Modern retrieval agents expose many configuration choices—LLM, retriever, number of documents, number of hops, and synthesis strategy—each shaping both
answer quality and serving cost. Today, these pipelines are typically hand-tuned
once per workload, leaving substantial per-query optimization untapped. We formulate Query2Conf : given a natural-language query and either an accuracy or
a budget target, select from a predefined pipeline catalog the configuration that
minimizes cost (or maximizes accuracy) at inference time. We propose BRANE
which uses an LLM to convert each query into workload-specific characteristics,
then trains a lightweight per-configuration predictor that estimates whether the
pipeline will answer the query correctly. At inference time, BRANE selects the
configuration that maximizes predicted correctness penalized by cost, exposing a
tunable cost–quality tradeoff without retraining. Across MuSiQue, BrowseCompPlus, and FinanceBench, BRANE consistently pushes the cost–quality Pareto
frontier, matches the best fixed configuration’s accuracy at up to 89% lower cost,
and outperforms LLM-routing, rule-based, and fine-tuned Qwen3-4B baselines.
These results show that per-query configuration of the full retrieval pipeline is a
practical alternative to static workload-level tuning.
Natural Language Query to Configuration for Retrieval Agents · UC Berkeley · University of Washington · Microsoft Azure Research - Systems · 26 May 2026