World models have shown great utility in improving the task performance of embodied agents. While prior work largely focuses on pixel-space world models, these approaches face practical limitations in mobile agent settings, where predicting complex visual GUI elements in future states is often difficult. In this work, we explore an alternative formulation of world modeling for mobile agents, where state transitions are described in natural language rather than predicting screenshot pixels. First, we introduce MobileWorldBench, a benchmark that evaluates the ability of vision-language models (VLMs) to function as world models for mobile agents. Second, we release MobileWorld, a large-scale dataset consisting of 1.4M samples, that improves the world modeling capabilities of VLMs. Finally, we propose a novel framework that integrates VLM world models into the planning framework of mobile agents, demonstrating that semantic world models can directly benefit mobile agents by improving task success rates.

Image Editing has made incredible progress in recent years. Earliest work only supported caption-guided editing. Recently, free-form text instructions and reference images are incorporated to allow more flexibility. However, existing methods still struggle with complicated editing instructions involving multiple objects or reference images. We present InstructAny2Pix, a novel image editing model that leverages a multi-modal LLM to execute complicated edit instructions. Compared with previous, works, InstructAny2Pix extends the flexibility of edit instructions in three ways: First, it can perform complex instructions involving multiple object edits; Second, it supports interleaving text instructions with multiple reference images; Third, it supports audio and music inputs as part of edit prompts, unlocking many creative applications, such as album cover generation and music-inspired merchandise design. To evaluate the effectiveness of InstructAny2Pix, we propose two new benchmark datasets MM-Inst and Dream-booth++ consisting of human written, multi-modal prompts. InstructAny2Pix outperforms baselines in these two proposed multi-modal benchmarks, as well as conventional image editing benchmarks such as InstructPix2Pix.