{"ID":5438617,"CreatedAt":"2026-07-01T01:17:58.482524686Z","UpdatedAt":"2026-07-03T04:20:05.427450767Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.31114","arxiv_id":"2606.31114","title":"Revealing Safety-Critical Scenarios for UTM via Transformer","abstract":"Unmanned Traffic Management (UTM) systems are cloud-based platforms designed to manage and coordinate multiple aerial vehicles remotely. UTM systems are safety-critical which cannot tolerate failures like crash or collision. To reveal latent vulnerabilities, there are neither optimal failure-exposing demonstrations nor clear reward signals. Additionally, UTM's self-healing capability introduces the ``long-tail effect'' of critical failures. We propose framing UTM vulnerability discovery as a sequence modeling problem amenable to transformer-based RL architectures. Our approach leverages attention mechanisms to directly model the relationship among system states, and predict optimal actions. Our framework introduces a Policy Model that generates targeted test scenarios and an Action Sampler that enforces domain constraints. We use a risk-based reward function to guide exploration. Through extensive evaluation on a 700-hour simulation study, we demonstrate an 8$\\times$ improvement in vulnerability discovery efficiency compared to expert-guided testing. It also discovers critical edge cases that traditional methods have missed.","short_abstract":"Unmanned Traffic Management (UTM) systems are cloud-based platforms designed to manage and coordinate multiple aerial vehicles remotely. UTM systems are safety-critical which cannot tolerate failures like crash or collision. To reveal latent vulnerabilities, there are neither optimal failure-exposing demonstrations nor...","url_abs":"https://arxiv.org/abs/2606.31114","url_pdf":"https://arxiv.org/pdf/2606.31114v1","authors":"[\"Huaze Tang\",\"Bill Zeng\",\"Chao Wang\",\"Zhenpeng Shi\",\"Qian Zhang\",\"Wenbo Ding\"]","published":"2026-06-30T04:21:20Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Transformer\",\"LoRA\"]","has_code":false}