Natural Language Query to Configuration for Retrieval Agents

Problem
This preprint addresses the gap in the optimization of retrieval agent configurations for natural language queries. Current systems typically rely on static, workload-level tuning, which does not leverage the potential for per-query optimization. The authors identify the need for a method that dynamically selects configurations from a predefined catalog based on the specific characteristics of incoming queries, aiming to balance accuracy and cost effectively.

Method
The authors introduce BRANE, a novel framework that utilizes a large language model (LLM) to transform natural language queries into workload-specific characteristics. The architecture consists of two main components: a query-to-characteristics converter and a lightweight per-configuration predictor. The converter extracts features from the query, while the predictor estimates the likelihood of correct answers for each configuration in the pipeline. At inference time, BRANE employs a selection mechanism that maximizes predicted correctness while penalizing for cost, allowing for a tunable trade-off between quality and expense without necessitating retraining. The training process involves a diverse set of configurations and queries, although specific details on the training compute and dataset sizes are not disclosed.

Results
BRANE demonstrates significant performance improvements across multiple benchmarks, including MuSiQue, BrowseComp-Plus, and FinanceBench. The framework achieves up to 89% lower costs while matching the accuracy of the best fixed configuration. In comparative evaluations, BRANE outperforms existing methods, including LLM-routing, rule-based approaches, and fine-tuned Qwen3-4B baselines. The results indicate a clear advancement in the cost-quality Pareto frontier, showcasing the effectiveness of per-query configuration over static tuning.

Limitations
The authors acknowledge that BRANE relies on a predefined catalog of configurations, which may limit its adaptability to novel or unforeseen query types. Additionally, the performance gains are contingent on the quality of the LLM used for query transformation, which may vary across different domains. The paper does not address the scalability of the approach in environments with rapidly changing workloads or the potential overhead introduced by the per-query prediction process.

Why it matters
This work has significant implications for the design of retrieval systems, particularly in contexts where cost efficiency is critical. By enabling dynamic configuration selection based on query characteristics, BRANE paves the way for more responsive and cost-effective retrieval agents. This approach could influence future research on adaptive systems and the integration of LLMs in optimizing complex pipelines, potentially leading to broader applications in AI-driven information retrieval and decision-making processes.

Authors: Melissa Z. Pan, Negar Arabzadeh, Mathew Jacob, Fiodar Kazhamiaka, Esha Choukse, Matei Zaharia
Source: arXiv:2605.27361
URL: https://arxiv.org/abs/2605.27361v1