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ASTRA: A Scalable Next-Generation ATCO Training Simulator with Autonomous Simpilots

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arXiv:2606. 18319v1 Announce Type: new Abstract: Air Traffic Control Operators (ATCOs) are vital in ensuring the safe, orderly, and efficient flow of air traffic, yet training capacity is constrained by reliance on specialized human trainers known as simpilots, who must role-play both pilots and ATCOs in a simulated airspace.

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Authors: Ethan Chew, Enjia Wu, Iruss Eng Wei Yeow, Ian Weiqin Lim, Ranen Sim, Brandon Koh Ziheng, Kaleb Nim, Caden Toh Jun Yi, Wei Dong Soin, Darius Kai Keat Koh, Galen King Yu Tay, Prannaya Gupta, Jonathan Ee Fang Koong, Yong Zhi Lim
Released: 2026-06-18 (21d ago)
Topic: Related to current project
Relation to my work: Read next because ASTRA: A Scalable Next-Generation ATCO Training Simulator with Autonomous Simpilots overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: text, word, eval, source, line, rate, control, trained. Source: arxiv cs.LG (Machine Learning).
arXiv: 2606.18319

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