ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）

chapter5/paper-to-ppt/validation/runs/exp5-4-real-pdf-both-20260730-v9/dual_round1_review.json

@@ -0,0 +1,30 @@
+{
+  "overall_score": 95,
+  "pass": true,
+  "issues": [
+    {
+      "page": 13,
+      "issue_type": "overcrowded",
+      "severity": "low",
+      "suggestion": "将第二点硬件信息拆分为两行，将第三点优化器参数单独一行显示，减少单行文字长度"
+    },
+    {
+      "page": 14,
+      "issue_type": "overcrowded",
+      "severity": "low",
+      "suggestion": "将第一点BLEU分数说明拆分为两行，将第三点训练成本比较单独一行显示"
+    },
+    {
+      "page": 15,
+      "issue_type": "image_size",
+      "severity": "low",
+      "suggestion": "适当放大图片尺寸，确保文字和连接线清晰可见，可考虑单独一页展示此图"
+    },
+    {
+      "page": 18,
+      "issue_type": "overcrowded",
+      "severity": "low",
+      "suggestion": "将第一点复杂度说明拆分为两行，改为\"O(n²) complexity for sequence length n\"和\"Challenging for very long sequences\""
+    }
+  ]
+}

chapter5/paper-to-ppt/validation/runs/exp5-4-real-pdf-both-20260730-v9/dual_round1_slides.md

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+---
+theme: default
+---
+
+# Attention Is All You Need
+## A Revolutionary Architecture for Sequence Transduction
+Ashish Vaswani et al.  
+NIPS 2017
+
+---
+
+## Background
+- Dominant sequence models rely on RNNs/LSTMs/GRUs
+- Encoder-decoder architectures with auxiliary attention
+- Sequential computation limits parallelization
+- Long-range dependencies are challenging to model
+
+---
+
+## Motivation
+- Recurrent networks: Inherently sequential, poor parallelization
+- Convolutional networks: Limited receptive field, layered dependencies
+- Both: Computation grows with distance between positions
+- Need for architecture with parallelization and global dependencies
+
+---
+
+## Transformer: Key Innovation
+- First sequence transduction model based entirely on attention
+- Dispenses with recurrence and convolutions entirely
+- Significantly more parallelizable than RNN/CNN models
+- Achieves state-of-the-art results with lower training cost
+
+---
+
+## Transformer Architecture (Figure 1)
+
+<img src="/paper_figure_1_transformer.png" style="max-height: 460px; width: 100%; object-fit: contain;" />
+
+Encoder (left) and decoder (right) stacks with self-attention and feed-forward layers.
+
+---
+
+## Encoder Structure
+- Stack of 6 identical layers
+- Each layer: Multi-head self-attention + feed-forward network
+- Residual connections around each sub-layer
+- Layer normalization; output dimension dmodel=512
+
+---
+
+## Decoder Structure
+- Stack of 6 identical layers
+- Three sub-layers: Masked self-attention, encoder-decoder attention, feed-forward
+- Residual connections and layer normalization
+- Masking prevents attending to future positions
+
+---
+
+## Attention Mechanism
+- Maps query (Q) and key-value (K,V) pairs to output
+- Output: Weighted sum of values, weights from query-key compatibility
+- Two primary types: Additive attention and dot-product attention
+- Transformer uses scaled dot-product attention with multi-head extension
+
+---
+
+## Scaled Dot-Product Attention
+- Compute dot products of Q with all K
+- Scale by 1/√dk to prevent softmax gradient vanishing
+- Apply softmax to get weights on values
+- Formula: Attention(Q,K,V) = softmax(QKT/√dk)V
+
+---
+
+## Multi-Head Attention
+- Project Q, K, V h times with learned linear projections
+- Perform attention in parallel on each projection (heads)
+- Concatenate results and project to final output
+- h=8 heads, dk=dv=dmodel/h=64 (total cost similar to single-head)
+
+---
+
+## Self-Attention Advantages
+- Computational complexity: O(n²·d) vs O(n·d²) for RNNs
+- Parallelization: O(1) sequential operations vs O(n) for RNNs
+- Long-range dependencies: Constant path length vs O(n) for RNNs
+- Interpretability: Attention distributions reveal dependency patterns
+
+---
+
+## Positional Encoding
+- Inject sequence order information (no recurrence/convolution)
+- Added to input embeddings (same dimension dmodel=512)
+- Uses sine/cosine functions with varying frequencies
+- Supports learning of relative position relationships
+
+---
+
+## Training Setup
+- Datasets: WMT 2014 EN-DE (4.5M) and EN-FR (36M) sentence pairs
+- Hardware: 8 NVIDIA P100 GPUs; base model trained 12h, big model 3.5 days
+- Optimizer: Adam (β1=0.9, β2=0.98, ϵ=10⁻⁹) with linear warmup learning rate
+- Regularization: Residual dropout (Pdrop=0.1), label smoothing (ϵls=0.1)
+
+---
+
+## Translation Performance
+- EN-DE: 28.4 BLEU (+2+ over previous SOTA including ensembles)
+- EN-FR: 41.8 BLEU (new single-model state-of-the-art)
+- Training cost: Significantly lower than competitors (e.g., 1/4 of prior SOTA)
+- Base model outperforms most previous models at fraction of training time
+
+---
+
+## Long-Distance Attention (Figure 3)
+
+<img src="/paper_figure_3_long_distance.png" style="max-height: 460px; width: 100%; object-fit: contain;" />
+
+Encoder self-attention linking "making" to distant "more difficult" (layer 5).
+
+---
+
+## Anaphora Attention (Figure 4)
+
+<img src="/paper_figure_4_anaphora.png" style="max-height: 460px; width: 100%; object-fit: contain;" />
+
+Attention heads resolving "its" to "Law" and "application" (layer 5).
+
+---
+
+## Generalization: Constituency Parsing
+- 4-layer Transformer with dmodel=1024
+- WSJ only (40K sentences): 91.3 F1 (comparable to SOTA)
+- Semi-supervised (17M sentences): 92.7 F1 (outperforms most prior models)
+- Demonstrates transferability to non-translation tasks
+
+---
+
+## Limitations
+- O(n²) complexity for sequence length n (challenging for very long sequences)
+- Requires explicit positional encoding for sequence order
+- Generation remains auto-regressive (sequential)
+- Less explored for non-text modalities (images, audio, video)
+
+---
+
+## Conclusion
+- Transformer establishes new SOTA in machine translation
+- Significantly faster training due to parallelization
+- Self-attention effectively captures global dependencies
+- Generalizes well to other sequence tasks beyond translation
+
+---
+
+## Future Work
+- Extend to multi-modal inputs/outputs (images, audio, video)
+- Develop local restricted attention for long sequences
+- Reduce sequential constraints in generation process
+- Explore more interpretable attention patterns