龙雏：Transformer 与大脑模型之间的缺失环节

Dragon Hatchling introduces a novel language model architecture designed to emulate the functional characteristics of a small brain. This model employs a brain-like network where numerous small components interact exclusively with their immediate neighbors, collectively facilitating memory and reasoning processes. Memory in Dragon Hatchling is distributed across the connections between these components, termed synaptic memory, rather than being confined to a single, centralized unit. This distributed memory paradigm significantly enhances the model's interpretability. Capable of rapid execution on graphics cards, Dragon Hatchling demonstrates performance comparable to larger models on common language tasks. A key feature is its localized activation, where specific parts of the model become active for individual concepts, making semantic identification straightforward. This provides robust interpretability, a highly valued attribute for researchers, enabling a clearer understanding of the model's decision-making rationale. The design offers a promising bridge between current Transformer architectures and biological brain models, fostering advancements in both efficiency and transparency within artificial intelligence.