检索Agent的配置优化是成本与质量的永恒博弈，BRANE让每个查询都能自动找到最优解，做RAG或搜索系统的团队可以直接参考其方法，省下大量手动调参时间。
现代检索Agent面临大量配置选择（如LLM、检索器、文档数量等），传统方法按工作负载静态调优，忽略了查询级别的优化空间。研究者提出BRANE系统，利用LLM将自然语言查询转化为工作负载特征，再训练轻量级预测器评估各配置的准确性。在推理时，BRANE根据成本与准确率目标自动选择最优配置，无需重新训练。在MuSiQue、BrowseComp-Plus和FinanceBench上，BRANE以最高89%的成本降低达到最佳固定配置的准确率，并优于LLM路由、规则和微调Qwen3-4B基线。这表明查询级别的全管道配置是静态调优的实用替代方案。
Natural Language Query to Configuration for Retrieval Agents
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 the problem: 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, BrowseComp-Plus, 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.