Xctopus: Hierarchical
Continual Learning
ExperimentalModular AI Architecture for Memory Preservation
What is Xctopus?
Xctopus is an Adaptive Knowledge Architecture designed to solve one of the most critical challenges in machine learning: Catastrophic Forgetting. Unlike traditional systems that overwrite previous knowledge when learning new information, Xctopus organizes knowledge into a dynamic, hierarchical structure of Knowledge Nodes that evolve organically based on the nature of the data.
Core System Architecture
Xctopus is structured into three fundamental layers that work in synergy to enable continuous learning:
Global Bayesian Orchestrator
The central intelligence layer that maintains global epistemic memory and manages uncertainty. It continuously updates priors and synthesizes information to ensure system-wide coherence across all specialized modules.
Autonomous Knowledge Nodes
Specialized local modules engineered for domain-specific processing. Each node implements Transformer-based encoding and independent belief adaptation, allowing for organic growth and local expertise without disrupting the global model.
Operational Control Layer
The framework's engine that supervises the system's lifecycle. It manages intelligent routing, ensures full traceability, and coordinates the modular scaling that allows Xctopus to expand its knowledge base while preserving architectural stability.
Want to Contribute?
Xctopus is an active research project in development. We are building a continual learning architecture that mitigates catastrophic forgetting.
Your contribution can help shape the future of continual learning in AI.
View Repository on GitHubHow Can You Help?
Documentation
Improve documentation, write guides, translate content, or create usage examples.
Code
Contribute features, fix bugs, optimize algorithms, or improve the architecture.
Design
Improve UI/UX, create visualizations, design diagrams, or suggest experience improvements.
Examples
Create use cases, example notebooks, tutorials, or demonstrate new applications.
This is a research project in development. All contributions are welcome, regardless of your experience level.
Lab Team
Research led by dedicated scientists exploring the frontiers of continual learning
Exploring modular AI architectures, Bayesian Neural Networks, and continual learning systems to solve catastrophic forgetting in neural networks.
For academic inquiries, research collaborations, or questions about Xctopus, please use the contact form below or reach out via the links above.
Open to collaborators! This project is participatory and community-driven. We're tackling real challenges in continual learning and catastrophic forgetting through collaborative work. If you're interested in contributing, sharing ideas, or working together on solutions, we'd love to hear from you.
Feedback & Collaboration
Ideas that matter: join modular research