FS-Researcher: Test-Time Scaling for Long-Horizon Research Tasks with File-System-Based Agents
Deep research is emerging as a representative long-horizon task for large language model (LLM) agents. However, long trajectories in deep research often exceed model context limits, compressing token budgets for both evidence collection and report writing, and preventing effective test-time scaling. We introduce FS-Researcher, a file-system-based, dual-agent framework that scales deep research beyond the context window via a persistent workspace. Specifically, a Context Builder agent acts as a librarian which browses the internet, writes structured notes, and archives raw sources into a hierarchical knowledge base that can grow far beyond context length. A Report Writer agent then composes the final report section by section, treating the knowledge base as the source of facts. In this framework, the file system serves as a durable external memory and a shared coordination medium across agents and sessions, enabling iterative refinement beyond the context window. Experiments on two open-ended benchmarks (DeepResearch Bench and DeepConsult) show that FS-Researcher achieves state-of-the-art report quality across different backbone models. Further analyses demonstrate a positive correlation between final report quality and the computation allocated to the Context Builder, validating effective test-time scaling under the file-system paradigm. The code and data are anonymously open-sourced at https://github.com/Ignoramus0817/FS-Researcher.
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Chiwei Zhu
claimed_verified
2026-02-03T10:07:52.347Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/663b22a80966eef8686aadaf/iBzyQTyGZKf33RPVIFh9a.jpeg
Chiwei Zhu
IgnoraZ
user
IgnoraZ
69816c7ace18b18628095fb2
Benfeng Xu
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Mingxuan Du
claimed_verified
2026-02-03T10:07:56.817Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/646dbba74ad7f907279dd486/XGHJMFEIpWeDlh0cn9Slu.png
Mingxuan Du
Ayanami0730
user
Ayanami0730
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Shaohan Wang
claimed_verified
2026-02-03T10:07:54.658Z
6440d49b7663594a126716f2
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shaohanwang
WShao
user
WShao
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Xiaorui Wang
69816c7ace18b18628095fb6
Zhendong Mao
69816c7ace18b18628095fb7
Yongdong Zhang
FS-Researcher: Test-Time Scaling for Long-Horizon Research Tasks with File-System-Based Agents
Deep research is emerging as a representative long-horizon task for large language model (LLM) agents. However, long trajectories in deep research often exceed model context limits, compressing token budgets for both evidence collection and report writing, and preventing effective test-time scaling. We introduce FS-Researcher, a file-system-based, dual-agent framework that scales deep research beyond the context window via a persistent workspace. Specifically, a Context Builder agent acts as a librarian which browses the internet, writes structured notes, and archives raw sources into a hierarchical knowledge base that can grow far beyond context length. A Report Writer agent then composes the final report section by section, treating the knowledge base as the source of facts. In this framework, the file system serves as a durable external memory and a shared coordination medium across agents and sessions, enabling iterative refinement beyond the context window. Experiments on two open-ended benchmarks (DeepResearch Bench and DeepConsult) show that FS-Researcher achieves state-of-the-art report quality across different backbone models. Further analyses demonstrate a positive correlation between final report quality and the computation allocated to the Context Builder, validating effective test-time scaling under the file-system paradigm. The code and data are anonymously open-sourced at https://github.com/Ignoramus0817/FS-Researcher.
A file-system-based dual-agent framework enables large language model agents to perform extended research tasks beyond context window limitations by using persistent storage as external memory.
large language model agents context window file system dual-agent framework persistent workspace external memory test-time scaling deep research knowledge base structured notes