---
title: "FalconCopilot — paper by Yan et al. (2026) — summary and ideas"
description: "The authors present FalconCopilot, which has two parts. 45 references. Among them: 0."
authors: ["Jingyuan Yan", "Qingchen Liu", "Qichao Ma", "Jiahu Qin"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.1500/"
doi: "10.18653/v1/2026.findings-acl.1500"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/yan-2026-falconcopilot-empowering-llms-integrated-human/"
---
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# FalconCopilot: Empowering LLMs Towards Integrated Human-Machine Systems for Aviation Autonomy

**Authors:** Jingyuan Yan, Qingchen Liu, Qichao Ma and Jiahu Qin · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.1500/) · [PDF](https://aclanthology.org/2026.findings-acl.1500.pdf) · [DOI 10.18653/v1/2026.findings-acl.1500](https://doi.org/10.18653/v1/2026.findings-acl.1500) · [OpenAlex](https://openalex.org/W7166809766)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Modern cockpits require pilots to combine long-horizon mission planning with precise, button-level operations, and this creates heavy cognitive load. Earlier LLM uses in aviation mostly act as retrieval tools or alarm systems. They do not plan tasks end to end or interact at the level of individual cockpit controls.

The authors present FalconCopilot, which has two parts. **Textual DCS** is middleware for DCS World. It converts multimodal cockpit signals and piloting knowledge into a two-level symbolic interface. **FalconAgent** is an LLM copilot built on that interface. It turns a pilot's mission goal into step-by-step cockpit instructions, assigns steps across crew members, and removes unnecessary procedures. A feedback loop links the two sides: the agent produces runtime verification functions to confirm each step against simulator state, and the human can send corrections back.

In a small human-in-the-loop study in an F-14A setup, the system shortened procedure-heavy tasks and reduced perceived workload to roughly the level of a human instructor. This suggests LLMs can support knowledge-intensive flight procedures.

## Key points
- **Textual DCS (released on GitHub):**
  - Its high-level interface uses LLMs to extract expert guidance (e.g., Chuck's Guide, Heatblur documentation) into an action space, and exposes mission state as JSON.
  - Its low-level interface builds a unified multimodal retrieval database with low-hallucination descriptions of cockpit controls.
- **FalconAgent modules:** planning, retrieval, compression (procedural pruning), crew allocation, and a verify module. The verify module generates checks such as whether the Master Arm switch is ON before moving to the next step.
- **Study design:** within-subjects, N=6 participants from novice to expert, three tasks (reconnaissance, air-to-air, air-to-ground). Four conditions were compared: a state-of-the-art baseline, Agentic Manual (no closed loop), Human Coach, and FalconCopilot. Data were analysed with Wilcoxon signed-rank tests.
- **Results:**
  - Task completion time was significantly better than the baseline on Tasks 1 and 3 (p=0.03, r=−1.0), with no difference on the skill-dominant air-to-air task.
  - On Task 3, completion time showed no significant difference from the Human Coach (p=0.09).
  - Weighted NASA-TLX workload was lower than both the baseline and Agentic Manual (p=0.03), and not significantly different from the Human Coach (p=0.22).
- **Limitations:** the system gives no help with aircraft maneuvering, and LLM latency limits its usefulness when rapid tactical changes are needed.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Jingyuan Yan, Qingchen Liu, Qichao Ma and Jiahu Qin. 2026. FalconCopilot: Empowering LLMs Towards Integrated Human-Machine Systems for Aviation Autonomy. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{yan2026falconcopilot,
  title = {{FalconCopilot: Empowering LLMs Towards Integrated Human-Machine Systems for Aviation Autonomy}},
  author = {Jingyuan Yan, Qingchen Liu, Qichao Ma and Jiahu Qin},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1500},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1500}
}
```

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*Updated 2 November 2026*