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-c

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ethics.ai (10 April 2026), “PilotBench: A Benchmark for General Aviation Agents with Safety Constraints,” evidence record 6082, https://ethics.ai/record/6082 (originally published by arXiv).

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