SOURCE-LINKED INTELLIGENCE
CoCoBench: A Cooperative Coordination Benchmark for Embodied Multi-Agent Task Planning
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 coordina
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-28T12:29:23.000Z
First collected: 2026-09-21T08:02:06.831Z. This is not the publication date.