AppDeltaWorld: Transition-Grounded Delta Code World Model for Mobile GUI Agents

• URL: http://arxiv.org/abs/2608.05891v1
• Date: Thu, 06 Aug 2026 11:15:41 GMT
• Title: AppDeltaWorld: Transition-Grounded Delta Code World Model for Mobile GUI Agents
• Abstract summary: AppDeltaWorld is a transition-grounded delta code world model that predicts the next GUI as a reachable code update.<n>As a world model, AppDeltaWorld achieves the highest fidelity on CMGUIBench-500 under Code2World evaluation.<n>World-model-based test-time reinforcement learning enables policy adaptation and shows further improvements without additional interaction with real apps.
• Score: 46.83090877984068
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mobile GUI agents can operate apps through pixel perception and touch actions, making them a promising interface for collecting and improving long-horizon mobile interaction policies. However, real trajectories are difficult to obtain for sensitive apps and privacy-critical operations. At the same time, existing simulated environments are costly to scale up, and GUI world models still suffer from unstable generation, limited modality coverage, and inconsistent action-transition logic. To address these limitations, we propose AppDeltaWorld, a transition-grounded delta code world model that predicts the next GUI as a reachable code update rather than as an unconstrained image or text description. AppDeltaWorld retrieves app-specific Level-1 HTML references under an action-transition constraint, generates Level-2 executable HTML conditioned on the current screen, action, predicted next-screen text, and retrieved structure, and inserts generated visual assets into image slots before browser rendering. As a world model, AppDeltaWorld achieves the highest fidelity on CMGUIBench-500 under Code2World evaluation, with clear gains in structural layout and UI element reconstruction over image-only and code-only baselines. As a training environment, AppDeltaWorld supports filtered closed-loop SFT data construction that, when combined with public supervision, enables AppDeltaAgent to achieve state-of-the-art performance on AndroidLens and consistent gains on MobileGym and MobileWorld. Moreover, world-model-based test-time reinforcement learning enables policy adaptation and shows further improvements without additional interaction with real apps.

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