在预算约束下，优化搜索深度和检索策略可显著提升大模型问答准确率。
Quantifying the Accuracy and Cost Impact of Design Decisions in Budget-Constrained Agentic LLM Search
- 通过控制实验量化搜索深度、检索方式与生成预算对性能的影响
- 混合关键词与密集检索+轻量重排序带来最大平均准确率提升
- 更大生成预算在复杂推理任务中效果更明显，适合实际部署调参
代理式检索增强生成（RAG）系统结合迭代搜索、规划提示和检索后端，但在实际部署中需对工具调用次数和生成令牌数施加明确预算。我们提出一项受控测量研究，分析搜索深度、检索策略和生成预算在固定约束下对准确率和成本的影响。利用无模型的预算约束代理搜索评估框架（BCAS），该框架可揭示剩余预算并控制工具使用，我们在六种大模型和三个问答基准上进行对比实验。结果显示：在不同模型和数据集上，准确率随搜索次数增加而提升，但存在小幅度上限；混合关键词与密集检索并配合轻量重排序，在消融实验中带来最大平均收益；更大的生成预算在类似HotpotQA的综合推理任务中最为有效。这些结果为配置预算受限的代理检索流水线提供了实用指导，并附有可复现的提示和评估设置。
原文摘要 · Abstract (English)
Agentic Retrieval-Augmented Generation (RAG) systems combine iterative search, planning prompts, and retrieval backends, but deployed settings impose explicit budgets on tool calls and completion tokens. We present a controlled measurement study of how search depth, retrieval strategy, and completion budget affect accuracy and cost under fixed constraints. Using Budget-Constrained Agentic Search (BCAS), a model-agnostic evaluation harness that surfaces remaining budget and gates tool use, we run comparisons across six LLMs and three question-answering benchmarks. Across models and datasets, accuracy improves with additional searches up to a small cap, hybrid lexical and dense retrieval with lightweight re-ranking produces the largest average gains in our ablation grid, and larger completion budgets are most helpful on HotpotQA-style synthesis. These results provide practical guidance for configuring budgeted agentic retrieval pipelines and are accompanied by reproducible prompts and evaluation settings.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。