NeuralOS: Towards Simulating Operating Systems via Neural Generative Models
This work addresses the problem of creating adaptive neural interfaces for human-computer interaction, but it is incremental as it builds on existing neural methods for GUI simulation.
The authors tackled the problem of simulating operating system GUIs by predicting screen frames from user inputs, resulting in a model that renders realistic sequences and captures mouse interactions accurately, though keyboard interactions remain challenging.
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.