# SCOPE: A Lightweight-training LLM Framework for Air Traffic Control Readback Monitoring Source: [SCOPE: A Lightweight-training LLM Framework for Air Traffic Control Readback Monitoring]() Collection index: [llms.txt]() Data updated: 2026-05-30T05:00:04.259Z Authors: Qihan Deng, Minghua Zhang, Yang Yang, Zhenyu Gao Published: 2026-05-28T07:56:24.000Z ## Abstract Pilot readback of Air Traffic Control (ATC) voice instructions is a primary safeguard against miscommunication in air transportation. However, readback anomalies remain implicated in approximately 80% of aviation incidents. This vulnerability is further exacerbated by rising traffic volume and elevated cognitive workload, thereby motivating automated readback monitoring by machine. Traditional rule-based and machine learning approaches struggle to generalize across the highly variable and evolving phraseology of air traffic controller-pilot communications. While Large Language Models (LLMs) have opened a new avenue through their strong reasoning and generalization capabilities, existing approaches still face deployment and computational barriers in practice. In this work, we propose Semantic reasoning for Communication via Open-set Plug-in with Examples (SCOPE), a novel lightweight-training LLM framework that advances both the efficiency and accuracy of machine-based ATC readback monitoring. The core idea is to couple a plug-in open-set classifier with a carefully designed in-context learning mechanism on top of a frozen LLM. Extensive experiments on the semi-synthetic communication dataset show that SCOPE attains superior accuracy while delivering the low-latency response required for operational environments. Under a few-shot setting, SCOPE achieves 91.05% accuracy in open-set detection and corrects 96.63% of anomalous readbacks, thereby outperforming the strongest available baselines while providing explanations for its decisions. These findings demonstrate the potential of our framework as a practical pathway toward interpretable and controllable ATC readback monitoring. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Readback Anomaly Detection on APCP](): Readback Anomaly Detection; APCP; Accuracy: 91.05. - [Joint Slot Filling and Intent Recognition on APCP](): Joint Slot Filling and Intent Recognition; APCP; Intent Accuracy: 95.22. ## Original sources and implementation [Original paper]() [PDF]()