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• Existing mobile GUI agent approaches face critical limitations due to scarcity of high-quality multilingual datasets, particularly for non-English ecosystems.limitation 90%
• Existing mobile GUI agent approaches have inefficient history representation methods.limitation 90%
• The authors constructed a human-verified Chinese mobile GUI dataset with 18k grounding samples and 121k navigation steps across 44 applications.method 95%
• The authors created a Chinese navigation benchmark featuring multi-choice action annotations.method 90%
• SecAgent uses a semantic context mechanism that distills history screenshots and actions into concise natural language summaries.technical 90%
• The semantic context mechanism significantly reduces computational costs while preserving task-relevant information.technical 85%
• SecAgent outperforms similar-scale baselines on navigation benchmarks.result 85%
• SecAgent achieves performance comparable to 7B-8B models on navigation benchmarks despite being a 3B scale model.result 85%

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