Ensuring accuracy in AI-assisted flight planning.

Flight planning is not one question with one answer. It is a sequence of judgments about route, weather, aircraft capability, fuel, airspace, and alternates. An error early in that sequence can change everything that follows. That makes planning a useful test of whether a model can reason carefully across multiple steps.

A meaningful evaluation does more than ask a model to propose a route. It checks whether the model notices constraints, explains trade-offs, and revises its answer when new information changes the situation. It should be willing to pause when a required input is absent rather than inventing a value and continuing as though it were confirmed.

Tool access changes the problem, but it does not remove the need for judgment. A model may retrieve weather or route data and still misread its significance. Comparing tool-assisted and unaided performance shows where external information helps and where reasoning remains the limiting factor.

Our AvBench work includes flight-planning scenarios for this reason. These tests are not a substitute for approved planning tools or pilot review. They are a way to understand how reliably language models handle aviation decisions and to make weaknesses visible before anyone mistakes fluent output for a flight-ready plan.