MECoBench: A Systematic Study of Multimodal Agent Collaboration in Embodied Environments

Source

Source name: arXiv
Source kind: paper
Authors / organization: Qingyun Liu, Jiwen Zhang, Jingyi Hu, Siyuan Wang, Zhongyu Wei
Venue: arXiv: cs.MA
Published: 2026-06-30
Collected: 2026-07-01
URL: http://arxiv.org/abs/2606.31966v1
PDF: https://arxiv.org/pdf/2606.31966v1
DOI: UNKNOWN

Plain-Language Brief

The research paper introduces MECoBench, a benchmark for evaluating the collaboration abilities of multimodal agents in visually grounded environments. The study explores three key findings: collaboration generally improves task completion, communication is essential for collaboration gains, and collaboration enhances robustness under noisy conditions. Code and dataset are available.

What the Document Says

The paper presents MECoBench, a multimodal embodied cooperation benchmark with an evaluation platform.
The benchmark includes various real-world tasks, two cooperation structures, and three collaboration modes.
Through experiments across multiple MLLMs, the study summarizes three findings about multimodal embodied collaboration.
Findings include: (i) Collaboration improves task completion but benefits depend on coordination complexity.
(ii) Communication is essential for collaboration gains, and the best collaboration mode depends on team size and model capability.
(iii) Collaboration improves robustness under noisy priors and exploration conditions.
MECoBench aims to understand mechanisms and limits of multimodal embodied collaboration.

Key Concepts Mentioned

multimodal agents
embodied environments
collaboration
task completion
coordination complexity
communication
team size
model capability
noisy priors
exploration conditions

Claims Made by the Document

Collaboration improves embodied task completion.
Benefits of collaboration depend on balancing collaborative gains against coordination complexity.
Communication is essential to collaboration gains.
The best collaboration mode depends on team size and model capability.
Collaboration improves robustness under noisy priors and exploration conditions.

Evidence Used

Results from extensive experiments across various MLLMs

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Recent multimodal large language models (MLLMs) have strong potential as embodied agents, but their ability to collaborate in visually grounded environments remains underexplored. To address this gap, we introduce MECoBench, a multimodal embodied cooperation benchmark with an evaluation platform spanning diverse real-world tasks, two cooperation structures, and three collaboration modes. Through extensive experiments across various MLLMs, we summarize three key findings: (i) Collaboration generally improves embodied task completion, but its benefits depend on balancing collaborative gains against coordination complexity. (ii) Communication is essential to collaboration gains, while the best collaboration mode depends on team size and model capability. (iii) Moreover, collaboration improves robustness under noisy priors and exploration conditions. Generally, MECoBench provides a systematic testbed for understanding the mechanisms and limits of multimodal embodied collaboration. Code and dataset are available at https://github.com/q-i-n-g/MECoBench.