You Only Look at Screens: Multimodal Chain-of-Action Agents
On the map: LLM agents and tool use → GUI and mobile agents
Abstract (arXiv)
Autonomous graphical user interface (GUI) agents aim to facilitate task automation by interacting with the user interface without manual intervention. Recent studies have investigated eliciting the capabilities of large language models (LLMs) for effective engagement in diverse environments. To align with the input-output requirement of LLMs, most existing approaches are developed under a sandbox setting where they rely on external tools and application-specific APIs to parse the environment into textual elements and interpret the predicted actions. Consequently, those approaches often grapple with inference inefficiency and error propagation risks. To mitigate the challenges, we introduce Auto-GUI, a multimodal solution that directly interacts with the interface, bypassing the need for environment parsing or reliance on application-dependent APIs. Moreover, we propose a chain-of-action technique -- leveraging a series of intermediate previous action histories and future action plans -- to help the agent decide what action to execute. We evaluate our approach on a new device-control benchmark AITW with 30K unique instructions, spanning multi-step tasks such as application operation, web searching, and web shopping. Experimental results show that Auto-GUI achieves state-of-the-art performance with an action type prediction accuracy of 90% and an overall action success rate of 74%. Code is publicly available at https://github.com/cooelf/Auto-GUI.
Key facts. Cited by 185 papers in the map (data through 7 Oct 2026). Placed in GUI and mobile agents, one of 16 directions in LLM agents and tool use. Builds on 31 of its 58 references that are in the map. First posted on arXiv 20 Sep 2023.
Builds on
31 of 58 references.
References · 31
- 2017Attention Is All You Needcited by 59,932
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
- 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
- 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
- 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Modelscited by 6,069
- 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
- 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
- 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
- 2022Scaling Instruction-Finetuned Language Modelscited by 2,997
- 2023A Survey on Large Language Model based Autonomous Agentscited by 2,203
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- 2021Multitask Prompted Training Enables Zero-Shot Task Generalizationcited by 1,462
- 2023AgentBench: Evaluating LLMs as Agentscited by 1,211
- 2021Show Your Work: Scratchpads for Intermediate Computation with Language Modelscited by 871
- 2023Multimodal Chain-of-Thought Reasoning in Language Modelscited by 740
- 2022Automatic Chain of Thought Prompting in Large Language Modelscited by 684
- 2023Cognitive Architectures for Language Agentscited by 422
- 2023A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesiscited by 350
- 2023Android in the Wild: A Large-Scale Dataset for Android Device Controlcited by 314
- 2023Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memorycited by 288
- 2022META-GUI: Towards Multi-modal Conversational Agents on Mobile GUIcited by 81
- 2022Enabling Conversational Interaction with Mobile UI using Large Language Modelscited by 66
- 2021Screen Recognition: Creating Accessibility Metadata for Mobile Applications from Pixelscited by 57
- 2021Good for Misconceived Reasons: An Empirical Revisiting on the Need for Visual Context in Multimodal Machine Translationcited by 44
- 2022On Vision Features in Multimodal Machine Translationcited by 31
- 2022Towards Better Semantic Understanding of Mobile Interfacescited by 24
- 2022Task Compass: Scaling Multi-task Pre-training with Task Prefixcited by 14
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Citing papers per year: 2023: 8; 2024: 57; 2025: 81; 2026: 39.
185 papers.
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