Improving Proficiency and Efficiency of Android GUI Agents via Self-Generating Tool Actions
- URL: http://arxiv.org/abs/2609.06792v1
- Date: Sun, 06 Sep 2026 19:22:53 GMT
- Title: Improving Proficiency and Efficiency of Android GUI Agents via Self-Generating Tool Actions
- Abstract summary: DroidTool is a framework for augmenting agents with self-generated tools.<n>DroidTool employs an agentic workflow featuring stages: proposal, implementation, test generation and execution, and repair.
- Score: 24.76596973597511
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Android agents using a hybrid action space that combines GUI actions and tool actions (e.g., accessing application data via APIs) remain largely underexplored, mainly due to the excessive effort required to create tools. To address this gap, we introduce DroidTool, a framework for augmenting the agents with self-generated tools, which are realized as Python functions operating on application states (e.g., a database). To create tools with minimal human labor, DroidTool employs an agentic workflow featuring stages: proposal, implementation, test generation and execution, and repair. Notably, when testing the created tools for verification, it constructs relational tests across relevant tools for natural preparation of appropriate test preconditions and improved test coverage, rather than testing each tool separately. The GUI agents augmented with the generated tools achieved approximately 4.47%p higher performance with approximately 20.05% fewer interactions than the GUI-only agents, averaged across representative benchmarks: AndroidWorld, B-MoCA, and MobileSafetyBench.
Related papers
- SING: Synthetic Intention Graph for Scalable Active Tool Discovery in LLM Agents [64.59100414726556]
Large language model (LLM) agents increasingly rely on harnesses that manage context, tools, and multi-turn execution.<n>Retrieval-augmented tool selection offers a natural alternative, but existing one-shot retrieval methods fail to align isolated tool descriptions with the agent's true task intention.<n>We propose SING, an intention-aware active tool discovery framework that builds an intention-tool graph linking user intentions, tool capabilities, and tool collaboration patterns.
arXiv Detail & Related papers (2026-06-15T11:37:37Z) - ToolOmni: Enabling Open-World Tool Use via Agentic learning with Proactive Retrieval and Grounded Execution [49.30293260493709]
Large Language Models (LLMs) enhance their problem-solving capability by utilizing external tools.<n>Existing methods relying on static embedding retrieval or parameter memorization struggle to align user intent with tool semantics.<n>We present Tool Omni, a unified agentic framework that enables LLMs for open-world tool use by proactive retrieval and grounded execution.
arXiv Detail & Related papers (2026-04-15T12:26:10Z) - Learning to Rewrite Tool Descriptions for Reliable LLM-Agent Tool Use [21.666294374943178]
We propose a curriculum learning framework that transfers supervision from trace-rich settings to trace-free deployment.<n> Experiments show consistent gains on unseen tools, strong cross-domain generalization, and robustness as the number of candidate tools scales to over 100.
arXiv Detail & Related papers (2026-02-23T23:50:24Z) - AdaReasoner: Dynamic Tool Orchestration for Iterative Visual Reasoning [66.24374176797075]
We introduce textbfAdaReasoner, a family of multimodal models that learn tool use as a general reasoning skill rather than as tool-specific or explicitly supervised behavior.<n>AdaReasoner is enabled by (i) a scalable data curation pipeline exposing models to long-horizon, multi-step tool interactions; (ii) Tool-GRPO, a reinforcement learning algorithm that prioritizes tool selection and sequencing based on end-task success; and (iii) an adaptive learning mechanism that dynamically regulates tool usage.
arXiv Detail & Related papers (2026-01-26T16:04:43Z) - DeepAgent: A General Reasoning Agent with Scalable Toolsets [111.6384541877723]
DeepAgent is an end-to-end deep reasoning agent that performs autonomous thinking, tool discovery, and action execution.<n>To address the challenges of long-horizon interactions, we introduce an autonomous memory folding mechanism that compresses past interactions into structured episodic, working, and tool memories.<n>We develop an end-to-end reinforcement learning strategy, namely ToolPO, that leverages LLM-simulated APIs and applies tool-call advantage attribution to assign fine-grained credit to the tool invocation tokens.
arXiv Detail & Related papers (2025-10-24T16:24:01Z) - TheMCPCompany: Creating General-purpose Agents with Task-specific Tools [12.249551019598442]
We introduce TheMCPCompany, a benchmark for evaluating tool-calling agents on tasks that involve interacting with various real-world services.<n>We also provide manually annotated ground-truth tools for each task.<n>Overall, our work shows that the most advanced reasoning models are effective at discovering tools in simpler environments, but seriously struggle with navigating complex enterprise environments.
arXiv Detail & Related papers (2025-10-22T06:42:01Z) - ToolLibGen: Scalable Automatic Tool Creation and Aggregation for LLM Reasoning [80.10274552177096]
Large Language Models (LLMs) equipped with external tools have demonstrated enhanced performance on complex reasoning tasks.<n>The widespread adoption of this tool-augmented reasoning is hindered by the scarcity of domain-specific tools.<n>We propose a systematic approach to automatically an unstructured collection of tools into a structured tool library.
arXiv Detail & Related papers (2025-10-09T04:11:16Z) - ToolFuzz -- Automated Agent Tool Testing [5.174808367448261]
ToolFuzz is designed to discover two types of errors: (1) user queries leading to tool runtime errors and (2) user queries that lead to incorrect agent responses.<n>We show that ToolFuzz identifies 20x more erroneous inputs compared to the prompt-engineering approaches.
arXiv Detail & Related papers (2025-03-06T14:29:52Z) - LLM Agents Making Agent Tools [2.5529148902034637]
Tool use has turned large language models (LLMs) into powerful agents that can perform complex multi-step tasks.<n>But these tools must be implemented in advance by human developers.<n>We propose ToolMaker, an agentic framework that autonomously transforms papers with code into LLM-compatible tools.
arXiv Detail & Related papers (2025-02-17T11:44:11Z) - AgentStudio: A Toolkit for Building General Virtual Agents [57.02375267926862]
General virtual agents need to handle multimodal observations, master complex action spaces, and self-improve in dynamic, open-domain environments.<n>AgentStudio provides a lightweight, interactive environment with highly generic observation and action spaces.<n>It integrates tools for creating online benchmark tasks, annotating GUI elements, and labeling actions in videos.<n>Based on our environment and tools, we curate an online task suite that benchmarks both GUI interactions and function calling with efficient auto-evaluation.
arXiv Detail & Related papers (2024-03-26T17:54:15Z) - Autonomous Large Language Model Agents Enabling Intent-Driven Mobile GUI
Testing [17.24045904273874]
We propose DroidAgent, an autonomous GUI testing agent for Android.
It is based on Large Language Models and support mechanisms such as long- and short-term memory.
DroidAgent achieved 61% activity coverage, compared to 51% for current state-of-the-art GUI testing techniques.
arXiv Detail & Related papers (2023-11-15T01:59:40Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.