{
  "id": 6589,
  "url": "https://arxiv.org/abs/2603.28561v2",
  "title": "Fine-Tuning Large Language Models for Cooperative Tactical Deconfliction of Small Unmanned Aerial Systems",
  "summary": "The growing deployment of small Unmanned Aerial Systems (sUASs) in low-altitude airspaces has increased the need for reliable tactical deconfliction under safety-critical constraints. Tactical deconfliction involves short-horizon decision-making in dense, partially observable, and heterogeneous multi-agent environments, where both cooperative separation assurance and operational efficiency must be maintained. While Large Language Models (LLMs) exhibit strong reasoning capabilities, their direct ",
  "authors": "Iman Sharifi, Alex Zongo, Peng Wei",
  "category": "research",
  "topics": "agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-30T15:22:27.000Z",
  "fetched_at": "2026-07-14T16:32:37.308Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6589",
  "original_url": "https://arxiv.org/abs/2603.28561v2",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}