arXiv:2606.04627v1 Announce Type: new Abstract: Mobile agents are increasingly expected to operate everyday applications from screenshots and language goals, where reliable control requires reasoning over screen affordances, multi-step navigation, and future state changes. However, many agents externalize this computation as long textual chains of thought, which slows interaction, increases supervision cost, and complicates deployment. We introduce MIRAGE, a framework that learns continuous latent reasoning representations from visible textual reasoning traces. MIRAGE transfers explicit reason
The proliferation of AI agents operating on complex digital interfaces necessitates more efficient and less resource-intensive reasoning mechanisms, pushing research towards implicit reasoning solutions.
This development addresses key bottlenecks in the deployment and scalability of AI agents, making them more practical for real-world applications by reducing computational overhead and improving interaction speed.
AI agents can now potentially learn complex reasoning from simpler textual traces, enabling more efficient and autonomous operation from visual inputs and language goals.
- · AI agent developers
- · Automation software providers
- · Companies using AI for workflow automation
- · Manual workflow operators
More robust and scalable mobile AI agents become deployable across various sectors.
Increased adoption of AI agents could significantly reduce operational costs and accelerate task completion.
The enhanced efficiency of AI agents may lead to faster integration into human-centric workflows, reshaping job markets and organizational structures.
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Read at arXiv cs.AI