@inproceedings{yan-etal-2026-falconcopilot,
title = "{F}alcon{C}opilot: Empowering {LLM}s Towards Integrated Human-Machine Systems for Aviation Autonomy",
author = "Yan, Jingyuan and
Liu, Qingchen and
Ma, Qichao and
Qin, Jiahu",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1500/",
doi = "10.18653/v1/2026.findings-acl.1500",
pages = "30001--30026",
ISBN = "979-8-89176-395-1",
abstract = "Complex flight tasks demand both intricate, long-horizon decision-making and precise operations, which imposes immense cognitive, knowledge and experience demands for pilots and highlights the need for advanced copilot systems. While Large Language Models (LLMs) bring powerful potential to this area, a comprehensive LLM-based copilot system{---}one that addresses deficiencies in task-level adaptability and fine-grained decision support while integrating with a high-fidelity environment{---}is critically lacking. To address this gap, we present FalconCopilot, pioneering the first such comprehensive system, composed of two parts: 1) Textual DCS, an interface built upon Digital Combat Simulator (DCS) World that unifies multi-modal cockpit data and piloting knowledge into a stable semantic interface for LLMs. Building on this interface, we introduce 2) FalconAgent, an LLM-powered copilot agent that performs optimized task planning, incorporating capabilities for multi-crew task allocation and procedural pruning. Our built-in human-AI interaction is grounded by a bidirectional feedback loop of runtime verification and human correction. In human-in-the-loop experiment, FalconCopilot shortens task completion time while attaining a level of performance approaching that of a human instructor."
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<abstract>Complex flight tasks demand both intricate, long-horizon decision-making and precise operations, which imposes immense cognitive, knowledge and experience demands for pilots and highlights the need for advanced copilot systems. While Large Language Models (LLMs) bring powerful potential to this area, a comprehensive LLM-based copilot system—one that addresses deficiencies in task-level adaptability and fine-grained decision support while integrating with a high-fidelity environment—is critically lacking. To address this gap, we present FalconCopilot, pioneering the first such comprehensive system, composed of two parts: 1) Textual DCS, an interface built upon Digital Combat Simulator (DCS) World that unifies multi-modal cockpit data and piloting knowledge into a stable semantic interface for LLMs. Building on this interface, we introduce 2) FalconAgent, an LLM-powered copilot agent that performs optimized task planning, incorporating capabilities for multi-crew task allocation and procedural pruning. Our built-in human-AI interaction is grounded by a bidirectional feedback loop of runtime verification and human correction. In human-in-the-loop experiment, FalconCopilot shortens task completion time while attaining a level of performance approaching that of a human instructor.</abstract>
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%0 Conference Proceedings
%T FalconCopilot: Empowering LLMs Towards Integrated Human-Machine Systems for Aviation Autonomy
%A Yan, Jingyuan
%A Liu, Qingchen
%A Ma, Qichao
%A Qin, Jiahu
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F yan-etal-2026-falconcopilot
%X Complex flight tasks demand both intricate, long-horizon decision-making and precise operations, which imposes immense cognitive, knowledge and experience demands for pilots and highlights the need for advanced copilot systems. While Large Language Models (LLMs) bring powerful potential to this area, a comprehensive LLM-based copilot system—one that addresses deficiencies in task-level adaptability and fine-grained decision support while integrating with a high-fidelity environment—is critically lacking. To address this gap, we present FalconCopilot, pioneering the first such comprehensive system, composed of two parts: 1) Textual DCS, an interface built upon Digital Combat Simulator (DCS) World that unifies multi-modal cockpit data and piloting knowledge into a stable semantic interface for LLMs. Building on this interface, we introduce 2) FalconAgent, an LLM-powered copilot agent that performs optimized task planning, incorporating capabilities for multi-crew task allocation and procedural pruning. Our built-in human-AI interaction is grounded by a bidirectional feedback loop of runtime verification and human correction. In human-in-the-loop experiment, FalconCopilot shortens task completion time while attaining a level of performance approaching that of a human instructor.
%R 10.18653/v1/2026.findings-acl.1500
%U https://aclanthology.org/2026.findings-acl.1500/
%U https://doi.org/10.18653/v1/2026.findings-acl.1500
%P 30001-30026
Markdown (Informal)
[FalconCopilot: Empowering LLMs Towards Integrated Human-Machine Systems for Aviation Autonomy](https://aclanthology.org/2026.findings-acl.1500/) (Yan et al., Findings 2026)
ACL