# AirCopBench: A Benchmark for Multi-drone Collaborative Embodied Perception and Reasoning

arXiv: https://arxiv.org/abs/2511.11025 · HTML: https://mlsanity.com/paper/2511.11025/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning.
- Builds on 30 of its 51 references that are in the map.
- First posted on arXiv 14 Nov 2025.

## Placed in

- LLM reasoning / [Spatial and embodied reasoning](https://mlsanity.com/lr/spatial-and-embodied-reasoning/)
- Multimodal LLMs / vision-language models / [Embodied and 3D vision-language models](https://mlsanity.com/mllm/embodied-and-3d-vision-language-models/)

## Builds on (most cited first, 30 of 30)

- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond](https://mlsanity.com/paper/2308.12966/) (2023)
- [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024)
- [InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models](https://mlsanity.com/paper/2504.10479/) (2025)
- [LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://mlsanity.com/paper/2403.13372/) (2024)
- [MiniCPM-V: A GPT-4V Level MLLM on Your Phone](https://mlsanity.com/paper/2408.01800/) (2024)
- [Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling](https://mlsanity.com/paper/2501.17811/) (2025)
- [Phi-4 Technical Report](https://mlsanity.com/paper/2412.08905/) (2024)
- [PaliGemma: A versatile 3B VLM for transfer](https://mlsanity.com/paper/2407.07726/) (2024)
- [Kimi-VL Technical Report](https://mlsanity.com/paper/2504.07491/) (2025)
- [MM-LLMs: Recent Advances in MultiModal Large Language Models](https://mlsanity.com/paper/2401.13601/) (2024)
- [MuirBench: A Comprehensive Benchmark for Robust Multi-image Understanding](https://mlsanity.com/paper/2406.09411/) (2024)
- [Ovis: Structural Embedding Alignment for Multimodal Large Language Model](https://mlsanity.com/paper/2405.20797/) (2024)
- [The Revolution of Multimodal Large Language Models: A Survey](https://mlsanity.com/paper/2402.12451/) (2024)
- [MiMo-VL Technical Report](https://mlsanity.com/paper/2506.03569/) (2025)
- [Ovis-U1 Technical Report](https://mlsanity.com/paper/2506.23044/) (2025)
- [EmbodiedCity: A Benchmark Platform for Embodied Agent in Real-world City Environment](https://mlsanity.com/paper/2410.09604/) (2024)
- [How to Enable LLM with 3D Capacity? A Survey of Spatial Reasoning in LLM](https://mlsanity.com/paper/2504.05786/) (2025)
- [UrbanVideo-Bench: Benchmarking Vision-Language Models on Embodied Intelligence with Video Data in Urban Spaces](https://mlsanity.com/paper/2503.06157/) (2025)
- [Evaluating MLLMs with Multimodal Multi-image Reasoning Benchmark](https://mlsanity.com/paper/2506.04280/) (2025)
- [MLLM-For3D: Adapting Multimodal Large Language Model for 3D Reasoning Segmentation](https://mlsanity.com/paper/2503.18135/) (2025)
- [Seeing from Another Perspective: Evaluating Multi-View Understanding in MLLMs](https://mlsanity.com/paper/2504.15280/) (2025)
- [Skywork-R1V3 Technical Report](https://mlsanity.com/paper/2507.06167/) (2025)
- [Hallucination at a Glance: Controlled Visual Edits and Fine-Grained Multimodal Learning](https://mlsanity.com/paper/2506.07227/) (2025)
- [BEDI: A Comprehensive Benchmark for Evaluating Embodied Agents on UAVs](https://mlsanity.com/paper/2505.18229/) (2025)
- [The Point, the Vision and the Text: Does Point Cloud Boost Spatial Reasoning of Large Language Models? A Bias-Controlled Study](https://mlsanity.com/paper/2504.04540/) (2025)
- [When Large Language Models Meet UAV Projects: An Empirical Study from Developers' Perspective](https://mlsanity.com/paper/2509.12795/) (2025)

## Built on by (most cited first, 20 of 20)

- [α3-Bench: A Unified Benchmark of Safety, Robustness, and Efficiency for LLM-Based UAV Agents over 6G Networks](https://mlsanity.com/paper/2601.03281/) (2026)
- [OralGPT-Plus: Learning to Use Visual Tools via Reinforcement Learning for Panoramic X-ray Analysis](https://mlsanity.com/paper/2603.06366/) (2026)
- [Large Language Model-Assisted UAV Operations and Communications: A Multifaceted Survey and Tutorial](https://mlsanity.com/paper/2602.19534/) (2026)
- [Are VLMs Lost Between Sky and Space? LinkS2Bench for UAV-Satellite Dynamic Cross-View Spatial Intelligence](https://mlsanity.com/paper/2604.02020/) (2026)
- [MECoBench: A Systematic Study of Multimodal Agent Collaboration in Embodied Environments](https://mlsanity.com/paper/2606.31966/) (2026)
- [α3-SecBench: A Large-Scale Evaluation Suite of Security, Resilience, and Trust for LLM-based UAV Agents over 6G Networks](https://mlsanity.com/paper/2601.18754/) (2026)
- [SpatialUAV: Benchmarking Spatial Intelligence for Low-Altitude UAV Perception, Collaboration, and Motion](https://mlsanity.com/paper/2606.27876/) (2026)
- [AirGroundBench: Probing Spatial Intelligence in Multimodal Large Models under Heterogeneous Multi-View Embodied Collaboration](https://mlsanity.com/paper/2606.28049/) (2026)
- [ScoutVLA: UAV-Centric Active Perception via a Dual-Expert VLA Model for Open-World Embodied Question Answering](https://mlsanity.com/paper/2606.14772/) (2026)
- [MultiUAV-Plat: An LLM-Oriented Platform, Benchmark and Framework for Multi-UAV Collaborative Task Planning](https://mlsanity.com/paper/2606.31073/) (2026)
- [CosFly-VLA: A Spatially Aware Vision-Language-Action Model for UAV Tracking](https://mlsanity.com/paper/2607.15004/) (2026)
- [Towards Embodied Air-Ground Cooperative Object Search: Benchmark, Dataset and Agentic Method](https://mlsanity.com/paper/2609.08402/) (2026)
- [Efficient Transceiver Design for Aerial Image Transmission and Large-scale Scene Reconstruction](https://mlsanity.com/paper/2604.11098/) (2026)
- [AeroVerse-SatAgent: UAV-Satellite Collaborative Spatial Reasoning Inspired by the Dual Visual Pathway Theory of Cognitive Neuroscience](https://mlsanity.com/paper/2606.31467/) (2026)
- [MulRobBench: A Decision-Level Benchmark for Safe and Security-Policy-Compliant Multimodal UAV Agents](https://mlsanity.com/paper/2607.23870/) (2026)
- [Lingjing: A Simulation Testbed for Multi-Agent Embodied Tasks in Open-Ended Cities](https://mlsanity.com/paper/2608.08045/) (2026)
- [Evaluating Multimodal LLMs as Generalist Vision-Language-Action Agents for Drone Control: Commanding, Approaching, Tracking and Searching](https://mlsanity.com/paper/2609.01404/) (2026)
- [CoRef-GS: Cooperative Referring Gaussian Splatting for Multi-Agent Scene Understanding](https://mlsanity.com/paper/2609.20586/) (2026)
- [PhysAI-Bench: A Benchmark for LLM-Based Agentic Decision-Making in Autonomous UAV-Centric Physical AI](https://mlsanity.com/paper/2609.23695/) (2026)
- [The Gap Is in the Geometry: A Convergent Meta-Analysis of Multi-Facet Aerial and Remote Sensing Video Understanding](https://mlsanity.com/paper/2610.19102/) (2026)

## Abstract (arXiv)

Multimodal Large Language Models (MLLMs) have shown promise in single-agent vision tasks, yet benchmarks for evaluating multi-agent collaborative perception remain scarce. This gap is critical, as multi-drone systems provide enhanced coverage, robustness, and collaboration compared to single-sensor setups. Existing multi-image benchmarks mainly target basic perception tasks using high-quality single-agent images, thus failing to evaluate MLLMs in more complex, egocentric collaborative scenarios, especially under real-world degraded perception conditions.To address these challenges, we introduce AirCopBench, the first comprehensive benchmark designed to evaluate MLLMs in embodied aerial collaborative perception under challenging perceptual conditions. AirCopBench includes 14.6k+ questions derived from both simulator and real-world data, spanning four key task dimensions: Scene Understanding, Object Understanding, Perception Assessment, and Collaborative Decision, across 14 task types. We construct the benchmark using data from challenging degraded-perception scenarios with annotated collaborative events, generating large-scale questions through model-, rule-, and human-based methods under rigorous quality control. Evaluations on 40 MLLMs show significant performance gaps in collaborative perception tasks, with the best model trailing humans by 24.38% on average and exhibiting inconsistent results across tasks. Fine-tuning experiments further confirm the feasibility of sim-to-real transfer in aerial collaborative perception and reasoning.