Mitigating Catastrophic Forgetting
in Continual Learning
ExperimentalXctopus is a modular Bayesian research framework
integrating BNNs, Transformers, and hierarchical knowledge nodes.
What is Xctopus?
α Alpha[Research Framework in Alpha] Xctopus is a hierarchical continual learning framework designed to address one of the most critical challenges in machine learning: catastrophic forgetting. Unlike traditional monolithic systems that overwrite prior knowledge when learning new information, Xctopus organizes learning into a dynamic hierarchy of modular Knowledge Nodes, orchestrated through probabilistic control mechanisms and evolving incrementally based on the structure and distribution of incoming data.
Core System Architecture
Xctopus is structured into fundamental components that work in synergy to enable continuous learning:
Global Bayesian Orchestrator
The central Filter Bayesian that processes initial inputs and manages probabilistic routing. It estimates domain probabilities, directs information to appropriate Knowledge Nodes, and triggers creation of new nodes when evidence supports novel subdomains.
Autonomous Knowledge Nodes
Specialized local modules containing PEFT (LoRA) Adapters and Local Filters for domain-specific adaptation. Each node processes inputs through efficient parameter tuning and local belief updates, generating outputs including ID, centroid, mass, and variance.
Transformer Base
The foundational language model that provides core encoding capabilities. Interacts with the KN Repository to access and integrate knowledge from specialized nodes, serving as the primary computational engine for natural language understanding.
KN Repository
Central storage for Knowledge Node statistics and outputs. Maintains ID, centroid, mass, variance, and PEFT weights for all nodes, enabling efficient retrieval and integration by the Transformer Base component.
Processing Line
Manages the creation of new Knowledge Nodes when evidence supports novel subdomains. Initializes parameters from global priors and ensures seamless integration with existing architecture.
Post-processing
Includes Quality Evaluator and Feedback Dispatcher for system optimization. Evaluates output quality and dispatches feedback loops to update mass and variance in the Orchestrator, ensuring continuous improvement.
Operational Control Layer
The system's engine that orchestrates the complete processing pipeline. Coordinates intelligent routing, ensures full traceability, and enables modular scaling while preserving architectural stability through continuous feedback loops.
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
Create visualizations, improve UI/UX, design architecture diagrams, or enhance user experience.
Research
Contribute to research papers, test hypotheses, analyze results, or propose new approaches.
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.