Xctopus: research lab developing a modular continual-learning framework
A living experimental environment for testing new memory-driven architectures and adaptive cognitive models in AI.
This is an evolving project. Here we document progress, experiments, preliminary results, architectural concepts, and technical reflections on continual learning.
What is Xctopus?
An experimental modular learning framework where specialized Knowledge Modules collaborate under a central Bayesian Node that maintains global memory, belief updates, and uncertainty. This allows the system to integrate new information without overwriting existing knowledge.
Preserve previous knowledge without full retraining.
Efficiently integrate new knowledge.
Coordinate modules with hierarchical memory and efficient distribution.
Optimize computational resources through modularization and Bayesian uncertainty control.
Architecture
A modular system where Knowledge Nodes process information independently while a central Bayesian Node maintains global memory, coherence, and belief updates.
Bayesian Node
(Central Coordinator)
Maintains global epistemic memory, manages uncertainty, updates priors, and ensures coherence across the entire system.
Knowledge Nodes
Specialized local modules designed for:
- Domain-specific processing
- Local belief adaptation
- Transformer-based encoding
- Feedback loops with the Bayesian Node
Each node can evolve independently while contributing to the global model.
Orchestration Layer
Supervises the lifecycle of the system:
- Routing information between nodes
- Managing training/evaluation workflows
- Ensuring consistency, traceability, and modular scalability
It enables the framework to grow organically without breaking previous modules.
This architecture enables modularity, specialization, and scalability, inspired by biological systems and hierarchical learning.
Read Full ConceptsWant 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.
Research Log
We document real progress: experiments, technical decisions, and learnings while building Xctopus.
→ Read the LogLab 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