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This research addresses a critical scalability bottleneck in multi-agent computer use systems by introducing a spine-branch architecture that avoids the impossible task of merging virtual machine states. By executing parallel subtasks on disposable branch VMs while maintaining continuous state only on a central spine, the framework significantly improves success rates and reduces operational costs for long-horizon tasks. This architectural innovation is essential for advancing the efficiency and reliability of autonomous agent deployments in complex environments.
Spine-Branch Coordination for Multi-agent Computer Use
arXiv:2608.22077v1 Announce Type: new Abstract: Computer use agents (CUAs) are increasingly deployed as multi-agent systems that decompose a task into multiple subtasks executed across parallel virtual machines (VMs). However, a critical physical bottleneck is that the state of two VMs cannot be merged. Previous systems handle this ad-hoc rather than treating it as a first-class concern. We propose Spine-Branch Coordination for multi-agent computer use, a framework that decomposes a task into a "spine-branch" graph, where the spine carries the main task flow with continuous VM state and branch tasks execute in parallel to collect information the spine needs to complete the task. Branch VMs are discarded once their tasks finish, so no VM merging ever occurs. Experiments show that on 200 long-horizon tasks from Odysseys and across three CUA backbones, Spine-Branch improves success rate over the baseline system by 6.0% to 16.5%, while reducing per-task cost by 34% to 70%, indicating that explicitly modeling VM-state merging constraint enables multi-agent computer use to scale efficiently.
Abstract:Computer use agents (CUAs) are increasingly deployed as multi-agent systems that decompose a task into multiple subtasks executed across parallel virtual machines (VMs). However, a critical physical bottleneck is that the state of two VMs cannot be merged. Previous systems handle this ad-hoc rather than treating it as a first-class concern. We propose Spine-Branch Coordination for multi-agent computer use, a framework that decomposes a task into a "spine-branch" graph, where the spine carries the main task flow with continuous VM state and branch tasks execute in parallel to collect information the spine needs to complete the task. Branch VMs are discarded once their tasks finish, so no VM merging ever occurs. Experiments show that on 200 long-horizon tasks from Odysseys and across three CUA backbones, Spine-Branch improves success rate over the baseline system by 6.0% to 16.5%, while reducing per-task cost by 34% to 70%, indicating that explicitly modeling VM-state merging constraint enables multi-agent computer use to scale efficiently.
Computation and Language (cs.CL); Multiagent Systems (cs.MA)
arXiv:2608.22077 [cs.CL]
(or arXiv:2608.22077v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2608.22077 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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