Fine-Tuning Large Language Models for Cooperative Tactical Deconfliction of Small Unmanned Aerial Systems

Fine-Tuning Large Language Models for Cooperative Tactical Deconfliction of Small Unmanned Aerial Systems | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/fine-tuning-large-language-models-for-cooperative-tactical-deconfliction-of-small-unmanned-aerial-systems

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Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-03-31
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 36
Source count: 3
Coverage: 67%

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Canonical ID fine-tuning-large-language-models-for-cooperative-tactical-deconfliction-of-small-unmanned-aerial-systems | Route /signal-canvas/fine-tuning-large-language-models-for-cooperative-tactical-deconfliction-of-small-unmanned-aerial-systems

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Evidence Receipt

Route status: building

Claims: 8

References: 36

Proof: Verification pending

Freshness state: computing

Source paper: Fine-Tuning Large Language Models for Cooperative Tactical Deconfliction of Small Unmanned Aerial Systems

PDF: https://arxiv.org/pdf/2603.28561v1

Repository: https://github.com/cvpr-org/author-kit

Source count: 3

Coverage: 67%

Last proof check: 2026-03-31T20:30:20.275Z

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Ready for execution: Fine-Tuning Large Language Models for Cooperative Tactical Deconfliction of Small Unmanned Aerial Systems

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Subject: Fine-Tuning Large Language Models for Cooperative Tactical Deconfliction of Small Unmanned Aerial Systems

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Last commit

5/28/2026

Forks

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Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
We demonstrate that parameter-efficient Supervised Fine-Tuning (SFT) with Low-Rank Adaptation (LoRA) [17] substantially improves LLM decision accuracy, behavioral consistency, and separation safety compared to a pretrained baseline, as validated through both offline evaluation and closed-loop simulation.
Implicationpartial
Explicitly stated as a key contribution in the abstract and supported by quantitative results in Table 2.
Verificationpartial
partial
Evidencepartial
The Base model achieves an accuracy of 27%, underscoring the challenge posed by tactical deconfliction for general-purpose LLMs without domain adaptation.
Implicationpartial
Directly stated numeric result from Table 2 in the analysis.
Verificationpartial
partial
Evidencepartial
In contrast, SFT with LoRA substantially improves performance, achieving an accuracy of 88% and an F1-score of 69%
Implicationpartial
Directly stated numeric result from Table 2 in the analysis.
Verificationpartial
partial
Evidencepartial
GRPO provides additional coordination benefits but exhibits reduced robustness when interacting with heterogeneous agent policies.
Implicationpartial
Directly stated in the abstract as a key finding.
Verificationpartial
partial
Evidencepartial
We propose a simulation-to-language data generation pipeline based on the BlueSky air traffic simulator that produces rule-consistent deconfliction datasets reflecting established safety practices.
Implicationpartial
Explicitly stated as a proposed method in the abstract and detailed in the analysis.
Verificationpartial
partial
Evidencepartial
While Large Language Models (LLMs) exhibit strong reasoning capabilities, their direct application to air traffic control remains limited by insufficient domain grounding and unpredictable output inconsistency.
Implicationpartial
Directly stated as a limitation in the abstract.
Verificationpartial
partial
Evidencepartial
GRPO537540 50
Implicationpartial
Directly stated numeric result from Table 2 in the analysis (GRPO: 53%, SFT: 88%).
Verificationpartial
partial
Evidencepartial
As major companies increasingly deploy sUAS fleets in shared airspace, safety- and privacy-related constraints have become central considerations to their operational frameworks. Due to proprietary concerns and regulatory sensitivities, high-fidelity operational data relevant to tactical decon
Implicationpartial
Strongly implied as the motivation for the simulation pipeline, stated in the context of dataset generation.
Verificationpartial
partial

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Fine-Tuning Large Language Models for Cooperative Tactical Deconfliction of Small Unmanned Aerial Systems

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