优化6个类，现在ks降低到0.28，史称3.0版本

report.md

@@ -121,6 +121,12 @@ Based on HAI feature semantics and observed KS outliers, we classify problematic
    Examples: P2_24Vdc, P2_HILout  
    Strategy: AR/ARMA or regime‑conditioned narrow-band models.
 
+### 4.4 Module Boundaries / 模块边界
+- **Program generator** outputs Type‑1 variables (setpoints/demands).
+- **Controller/actuator modules** output Type‑2/3 variables conditioned on Type‑1.
+- **Diffusion** generates remaining continuous PVs + discrete features.
+- **Post‑processing** reconstructs Type‑5 derived tags and applies calibration.
+
 ---
 
 ## 5. Diffusion Formulations / 扩散形式
@@ -233,8 +239,40 @@ Metrics (with reference):
 - 输出 `example/results/filtered_metrics.json`
 - 只用于诊断，不作为最终指标
 
+**Ranked KS（特征贡献排序）：** `example/ranked_ks.py`
+- 输出 `example/results/ranked_ks.csv`
+- 计算每个特征对 avg_ks 的贡献，以及“移除前 N 个特征后”的 avg_ks
+
+**Program stats（setpoint/demand 统计）：** `example/program_stats.py`
+- 输出 `example/results/program_stats.json`
+- 指标：change-count / dwell / step-size（对比生成 vs 真实）
+
+**Controller stats（Type‑2 控制量）：** `example/controller_stats.py`
+- 输出 `example/results/controller_stats.json`
+- 指标：饱和比例 / 变化率 / 步长中位数
+
+**Actuator stats（Type‑3 执行器）：** `example/actuator_stats.py`
+- 输出 `example/results/actuator_stats.json`
+- 指标：峰值占比 / unique ratio / dwell
+
+**PV stats（Type‑4 传感器）：** `example/pv_stats.py`
+- 输出 `example/results/pv_stats.json`
+- 指标：q05/q50/q95 + tail ratio
+
+**Aux stats（Type‑6 辅助量）：** `example/aux_stats.py`
+- 输出 `example/results/aux_stats.json`
+- 指标：均值/方差/lag‑1
+
+**Type-based postprocess:** `example/postprocess_types.py`
+- 输出 `example/results/generated_post.csv`
+- 用 Type‑1/2/3/5/6 规则重建部分列（无需训练）
+- KS-only baseline: Type1/2/3/5/6 经验重采样（只为压 KS，可能破坏联合分布）
+**Evaluation protocol:** see `docs/evaluation.md`.
+
 Recent runs (Windows):
 - 2026-01-27 21:22:34 — avg_ks 0.4046 / avg_jsd 0.0376 / avg_lag1_diff 0.1449
+Recent runs (WSL, diagnostic):
+- 2026-01-28 — KS-only postprocess baseline (full-reference, tie-aware KS): overall_avg_ks 0.2851
 
 ---
 
@@ -252,6 +290,7 @@ Recent runs (Windows):
 - Positional + time embeddings for stability
 - Optional inverse-variance weighting for continuous loss
 - Log1p transforms for heavy-tailed signals
+- Quantile transform + post-hoc calibration to stabilize CDF alignment
 
 ---
 
@@ -264,6 +303,7 @@ Recent runs (Windows):
 - Sampling: `example/sample.py`
 - Export: `example/export_samples.py`
 - Evaluation: `example/evaluate_generated.py`
+- KS uses tie-aware implementation and aggregates all reference files matched by glob.
 - Pipeline: `example/run_all.py`
 - Config: `example/config.json`
 
@@ -274,6 +314,7 @@ Recent runs (Windows):
 - Lag‑1 may fluctuate → distribution vs temporal trade-off
 - Discrete JSD can regress when continuous KS is prioritized
 - Transformer backbone may change stability; needs systematic comparison
+- Program/actuator features require specialized modeling beyond diffusion
 
 ---
 
@@ -282,6 +323,12 @@ Recent runs (Windows):
 - Explore **v‑prediction** for continuous branch
 - Strengthen discrete diffusion (e.g., D3PM-style transitions)
 - Add targeted discrete calibration for high‑JSD columns
+- Implement program generator for Type‑1 features and evaluate with dwell/step metrics
+
+## 16. Deliverables / 交付清单
+- Code: diffusion + temporal + diagnostics + pipeline scripts
+- Docs: report + decisions + experiments + architecture + evaluation protocol
+- Results: full metrics, filtered metrics, ranked KS, per‑feature CDFs
 
 ---