Cognitive Kernel-Pro: A Framework for Deep Research Agents and Agent Foundation Models Training
Abstract
General AI Agents are increasingly recognized as foundational frameworks for the next generation of artificial intelligence, enabling complex reasoning, web interaction, coding, and autonomous research capabilities. However, current agent systems are either closed-source or heavily reliant on a variety of paid APIs and proprietary tools, limiting accessibility and reproducibility for the research community. In this work, we present \textbf{Cognitive Kernel-Pro}, a fully open-source and (to the maximum extent) free multi-module agent framework designed to democratize the development and evaluation of advanced AI agents. Within Cognitive Kernel-Pro, we systematically investigate the curation of high-quality training data for Agent Foundation Models, focusing on the construction of queries, trajectories, and verifiable answers across four key domains: web, file, code, and general reasoning. Furthermore, we explore novel strategies for agent test-time reflection and voting to enhance agent robustness and performance. We evaluate Cognitive Kernel-Pro on GAIA, achieving state-of-the-art results among open-source and free agents. Notably, our 8B-parameter open-source model surpasses previous leading systems such as WebDancer and WebSailor, establishing a new performance standard for accessible, high-capability AI agents. Code is available at https://github.com/Tencent/CognitiveKernel-Pro
Cite
@article{arxiv.2508.00414,
title = {Cognitive Kernel-Pro: A Framework for Deep Research Agents and Agent Foundation Models Training},
author = {Tianqing Fang and Zhisong Zhang and Xiaoyang Wang and Rui Wang and Can Qin and Yuxuan Wan and Jun-Yu Ma and Ce Zhang and Jiaqi Chen and Xiyun Li and Yonglin Wang and Jingchen Ni and Tianshi Zheng and Chun Chen and Wenhao Yu and Zhenwen Liang and Hongming Zhang and Haitao Mi and Dong Yu},
journal= {arXiv preprint arXiv:2508.00414},
year = {2026}
}
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21 pages