reports/paper_writing_guide.md

# CS3319 论文写作辅助文档(Paper Writing Guide)

> 本文档为论文撰写的"素材库 + 脚手架"。所有数字均来自项目真实实验产物(各 `validation_summary.csv` / `*_ablation.csv` / `error_analysis_buckets.csv` / `dynamic_summary.csv` / `notes/experiment_history.md` / 图结构统计),可直接核对、直接复制进论文。验证集 F1 均基于同一 `split_seed=202` 划分,严格可比。

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## 0. 一页速览(TL;DR)

| 项 | 内容 |
|---|---|
| **任务** | 学术异构图上的作者-论文链接预测(reading recommendation),F1 评估 |
| **数据** | 6,611 作者 / 79,937 论文;合著边 9,663 / 引用边 327,113 / 训练阅读边 682,421 / 测试对 2,047,262;训练图密度仅 0.129% |
| **最终方法** | 两阶段堆叠:Stage1 多个嵌入/分数源(LightGCN、BPR-MF、DeepWalk、Node2Vec)→ 259 维特征工程 → Stage2 LightGBM 二级学习器 → rank-cutoff 决策 |
| **最终成绩** | 验证 F1 = **0.9669**,AUC = 0.9949;公开 LB F1 = **0.9663** |
| **核心叙事** | 方法演进"三级跳":0.939(单 GNN)→ 0.956(特征堆叠)→ 0.967(高阶引用传播) |
| **三大卖点** | ① 高阶有向引用传播(`A-P-P^k`、`A-A-P-P^k`,fwd/bwd/undir);② 多源异构特征 + 二级 LightGBM 堆叠;③ rank-cutoff 决策规避验证-测试分布漂移 |

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## 1. 论文定位与核心卖点

### 1.1 定位
这是一个**课程项目向正式论文靠拢**的工作(参考 `data_and_docs/advice.md` 的 reviewer 反馈)。建议定位为 **"基于异构图特征工程与高阶传播的学术阅读推荐"**,强调:
- 不是"盲目堆复杂模型",而是**根据任务特点(稀疏 + 冷启动)选择 LightGCN + 显式高阶传播**;
- 方法**可解释、可复现、CPU 即可跑**。

### 1.2 三个核心卖点(每个都要在论文里给证据)

| 卖点 | 证据 | 出处 |
|---|---|---|
| **高阶引用传播带来第三级突破** | `+high-order directed` 使 F1 从 0.9650 → 0.9669(+0.0019),n_features 190→259 | `high_order_graph_stack/validation_summary.csv` |
| **特征堆叠远胜单一 GNN** | LightGCN 单模型 0.9386 → 堆叠 0.9559(+0.0173,最大单步增益) | `dynamic_summary.csv` + `post95_ablation/ablation_table.csv` |
| **rank-cutoff 决策更稳健** | 验证集 1:1 人工划分导致概率阈值在测试上漂移到 0.52;rank-cutoff 在 ratio∈[0.498,0.502] 上 F1 几乎不变 | `stack_ratio_analysis.csv` + `threshold_submission_summary.csv` |

### 1.3 一句话主旨(可放 Introduction 末尾)
> *We show that, on a sparse academic reading-recommendation graph, **higher-order directed citation propagation** combined with a **multi-source feature stacker** yields large gains over single-graph neural models, and that a **rank-based decision rule** is markedly more robust than probability thresholding under the validation–test distribution shift.*

---

## 2. 建议的论文结构与逐章写作指南

### Abstract(模板,中英可填)
> Link prediction on heterogeneous academic graphs underpins reading-recommendation systems, yet suffers from severe sparsity and cold start. We study the author–paper recommendation task on a GeoScience academic network (6,611 authors, 79,937 papers). We propose a two-stage framework: (1) multiple embedding/score producers — LightGCN, BPR-MF, DeepWalk, and Node2Vec — and (2) a LightGBM second-stage stacker over **259 engineered features**, including **higher-order directed citation propagation** along `Author-Paper-Paper^k` and `Author-Author-Paper-Paper^k` meta-paths. On a held-out validation split our method reaches **F1 = 0.9669 (AUC = 0.9949)**, improving over the best single LightGCN (0.9386) by **+2.8 points**, and over the content/feature stacker (0.9559) by **+1.1 points**; the public-leaderboard F1 is **0.9663**. We further show that a **rank-cutoff decision rule** is more stable than probability thresholding under distribution shift.

### 1 Introduction
**要讲清四件事(呼应 advice.md):**
1. **问题定义清晰**:`author-paper link prediction`,不是普通分类——这是 reviewer 最看重的点。给出图怎么建、节点是什么、边是什么、label 是什么、prediction target 是什么。
2. **冷启动是 GNN 在推荐中的核心价值**:图结构能传播稀疏节点信息(56.4% 的作者只有 1 个合著者;12,042 篇论文从未被读、16,074 篇从未被引)。
3. **动机**:单 GNN 受限于稀疏与未校准概率;我们用**高阶传播 + 堆叠 + rank 决策**三管齐下。
4. **贡献(contributions,用 bullet)**:
   - 设计有向/无向高阶引用传播特征族(24+45 维);
   - 系统集成 7 个随机游走配置 + LightGCN/BPR-MF/内容特征 为 259 维二级特征;
   - 揭示验证-测试分布漂移,提出 rank-cutoff 决策;
   - 充分的消融、误差分析与跨种子稳定性。

### 2 Related Work
建议三层 baseline 框架(直接采用 advice.md 的建议):
- **Level 1 启发式**:popularity、common neighbor、Adamic-Adar(在 author-paper 二部图上);
- **Level 2 传统图嵌入**:BPR / implicit MF、DeepWalk、Node2Vec;
- **Level 3 GNN**:LightGCN、(Hetero)GNN。
本工作 = **Level 2/3 多源融合 + 高阶 meta-path 特征**,位于 Level 3 之上。需补一句 **meta-path 经典性**:`Author→Author→Paper`、`Author→Paper→Paper` 是异构图推荐的关键词。

### 3 Problem Formulation & Dataset
- **形式化**:异构图 $G=(V,E)$,$V=A\cup P$(作者、论文),边类型 $\{$reads, co-author, cites$\}$。给定测试对集合 $\mathcal{T}\subseteq A\times P$,预测 $\hat y\in\{0,1\}$。
- **数据集统计表(见 §3.1,可直接放论文 Table 1)**。
- **评估**:F1;公开榜基于 50% 测试集。
- ⚠️ **必须写一句防 label leakage**(advice.md 强调):
  > *"We carefully avoid information leakage: citation/co-authorship edges are built only from the provided static networks, and validation edges are held out of the LightGCN message-passing graph."*

### 4 Method(论文核心章)
按四小节组织,**每节给可复述的素材**:

**(a) Stage-1 Score Producers**
- LightGCN:异构 4 层,边类型 `(author,ref,paper)/(paper,beref,author)/(paper,cite,paper)/(author,coauthor,author)`;作者用可训练 embedding,论文投影 `feature.pkl`(512 维 USE)→ embed;层输出加权求和;BPR 损失 + **硬负采样**(random 50% / popular 25% / co-author pool 25%)。最佳配置 L=2, d=512, BPR。
- BPR-MF:`Embedding(6611,256)×Embedding(79937,256)+bias`,AdamW,220 epoch。
- DeepWalk / Node2Vec:7 个配置(见 §4.3),每个生成 11 维 pair 特征。

**(b) Feature Engineering(259 维,见 §4.2 完整构成表)**
- 显式图/meta-path 特征(18 维:`aap`/`app`/`apap` 计数与比率、Jaccard、度等);
- 高阶引用传播(见下,**创新点**);
- content-rich(18 维)、content mean-cos、top-k 相似度、负证据、变体分数。

**(c) Higher-Order Directed Citation Propagation(创新点,单独一小节)**
- 在行归一化邻接矩阵上做稀疏传播 + top-k 剪枝(k=1500):
  - `A-P-P^k`(k=1..4):作者经由阅读论文沿引用图传播;
  - `A-A-P-P^k`(k=0..3):先经合著聚合再传播;
  - 三方向:**forward / backward / undirected** 引用;
- 每跳产出 **raw / popularity-normalized(除以 `log1p(paper_ref_deg+cite_deg)`)/ log / delta** 变体。
- 论证:**冷启动作者**(阅读历史少)可通过合著者与引用社区获得信号——这正是 GNN 在稀疏推荐中的价值。

**(d) Stage-2 LightGBM Stacker & Decision Rule**
- 5 折 StratifiedKFold 出 OOF 分数用于无泄漏评估;
- 最终超参:`num_leaves=15, reg_lambda=8.0, min_child_samples=100, n_estimators=1400, lr=0.022`(强正则,防过拟合);
- **决策规则(关键)**:按最终分数排序,预测 top-50% 为正,并**强制训练-测试重叠的已知正例为 1**(占测试集 25.6%)。

### 5 Experiments
- **5.1 Setup**:split_seed=202,train_frac=0.9 的"notebook-style"划分(10% 训练边作验证正例 + 等量随机负例,1:1)。说明这是**人工 1:1 划分**,故阈值不直接迁移到测试。
- **5.2 Main Results / Progression**(Table 2,见 §3.2)——**最重要的表**。
- **5.3 Ablation**(Table 3,见 §3.3):逐步加入特征族的增益。
- **5.4 Architecture Sensitivity**(Table 4 / 图):LightGCN dim/layers/loss。
- **5.5 Cross-seed Stability**:F1 方差 < 0.001。

### 6 Analysis & Discussion
- **6.1 Error Analysis by Group**(见 §3.4):`has_local_evidence=0` 的 F1 仅 0.573 vs =1 的 0.969——**冷启动/无局部证据是主要误差源**;按度分桶的低度节点性能下降。
- **6.2 Calibration & Decision Robustness**:校准曲线 + ratio-F1 曲线,论证 rank-cutoff。
- **6.3 Feature Importance**(若导出):高阶传播族贡献占比。
- **6.4 Limitations**:数据匿名无领域标签;验证集人工 1:1 与真实正例比例不符;`rank top-50%` 的假设。

### 7 Conclusion
回扣三级跳 + 三个贡献 + 未来工作(可加领域标签/图注意力/校准方法如 Platt/Isotonic)。

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## 3. 核心数据表格(可直接复制进论文)

### 3.1 Table 1 — Dataset Statistics
| Entity / Relation | Count | Note |
|---|---:|---|
| Authors | 6,611 | — |
| Papers | 79,937 | 512-d USE features |
| Co-authorship edges | 9,663 | undirected |
| Citation edges | 327,113 | directed |
| Train read edges (author–paper) | 682,421 | positives |
| Test pairs | 2,047,262 | to predict |
| Bipartite density | 1.29×10⁻³ | 0.129% |
| Co-author connected components | 1,508 | largest = 873 |
| Authors with degree = 1 | 56.4% | severe sparsity |
| Papers never read | 12,042 | cold-start items |
| Papers never cited | 16,074 | — |

**度分布(用于 log-log 图)**:
- 合著度:median=1, P90=6, P99=28, max=88
- 论文被读度:median=4, mean=10.05, P99=103, max=2,582
- 论文被引(入)度:median=3, mean=5.12, P99=34, max=777
- 作者阅读数(训练):median=74, mean=103.4, max=5,727

### 3.2 Table 2 — Performance Progression(主角表 ★)
| # | Method | Val F1 | ΔF1 | #Feat | Public LB F1 |
|---|---|---:|---:|---:|---:|
| 0 | Official notebook baseline (HeteroMeanConv+hinge) | ~0.885 | — | — | — |
| 1 | LightGCN single (L2, d512, BPR, best) | 0.9386 | — | — | 0.9304 (6-model ens) |
| 2 | + explicit graph/meta-path + rank (stacking baseline) | 0.9560 | +0.0174 | 22 | — |
| 3 | + neg-evidence + top-k + variant scores | 0.9571 | +0.0011 | 76 | 0.9576 |
| 4 | + content mean-cos | 0.9576 | +0.0005 | 80 | — |
| 5 | + BPR-MF | 0.9593 | +0.0017 | 84 | 0.9600 |
| 6 | + rich content (18-d) | 0.9599 | +0.0006 | 102 | — |
| 7 | + DeepWalk + Node2Vec | 0.9621 | +0.0022 | 100 | 0.9625 |
| 8 | + 7-block random-walk ensemble | 0.9649 | +0.0028 | 190 | — |
| 9 | + high-order propagation (undirected) | 0.9666 | +0.0017 | 214 | — |
| **10** | **+ high-order directed (FINAL)** | **0.9669** | **+0.0003** | **259** | **0.9663** |

> 三级跳标注:**① +0.0173**(行 1→2,堆叠)**② +0.0030**(行 6→8,随机游走)**③ +0.0020**(行 8→10,高阶传播)。

### 3.3 Table 3 — Feature-Group Ablation(同 split,逐步增益)
| Stage | Val F1 | n_features | Source CSV |
|---|---:|---:|---|
| baseline_stacking | 0.9560 | 22 | `post95_ablation/ablation_table.csv` |
| + ensemble_lgcn_score_features | 0.9571 | 76 | 同上 |
| + content_mean_cos | 0.9576 | 80 | `extra_score_sources/extra_score_ablation.csv` |
| + bpr_mf | 0.9593 | 84 | 同上 |
| + rich_content_features | 0.9599 | 102 | `content_rich/content_rich_ablation.csv` |
| + node2vec | 0.9621 | 100 | `node2vec_deepwalk/node2vec_deepwalk_ablation.csv` |
| + deepwalk | 0.9618 | 92 | 同上 |
| rwens_7model | 0.9649 | 172 | `randomwalk_systematic/ensemble_7_ablation.csv` |

### 3.4 Table 4 — High-Order Propagation Ablation(创新点消融 ★)
| Stage | Val F1 | AUC | Precision | Recall | n_features |
|---|---:|---:|---:|---:|---:|
| base_highorder | 0.9643 | 0.9941 | 0.9654 | 0.9632 | 108 |
| rich_rw7 | 0.9650 | 0.9946 | 0.9664 | 0.9635 | 190 |
| rich_rw7_highorder | 0.9666 | 0.9949 | 0.9671 | 0.9660 | 214 |
| **rich_rw7_highorder_directed** | **0.9669** | **0.9949** | 0.9667 | 0.9670 | **259** |

### 3.5 Table 5 — Error Analysis by Group(支撑 Discussion)
| Group | Bucket | F1 | Insight |
|---|---|---:|---|
| has_local_evidence | 0 | 0.573 | 无局部证据 = 主要误差源 |
| has_local_evidence | 1 | 0.969 | 有局部证据几乎全对 |
| author_degree | [-inf,1) | 0.500 | 冷启动作者极差 |
| author_degree | [1,3) | 0.667 | 低度作者差 |
| author_degree | [50,inf) | 高(见 CSV) | 高度作者好 |
| LightGCN_score | [0.51,1.27] | 0.275 | 中间分数段严重误判(校准差) |

### 3.6 Table 6 — Decision Rule Robustness(支撑 rank-cutoff 论点)
| Rule | Positive ratio on test | Stability |
|---|---:|---|
| probability threshold th=0.455 | 0.5250 | 漂移到 52.5% |
| probability threshold th=0.500 | 0.5199 | 仍漂移 |
| **rank-cutoff ratio=0.500** | **0.5000** | **稳定 50%** |

> ratio∈[0.498, 0.502] 时验证 F1 几乎不变(0.9554–0.9559,stacking 阶段;final 阶段全 0.9669)。

### 3.7 LightGCN 架构敏感性(支撑 5.4)
- 最佳:L=2, d=512, BPR → 0.9386;L=3,d=512 → 0.9381(接近)。
- **损失函数**:BPR(0.9386)≫ BCE(0.8612);hinge 居中。**BPR 显著优于 BCE** 是一个干净结论。
- 跨 seed(201–204)F1 ∈ [0.9365, 0.9386],方差 < 0.001 → **稳定性强**。

---

## 4. 方法描述素材(可直接改写进 Method 章)

### 4.1 LightGCN(Stage-1 主力)
> LightGCN adapted to the heterogeneous academic graph. Author nodes use learnable embeddings; paper nodes project the 512-d USE features via a linear layer. We stack `L` propagation layers that aggregate over four edge types — `(author, ref, paper)`, `(paper, beref, author)`, `(paper, cite, paper)`, `(author, coauthor, author)` — each followed by symmetric-degree normalization. The final representation is the uniform (or learnable-weighted) mean over all layers, following LightGCN. The model is trained with the **BPR ranking loss** and **hard negative sampling**: 50% random negatives, 25% sampled from popular papers (top-30% by read-degree), and 25% from each author's co-author paper pool — explicitly targeting confusable items in the same citation/co-author community.

### 4.2 259 维特征完整构成(放 Method 或附录)
| 组 | 维数 | 说明 |
|---|---:|---|
| LightGCN rank | 4 | score / global-rank / author-pct / author-rank |
| Explicit graph & meta-path | 18 | `aap`/`app`/`apap` 计数与比率、Jaccard(ref/cited-by)、度等 |
| Negative evidence | 8 | 反向证据特征 |
| Top-k content similarity | 3 | top-1/3/5 USE cosine |
| LightGCN variant scores | 43 | 20 个变体×(z,rank)+3 聚合 |
| content mean-cos | 4 | raw/z/rank/author-rank |
| BPR-MF | 4 | raw/z/rank/author-rank |
| **X_base 小计** | **84** | |
| Rich content | 18 | center-cos、top-k、frac>0.5/0.7、local-pct 等 |
| 7× RW blocks | 77 | 每块 11:dot/cos/hadamard/absdiff/l2 + 全局/作者 rank & pct |
| RW aggregate | 11 | 7 块的 mean/std/max/min + agreement |
| High-order undirected | 24 | `A-P-P^k`,`A-A-P-P^k` × {raw,popnorm,log} |
| High-order directed | 45 | fwd/bwd/undir × {raw,popnorm,delta} |
| **总计** | **259** | |

### 4.3 七个随机游走配置(放 Method/附录)
| Version | Graph | Method | dim | walk_len |
|---|---|---|---:|---:|
| dw_base_d128_l40 | full | DeepWalk | 128 | 40 |
| dw_long_d128_l80 | full | DeepWalk | 128 | 80 |
| dw_highdim_d256_l40 | full | DeepWalk | 256 | 40 |
| dw_d256_l80 | full | DeepWalk | 256 | 80 |
| dw_seed3407_d128_l40 | full | DeepWalk | 128 | 40 |
| dw_graph_ap_pp | AP+PP | DeepWalk | 128 | 40 |
| n2v_p2_q1_d128_l40 | full | Node2Vec(p=2,q=1) | 128 | 40 |

> RW ensemble 规模实验:1→5→7 模型,F1 0.9627→0.9639→0.9649,**多样性带来稳定增益**。

### 4.4 高阶传播(创新点,精炼描述)
> We construct higher-order citation-propagation features by iterative sparse matrix multiplication on row-normalized adjacency matrices with top-`k` row pruning (`k=1500`). Two meta-path families are used: `A-P-P^k` (an author's read papers propagated `k` hops along the citation graph, `k=1..4`) and `A-A-P-P^k` (first aggregated through co-authors, then propagated, `k=0..3`). To disentangle citation directionality, we build **forward / backward / undirected** variants. For each hop we emit raw, popularity-normalized, log-transformed, and inter-hop delta signals, yielding 24 (undirected) + 45 (directed) = 69 features. These features let **cold-start authors** inherit signal from their co-author and citation communities — the core value of graph structure in sparse recommendation.

### 4.5 决策规则(精炼描述)
> Because the validation split is an artificial 1:1 positive/negative construction, the LightGBM probabilities are **not calibrated** to the test distribution: a probability threshold tuned on validation drifts the test positive rate to ≈0.52. We therefore adopt a **rank-cutoff decision rule**: sort test pairs by final score, predict the top-50% as positive, and force the 25.6% of test pairs that overlap the training graph to positive. This is markedly more stable — F1 is flat across ratio ∈ [0.498, 0.502].

---

## 5. 图表规划清单(每张图的位置/目的/caption)

| 图 | 章节 | 目的 | Caption 模板 |
|---|---|---|---|
| Fig 1 Dataset overview | §3 | 稀疏/冷启动 | "Degree distributions (log-log) and graph statistics of the academic network; 56% of authors have a single co-author." |
| Fig 2 Framework | §4 | 方法总览 | "Two-stage stacking framework: score producers → 259-d feature engineering → LightGBM → rank-cutoff decision." |
| **Fig 3 Progression** ★ | §5.2 | 三级跳 | "Validation F1 progression. Three breakthroughs: +0.017 (stacking), +0.003 (random walks), +0.002 (higher-order propagation)." |
| Fig 4 High-order ablation | §5.3 | 创新点 | "Ablation of higher-order directed citation propagation; adding directed propagation yields the best F1 (0.9669) at 259 features." |
| Fig 5 PR/ROC | §5.2 | 判别力 | "Precision-Recall and ROC curves across model stages; AUC rises from 0.984 to 0.995." |
| Fig 6 Feature importance | §6.3 | 可解释 | "Grouped feature importance; higher-order propagation and random-walk blocks dominate." |
| Fig 7 Error & robustness | §6 | 冷启动+决策 | "(a) F1 by author/paper degree (cold-start drops); (b) calibration; (c) rank-cutoff vs probability-threshold stability." |

> 附录图:USE embedding UMAP(度数着色)、14-LightGCN 相关性热图、超参热图、RW ensemble size vs F1。

---

## 6. Research Questions 与实验对应(advice.md 建议加 RQ)

| RQ | 问题 | 对应实验/证据 |
|---|---|---|
| **RQ1** | 不同边类型/传播阶对推荐性能的贡献? | 高阶消融(Table 4):`A-P-P^k` vs `A-A-P-P^k`;fwd/bwd/undir |
| **RQ2** | 冷启动作者能否从图结构获益? | 误差分桶(Table 5):低度作者靠 `has_local_evidence` 与合著传播 |
| **RQ3** | 硬负采样 vs 随机负采样? | LightGCN 训练采用混合硬负采样(random/popular/coauthor 50/25/25) |
| **RQ4** | 概率阈值 vs rank 决策的稳健性? | Table 6 + ratio-F1 曲线 |
| **RQ5** | 多源特征堆叠 vs 单 GNN? | Table 2 主结果 |

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## 7. 写作注意事项(reviewer 视角)

1. **防 label leakage**——必须明确写出验证边不进入 LightGCN 消息传递图,引用/合著网络是静态的(advice.md 重点)。
2. **评估协议**——说明验证集是人工 1:1;F1 对阈值敏感,故补充 Precision/Recall/AUC(advice.md 建议)。
3. **不要只写"用了更高级模型"**——强调"根据任务特点选模型"(LightGCN 比 HGT 在小数据上更稳,呼应 advice.md)。
4. **meta-path 明确化**——把 `A-A-P`、`A-P-P` 写成正式 meta-path 术语,是异构图推荐的关键词。
5. **诚实标注局限**——数据匿名无领域标签;t`op-50%` 是基于榜榜反馈的假设;未来可加校准(Platt/Isotonic)与图注意力。
6. **复现性**——所有结果 CPU 可复现,代码与缓存随包提供;F1 跨种子方差 <0.001。
7. **数字一致性**——论文里所有 F1 必须与 `validation_summary.csv` / `dynamic_summary.csv` 一致;最终 0.9669(val)/0.9663(LB)。

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## 8. 中英术语对照

| 中文 | 英文(论文用) |
|---|---|
| 作者-论文链接预测 | author–paper link prediction |
| 异构图 | heterogeneous graph |
| 合著网络 | co-authorship network |
| 引用网络 | citation network |
| 二部图 | bipartite graph |
| 冷启动 | cold start |
| 矩阵分解 | matrix factorization (MF) |
| 随机游走 | random walk (DeepWalk / Node2Vec) |
| 高阶传播 | higher-order propagation |
| 元路径 | meta-path |
| 堆叠/二级学习器 | stacking / second-stage (meta) learner |
| 负采样 | negative sampling (hard negatives) |
| 秩截断决策 | rank-cutoff decision rule |
| 校准 | calibration |
| 分布漂移 | distribution shift |
| 消融 | ablation (study) |
| 特征重要性 | feature importance |

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## 附录 A:关键文件速查(写论文/核对数据用)

| 用途 | 文件 |
|---|---|
| 最终成绩 | `validation_runs/dynamic_seed202/high_order_graph_stack/validation_summary.csv` |
| LightGCN 超参搜索 | `validation_runs/dynamic_summary.csv` |
| 堆叠消融 | `validation_runs/dynamic_seed202/post95_ablation/ablation_table.csv` |
| 内容/BPR 消融 | `validation_runs/dynamic_seed202/extra_score_sources/extra_score_ablation.csv` |
| 随机游走消融 | `validation_runs/dynamic_seed202/randomwalk_systematic/{small,graph}_ablation_table.csv`、`ensemble_{5,7}_ablation.csv` |
| 误差分桶 | `validation_runs/dynamic_seed202/error_group_calibration/error_analysis_buckets.csv` |
| ratio-F1 稳健性 | `validation_runs/stack_ratio_analysis.csv` |
| 决策规则漂移 | `validation_runs/dynamic_seed202/high_order_graph_stack/threshold_submission_summary.csv` |
| 实验演进叙事 | `notes/experiment_history.md`、`reports/{preliminary_report,exploration_summary,final_report}.md` |
| 最终提交 | `validation_runs/dynamic_seed202/high_order_graph_stack/submissions/submission_rich_rw7_highorder_directed_r0.500000.csv` |