Unverified50% confidenceFactExact time
A forward pass is the process of a neural network computing once completely from the input layer to the output layer; an autoregressive language model needs N forward passes to generate N tokens, with latency growing linearly with output length; a classifier-style model needs only one forward pass.
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10/13/2026
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VerifiedThe forward propagation of a neural network is essentially matrix multiplication.75% similarUnverified输出生成是严格串行的自回归过程，每生成一个token都需要一次完整的模型前向传播74% similarUnverified前向传播（forward pass）描述数据从输入层经过每一层网络变换最终输出预测结果的完整流程74% similarUnverifiedA single forward pass means the model only needs to compute the network weights in full once rather than looping autoregressively token by token, giving it a structural advantage in latency and computational resource consumption.72% similarUnverified用纯 NumPy 从零实现神经网络需要手动实现前向传播、反向传播、梯度计算和参数更新，是理解神经网络底层原理的有效方法72% similar
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