PilotBench: A Benchmark for General Aviation Agents with Safety Constraints

Researchers introduce PilotBench, a benchmark evaluating large language models on safety-critical aviation tasks using 708 real-world flight trajectories. The study reveals a fundamental trade-off: traditional forecasters achieve superior numerical precision (7.01 MAE) while LLMs provide better instruction-following (86-89%) but with significantly degraded prediction accuracy (11-14 MAE), exposing brittleness in implicit physics reasoning for embodied AI applications.

PilotBench represents a crucial empirical investigation into the reliability of LLMs for safety-critical physical reasoning—a domain increasingly relevant as AI systems transition from text-only applications toward embodied agents controlling real-world systems. The research systematically quantifies a previously theoretical concern: LLMs excel at semantic understanding and instruction adherence but fundamentally struggle with precise physics-governed prediction, particularly under dynamic complexity.

This work emerges from a broader industry challenge where AI developers seek to deploy LLMs in safety-constrained environments. Traditional machine learning forecasters, trained specifically on numerical prediction tasks, maintain superior accuracy margins but cannot interpret semantic instructions or adapt to novel scenarios. The study's identification of a Precision-Controllability Dichotomy provides empirical grounding for what practitioners have observed anecdotally across robotics, autonomous systems, and industrial applications.

The phase-stratified analysis revealing performance degradation during high-workload flight phases (Climb, Approach) directly impacts development roadmaps for autonomous aviation and other critical systems. Organizations developing AI-powered control systems must now account for this documented weakness when evaluating LLM integration. The research suggests that hybrid architectures—combining LLMs' symbolic reasoning with specialized numerical forecasters—represent a more viable near-term path than pure LLM-based solutions for safety-critical domains.

Future validation will focus on whether hybrid approaches can eliminate the observed trade-off or whether fundamental architectural constraints prevent LLMs from achieving both semantic understanding and physics precision simultaneously. This finding influences both AI safety research priorities and commercial decisions around autonomous system design.

• →LLMs demonstrate 86-89% instruction adherence but 11-14 MAE prediction error versus 7.01 MAE for traditional forecasters in safety-critical aviation tasks
• →Performance degradation occurs sharply during high-workload flight phases, indicating brittle implicit physics models rather than consistent capability limitations
• →Hybrid architectures combining LLM semantic reasoning with specialized numerical forecasters appear necessary for safety-constrained embodied AI applications
• →PilotBench's 708 real-world trajectories across nine distinct flight phases provide rigorous benchmark foundation for future aviation AI development
• →The Precision-Controllability Dichotomy represents a fundamental trade-off that extends beyond aviation to broader embodied AI and robotics domains