CrossMAS: A Deep Dive into Multi-Agent Collaboration Across Distributed Environments

Language-model agents can complete complex tasks with tools, and multi-agent systems demonstrate collaborative execution. These advances motivate delegating tasks spanning multiple systems and participants to agents. Many systems rely on human-designed roles and workflows, with studies focusing on overall performance. Yet dependencies and failures during execution may require handoffs beyond these plans. Greater autonomy thus requires agents to organize cooperation themselves, but how they do so with local access and no predefined task assignments remains poorly understood. CrossMAS studies task completion and autonomous cooperation when information, mutable state, and action authority are distributed across nodes. Across four task families and four models, we combine matched comparisons of six organizations with trajectory analysis. Across these tasks, autonomous MAS average 0.394–0.437 completion, below the single-decision references Merge (0.604) and Central (0.538); organizational support yields no consistent gain. Agents exchange contributions and act on them, but adopting incorrect contributions, confusing evidence sources or action targets, and leaving downstream dependencies unresolved prevent local progress from becoming joint completion.