CellularFlow Architecture Claims 83.9% Domain Retention by Replacing Transformer FFN Layers

A researcher named Celcilin C S has proposed CellularFlow, an alternative neural network architecture designed to address catastrophic forgetting in large language models. The core problem it targets is the tendency of models like LLaMA, Mistral, and GPT-4 to lose previously learned domain knowledge when sequentially trained on new subjects. CellularFlow replaces the standard dense feed-forward network layers in Transformers with addressable, multi-head associative memory banks that can be selectively updated without overwriting existing knowledge. The architecture reportedly achieves zero-backpropagation streaming learning and retains 83.9% of domain-specific knowledge across sequential training tasks. To prevent routing failures in sparse memory access, the system injects small Gaussian noise during training to ensure all memory slots receive gradient updates.