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The MaP is proposed, a unified framework that seamlessly harmonizes divergent GUI navigation tasks and effectively mitigates gradient conflicts and significantly outperforms the direct mixture training, establishing a robust paradigm for multi-task GUI navigation.
Real-world GUI agents can achieve over 72% success in complex mobile tasks by leveraging a unique hybrid training approach that integrates real-device execution and adaptive learning from failures.
Achieving state-of-the-art performance in in-image machine translation, UniTranslator reveals a powerful synergy between translation understanding and image generation.
Autonomous exploration by an LLM agent dramatically outperforms both rigid retrieval workflows and supervised fine-tuning for temporal knowledge graph question answering, achieving state-of-the-art results in a zero-shot setting.
