解耦規劃與執行：面向深度搜索的層次化推理框架

摘要

在現實世界的搜索場景中，複雜的信息需求要求跨多種來源進行深度推理和知識綜合，而傳統的檢索增強生成（RAG）管道難以有效應對。當前基於推理的方法存在一個根本性限制：它們使用單一模型來處理高層次規劃和詳細執行，導致推理效率低下且可擴展性有限。本文介紹了HiRA，這是一種分層框架，將戰略規劃與專業執行分離。我們的方法將複雜的搜索任務分解為專注的子任務，將每個子任務分配給配備外部工具和推理能力的領域特定代理，並通過結構化的集成機制協調結果。這種分離防止了執行細節干擾高層次推理，同時使系統能夠利用專業知識進行不同類型的信息處理。在四個複雜的跨模態深度搜索基準上的實驗表明，HiRA顯著優於最先進的RAG和基於代理的系統。我們的結果顯示了在答案質量和系統效率方面的改進，突出了分離規劃和執行對於多步驟信息尋求任務的有效性。我們的代碼可在https://github.com/ignorejjj/HiRA獲取。

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.

解耦規劃與執行：面向深度搜索的層次化推理框架

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

摘要

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