Natural Language Query to Configuration for Retrieval Agents

Researchers introduce BRANE, an AI system that dynamically selects optimal configurations for retrieval agents by analyzing natural-language queries at inference time. The method reduces serving costs by up to 89% while maintaining accuracy, demonstrating that per-query optimization outperforms traditional static pipeline tuning across multiple benchmarks.

BRANE addresses a fundamental inefficiency in modern retrieval-augmented generation (RAG) systems: most organizations manually tune pipeline configurations once per workload, ignoring substantial optimization opportunities at query time. The system uses LLMs to extract query-specific characteristics, then deploys lightweight predictors to estimate whether each preconfigured pipeline will answer correctly, selecting the option that balances cost and accuracy. This approach is pragmatically valuable because retrieval pipelines involve numerous tunable parameters—model choice, retriever type, document count, hop depth, and synthesis strategy—each affecting both response quality and infrastructure expenses.

The technical contribution reflects broader industry trends toward intelligent resource allocation in AI systems. As companies deploy large language models at scale, cost optimization has become as critical as accuracy, particularly in production environments serving thousands of queries daily. Traditional fine-tuning approaches require retraining when conditions change, making them inflexible for dynamic workloads. BRANE's per-query approach sidesteps this limitation through clever use of predictive modeling and predefined pipeline catalogs.

Benchmark results across MuSiQue, BrowseComp-Plus, and FinanceBench demonstrate meaningful improvements: matching best fixed configurations' accuracy at 89% lower cost while outperforming LLM-routing and rule-based baselines. The 89% cost reduction figure is particularly significant for cost-sensitive applications like financial analysis or enterprise search. The system exposes a tunable Pareto frontier, letting organizations explicitly trade accuracy for serving expense without complex retraining workflows.

Looking forward, this work validates per-query optimization as a practical alternative to monolithic configuration strategies. As retrieval systems become more complex and diverse workloads proliferate, similar dynamic allocation techniques will likely become standard infrastructure patterns. The scalability of lightweight predictors across heterogeneous pipelines remains an interesting open question.

• →BRANE dynamically selects retrieval pipeline configurations at query time, reducing costs by up to 89% without sacrificing accuracy.
• →The system uses LLM-extracted query characteristics and lightweight predictors to estimate pipeline performance before selection.
• →Per-query optimization outperforms static workload-level tuning and LLM-routing baselines across multiple financial and knowledge-retrieval benchmarks.
• →The approach eliminates expensive retraining cycles by exposing a tunable cost-quality tradeoff through predefined pipeline catalogs.
• →Results suggest per-query configuration management will become a practical standard for scaling retrieval systems in production environments.