Cognitive Kernel-Pro: A Framework for Deep Research Agents and Agent
  Foundation Models Training
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 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
6890eddff01a094725f833f8
Tianqing Fang
6890eddff01a094725f833f9
Zhisong Zhang
6890eddff01a094725f833fa
Xiaoyang Wang
6890eddff01a094725f833fb
Rui Wang
6890eddff01a094725f833fc
Can Qin
6890eddff01a094725f833fd
Yuxuan Wan
6890eddff01a094725f833fe
Jun-Yu Ma
6890eddff01a094725f833ff
Ce Zhang
6890eddff01a094725f83400
Jiaqi Chen
6890eddff01a094725f83401
Xiyun Li
6890eddff01a094725f83402
Hongming Zhang
6890eddff01a094725f83403
Haitao Mi
6890eddff01a094725f83404
Dong Yu
Cognitive Kernel-Pro: A Framework for Deep Research Agents and Agent
  Foundation Models Training
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 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
Cognitive Kernel-Pro is an open-source multi-module agent framework that enhances AI agent robustness and performance through data curation and novel test-time strategies, achieving state-of-the-art results.
Agent Foundation Models queries trajectories verifiable answers agent test-time reflection agent voting GAIA WebDancer WebSailor