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          content="A Comprehensive Survey of Agents for Computer Use: Foundations, Challenges, and Future Directions">
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          content="A Comprehensive Survey of Agents for Computer Use: Foundations, Challenges, and Future Directions"/>
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                    <h1 class="title is-1 publication-title">A Comprehensive Survey of Agents for Computer Use: Foundations, Challenges, and Future Directions</h1>
                    <div class="is-size-5 publication-authors">
                        <!-- Paper authors -->
                        <span class="author-block">
                <a href="https://www.linkedin.com/in/pascal-sager-3b7403168/"
                   target="_blank">Pascal J. Sager</a><sup>*</sup>,</span>
                        <span class="author-block">
                  <a href="https://www.linkedin.com/in/benjamin-meyer-b87077151/"
                     target="_blank">Benjamin Meyer</a><sup>*</sup>,</span>
                        <span class="author-block">
                    <a href="https://www.linkedin.com/in/peng520/" target="_blank">Peng Yan</a>,
                  </span>
                        <span class="author-block">
                    <a href="https://www.linkedin.com/in/rebekka-von-wartburg-kottler-3b807b298/" target="_blank">Rebekka von Wartburg-Kottler</a>,
                  </span>
                        <span class="author-block">
                    <a href="https://www.linkedin.com/in/layan-etaiwi/" target="_blank">Layan Etaiwi</a>,
                  </span>
                        <span class="author-block">
                    <a href="https://www.linkedin.com/in/aref-enayati-a2bab51a6/" target="_blank">Aref Enayati</a>,
                  </span>
                        <span class="author-block">
                    <a href="https://www.linkedin.com/in/gabriel-nobel-aa3672187/" target="_blank">Gabriel Nobel</a>,
                  </span>
                        <span class="author-block">
                    <a href="https://www.linkedin.com/in/ahmed-abdulkadir-2431b814/"
                       target="_blank">Ahmed Abdulkadir</a>,
                  </span>
                        <span class="author-block">
                    <a href="https://www.linkedin.com/in/benjamingrewe/" target="_blank">Benjamin F. Grewe</a>,
                  </span>
                        <span class="author-block">
                    <a href="https://www.linkedin.com/in/thilo-stadelmann/" target="_blank">Thilo Stadelmann</a>
                  </span>
                    </div>

                    <div class="is-size-5 publication-authors">
                        <span class="author-block">Zurich University of Applied Sciences | University of Zurich | ETH Zurich | AlpineAI AG<br>ArXiv 2025</span>
                        <span class="eql-cntrb"><small><br><sup>*</sup>Indicates Equal Contribution</small></span>
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                        <span>Paper</span>
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                  <span>arXiv</span>
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            <h2 class="subtitle has-text-centered">
                Overview: (a) An example of a task for an agent for computer use (ACU): A user specifies a task ("propose meeting dates by email") and the agent executes it.
(b) We structure the literature on ACUs based on three key <i>perspectives</i> corresponding to the main differentiating aspects:
(1) The shared <i>domain</i> properties across computer environments (e.g., Web, Android).
(2) The means of <i>interaction</i> between the agent and the environment as manifested in the observation and action spaces.
(3) The <i>agent</i> components: how an agent acts through a policy while tracking the past in memory and how an agent learns to act.
            </h2>
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</section>
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                <h2 class="title is-3">Abstract</h2>
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                    <p>
                        <p>
                        <b>Background</b>:
                        Agents for computer use (ACUs) are systems that execute complex tasks on digital devices -- such as personal computers or mobile phones -- given instructions in natural language. These agents automate tasks by controlling software through low-level actions like mouse clicks and touchscreen gestures. However, despite rapid progress, ACUs are not yet mature for everyday use.
                        </p>
                        <p>
                        <b>Objectives</b>:
                        This survey examines the current state-of-the-art, identifies trends, and points out research gaps in the development of practical ACUs. The goal is to provide a comprehensive review and analysis that helps advance general-purpose, robust, and scalable agents for real-world computer use.
                        </p>
                        <p>
                        <b>Methods</b>:
                         We introduce a multifaceted taxonomy of ACUs across three dimensions: (I) the <i>domain perspective</i>, characterizing the contexts in which agents operate; (II) the <i>interaction perspective</i>, describing observation modalities (e.g., screenshots, HTML) and action modalities (e.g., mouse, keyboard, code execution); and (III) the <i>agent perspective</i>, detailing how agents perceive, reason, and learn. We review 87 original research papers about ACUs and 33 relevant datasets, covering both foundation model-based and specialized approaches.
                        </p>
                        <p>
                        <b>Results</b>:
                        Our taxonomy comprehensively structures state-of-the-art approaches and establishes the groundwork for guiding future ACU research. We found that the field is transitioning from specialized agents toward foundation-model-based agents, a shift from text to image-based observation space, and an increasing adoption of behavior cloning methodologies. Furthermore, we identify six key research gaps: insufficient generalization, inefficient learning, limited planning, low task complexity in benchmarks, non-standardized evaluation, and a disconnect between research and practical conditions.
                        </p>
                        <p>
                        <b> Conclusions</b>:
                        To continue rapid improvements in the field, we recommend focusing on: (a) vision-based observations and low-level control to enhance generalization; (b) adaptive learning beyond static prompting; (c) effective planning and reasoning capabilities; (d) realistic, high-complexity benchmarks; (e) standardized evaluation criteria based on task success; and (f) aligning agent design with real-world deployment constraints. Collectively, our findings and proposed directions help develop more general-purpose agents for everyday digital tasks.
                        </p>
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                    <h2 class="subtitle has-text-centered">
                        Agent distribution per application domain.
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                        Agent distribution per action space.
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                        Agent distribution per environment learning strategy.
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            <!-- Paper video. -->
            <h2 class="title is-3">Agent Overview</h2>

            <a href="static/files/agents.csv" download="agents.csv" class="button m-2">Download CSV</a>

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                <div class="column is-full">
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                            style="width: 100%; height: 500px; border: none;"
                            src="static/tables/agents.html"></iframe>
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            <h2 class="title is-3">Datasets Overview</h2>

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        <pre><code>@misc{sager_acu_2025,
      title={A Comprehensive Survey of Agents for Computer Use: Foundations, Challenges, and Future Directions},
      author={Pascal J. Sager and Benjamin Meyer and Peng Yan and Rebekka von Wartburg-Kottler and Layan Etaiwi and Aref Enayati and Gabriel Nobel and Ahmed Abdulkadir and Benjamin F. Grewe and Thilo Stadelmann},
      year={2025},
      eprint={2501.16150},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2501.16150},
}</code></pre>
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</section>
<!--End BibTex citation -->


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