HierSearch: A Hierarchical Enterprise Deep Search Framework Integrating
  Local and Web Searches
Authors: name: Jiejun Tan, name: Zhicheng Dou, name: Yan Yu, name: Jiehan Cheng, name: Qiang Ju, name: Jian Xie, name: Ji-Rong Wen

Recently, large reasoning models have demonstrated strong mathematical and
coding abilities, and deep search leverages their reasoning capabilities in
challenging information retrieval tasks. Existing deep search works are
generally limited to a single knowledge source, either local or the Web.
However, enterprises often require private deep search systems that can
leverage search tools over both local and the Web corpus. Simply training an
agent equipped with multiple search tools using flat reinforcement learning
(RL) is a straightforward idea, but it has problems such as low training data
efficiency and poor mastery of complex tools. To address the above issue, we
propose a hierarchical agentic deep search framework, HierSearch, trained with
hierarchical RL. At the low level, a local deep search agent and a Web deep
search agent are trained to retrieve evidence from their corresponding domains.
At the high level, a planner agent coordinates low-level agents and provides
the final answer. Moreover, to prevent direct answer copying and error
propagation, we design a knowledge refiner that filters out hallucinations and
irrelevant evidence returned by low-level agents. Experiments show that
HierSearch achieves better performance compared to flat RL, and outperforms
various deep search and multi-source retrieval-augmented generation baselines
in six benchmarks across general, finance, and medical domains.