MIRAGE: New Framework Enables Mobile Agents to Reason Internally Without Generating Long Text Explanations
Researchers introduced MIRAGE, a framework that allows mobile agents operating on smartphones to perform reasoning internally using compressed hidden states rather than generating lengthy text explanations. The approach combines implicit reasoning representations with generative world models that predict future interface states. This advancement reduces computational overhead and token generation by 75% while maintaining or improving task performance on mobile automation benchmarks.
MIRAGE is a new AI framework designed to improve how mobile agents—systems that control smartphones based on screenshots and language instructions—perform complex tasks. Rather than externalizing reasoning as long chains of text (which slows interaction and increases computational cost), MIRAGE learns to compress explicit reasoning into compact hidden states that the agent uses internally. The framework also incorporates a generative world-model component that aligns latent reasoning vectors with future screenshots, enabling the agent to anticipate upcoming interface states before taking action. Testing on AndroidWorld and AndroidControl benchmarks showed that MIRAGE matches the performance of explicit chain-of-thought approaches while reducing decoded-token generation by 3-5x and improving baseline instruction-tuned models by 10.2 points, while generating over 75% fewer tokens overall.
What's missing
The paper does not discuss potential limitations of the approach, such as failure modes when world-model predictions diverge significantly from actual interface states, generalization to novel applications unseen during training, or computational requirements during the training phase versus inference.
What different sources said
- arXiv cs.AICenter
MIRAGE: Mobile Agents with Implicit Reasoning and Generative World Models
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