HierSearch: A Hierarchical Enterprise Deep Search Framework Integrating
  Local and Web Searches
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.
689ac80efab6fdd2e52ac510
Jiejun Tan
claimed_verified
2025-08-13T07:19:26.764Z
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Jiejun Tan
user
zstanjj
689ac80efab6fdd2e52ac511
Zhicheng Dou
689ac80efab6fdd2e52ac512
Yan Yu
689ac80efab6fdd2e52ac513
Jiehan Cheng
689ac80efab6fdd2e52ac514
Qiang Ju
claimed_verified
2025-09-03T08:30:15.134Z
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sdujq
user
sdujq
689ac80efab6fdd2e52ac515
Jian Xie
689ac80efab6fdd2e52ac516
Ji-Rong Wen
HierSearch: A Hierarchical Enterprise Deep Search Framework Integrating
  Local and Web Searches
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.
HierSearch, a hierarchical agentic deep search framework using hierarchical RL, improves performance in multi-source retrieval tasks by coordinating local and Web search agents and refining knowledge.
reinforcement learning hierarchical RL local deep search agent Web deep search agent planner agent knowledge refiner hallucinations irrelevant evidence multi-source retrieval-augmented generation benchmarks