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🧠 AI🟢 BullishImportance 7/10

OpenSeeker: Democratizing Frontier Search Agents by Fully Open-Sourcing Training Data

arXiv – CS AI|Yuwen Du, Rui Ye, Shuo Tang, Xinyu Zhu, Yijun Lu, Yuzhu Cai, Siheng Chen|
🤖AI Summary

Researchers have introduced OpenSeeker, the first fully open-source search agent that achieves frontier-level performance using only 11,700 training samples. The model outperforms existing open-source competitors and even some industrial solutions, with complete training data and model weights being released publicly.

Key Takeaways
  • OpenSeeker is the first fully open-source search agent to achieve state-of-the-art performance across multiple benchmarks.
  • The model was trained on only 11,700 synthesized samples using two key innovations: fact-grounded QA synthesis and denoised trajectory synthesis.
  • OpenSeeker significantly outperforms the previous best open-source agent DeepDive with 29.5% vs 15.3% on BrowseComp benchmark.
  • The model even surpasses industrial competitors like Tongyi DeepResearch on Chinese benchmarks despite simpler training methods.
  • Complete training dataset and model weights are being released to democratize AI search agent research.
Read Original →via arXiv – CS AI
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