OpenSeeker: Democratizing Frontier Search Agents by Fully Open-Sourcing Training Data
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Yuwen Du
Shanghai Jiao Tong University
Shuo Tang
Shanghai Jiao Tong University
Xinyu Zhu
Shanghai Jiao Tong University
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References (22)
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Founder's Pitch
"Fully open-source search agent democratizing high-performance frontier search through open data and code."
Commercial Viability Breakdown
0-10 scaleHigh Potential
3/4 signals
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3/4 signals
Series A Potential
4/4 signals
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Why It Matters
OpenSeeker democratizes access to high-performance search models, which have traditionally been exclusive to large corporations due to proprietary datasets.
Product Angle
Productize OpenSeeker as an API for third-party developers and researchers to build applications on top of it that require advanced search capabilities and data-driven insights.
Disruption
OpenSeeker can displace proprietary search agents by providing equivalent or superior performance with transparency and cost-efficiency, promoting innovation and reducing barriers in the research community.
Product Opportunity
The need for high-quality search capabilities is significant in edtech, research institutions, and enterprises seeking competitive intelligence. These sectors will benefit from improved automated search capabilities and open-source accessibility, reducing dependency on costly proprietary agents.
Use Case Idea
An accessible AI-driven platform for educational or enterprise research that leverages OpenSeeker to provide deep, multi-faceted insights from web data.
Science
OpenSeeker uses scalable QA synthesis and denoised trajectory synthesis to create complex, multi-hop reasoning datasets that train search agents to perform at state-of-the-art levels. It involves reverse-engineering web graphs and controlling complexity through entity obfuscation, enabling deep reasoning required for search tasks.
Method & Eval
Tested on BrowseComp, BrowseComp-ZH, xbench-DeepSearch, and WideSearch, OpenSeeker achieved state-of-the-art performance using a single training run with default parameters, beating both open-source and some proprietary models.
Caveats
Although promising, the model's performance heavily depends on the quality of web data, and potential biases in dataset creation may impact results. Resource constraints during training indicate room for optimization.
Author Intelligence
Yuwen Du
Rui Ye
Shuo Tang
Xinyu Zhu
Yijun Lu
Yuzhu Cai
Siheng Chen
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