# Agent S2: A Compositional Generalist-Specialist Framework for Computer Use Agents

arXiv: https://arxiv.org/abs/2504.00906 · HTML: https://mlsanity.com/paper/2504.00906/

## Key facts

- Cited by 159 papers in the map (data through 14 Oct 2026).
- Placed in GUI and mobile agents, one of 16 directions in LLM agents and tool use.
- Builds on 22 of its 27 references that are in the map.
- First posted on arXiv 1 Apr 2025.

## Placed in

- LLM agents and tool use / [GUI and mobile agents](https://mlsanity.com/agents/gui-and-mobile-agents/)

## Builds on (most cited first, 22 of 22)

- [Mind2Web: Towards a Generalist Agent for the Web](https://mlsanity.com/paper/2306.06070/) (2023)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments](https://mlsanity.com/paper/2404.07972/) (2024)
- [CogAgent: A Visual Language Model for GUI Agents](https://mlsanity.com/paper/2312.08914/) (2023)
- [An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning](https://mlsanity.com/paper/2308.08747/) (2023)
- [UI-TARS: Pioneering Automated GUI Interaction with Native Agents](https://mlsanity.com/paper/2501.12326/) (2025)
- [AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agents](https://mlsanity.com/paper/2405.14573/) (2024)
- [Navigating the Digital World as Humans Do: Universal Visual Grounding for GUI Agents](https://mlsanity.com/paper/2410.05243/) (2024)
- [OS-ATLAS: A Foundation Action Model for Generalist GUI Agents](https://mlsanity.com/paper/2410.23218/) (2024)
- [Android in the Wild: A Large-Scale Dataset for Android Device Control](https://mlsanity.com/paper/2307.10088/) (2023)
- [Aguvis: Unified Pure Vision Agents for Autonomous GUI Interaction](https://mlsanity.com/paper/2412.04454/) (2024)
- [Windows Agent Arena: Evaluating Multi-Modal OS Agents at Scale](https://mlsanity.com/paper/2409.08264/) (2024)
- [OS-Copilot: Towards Generalist Computer Agents with Self-Improvement](https://mlsanity.com/paper/2402.07456/) (2024)
- [OmniParser for Pure Vision Based GUI Agent](https://mlsanity.com/paper/2408.00203/) (2024)
- [Agent S: An Open Agentic Framework that Uses Computers Like a Human](https://mlsanity.com/paper/2410.08164/) (2024)
- [Aria-UI: Visual Grounding for GUI Instructions](https://mlsanity.com/paper/2412.16256/) (2024)
- [VisualWebBench: How Far Have Multimodal LLMs Evolved in Web Page Understanding and Grounding?](https://mlsanity.com/paper/2404.05955/) (2024)
- [Learn-by-interact: A Data-Centric Framework for Self-Adaptive Agents in Realistic Environments](https://mlsanity.com/paper/2501.10893/) (2025)
- [ExACT: Teaching AI Agents to Explore with Reflective-MCTS and Exploratory Learning](https://mlsanity.com/paper/2410.02052/) (2024)
- [Caution for the Environment: Multimodal LLM Agents are Susceptible to Environmental Distractions](https://mlsanity.com/paper/2408.02544/) (2024)
- [OSCAR: Operating System Control via State-Aware Reasoning and Re-Planning](https://mlsanity.com/paper/2410.18963/) (2024)
- [AgentStore: Scalable Integration of Heterogeneous Agents As Specialized Generalist Computer Assistant](https://mlsanity.com/paper/2410.18603/) (2024)

## Built on by (most cited first, 30 of 159)

- [Mobile-Agent-v3: Fundamental Agents for GUI Automation](https://mlsanity.com/paper/2508.15144/) (2025)
- [A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence](https://mlsanity.com/paper/2507.21046/) (2025)
- [OpenCUA: Open Foundations for Computer-Use Agents](https://mlsanity.com/paper/2508.09123/) (2025)
- [InfiGUI-R1: Advancing Multimodal GUI Agents from Reactive Actors to Deliberative Reasoners](https://mlsanity.com/paper/2504.14239/) (2025)
- [Agent Skills for Large Language Models: Architecture, Acquisition, Security, and the Path Forward](https://mlsanity.com/paper/2602.12430/) (2026)
- [GTA1: GUI Test-time Scaling Agent](https://mlsanity.com/paper/2507.05791/) (2025)
- [Mobile-Agent-v3.5: Multi-platform Fundamental GUI Agents](https://mlsanity.com/paper/2602.16855/) (2026)
- [SEAgent: Self-Evolving Computer Use Agent with Autonomous Learning from Experience](https://mlsanity.com/paper/2508.04700/) (2025)
- [ScaleCUA: Scaling Open-Source Computer Use Agents with Cross-Platform Data](https://mlsanity.com/paper/2509.15221/) (2025)
- [ScienceBoard: Evaluating Multimodal Autonomous Agents in Realistic Scientific Workflows](https://mlsanity.com/paper/2505.19897/) (2025)
- [ComputerRL: Scaling End-to-End Online Reinforcement Learning for Computer Use Agents](https://mlsanity.com/paper/2508.14040/) (2025)
- [RedTeamCUA: Realistic Adversarial Testing of Computer-Use Agents in Hybrid Web-OS Environments](https://mlsanity.com/paper/2505.21936/) (2025)
- [AgentSynth: Scalable Task Generation for Generalist Computer-Use Agents](https://mlsanity.com/paper/2506.14205/) (2025)
- [MobileRL: Online Agentic Reinforcement Learning for Mobile GUI Agents](https://mlsanity.com/paper/2509.18119/) (2025)
- [UI-Venus-1.5 Technical Report](https://mlsanity.com/paper/2602.09082/) (2026)
- [Step-GUI Technical Report](https://mlsanity.com/paper/2512.15431/) (2025)
- [OSWorld-Human: Benchmarking the Efficiency of Computer-Use Agents](https://mlsanity.com/paper/2506.16042/) (2025)
- [CoAct-1: Computer-using Multi-Agent System with Coding Actions](https://mlsanity.com/paper/2508.03923/) (2025)
- [UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents](https://mlsanity.com/paper/2505.21496/) (2025)
- [LLM-Powered GUI Agents in Phone Automation: Surveying Progress and Prospects](https://mlsanity.com/paper/2504.19838/) (2025)
- [UltraCUA: A Foundation Model for Computer Use Agents with Hybrid Action](https://mlsanity.com/paper/2510.17790/) (2025)
- [Look Before You Leap: A GUI-Critic-R1 Model for Pre-Operative Error Diagnosis in GUI Automation](https://mlsanity.com/paper/2506.04614/) (2025)
- [PolySkill: Learning Generalizable Skills Through Polymorphic Abstraction](https://mlsanity.com/paper/2510.15863/) (2025)
- [InfiniteWeb: Scalable Web Environment Synthesis for GUI Agent Training](https://mlsanity.com/paper/2601.04126/) (2026)
- [WebXSkill: Skill Learning for Autonomous Web Agents](https://mlsanity.com/paper/2604.13318/) (2026)
- [MLA-Trust: Benchmarking Trustworthiness of Multimodal LLM Agents in GUI Environments](https://mlsanity.com/paper/2506.01616/) (2025)
- [What Is Your AI Agent Buying? Evaluation, Biases, Model Dependence, & Emerging Implications for Agentic E-Commerce](https://mlsanity.com/paper/2508.02630/) (2025)
- [OS-Symphony: A Holistic Framework for Robust and Generalist Computer-Using Agent](https://mlsanity.com/paper/2601.07779/) (2026)
- [CUARewardBench: A Benchmark for Evaluating Reward Models on Computer-using Agent](https://mlsanity.com/paper/2510.18596/) (2025)
- [MGA: Memory-Driven GUI Agent for Observation-Centric Interaction](https://mlsanity.com/paper/2510.24168/) (2025)

## Abstract (arXiv)

Computer use agents automate digital tasks by directly interacting with graphical user interfaces (GUIs) on computers and mobile devices, offering significant potential to enhance human productivity by completing an open-ended space of user queries. However, current agents face significant challenges: imprecise grounding of GUI elements, difficulties with long-horizon task planning, and performance bottlenecks from relying on single generalist models for diverse cognitive tasks. To this end, we introduce Agent S2, a novel compositional framework that delegates cognitive responsibilities across various generalist and specialist models. We propose a novel Mixture-of-Grounding technique to achieve precise GUI localization and introduce Proactive Hierarchical Planning, dynamically refining action plans at multiple temporal scales in response to evolving observations. Evaluations demonstrate that Agent S2 establishes new state-of-the-art (SOTA) performance on three prominent computer use benchmarks. Specifically, Agent S2 achieves 18.9% and 32.7% relative improvements over leading baseline agents such as Claude Computer Use and UI-TARS on the OSWorld 15-step and 50-step evaluation. Moreover, Agent S2 generalizes effectively to other operating systems and applications, surpassing previous best methods by 52.8% on WindowsAgentArena and by 16.52% on AndroidWorld relatively. Code available at https://github.com/simular-ai/Agent-S.