Multi-Agent Computer Use
Summary
This paper proposes a multi-agent computer use (MACU) system that uses a manager model to decompose tasks into directed acyclic graphs for parallel execution by subagents. It demonstrates consistent improvements over single-agent baselines on multiple benchmarks and better test-time scaling.
View Cached Full Text
Cached at: 06/02/26, 03:37 PM
Paper page - Multi-Agent Computer Use
Source: https://huggingface.co/papers/2606.01533
Abstract
Multi-agent computer use systems outperform single-agent approaches on complex tasks by enabling parallel execution and dynamic task decomposition through directed acyclic graphs.
Computer use agents(CUAs) today are primarily deployed as single serial agents. This setup is suboptimal for complex long-horizon tasks that benefit fromtask decomposition,parallel execution, and consistent re-planning based on new information. In this paper, we argue that we should instead move towards evaluating and building multi-agent computer use (MACU) systems. These systems, which emphasize planning andparallel execution, alleviate many of the shortcomings of single-agent CUAs. We propose a general multi-agent setup in which a manager model decomposes computer use tasks as adirected acyclic graph(DAG), encoding relevant dependencies and goals for subagents. At each iteration, the manager dispatches parallel CUA subagents to carry out nodes on the ready frontier of the DAG, and continuously revises the DAG (adding, canceling, or rewriting nodes) as new findings arrive from subagents. This design treats the partially observable environment of computer use as a first class challenge: information that downstream agents may not be able to re-observe are retained and passed forward through the manager and DAG structure. We demonstrate that MACU consistently improves over strong single-agent baselines by 3.4-25.5% on desktop (OSWorld) and web navigation (Online-Mind2Web, WebTailBench, Odysseys) benchmarks, exhibits more favorable test-time scaling, and solves complex long-horizon tasks where single-agent CUAs get stuck. On Odysseys, a long-horizon web navigation benchmark, MACU improves average task completion wall-clock time by {sim} 1.5 times, demonstrating its efficacy in speeding up traditionally slow CUA pipelines. Our findings highlight that multi-agent coordinationis a promising axis for scalingcomputer use agentsto work productively for longer and more effectively. We release all code and interactive visualizations at https://jykoh.com/multi-agent-computer-use.
View arXiv pageView PDFProject pageAdd to collection
Models citing this paper0
No model linking this paper
Cite arxiv.org/abs/2606.01533 in a model README.md to link it from this page.
Datasets citing this paper0
No dataset linking this paper
Cite arxiv.org/abs/2606.01533 in a dataset README.md to link it from this page.
Spaces citing this paper0
No Space linking this paper
Cite arxiv.org/abs/2606.01533 in a Space README.md to link it from this page.
Collections including this paper1
Similar Articles
UrbanAgent: A Tool-Augmented Agent for Cross-System Urban Tasks
UrbanAgent is a tool-augmented agent framework that uses LLMs with code execution, API calls, and MCP to handle cross-system urban requests. The authors also introduce UrbanEval, a benchmark for evaluating task results and execution quality, achieving 71% success rate over baselines.
You don't need a multi-agent setup
An article arguing that multi-agent setups are often overused, providing guidelines on when to use single vs multi-agent architectures based on task parallelism and coordination needs.
We might be overusing multi-agent systems
The article argues that multi-agent systems are often overused in AI applications, suggesting that a single agent with good tools, strict state, and clear stop conditions can be more efficient, easier to debug, and cost-effective for many workflows.
Recursive Multi-Agent Systems
This paper introduces RecursiveMAS, a framework that extends recursive scaling principles to multi-agent systems for improved collaborative reasoning efficiency and accuracy. It demonstrates significant speedups and token reduction across various benchmarks compared to standard baselines.
Multi agent systems for complex tasks
Discusses multi-agent systems designed to handle complex tasks, likely covering coordination and collaboration among AI agents.