No Training, Better Flights: Test-Time Scaled VLMs for UAV Navigation

TL;DR - This paper improves UAV vision-language navigation at inference time by generating and refining multiple flight-plan candidates without retraining the model. Multi-criteria selection targets safer, more accurate, and goal-directed trajectories.

• Generates multiple candidate navigation plans in parallel.
• Uses self-correction to iteratively reassess and refine initial plans.
• Scores candidates on safety, goal alignment, and forward progress.
• Reports state-of-the-art performance using frozen UAV navigation VLMs.