mlsanityData to 7 Oct 2026

DroidCall: A Dataset for LLM-powered Android Intent Invocation

arXiv 2412.00402 ↗First posted 30 Nov 2024cs.AIcited by 4

On the map: LLM agents and tool use → GUI and mobile agents

Abstract (arXiv)

The growing capabilities of large language models in natural language understanding significantly strengthen existing agentic systems. To power performant on-device mobile agents for better data privacy, we introduce DroidCall, the first training and testing dataset for accurate Android intent invocation. With a highly flexible and reusable data generation pipeline, we constructed 10k samples in DroidCall. Given a task instruction in natural language, small language models such as Qwen2.5-3B and Gemma2-2B fine-tuned with DroidCall can approach or even surpass the capabilities of GPT-4o for accurate Android intent invocation. We also provide an end-to-end Android app equipped with these fine-tuned models to demonstrate the Android intent invocation process. The code and dataset are available at https://github.com/UbiquitousLearning/DroidCall.

Key facts. Cited by 4 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 41 of its 77 references that are in the map. First posted on arXiv 30 Nov 2024.

Builds on

41 of 77 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Mobile-Agent: Autonomous Multi-Modal Mobile Device Agent with Visual Perception29 Jan

Other references · 40

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2024The Llama 3 Herd of Modelscited by 14,192
  3. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  4. 2024Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolutioncited by 4,151
  5. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  6. 2023Qwen Technical Reportcited by 3,273
  7. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,674
  8. 2024Qwen2 Technical Reportcited by 2,076
  9. 2024Gemma 2: Improving Open Language Models at a Practical Sizecited by 1,928
  10. 2024Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phonecited by 1,883
  11. 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIscited by 1,880
  12. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,761
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  4. 2024DeepSeek-VL: Towards Real-World Vision-Language Understandingcited by 718
  5. 2023Towards General Text Embeddings with Multi-stage Contrastive Learningcited by 705
  6. 2024MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategiescited by 694
  7. 2023The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)cited by 677
  8. 2024AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agentscited by 450
  9. 2023ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Casescited by 383
  10. 2024AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agentscited by 359
  11. 2024DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligencecited by 325
  12. 2024ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilitiescited by 272
  13. 2024Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Securitycited by 257
  14. 2023Auto-GPT for Online Decision Making: Benchmarks and Additional Opinionscited by 250
  15. 2024ToolACE: Winning the Points of LLM Function Callingcited by 204
  16. 2024APIGen: Automated Pipeline for Generating Verifiable and Diverse Function-Calling Datasetscited by 188
  17. 2023AutoDroid: LLM-powered Task Automation in Androidcited by 178
  18. 2023ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Modelscited by 174
  19. 2023An LLM Compiler for Parallel Function Callingcited by 170
  20. 2024Small Language Models: Survey, Measurements, and Insightscited by 109
  21. 2024A Survey of Resource-efficient LLM and Multimodal Foundation Modelscited by 84
  22. 2024TinyAgent: Function Calling at the Edgecited by 55
  23. 2024AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learningcited by 53
  24. 2023LLMCad: Fast and Scalable On-device Large Language Model Inferencecited by 45
  25. 2024Octopus v2: On-device language model for super agentcited by 45
  26. 2024ShortcutsBench: A Large-Scale Real-world Benchmark for API-based Agentscited by 36
  27. 2023Mobile Foundation Model as Firmwarecited by 25
  28. 2024PhoneLM:an Efficient and Capable Small Language Model Family through Principled Pre-trainingcited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025ShadowNPU: System and Algorithm Co-design for NPU-Centric On-Device LLM Inferencecited by 4
  2. 2025Every Software as an Agent: Blueprint and Case Studycited by 2
  3. 2026Agent-Integrated Software: Interaction Contracts and Continuous Assurancecited by 1
  4. 2026From Tapping to Hopping: Augmenting Mobile GUI Agents with App-Native Deeplinkscited by 0

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