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On September 8, 2026, the AI company Pathway launched its latest large-model architecture – BDH (Dragon Hatchling) – on the Amazon SageMaker HyperPod platform. This architecture abandoned the traditional chain-of-thinking reasoning model and adopted a brain-inspired sparse local interactive neuron graph structure. By using potential space for iterative calculations to update internal memories and decode candidate answers, BDH can handle new problems without generating intermediate text traces or fine-tuning. This design effectively overcomes the challenges associated with traditional Transformer models, such as low推理 efficiency and poor long-term consistency due to fixed context windows, catastrophic forgetting, and high computational costs, while maintaining state adaptation without relying on weight updates during testing.
Pathway 推出基于大脑启发的 BDH（Dragon Hatchling）架构，利用 Amazon SageMaker HyperPod 扩展训练规模。该架构摒弃链式思维推理模式，通过在潜在空间进行迭代计算来更新内部记忆并解码候选答案，无需生成中间文本痕迹或微调即可处理新问题。BDH 采用稀疏局部交互的神经元图结构，在上下文适应中保持状态而不依赖测试时权重更新，有效克服了传统 Transformer 模型因固定上下文窗口、灾难性遗忘及高计算成本导致的推理效率与长期一致性难题。