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PilotBench: Safe Aviation AI Benchmark
π‘New benchmark exposes LLMs' aviation physics & safety gapsβvital for embodied AI.
β‘ 30-Second TL;DR
What Changed
708 real-world trajectories across 9 flight phases with 34-channel telemetry
Why It Matters
Reveals LLMs' physics reasoning gaps in safety-critical domains, guiding safer embodied AI development. Highlights need for hybrid systems combining semantic and numerical strengths. Advances benchmarking for aviation AI applications.
What To Do Next
Download PilotBench dataset from arXiv:2604.08987v1 and test your LLM on flight phases.
Who should care:Researchers & Academics
Key Points
- β’708 real-world trajectories across 9 flight phases with 34-channel telemetry
- β’Pilot-Score balances 60% regression accuracy and 40% safety/instruction adherence
- β’LLMs achieve 86-89% instruction-following but 11-14 MAE vs traditional 7.01
- β’Performance degrades in high-workload phases like Climb and Approach
- β’Motivates hybrid LLM-forecaster architectures
π°
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