PilotBench: A Benchmark for General Aviation Agents with Safety Constraints
Summary: arXiv:2604.08987v1 Announce Type: new
Abstract: As Large Language Models (LLMs) advance toward embodied AI agents operating in physical environments, a fundamental question emerges: can models trained on text corpora reliably reason about complex physics while adhering to safety constraints? We address this through PilotBench, a benchmark evaluating LLMs on safety-critical flight trajectory and attitude prediction.
Built from 708 real-world general aviation trajectories spanning nine operationally distinct flight phases with synchronized 34-channel telemetry, PilotBench systematically probes the intersection of semantic understanding and physics-governed prediction through comparative analysis of LLMs and traditional forecasters.
We introduce Pilot-Score, a composite metric balancing 60% regression accuracy with 40% instruction adherence and safety compliance. Comparative evaluation across 41 models uncovers a Precision-Controllability Dichotomy: traditional forecasters achieve superior MAE of 7.01 but lack semantic reasoning capabilities, while LLMs gain controllability with 86–89% instruction-following at the cost of 11–14 MAE precision.
Key Findings
- Precision-Controllability Dichotomy: Traditional forecasters excel in mean absolute error (MAE) but do not possess the ability for semantic reasoning.
- LLMs vs. Traditional Models: LLMs demonstrate a higher rate of instruction adherence (86–89%), but this comes at a cost of reduced precision (MAE of 11–14).
- Dynamic Complexity Gap: LLM performance notably declines in high-workload flight phases such as Climb and Approach, indicating potential weaknesses in their implicit physics modeling.
Phase-stratified analysis further exposes a Dynamic Complexity Gap, revealing that LLM performance degrades sharply in high-workload phases such as Climb and Approach. This suggests that their implicit models of physics may be brittle under complex operational conditions.
Implications for Future AI Development
These empirical discoveries motivate the exploration of hybrid architectures that combine the symbolic reasoning capabilities of LLMs with the numerical precision of specialized forecasters. By integrating the strengths of both approaches, PilotBench provides a rigorous foundation for advancing embodied AI in safety-constrained domains.
The implications of this research extend beyond aviation, potentially influencing various fields where safety and precision are paramount. As AI systems become increasingly integrated into critical real-world applications, understanding their limitations and strengths will be essential for ensuring safety and effectiveness.
Conclusion
PilotBench represents a significant step forward in evaluating the capabilities of AI models in safety-critical environments. By addressing the intersection of semantic understanding and physics-based prediction, researchers can better understand how to harness the power of LLMs while ensuring adherence to safety constraints. This benchmark paves the way for future innovations in embodied AI, ensuring that as we advance technology, we also prioritize safety and reliability.