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Bibliographic Details
Main Authors: Favreau, Pierre-Louis, Lo, Jean-Pierre, Guiguet, Clement, Simon-Meunier, Charles, Dehandschoewercker, Nicolas, Roush, Allen G., Goldfeder, Judah, Shwartz-Ziv, Ravid
Format: Preprint
Published: 2026
Subjects:
Online Access:https://arxiv.org/abs/2602.07787
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Table of Contents:
  • We present Minitap, a multi-agent system that achieves 100% success on the AndroidWorld benchmark, the first to fully solve all 116 tasks and surpassing human performance (80%). We first analyze why single-agent architectures fail: context pollution from mixed reasoning traces, silent text input failures undetected by the agent, and repetitive action loops without escape. Minitap addresses each failure through targeted mechanisms: cognitive separation across six specialized agents, deterministic post-validation of text input against device state, and meta-cognitive reasoning that detects cycles and triggers strategy changes. Ablations show multi-agent decomposition contributes +21 points over single-agent baselines; verified execution adds +7 points; meta-cognition adds +9 points. We release Minitap as open-source software. https://github.com/minitap-ai/mobile-use