解耦规划与执行：面向深度搜索的层次化推理框架

摘要

现实世界中的复杂信息检索需求要求跨多种来源进行深度推理与知识综合，而传统的检索增强生成（RAG）流程在此方面表现欠佳。当前基于推理的方法存在一个根本性局限：它们依赖单一模型同时处理高层规划与细节执行，导致推理效率低下且扩展性受限。本文提出HiRA，一种将战略规划与专业执行分离的层次化框架。该框架将复杂搜索任务分解为聚焦的子任务，为每个子任务配备具备外部工具与推理能力的领域特定代理，并通过结构化整合机制协调结果。这种分离避免了执行细节干扰高层推理，同时使系统能够针对不同类型的信息处理利用专业特长。在四个复杂、跨模态的深度搜索基准测试中，HiRA显著超越了最先进的RAG及基于代理的系统。我们的结果表明，在答案质量与系统效率上均有提升，凸显了在多层次信息寻求任务中解耦规划与执行的有效性。代码已发布于https://github.com/ignorejjj/HiRA。

English

Complex information needs in real-world search scenarios demand deep reasoning and knowledge synthesis across diverse sources, which traditional retrieval-augmented generation (RAG) pipelines struggle to address effectively. Current reasoning-based approaches suffer from a fundamental limitation: they use a single model to handle both high-level planning and detailed execution, leading to inefficient reasoning and limited scalability. In this paper, we introduce HiRA, a hierarchical framework that separates strategic planning from specialized execution. Our approach decomposes complex search tasks into focused subtasks, assigns each subtask to domain-specific agents equipped with external tools and reasoning capabilities, and coordinates the results through a structured integration mechanism. This separation prevents execution details from disrupting high-level reasoning while enabling the system to leverage specialized expertise for different types of information processing. Experiments on four complex, cross-modal deep search benchmarks demonstrate that HiRA significantly outperforms state-of-the-art RAG and agent-based systems. Our results show improvements in both answer quality and system efficiency, highlighting the effectiveness of decoupled planning and execution for multi-step information seeking tasks. Our code is available at https://github.com/ignorejjj/HiRA.

解耦规划与执行：面向深度搜索的层次化推理框架

Decoupled Planning and Execution: A Hierarchical Reasoning Framework for Deep Search

摘要

Support