Mitigating Catastrophic Forgetting
in Continual Learning
ExperimentalAn atomic geometric framework for structural decoupling
of domain knowledge via Bayesian physics.
What is Xctopus?
α Alpha[Research Framework in Alpha] Xctopus is an atomic geometric framework designed to decouple specialized clinical knowledge from core processing engines. It evolves through an expanding manifold of Knowledge Nodes, utilizing Bayesian physics to accurately map and resolve complex genomic evidence without catastrophic forgetting.
Core System Architecture
Xctopus is structured into fundamental components that work in synergy to enable continuous learning:
Anchor Topology Orchestrator
The central probabilistic filter that maps high-dimensional inputs to specific domains within the Anchor Topology. It manages manifold latent routing and triggers the creation of the next generation of Atomic Knowledge Nodes.
Autonomous Knowledge Nodes
Specialized atomic units representing domain-specific expertise. Each node is defined by its Geometric Metrics (Mass and Variance) and evolves via Incremental Atomic Learning, preserving architectural stability without catastrophic forgetting.
Structural Decoupling
A foundational strategy where the core Transformer serves as a general-purpose processor, decoupled from specialized knowledge. This allow for modular expert integration via the Geometric Engine without re-training the base model.
Geometric Repository
Distributed atomic store maintaining the state of the Geometric Manifold. It persists the Centroids, Mass, and Variance metrics required for Bayesian inference and high-fidelity interactive visualization.
V2.1 Physics Engine
Orchestrates the physical distribution of nodes using specificity tidal forces and radial repulsion. It ensures a collision-free and semantically coherent latent space during Manifold Expansion.
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.