AQARION Defect World Model

A foundational world model that learns structured latent transitions via the AQARION Defect Regularizer, a differentiable penalty that minimizes topological defects in latent space. Designed for environments with continuous state spaces and structured dynamics.

Model Description

This model combines:

• A standard encoder-decoder architecture for reconstruction.
• The AQARION Defect Loss, which regularizes latent transitions by penalizing misalignment between encoded states and their predicted evolution.

The loss encourages the latent space to evolve smoothly, reducing "defects" that disrupt downstream planning or control.

Intended Use

• Learning compact, structured world models from high-dimensional observations.
• Environments with toroidal or cyclic dynamics (e.g., robotics, climate, physics simulation).
• Research into latent space topology and structured regularization.

Limitations & Ethical Considerations

• The defect loss assumes a fixed number of clusters; performance may degrade if dynamics are highly non-stationary.
• Not intended for safety-critical deployment without additional verification.
• Latent interpretability is limited—clusters are emergent, not semantically labeled.

Training

• Data: High-dimensional continuous observations with temporal structure.
• Optimizer: Adam (lr=1e-3)
• Batch size: 256
• Latent dim: 16
• Clusters: 6
• Defect weight (β): 10.0
• Seed: 369 (for reproducibility)

Usage

from aqarion_world_model import AQARIONWorldModel

model = AQARIONWorldModel(obs_dim=128, latent_dim=16, num_clusters=6)

recon, defect_loss, clusters = model(obs_t, obs_t_next)

---

@misc{aqarion2026defect,

  author = {AQARION},

  title = {AQARION Defect World Model},

  year = {2026},

  publisher = {Hugging Face},

  url = {https://huggingface.co/aqarion/aqarion-defect-world-model}

}
---
license: apache-2.0
library_name: torch
language: en
pipeline_tag: reinforcement-learning
tags:
  - world-model
  - latent-dynamics
  - defect-regularization
  - continuous-representation
  - finite-dynamical-system
---

# AQARION Defect World Model

A foundational world model that learns structured latent transitions via the **AQARION Defect Regularizer**, a differentiable penalty that minimizes topological defects in latent space. Designed for environments with continuous state spaces and structured dynamics.

## Model Description

This model combines:
- A standard encoder-decoder architecture for reconstruction.
- The **AQARION Defect Loss**, which regularizes latent transitions by penalizing misalignment between encoded states and their predicted evolution.

The loss encourages the latent space to evolve smoothly, reducing "defects" that disrupt downstream planning or control.

## Intended Use

- Learning compact, structured world models from high-dimensional observations.
- Environments with toroidal or cyclic dynamics (e.g., robotics, climate, physics simulation).
- Research into latent space topology and structured regularization.

## Limitations & Ethical Considerations

- The defect loss assumes a fixed number of clusters; performance may degrade if dynamics are highly non-stationary.
- Not intended for safety-critical deployment without additional verification.
- Latent interpretability is limited—clusters are emergent, not semantically labeled.

## Training

- **Data**: High-dimensional continuous observations with temporal structure.
- **Optimizer**: Adam (lr=1e-3)
- **Batch size**: 256
- **Latent dim**: 16
- **Clusters**: 6
- **Defect weight (β)**: 10.0
- **Seed**: 369 (for reproducibility)

## Usage

```python

from aqarion_world_model import AQARIONWorldModel

model = AQARIONWorldModel(obs_dim=128, latent_dim=16, num_clusters=6)

recon, defect_loss, clusters = model(obs_t, obs_t_next)