NeuralOS: Towards Simulating Operating Systems via Neural Generative Models

2025-07-11

AI Summary

This work addresses the challenge of modeling operating-system-level GUI dynamics end-to-end for pixel-level screen frame generation from raw user inputs (mouse/keyboard events). We propose the first end-to-end neural simulation framework that jointly models GUI state evolution—using a recurrent neural network—and high-fidelity image synthesis—via a diffusion-based neural renderer—trained on large-scale real-world Ubuntu XFCE screen recordings. Our contributions are threefold: (1) unified modeling of GUI state transitions and corresponding visual outputs; (2) integration of AI agents to synthesize high-quality interactive trajectories, mitigating scarcity of human-annotated interaction data; and (3) superior performance over baselines on complex state-transition tasks such as application launching and window switching. Experiments demonstrate photorealistic reproduction of fine-grained interaction details—including cursor motion and animated transitions—advancing the frontier of generative human–computer interface modeling.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Computer Vision: Diffusion Models for Vision

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

We introduce NeuralOS, a neural framework that simulates graphical user interfaces (GUIs) of operating systems by directly predicting screen frames in response to user inputs such as mouse movements, clicks, and keyboard events. NeuralOS combines a recurrent neural network (RNN), which tracks computer state, with a diffusion-based neural renderer that generates screen images. The model is trained on a large-scale dataset of Ubuntu XFCE recordings, which include both randomly generated interactions and realistic interactions produced by AI agents. Experiments show that NeuralOS successfully renders realistic GUI sequences, accurately captures mouse interactions, and reliably predicts state transitions like application launches. Although modeling fine-grained keyboard interactions precisely remains challenging, NeuralOS offers a step toward creating fully adaptive, generative neural interfaces for future human-computer interaction systems.

Problem

• Simulating OS GUIs via neural models for screen prediction
• Combining RNN and diffusion renderer for state-aware rendering
• Training on Ubuntu XFCE data to capture GUI interactions

Innovation

• Neural framework simulating GUIs via generative models
• Combines RNN for state tracking with diffusion renderer
• Trained on large-scale Ubuntu XFCE interaction recordings