PilotBench: A Benchmark for General Aviation Agents with Safety Constraints
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 in...
| Authors | Yalun Wu, Haotian Liu, Zhoujun Li et al. |
|---|---|
| Primary category | cs.AI |
| Published | 10 April 2026 |
| Last updated at source | 10 April 2026 |
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