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mathematical-verification

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整数量化的严格数学验证:随机舍入、误差反馈、位拆分、精度分配、噪声整形、梯度恒等式、网络属性,NumPy/PyTorch 双库互证,开箱即跑。Independent, reproducible, dual-library (NumPy + PyTorch) verification of integer-quantization mathematics. Clone and run.

  • Updated Sep 11, 2026
  • Python

超度量(p-adic) Attention 的严格数学验证:公共前缀深度得分、树 softmax、层级聚合、离散 ℓ 无梯度 VAPO 学习,NumPy/PyTorch 双库互证。 Rigorous dual-library (NumPy+PyTorch) verification of ultrametric (p-adic) attention: tree softmax, hierarchical aggregation, derivative-free VAPO learning. Clone and run.

  • Updated Sep 12, 2026
  • Python

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