Cognitive Kernel: An Open-source Agent System towards Generalist Autopilots
Hongming Zhang, Xiaoman Pan, Hongwei Wang, Kaixin Ma, Wenhao Yu, Dong Yu
Abstract
We introduce Cognitive Kernel, an open-source agent system towards the goal of generalist autopilots. Unlike copilot systems, which primarily rely on users to provide essential state information, autopilot systems complete tasks from start to finish independently. This requires the system to acquire the missing state information actively. Cognitive Kernel adopts a dynamic programming design where the central policy model (a fine-tuned LLM) could initiate an environment state perception task, essentially another agent task, as needed. The results demonstrate that Cognitive Kernel achieves better or comparable performance to other closed-source systems on core autopilot capabilities. Cognitive Kernel is fully dockerized, ensuring everyone can deploy it privately and securely. We open-source the system to encourage further research on LLM-driven autopilot systems
BibTeX
@inproceedings{zhang-etal-2025-cognitive,
title = "Cognitive Kernel: An Open-source Agent System towards Generalist Autopilots",
author = "Zhang, Hongming and
Pan, Xiaoman and
Wang, Hongwei and
Ma, Kaixin and
Yu, Wenhao and
Yu, Dong",
editor = "Dziri, Nouha and
Ren, Sean (Xiang) and
Diao, Shizhe",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (System Demonstrations)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-demo.29/",
pages = "328--349",
ISBN = "979-8-89176-191-9"
}