Can a language model really understand aviation concepts?
Testing beyond terminology to see whether a model can apply ideas consistently across unfamiliar scenarios.
Knowing the vocabulary of aviation is not the same as understanding it. A model may correctly define a term and still misunderstand what changes when the weather, aircraft, or phase of flight changes. Evaluation should make that distinction visible.
We start with questions that ask for an explanation of a concept, then vary the setting. Can the model apply the same principle in a new scenario? Can it tell when an exception matters? Can it identify a premise that is incomplete or contradictory instead of simply answering the question as written? These are more revealing tests than recalling a definition alone.
Good evaluation also checks whether a model knows the limits of its knowledge. Aviation information can be location-specific and time-sensitive. When a task depends on a current chart, regulation, or weather report, a model should separate stable reasoning from details that need verification against an authoritative source.
AvBench is being developed to compare frontier language models on aviation queries, including tasks that require this kind of conceptual reasoning. We test without tools to understand a model’s unaided reasoning and with tools to examine how effectively it uses evidence. The goal is a more honest picture of capability, not a single impressive answer.
