NeuralOS: Towards Simulating Operating Systems via Neural Generative Models (2507.08800v1)

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.

• The paper introduces a neural framework that models OS GUIs as an autoregressive generative process driven by user inputs.
• It employs a hierarchical RNN and latent diffusion renderer to maintain state and generate precise screen frames in real time.
• Experiments demonstrate high cursor localization accuracy and robust state transition modeling, highlighting its interactive potential.

NeuralOS: Simulating Operating Systems with Neural Generative Models

The paper "NeuralOS: Towards Simulating Operating Systems via Neural Generative Models" (2507.08800) presents a neural framework for simulating the graphical user interfaces (GUIs) of operating systems by directly generating screen frames in response to user inputs. The work is situated at the intersection of generative modeling, interactive systems, and human-computer interaction, and addresses the challenge of creating fully adaptive, generative neural interfaces that can respond to arbitrary user actions in real time.

Problem Formulation and Motivation

NeuralOS formalizes the simulation of an OS GUI as an autoregressive generative modeling problem. At each timestep, the model predicts the next screen frame conditioned on the sequence of previous frames and the sequence of user input events (mouse movements, clicks, keyboard events). This formulation is distinct from standard video generation: the model must handle abrupt, user-driven state transitions (e.g., launching an application) and maintain accurate, persistent state tracking over long horizons.

The motivation is to move beyond rigid, pre-programmed interfaces and toward a paradigm where the entire user interface is generated and adapted by neural models, potentially enabling new forms of interaction, personalization, and integration across applications.

Model Architecture

NeuralOS adopts a modular architecture inspired by the separation of concerns in traditional operating systems:

• Hierarchical RNN State Tracker: A two-level LSTM-based RNN encodes user inputs and tracks the internal state of the simulated computer. The lower-level LSTM processes input embeddings (cursor, mouse, keyboard), while the upper-level LSTM integrates attention over the previous frame and maintains higher-level context. This design ensures constant per-timestep computational complexity, which is essential for real-time, long-horizon simulation.
• Latent Diffusion Renderer: Screen images are compressed into a latent space using an autoencoder. A UNet-based diffusion model then generates the next latent frame conditioned on the RNN state and an explicit spatial encoding of the cursor position (a Gaussian map). The generated latent is decoded back to a pixel image for display.
• Explicit Cursor Encoding: The model incorporates a spatial map centered at the cursor position, which is critical for precise cursor rendering and accurate simulation of interactive behaviors.

Training Pipeline

The authors introduce a multi-stage training strategy to address several practical challenges:

1. RNN Pretraining: The RNN is first trained to predict latent frames using an MSE loss. This provides a strong initialization and prevents the diffusion renderer from ignoring the RNN outputs during joint training.
2. Joint Training: The pretrained RNN and the diffusion renderer are optimized together using a diffusion loss, enabling the renderer to leverage the RNN's state representations.
3. Scheduled Sampling: To mitigate exposure bias and error accumulation during inference, scheduled sampling is employed: with a small probability, the model-generated frame is used as input during training instead of the ground-truth frame.
4. Context Length Extension: The context window is extended in later training stages to enable the model to capture long-term dependencies, with special handling for sequence truncation.
5. Curriculum on Challenging Transitions: Training is initially focused on challenging transitions (large frame differences) to prioritize learning of significant state changes, before expanding to the full dataset.
6. Finetuning with Real-User Data: After deployment, the model is further finetuned on real-user demonstrations to improve alignment with actual user behaviors.

Data Collection

A large-scale dataset is constructed using two complementary strategies:

• Agent-Based Demonstrations: Anthropic's Claude-3.5-Sonnet computer-use agent is used to systematically explore the OS state space, identifying interactable GUI elements and generating diverse interaction sequences via a search tree approach.
• Random Exploration: To avoid spurious correlations and increase coverage, random mouse and keyboard events are generated with heuristics to mimic natural user behavior.

Data is collected in parallel across 64 Docker containers running Ubuntu XFCE at $512 \times 384$ resolution, resulting in 2K agent-based and 120K random demonstrations (12TB compressed).

Experimental Results

The evaluation focuses on the model's ability to simulate realistic GUI sequences, accurately track cursor positions, and model state transitions:

• Cursor Localization: With explicit cursor encoding, NeuralOS achieves an average localization error of 1.6 (x) and 1.4 (y) pixels, corresponding to less than 0.5% of the frame dimensions. This is a strong quantitative result, demonstrating precise spatial modeling.
• State Transition Modeling: On a set of challenging transitions (clustered into 73 categories), NeuralOS achieves 37.7% accuracy in predicting the correct transition cluster, far exceeding the majority baseline (1.4%). The heatmap analysis shows that off-diagonal predictions often correspond to plausible alternative outcomes due to inherent OS timing variability.
• Ablation Studies: The necessity of each training stage is empirically validated. Without scheduled sampling, error accumulation degrades generation quality. Without joint training, the RNN outputs are blurry and lack cursor detail.
• Limitations: The model is currently limited to low resolution, cannot reliably simulate fine-grained keyboard input (e.g., typing in terminals), and runs at 1.8 fps on an H100 GPU. The system does not yet support installation of new software or external resource interaction.

Implications and Future Directions

Practical Implications:

• Adaptive Interfaces: NeuralOS demonstrates the feasibility of end-to-end generative user interfaces that can adapt in real time to arbitrary user actions, potentially enabling new forms of accessibility, personalization, and integration.
• Simulation and Testing: The framework could be used for automated testing of GUIs, training of RL agents in simulated OS environments, or as a foundation for interactive demos and prototyping.
• Data Generation: The approach provides a scalable method for generating large-scale, diverse interaction data, which could benefit downstream tasks such as GUI understanding or agent training.

Theoretical Implications:

• World Modeling Beyond Games: NeuralOS extends the paradigm of world models and generative environment simulators from games to the domain of operating systems, introducing new challenges in state tracking, event-driven transitions, and fine-grained spatial control.
• Bridging Modalities: The architecture suggests a path toward integrating language, vision, and action in a unified generative interface, where user intent (expressed via language or gesture) could directly drive interface generation.

Future Research Directions:

• Natural Language and Gesture Conditioning: Conditioning the generative process on high-level user intent could enable more intuitive and flexible interactions.
• Higher Resolution and Efficiency: Improving model efficiency and scaling to higher resolutions are necessary for practical deployment.
• Controllability and Safety: Mechanisms for user control, error correction, and safe execution will be critical as generative OS interfaces become more capable.
• Blurring Application Boundaries: The generative approach could dissolve traditional application silos, enabling seamless transitions between media, productivity, and communication within a unified, adaptive interface.

Conclusion

NeuralOS represents a significant step toward generative, adaptive operating system interfaces. The work demonstrates that neural models can learn to simulate complex, interactive GUIs with high spatial precision and robust state tracking, given sufficient data and carefully designed training protocols. While substantial challenges remain, the framework opens new avenues for research at the intersection of generative modeling, interactive systems, and human-computer interaction, with broad implications for the future of computing interfaces.