Decoupled Planning and Execution: A Hierarchical Reasoning Framework for
  Deep Search
Authors: name: Jiajie Jin, name: Xiaoxi Li, name: Guanting Dong, name: Yuyao Zhang, name: Yutao Zhu, name: Yang Zhao, name: Hongjin Qian, name: Zhicheng Dou

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.