World Model Architecture Scaffold

A modular world-model scaffold uses a strict ABC-based interface and dependency injection composition root, allowing you to hot-swap encoders, dynamics, and decoders without altering source code.

Dependency injection here uses a composition root to enforce dimensional consistency across components, ensuring encoder embed dim equals dynamics state dim and decoder input dim during project generation.

Yes, you can use Hydra-compatible YAML configs for config-driven project generation, providing ready-to-run templates for model, training, dataset, and hardware configurations in your world-model project.

Yes, the world-model scaffold supports optional memory and planner components for extended workflows, utilizing a registry-based factory pattern to integrate these components dynamically.

Using ABC-based interfaces for AI project scaffolding requires strict adherence to dimensional consistency constraints, meaning encoder, dynamics, and decoder dimensions must match exactly, limiting arbitrary component combinations.

Yes, you need PyTorch, PyYAML, and pytest installed, as these are the core dependencies required to run the scaffold generation and execute the resulting world-model project code.