🤖 AI Summary
This work addresses the dual challenges of catastrophic forgetting and data privacy in sequential learning with artificial neural networks by proposing a silicon-native modular architecture. The approach enforces parameter isolation through task-specific expert modules and an outlier-based distributed gating mechanism, while leveraging tight-bottleneck autoencoders to establish strict topological boundaries in high-dimensional embedding spaces, enabling unsupervised novelty detection and stable replay of known manifolds. During training, teacher learning, student distillation, and routing manifold acquisition are executed in parallel, and raw input data is discarded immediately after use, guaranteeing zero privacy leakage. Experiments demonstrate that the framework effectively mitigates forgetting in both computer vision and natural language processing tasks, preserving strong memory retention without compromising student fidelity, while fully complying with privacy regulations such as GDPR.
📝 Abstract
Catastrophic forgetting remains a primary hurdle in sequential task learning for artificial neural networks. We propose a silicon-native modular architecture that achieves structural parameter isolation using Task-Specific Experts and a distributed, outlier-based Gatekeeper. Moving beyond traditional sequential consolidation, our framework utilizes a Simultaneous Pipeline where Teacher learning, Student distillation, and Router manifold acquisition occur in parallel while raw data is present in a localized training session. This approach ensures computational efficiency and complies with privacy mandates like GDPR by deleting raw data as soon as a task is learned. We demonstrate that a Tight-Bottleneck Autoencoder (TB-AE) can effectively distinguish semantically crowded manifolds in high-dimensional latent spaces, overcoming the posterior collapse inherent to standard variational methods. By establishing strict topological boundaries, our TB-AE resolves latent space crowding in 4096-D LLM embeddings to provide a robust, unsupervised novelty signal. Furthermore, we validate an Autonomous Retrieval mechanism that confidently identifies returning manifolds, enabling stable lifelong learning without redundant module instantiation. Empirical results demonstrate that our ``Live Distillation'' approach acts as a natural regularizer, achieving strong retention across computer vision and natural language processing domains without suffering a student fidelity gap.