding the tested retrieval signals does not produce a reliable routing improvement over the query-only baseline. Some retrieval signals are associated with whether a later step will help, but that predictability does not consistently lead to bet- ter RUN/SKIP decisions. The main lesson is therefore methodological: retrieval-state features should not be credited with routing value unless they improve over a matched query-only control. Our results do not show that routing or retrieval state is generally useless; they show that the incremental value of retrieval signals must be demonstrated rather than assumed.
This work systematically analyze how retriever and generator complexity interacts across factoid and multi-hop question answering (QA), including bridge and composition reasoning tasks, and introduces DRAG, a query-adaptive framework for selecting retriever-generator configurations.
Neeraj Anand, Payel Santra, Partha Basuchowdhuri et al.· 0 citations
STAR is presented, a structure-aware adaptive retrieval framework for RAG that treats this mismatch as a problem of diagnosing evidence sufficiency and benefits from a control signal that preserves structurally distinct insufficiency patterns rather than collapsing them into a single scalar confidence estimate.
It is concluded that diversification should be used selectively, based on observable redundancy and evidence requirements, and introduces a query-adaptive rule that diversifies only when the effective number of distinct documents in the nearest-neighbor top-$k$ selection falls below the query's evidence requirement.
Guillaume Brouillette, Faustin Kagabo, Usef Faghihi et al.· 0 citations
QueryRoute is introduced, a benchmark that freezes the expensive artifacts needed to study this inference-time decision problem reproducibly: original queries, generated variants, ranked lists under multiple retrievers, retrieval scores, and per-query oracle labels.
Hai-Son Le, Negar Arabzadeh, Amin Bigdeli et al.· 0 citations
This approach is pre-retrieval and query-centric, making it robust in heterogeneous, case-like corpora and applicable across domains such as legal, healthcare, finance, and enterprise search.
Ye Xia, Emre Yamangil, Hai-Xun Wang· Proceedings of the 35th ACM...· 0 citations
Document retrieval increasingly supports high-stakes information access in finance, healthcare, and law. Modern retrieval pipelines vary both in modality (text or multimodal) and in retrieval architecture (dense or late-interaction). These choices impose a hard compromise: the most effective pipelines are too slow and...
Emre Kuru, Mehmet Onur Keskin, R. Farahbakhsh et al.· 0 citations
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