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

arXiv 2026 14.9 system, benchmark, application

TLDR

Introduces DroneCATS-Agent and DroneCATS benchmark to evaluate MLLMs as vision-language-action agents for drone control, finding small models navigate but fail at declaring arrival.

Reasoning

The paper presents a novel benchmark and architecture for studying MLLMs as drone-control agents, with a useful focus on scaling down to small models and identifying failure modes. However, the abstract is truncated and does not clarify whether evaluations are real-world or simulated, and the work is not centered on world models.

Read-first score

Read-first score 14.9, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 0.

Recency 5%

100

Uses a gentle age decay so recent papers surface without erasing older foundations. 2026

Methodology quality 15%

Screens visible abstract and analysis fields for experiment, dataset, baseline, metric, and limitation evidence. markers=benchmark

Reproducibility 15%

Screens links and visible text for paper, code, dataset, artifact, and repository signals. pdf=True; code=False; dataset=False; markers=none

Graph prestige 15%

5.7

PageRank-style prestige over the local citation graph built from available OpenAlex references. local_pagerank=0.0016

Topical relevance 25%

Uses existing LLM keyword relevance scores normalized to 0-100. world model,world simulator,generative world model,interactive world model,video world model,world dynamics prediction,model-based reinforcement learning world model

Citation impact 15%

Uses OpenAlex-shaped citation metadata as a bibliometric attention signal, separate from paper quality. cited_by_count=0

Citation velocity 10%

Citation velocity estimates citations per publication-year to reduce old-paper bias. velocity=0.00

Field roles

Frontier

Rank sensitivity

Stability: volatile; rank range: 27.

Keyword Scores

world model

world simulator

generative world model

interactive world model

video world model

world dynamics prediction

model-based reinforcement learning world model

Resources

Paper