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

• URL: http://arxiv.org/abs/2606.31966v1
• Date: Tue, 30 Jun 2026 17:07:53 GMT
• Title: MECoBench: A Systematic Study of Multimodal Agent Collaboration in Embodied Environments
• Abstract summary: multimodal large language models (MLLMs) have strong potential as embodied agents, but their ability to collaborate in visually grounded environments remains underexplored.<n>We introduce MECoBench, a multimodal embodied cooperation benchmark with an evaluation platform spanning diverse real-world tasks, two cooperation structures, and three collaboration modes.
• Score: 39.79361155591304
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• CollabSim: A CSCW-Grounded Methodology for Investigating Collaborative Competence of LLM Agents through Controlled Multi-Agent Experiments [32.043112677406064]
  CollabSim is a simulation framework that combines a theory-grounded definition of collaborative capabilities, controlled manipulation of interaction conditions, and action-level probing of agents' internal states.<n> Experiments show that CollabSim can capture condition effects, separate model performance patterns, and reveal task-dependent effects of agent design.
  arXiv  Detail & Related papers  (2026-06-04T17:06:22Z)
• EmCoop: A Framework and Benchmark for Embodied Cooperation Among LLM Agents [18.802912315746564]
  EmCoop is a benchmark framework for studying cooperation in embodied multi-agent systems.<n>Our framework separates a high-level cognitive layer from a low-level embodied interaction layer.<n>We propose generalizable, process-level metrics that diagnose collaboration quality and failure modes.
  arXiv  Detail & Related papers  (2026-02-27T22:28:33Z)
• MoCo: A One-Stop Shop for Model Collaboration Research [132.52160996841505]
  We present MoCo: a one-stop Python library of executing, benchmarking, and comparing model collaboration algorithms at scale.<n>MoCo features 26 model collaboration methods, spanning diverse levels of cross-model information exchange.<n>Extensive experiments with MoCo demonstrate that most collaboration strategies outperform models without collaboration.<n>We envision MoCo as a valuable toolkit to facilitate and turbocharge the quest for an open, modular, decentralized, and collaborative AI future.
  arXiv  Detail & Related papers  (2026-01-29T04:36:52Z)
• Cross-Task Experiential Learning on LLM-based Multi-Agent Collaboration [63.90193684394165]
  We introduce multi-agent cross-task experiential learning (MAEL), a novel framework that endows LLM-driven agents with explicit cross-task learning and experience accumulation.<n>During the experiential learning phase, we quantify the quality for each step in the task-solving workflow and store the resulting rewards.<n>During inference, agents retrieve high-reward, task-relevant experiences as few-shot examples to enhance the effectiveness of each reasoning step.
  arXiv  Detail & Related papers  (2025-05-29T07:24:37Z)
• MultiAgentBench: Evaluating the Collaboration and Competition of LLM agents [59.825725526176655]
  Large Language Models (LLMs) have shown remarkable capabilities as autonomous agents.<n>Existing benchmarks either focus on single-agent tasks or are confined to narrow domains, failing to capture the dynamics of multi-agent coordination and competition.<n>We introduce MultiAgentBench, a benchmark designed to evaluate LLM-based multi-agent systems across diverse, interactive scenarios.
  arXiv  Detail & Related papers  (2025-03-03T05:18:50Z)
• Multi-Agent Collaboration Mechanisms: A Survey of LLMs [6.545098975181273]
  Multi-Agent Systems (MASs) enable groups of intelligent agents to coordinate and solve complex tasks collectively.<n>This work provides an extensive survey of the collaborative aspect of MASs and introduces a framework to guide future research.
  arXiv  Detail & Related papers  (2025-01-10T19:56:50Z)
• What Makes Good Collaborative Views? Contrastive Mutual Information Maximization for Multi-Agent Perception [52.41695608928129]
  Multi-agent perception (MAP) allows autonomous systems to understand complex environments by interpreting data from multiple sources. This paper investigates intermediate collaboration for MAP with a specific focus on exploring "good" properties of collaborative view. We propose a novel framework named CMiMC for intermediate collaboration.
  arXiv  Detail & Related papers  (2024-03-15T07:18:55Z)
• Beyond Isolation: Multi-Agent Synergy for Improving Knowledge Graph Construction [6.020016097668138]
  CooperKGC is a novel framework challenging the conventional solitary approach of large language models (LLMs) in knowledge graph construction (KGC) CooperKGC establishes a collaborative processing network, assembling a team capable of concurrently addressing entity, relation, and event extraction tasks.
  arXiv  Detail & Related papers  (2023-12-05T07:27:08Z)
• UMC: A Unified Bandwidth-efficient and Multi-resolution based Collaborative Perception Framework [20.713675020714835]
  We propose a Unified Collaborative perception framework named UMC. It is designed to optimize the communication, collaboration, and reconstruction processes with the Multi-resolution technique. Our experiments prove that the proposed UMC greatly outperforms the state-of-the-art collaborative perception approaches.
  arXiv  Detail & Related papers  (2023-03-22T09:09:02Z)
• Structured Diversification Emergence via Reinforced Organization Control and Hierarchical Consensus Learning [48.525944995851965]
  We propose a structured diversification emergence MARL framework named scRochico based on reinforced organization control and hierarchical consensus learning. scRochico is significantly better than the current SOTA algorithms in terms of exploration efficiency and cooperation strength.
  arXiv  Detail & Related papers  (2021-02-09T11:46:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.