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In the Transformer architecture, the input of each token is processed through multiple layers of self-attention and feed-forward networks, and each layer produces a high-dimensional vector representation called a "hidden state"; the hidden states of all layers and all tokens together constitute the "activation space."
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11/4/2026
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VerifiedEach Transformer layer contains two core sub-modules: multi-head self-attention and a feed-forward neural network.77% similarVerifiedIn the Transformer architecture, the tokens of system instructions are placed at the very front of the attention mechanism, exerting a continuous constraining influence on all subsequently generated content.71% similarUnverifiedThe Transformer architecture was proposed by Vaswani et al. in 2017; the inference computation graph is composed of stacked Decoder layers, each containing multi-head self-attention, a feed-forward network, layer normalization, and residual connections71% similarUnverifiedThe residual stream is the core information channel of the Transformer architecture, where attention heads and MLP modules at each layer interact with the residual stream in a read-process-write-back manner.71% similarUnverified残差连接是 Transformer 的核心创新之一，每一层的输出是输入加上该层的变换结果70% similar
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