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Exploring Brain-like Dragon Hatchling (BDH) — a new AI model that learns on the fly, adapts like a brain, and challenges the transformer era.
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This article demystifies the Brain-like Dragon Hatchling (BDH), a neural architecture that keeps learning during inference using Hebbian “fast memory” while retaining pre-trained “slow” weights. BDH aims for interpretable reasoning, stable long-range behavior, modular model merging without catastrophic forgetting, and efficiency suited to GPUs and neuromorphic chips. A minimal Rust+tch proof-of-concept (XOR) illustrates the mechanics and why σ (fast memory) shines on sequence/context tasks, pointing toward practical lifelong learning systems.