arXiv:2607.00502v1 Announce Type: new Abstract: While long-horizon mobile GUI agents typically rely on thought-action-observation loops, they struggle to separate persistent task states from transient screen observations. As execution histories grow, this entanglement imposes a severe context burden, causing agents to forget initial requirements, hallucinate progress, or repeatedly interact with stale interfaces. To address this, we introduce Task-State Representation (TSR), a training-free framework that explicitly decouples task state from sensory input. Acting as a lightweight external wrap
The proliferation of complex mobile applications and the increasing sophistication of AI models for interaction highlight the immediate need for improved agentic reliability.
This development addresses a critical limitation in AI agents' ability to manage long-horizon tasks on graphical user interfaces, making them more robust and effective.
AI agents will be less prone to errors caused by context burden and stale interface interactions, leading to more reliable and longer autonomous operations.
- · AI agent developers
- · Mobile application developers
- · Automation software providers
- · Enterprises adopting AI for workflows
- · Companies relying on manual GUI interaction
- · Less robust AI agent frameworks
AI agents can now perform more complex, multi-step tasks on mobile devices with higher success rates.
This improved reliability could accelerate the deployment of autonomous mobile agents in customer service, personal assistance, and backend operations.
Increased agent autonomy might lead to a significant reduction in human intervention for routine digital tasks, shifting workflows and labor requirements.
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Read at arXiv cs.CL