Evaluating Multimodal LLMs as Generalist Vision-Language-Action Agents for Drone Control: Commanding, Approaching, Tracking and Searching

arXiv:2609.01404v2 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) are increasingly used to make decisions for drones, but existing evaluations are mostly limited to simulation, to a single drone, and to targets that are visible from the start and do not move. We present DroneCATS, a benchmark in which the MLLM alone controls the drone through four actions, and evaluate ten MLLMs from frontier models to small open models. The benchmark varies target visibility, target motion, scene and simulator, and extends to real drones. Beyond a single drone, it adds a multi

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