Decoupled Planning and Execution: A Hierarchical Reasoning Framework for
  Deep Search
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.
6867282b9db35afc9c304c83
Jiajie Jin
6867282b9db35afc9c304c84
Xiaoxi Li
6867282b9db35afc9c304c85
Guanting Dong
claimed_verified
2025-07-29T12:51:21.544Z
61cd4b833dd34ba1985e0753
https://cdn-avatars.huggingface.co/v1/production/uploads/61cd4b833dd34ba1985e0753/BfHfrwotoMESpXZOHiIe4.png
KABI
user
dongguanting
6867282b9db35afc9c304c86
Yuyao Zhang
6867282b9db35afc9c304c87
Yutao Zhu
6867282b9db35afc9c304c88
Yang Zhao
6867282b9db35afc9c304c89
Hongjin Qian
6867282b9db35afc9c304c8a
Zhicheng Dou
Decoupled Planning and Execution: A Hierarchical Reasoning Framework for
  Deep Search
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.
A hierarchical framework separates strategic planning from specialized execution in deep search tasks, improving answer quality and system efficiency over traditional RAG and agent-based systems.
retrieval-augmented generation RAG hierarchical framework strategic planning specialized execution domain-specific agents external tools reasoning capabilities structured integration mechanism complex search tasks cross-modal deep search benchmarks multi-step information seeking tasks