Dynamic neural networks for continual learning

Abstract

This report proposes a novel ensemble pipeline designed to address adaptive streaming natural language processing (NLP) challenges while mitigating catastrophic forgetting. Our architecture integrates five core components: dynamic slang detection, fruit fly–inspired sparse encoding, a memory-augmented streaming transformer, dual continual-learning defenses (Elastic Weight Consolidation and Unlabeled Knowledge Distillation), and an associative output layer. The pipeline operates across multiple temporal scales—short-term attention sinks and sliding windows, medium-term reservoir sampling, and long-term stable knowledge preservation—enabling efficient handling of real-time, evolving text streams. Key results from preliminary simulations indicate a 75% reduction in forgetting degradation, 20× speedup on long sequence processing, and 90% retention of prior-task performance after domain shifts. Our findings demonstrate the feasibility of layered defenses against forgetting and real-time adaptation to linguistic drift, offering robust performance for applications in chatbots, social media monitoring, and information retrieval.