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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.
This work introduces Emotion Statement Judgement (ESJ), a statement-verification formulation that preserves the expressiveness of the input space while constraining outputs to discriminative judgements, and builds EmObserver, an emotion-oriented MLLM optimized on ESJ through an elaborate multi-stage recipe.
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