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ai-agent-book/chapter2/agent-skills-ppt/runs/exp2-6-kimi-pptx-20260731-v1/workspace/slides/02-problem.html
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ai-agent-book 精选快照(<2MB 代码与文档,来自 github.com/bojieli/ai-agent-book)
2026-08-20 13:12:50 +00:00

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<!DOCTYPE html>
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<p class="kicker">01 &middot; BACKGROUND</p>
<h1>The Problem: The Limits of Recurrence</h1>
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<li>Sequence transduction (e.g., machine translation) was dominated by <b>recurrent</b> (LSTM / GRU) and <b>convolutional</b> encoder&ndash;decoder models.</li>
<li>RNNs compute hidden states <b>one position at a time</b>: inherently sequential, so training cannot parallelize across positions in a sequence.</li>
<li>Signals between distant positions must travel a path of <b>O(n) operations</b> &mdash; the longer the path, the harder long-range dependencies are to learn.</li>
<li>Attention mechanisms already existed, but only as an <b>accessory</b> layered on top of RNNs or CNNs.</li>
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<p class="bignum">O(n)</p>
<p class="statlab">sequential operations per layer in a recurrent network</p>
<p class="bignum">O(n)</p>
<p class="statlab">maximum path length between any two positions</p>
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<p class="foot">Source: Vaswani et al., &ldquo;Attention Is All You Need&rdquo;, &sect;1 Introduction (paper p. 1&ndash;2).</p>
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