SCOPE: A Lightweight-training LLM Framework for Air Traffic Control Readback Monitoring

Researchers introduce SCOPE, a lightweight LLM framework designed to monitor pilot readbacks of Air Traffic Control instructions, addressing a critical aviation safety gap where readback anomalies contribute to approximately 80% of aviation incidents. The system achieves 91% accuracy in detecting anomalies and 96.63% correction rates while requiring minimal computational overhead, offering a practical deployment pathway for automated safety monitoring in high-stakes operational environments.

SCOPE represents a targeted application of LLM technology to a high-consequence domain where traditional machine learning approaches have consistently fallen short. The aviation industry has long struggled with miscommunication between pilots and controllers—a deceptively simple problem complicated by natural language variation, regional dialects, and the nuanced phraseology of professional air traffic communications. Readback errors trigger cascading safety protocols that consume resources and introduce delays, yet current detection methods remain either too rigid or too computationally expensive for real-time deployment.

The framework's innovation lies in its hybrid architecture: coupling an open-set classifier with in-context learning mechanisms atop a frozen LLM. This design choice directly addresses deployment constraints that have hindered previous AI safety initiatives in aviation. By avoiding full model retraining and leveraging few-shot learning patterns, SCOPE reduces computational barriers while maintaining interpretability—a regulatory requirement in safety-critical domains.

The 96.63% readback correction rate carries significant operational implications. Airlines and air traffic control agencies face mounting pressure from increasing flight volumes and pilot fatigue. Automated monitoring that catches anomalies before they escalate provides measurable risk reduction without requiring human validation delays. The framework's ability to provide decision explanations also satisfies regulatory and liability concerns that typically slow technology adoption in aviation.

Looking ahead, success in this domain could establish a precedent for LLM deployment across other safety-critical infrastructure: maritime communications, industrial control systems, and emergency dispatch. The key signal to monitor is whether aviation regulators begin certifying SCOPE or similar systems for operational use, which would validate the broader thesis that lightweight, interpretable AI can address high-stakes communication monitoring.

• →SCOPE achieves 91% open-set detection accuracy and corrects 96.63% of anomalous readbacks using a frozen LLM with minimal training overhead.
• →The framework addresses a critical safety gap where readback errors contribute to approximately 80% of aviation incidents annually.
• →Lightweight architecture enables low-latency real-time deployment in operational air traffic control environments where computational constraints are strict.
• →Interpretability and explainability features satisfy regulatory requirements necessary for adoption in safety-critical aviation infrastructure.
• →Success with ATC readback monitoring could establish precedent for LLM deployment across other high-consequence domains like maritime and industrial control systems.