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 presents a pioneering neural architecture that combines hierarchical RNN state tracking with diffusion-based rendering to simulate OS GUIs from user inputs.
• It achieves strong quantitative performance in cursor localization and state transition accuracy, outperforming standard baselines.
• The study employs a multi-stage training pipeline and innovative data collection to advance adaptive, generative user interfaces.

NeuralOS: Simulating Operating Systems with Neural Generative Models

NeuralOS 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 system is architected as a combination of a hierarchical recurrent neural network (RNN) for state tracking and a diffusion-based neural renderer for frame generation. This work is positioned as a step toward fully generative, adaptive user interfaces, with the potential to fundamentally alter the paradigm of human-computer interaction.

Problem Formulation and Motivation

The core task is formalized 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). Unlike standard video generation, the model must handle abrupt, user-driven state transitions and maintain accurate, responsive state tracking over long horizons.

The motivation is to move beyond rigid, pre-programmed interfaces toward interfaces that are entirely generated and adapted by neural models, potentially allowing for new forms of interaction (e.g., via natural language or gestures) and blurring the boundaries between applications.

Architecture

NeuralOS is composed of two principal modules:

• Hierarchical RNN State Tracker:
  • Two-level LSTM architecture: the lower-level LSTM encodes user inputs and integrates visual information from the previous frame via attention; the upper-level LSTM further processes these representations.
  • Explicit feedback from the upper-level to the lower-level LSTM ensures context awareness and supports long-horizon state tracking with constant per-timestep computational complexity.
  • Cursor positions are encoded as Gaussian spatial maps, which are critical for precise cursor rendering in the generated frames.
• Diffusion-Based Renderer:
  • A UNet-based diffusion model generates latent representations of the next frame, conditioned on the RNN state and cursor map.
  • The model operates in a compressed latent space (via a custom autoencoder), enabling tractable training and inference at reduced spatial resolution.

The architecture is designed to be modular, mirroring the separation between OS kernel state and GUI rendering in traditional systems, but implemented entirely with neural components.

Training Methodology

A multi-stage training pipeline is employed to address the unique challenges of this domain:

1. RNN Pretraining: The RNN is first pretrained 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 jointly optimized with a diffusion loss, allowing the renderer to leverage the RNN's state representations.
3. Scheduled Sampling: To mitigate exposure bias and error accumulation during autoregressive inference, scheduled sampling is used: the model is occasionally fed its own generated frames as input during training.
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 Learning: 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 interaction data, improving alignment with actual user behavior.

Data Collection

A large-scale dataset is constructed using two strategies:

• Agent-Based Demonstrations:

Anthropic's Claude-3.5-Sonnet computer-use agent is used to systematically explore the OS state space, building a search tree of GUI states and transitions. This enables efficient coverage of diverse interaction scenarios.

• Random Exploration:

To avoid spurious correlations and ensure robustness, random interaction data is generated with heuristics to mimic natural user behavior (e.g., Bezier curves for mouse movement, explicit double-clicks).

Data is collected in parallel across 64 Dockerized Ubuntu XFCE environments at $512 \times 384$ resolution, resulting in 2K agent-based and 120K random demonstrations (30 seconds each at 15 fps), compressed to 12TB of latent data.

Experimental Results

Quantitative Evaluation:

• Cursor Localization:

With explicit cursor position maps, NeuralOS achieves mean localization errors of 1.6 (x) and 1.4 (y) pixels, corresponding to <0.5% of the frame dimensions. This is a strong result, significantly outperforming ablations without spatial encoding.

• State Transition Modeling:

On challenging transitions (clustered into 73 categories), NeuralOS achieves 37.7% accuracy (diagonal in the transition heatmap), far above the majority baseline (1.4%). Off-diagonal predictions often reflect valid alternative outcomes due to inherent OS timing variability.

• Ablation Studies:
  • Without joint training, the RNN outputs are blurry and lack precise cursor localization.
  • Without scheduled sampling, error accumulation leads to rapid degradation in frame quality.

Resource Requirements:

• Training required 17,000 GPU hours on 8×H200 (141GB) and 6,000 GPU hours on 8×H100 (80GB) servers.
• Inference speed is 1.8 fps on a single H100 GPU.

Limitations

• Resolution and Fidelity:

The model operates at low resolution ($512 \times 384$), and fine-grained keyboard interactions (e.g., typing in terminals) are not reliably captured.

• Performance:

Inference is slow (1.8 fps), limiting real-time interactivity.

• Scope:

The model does not support installation of new software, internet connectivity, or advanced forms of controllability.

• Generalization:

While the model generalizes across a range of OS states, some transitions remain challenging, and the system is not yet competitive with real OSs in terms of flexibility or responsiveness.

Implications and Future Directions

Practical Implications:

• Adaptive Interfaces:

NeuralOS demonstrates the feasibility of fully generative, adaptive user interfaces, where the entire GUI is synthesized in response to user actions.

• Personalization and Accessibility:

Such systems could enable highly personalized interfaces, potentially controlled via natural language or other modalities, and could improve accessibility for users with diverse needs.

• Application Blurring:

The generative approach may dissolve traditional application boundaries, enabling, for example, the transformation of passive media into interactive experiences at the OS level.

Theoretical Implications:

• World Modeling:

NeuralOS extends the paradigm of world models from games and simulated environments to the domain of general-purpose computing interfaces.

• Neural State Tracking:

The hierarchical RNN architecture provides a template for efficient, long-horizon state tracking in interactive generative systems.

Future Work:

• Higher Resolution and Efficiency:

Scaling to higher resolutions and improving inference speed are necessary for practical deployment.

• Richer Modalities:

Conditioning on natural language, gestures, or other input modalities could further enhance flexibility.

• Controllability and Tool Use:

Integrating mechanisms for explicit tool invocation, software installation, and internet access would move toward a fully functional neural OS.

• Generalization and Robustness:

Further research is needed to ensure robust generalization across diverse OS environments and user behaviors.

Conclusion

NeuralOS provides a proof-of-concept for simulating operating system GUIs with neural generative models, combining hierarchical RNN state tracking with diffusion-based rendering. The system achieves strong results in cursor localization and state transition modeling, and introduces a scalable training and data collection pipeline. While significant limitations remain, this work lays the groundwork for future research on fully generative, adaptive user interfaces and neural operating systems. The open-source release of code, models, and an interactive demo facilitates further exploration and benchmarking in this emerging area.