Neural Consolidation Process: Selective Tagging and Validated Capture for Continual Learning

Continual learning in deep neural networks is severely bottlenecked by catastrophic forgetting. While the human brain elegantly resolves this stability-plasticity dilemma through systems memory consolidation, i.e., gradually transferring short-term memory into stable neocortical traces, mimicking this in artificial networks without destructive overwriting remains an open challenge. In this paper, we propose **Neural Consolidation Process (NeuralCP)**, a novel continual learning framework inspired by the biological Synaptic Tagging and Capture (STC) mechanism. NeuralCP models short-term memory using a lightweight LoRA module to rapidly encode task-specific knowledge. Instead of accumulating task-specific adapters or relying on rigid parameter-space regularization, NeuralCP employs function-space attribution to explicitly tag modules responsible for critical representation shifts. We then introduce a replay-validated capture mechanism, utilizing a minimal calibration set strictly to filter for highly beneficial and consistently recallable memory tags. Finally, these validated tags are consolidated into the backbone via residual distillation within a stable-plastic subspace decomposition, dedicatedly protecting previously consolidated knowledge while maintaining future adaptability. Crucially, NeuralCP introduces zero additional parameters or overhead at inference. Extensive evaluations across eight sequential tasks demonstrate that NeuralCP promotes average continual learning scores by **22.72, 7.39, and 8.05** points on Qwen3-0.6B, 4B, and 8B respectively, setting a new state-of-the-art. Besides, NeuralCP exhibits consistent improvements and scalability on visual Transformers.