CoCoBench: A Cooperative Coordination Benchmark for Embodied Multi-Agent Task Planning

2026-08-28

AI Summary

本文提出CoCoBench，一个用于评估多智能体在家庭任务中协作的基准，通过四种协调结构诊断多智能体协作问题。

Abstract

Agent systems powered by multimodal large language models (MLLMs) have advanced rapidly in recent years, yet existing embodied-agent benchmarks still lack fine-grained diagnostics for multi-agent coordination. Most benchmarks either focus on single-agent task completion or summarize multi-agent behavior with overall task success rates, which can obscure coordination failures such as duplicated work, violations of ordering constraints, resource contention, and desynchronized handoffs. In this paper, we introduce CoCoBench, a construct-level benchmark for evaluating multi-agent embodied coordination in executable household tasks. CoCoBench contains 897 oracle-validated instances organized around four recurring coordination constructs: task allocation, sequential ordering, mutual exclusion, and handoff coordination. In addition to task success rate, CoCoBench provides construct-level scores that measure whether agents coordinate effectively. We evaluate 11 leading MLLMs across different coordination modes, observation inputs, and numbers of agents. The results show that coordination ability is highly construct-specific: strong overall performance does not imply balanced competence across different coordination types. These findings point to new directions for designing targeted model architectures and improving multi-agent coordination ability.

Problem

• multi-agent coordination
• embodied agents
• task planning
• benchmark
• diagnostics

Innovation

• multi-agent coordination
• embodied task planning
• construct-level benchmark
• coordination constructs
• MLLMs