# Human-Centered LLM-Agent User Interface: A Position Paper

arXiv: https://arxiv.org/abs/2405.13050 · HTML: https://mlsanity.com/paper/2405.13050/

## Key facts

- Cited by 3 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 24 of its 34 references that are in the map.
- First posted on arXiv 19 May 2024.

## Placed in

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

## Builds on (most cited first, 24 of 24)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs](https://mlsanity.com/paper/2307.16789/) (2023)
- [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](https://mlsanity.com/paper/2303.17580/) (2023)
- [Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models](https://mlsanity.com/paper/2303.04671/) (2023)
- [AppAgent: Multimodal Agents as Smartphone Users](https://mlsanity.com/paper/2312.13771/) (2023)
- [You Only Look at Screens: Multimodal Chain-of-Action Agents](https://mlsanity.com/paper/2309.11436/) (2023)
- [TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs](https://mlsanity.com/paper/2303.16434/) (2023)
- [GPT-4V in Wonderland: Large Multimodal Models for Zero-Shot Smartphone GUI Navigation](https://mlsanity.com/paper/2311.07562/) (2023)
- [AssistGPT: A General Multi-modal Assistant that can Plan, Execute, Inspect, and Learn](https://mlsanity.com/paper/2306.08640/) (2023)
- [Small LLMs Are Weak Tool Learners: A Multi-LLM Agent](https://mlsanity.com/paper/2401.07324/) (2024)
- [POP909: A Pop-song Dataset for Music Arrangement Generation](https://mlsanity.com/paper/2008.07142/) (2020)
- [If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents](https://mlsanity.com/paper/2401.00812/) (2024)
- [Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven Agents](https://mlsanity.com/paper/2402.09205/) (2024)
- [Confucius: Iterative Tool Learning from Introspection Feedback by Easy-to-Difficult Curriculum](https://mlsanity.com/paper/2308.14034/) (2023)
- [ControlLLM: Augment Language Models with Tools by Searching on Graphs](https://mlsanity.com/paper/2310.17796/) (2023)
- [DroidBot-GPT: GPT-powered UI Automation for Android](https://mlsanity.com/paper/2304.07061/) (2023)
- [Investigating on Incorporating Pretrained and Learnable Speaker Representations for Multi-Speaker Multi-Style Text-to-Speech](https://mlsanity.com/paper/2103.04088/) (2021)
- [Loop Copilot: Conducting AI Ensembles for Music Generation and Iterative Editing](https://mlsanity.com/paper/2310.12404/) (2023)
- [AutoMMLab: Automatically Generating Deployable Models from Language Instructions for Computer Vision Tasks](https://mlsanity.com/paper/2402.15351/) (2024)
- [WebWISE: Web Interface Control and Sequential Exploration with Large Language Models](https://mlsanity.com/paper/2310.16042/) (2023)
- [Adaptive Multimodal Music Learning via Interactive-haptic Instrument](https://mlsanity.com/paper/1906.01197/) (2019)
- [Interactive Rainbow Score: A Visual-centered Multimodal Flute Tutoring System](https://mlsanity.com/paper/2004.13908/) (2020)

## Built on by (most cited first, 3 of 3)

- [GUI-Robust: A Comprehensive Dataset for Testing GUI Agent Robustness in Real-World Anomalies](https://mlsanity.com/paper/2506.14477/) (2025)
- [SHIELDA: Structured Handling of Exceptions in LLM-Driven Agentic Workflows](https://mlsanity.com/paper/2508.07935/) (2025)
- [Componentization: Decomposing Monolithic LLM Responses into Manipulable Semantic Units](https://mlsanity.com/paper/2509.08203/) (2025)

## Abstract (arXiv)

Large Language Model (LLM) -in-the-loop applications have been shown to effectively interpret the human user's commands, make plans, and operate external tools/systems accordingly. Still, the operation scope of the LLM agent is limited to passively following the user, requiring the user to frame his/her needs with regard to the underlying tools/systems. We note that the potential of an LLM-Agent User Interface (LAUI) is much greater. A user mostly ignorant to the underlying tools/systems should be able to work with a LAUI to discover an emergent workflow. Contrary to the conventional way of designing an explorable GUI to teach the user a predefined set of ways to use the system, in the ideal LAUI, the LLM agent is initialized to be proficient with the system, proactively studies the user and his/her needs, and proposes new interaction schemes to the user. To illustrate LAUI, we present Flute X GPT, a concrete example using an LLM agent, a prompt manager, and a flute-tutoring multi-modal software-hardware system to facilitate the complex, real-time user experience of learning to play the flute.