diff --git a/README_windows_friendly_format.md b/README_windows_friendly_format.md
new file mode 100644
index 0000000000000000000000000000000000000000..3e86bfb64c8cc95b2d33905cbd47c09bec0ea18e
--- /dev/null
+++ b/README_windows_friendly_format.md
@@ -0,0 +1,36 @@
+# Windows-Friendly Prepared Dataset
+
+This dataset root is generated from the original `new_data` and keeps the old dataset untouched.
+
+## Goals
+
+- reduce file-count and random gzip/json parsing on Windows
+- keep split data already laid out by split
+- keep samples in source shard order so later training can stream sequentially
+- avoid requiring the old `*_refs.npz` indirection during later training
+
+## Layout
+
+- `manifest.json`: top-level dataset manifest for the new format
+- `source_manifest.original.json`: copied manifest from the original dataset
+- `grids.json`, `materials_catalog.json`, `merged_stats.json`, `dedup_stats.json`
+- `windows_seq/<split>/manifest.json`
+- `windows_seq/<split>/chunk_xxxxxx_target_field.npy`
+- `windows_seq/<split>/chunk_xxxxxx_meta.npz`
+
+## Chunk schema
+
+- `chunk_*_target_field.npy`
+  - dtype: `uint16`
+  - shape: `[N, 774]`
+  - decode later with `float32 = uint16 * 0.001`
+- `chunk_*_meta.npz`
+  - fixed-size arrays for sample id, constraints, and structure labels
+  - variable-length sequences are padded to `max_layers=24`
+
+## Build config
+
+- selected_splits: train, val, test
+- chunk_size: 32768
+- sample_order: source_shard_order
+- recommended later train order: sequential, no extra shuffle
diff --git "a/README_\346\227\247\347\211\210\346\240\274\345\274\217\345\217\202\350\200\203.md" "b/README_\346\227\247\347\211\210\346\240\274\345\274\217\345\217\202\350\200\203.md"
new file mode 100644
index 0000000000000000000000000000000000000000..eceb3eda24497eebbf042bf2a9c60f09d2ac8906
--- /dev/null
+++ "b/README_\346\227\247\347\211\210\346\240\274\345\274\217\345\217\202\350\200\203.md"
@@ -0,0 +1,352 @@
+# Prepared Training Dataset 使用说明
+
+本目录是已经整理好的统一训练数据集视图，不再是母本生成目录。
+
+公开仓库建议：
+- 保留本说明文档和必要的数据协议说明
+- 不直接提交 `shards/`、`splits/`、大体量 `npz/jsonl.gz` 数据文件
+- 通过环境变量或本地路径在私有环境中挂载真实数据
+
+目标：
+- 直接作为后续主模型训练输入
+- 不再重复做多源合并、跨目录去重、目标场拼接
+- 读取时动态采样条件模式
+
+## 1. 目录结构
+
+本目录下的主要文件：
+
+- `manifest.json`
+  - 数据集总说明
+  - 记录输出根目录、源目录、split 比例、target field 顺序等
+- `grids.json`
+  - 固定波长网格
+  - target field 的全局布局定义
+- `materials_catalog.json`
+  - 14 种材料、角色映射、substrate 选项
+- `merged_stats.json`
+  - 合并整理后的整体统计
+- `dedup_stats.json`
+  - 跨目录去重统计
+- `shards/`
+  - `shard_xxxxxx.jsonl.gz`
+    - 每条样本的结构标签、来源、约束、target field 引用
+  - `shard_xxxxxx_target_field.npz`
+    - 每条样本拼接后的统一目标场，存储格式 `uint16_x1000`
+  - `shard_xxxxxx_index.npz`
+    - 轻量索引，用于快速生成 split ref
+- `splits/`
+  - `train_ids.txt / val_ids.txt / test_ids.txt`
+  - `hard_test_ids.txt / ood_test_ids.txt`
+  - `train_refs.npz / val_refs.npz / test_refs.npz`
+  - 推荐训练时直接用 `*_refs.npz`
+
+## 2. Split 比例
+
+当前整理后的正式 split 比例是：
+
+- `train = 80%`
+- `test = 15%`
+- `val = 5%`
+
+额外还有：
+
+- `hard_test`
+- `ood_test`
+
+这两个不是从 `test` 中扣除，而是额外评估视图。
+
+## 3. 统一 target field 协议
+
+每条样本的目标场已经被统一拼接成一个一维序列：
+
+1. `visible R`
+2. `visible T`
+3. `nir R`
+4. `nir T`
+5. `ir E_normal`
+
+固定长度：
+
+- `vis_R`: 101
+- `vis_T`: 101
+- `nir_R`: 173
+- `nir_T`: 173
+- `ir_E_normal`: 226
+- 合计：`774`
+
+因此：
+
+- `target_field.shape = [774]`
+
+数值存储：
+
+- 磁盘上：`uint16_x1000`
+- 读取后：`float32`，范围 `0~1`
+
+## 4. 读取时返回什么
+
+建议统一通过：
+
+- `coolhouse3_diffusion/new_dataset_maker/dataset.py`
+
+中的：
+
+- `PreparedTrainingDataset`
+
+读取。
+
+### 单条样本返回字段
+
+`PreparedTrainingDataset[idx]` 返回：
+
+- `sample_id`
+- `target_field`
+- `condition_mask`
+- `objective_weights`
+- `window_ids`
+- `channel_ids`
+- `position_ids`
+- `constraint_field`
+- `structure_labels`
+- `condition_mode`
+
+其中：
+
+- `target_field`
+  - `float32[774]`
+- `condition_mask`
+  - `float32[774]`
+  - 1 表示该位置作为已知条件输入
+- `objective_weights`
+  - `float32[774]`
+  - 默认与 `condition_mask` 同步
+- `window_ids / channel_ids / position_ids`
+  - token 级辅助编码字段
+- `constraint_field`
+  - 全局约束信息
+- `structure_labels`
+  - 监督标签
+
+## 5. 条件模式怎么用
+
+读取时不需要把一个母本样本提前展开成很多份。
+
+正确用法是：
+- 一个样本只存一份完整 `target_field`
+- 训练时动态采样 condition mode
+- 再生成 `condition_mask` 和 `objective_weights`
+
+当前支持：
+
+- `full_condition`
+- `vis_only`
+- `nir_only`
+- `ir_only`
+- `vis_nir`
+- `vis_ir`
+- `nir_ir`
+- `custom_channel_subset`
+- `random`
+
+推荐：
+
+- 训练阶段：`condition_mode="random"`
+- 验证阶段：固定几种模式分别跑
+- 测试阶段：按任务定义固定模式
+
+## 6. constraint_field 的用法
+
+`constraint_field` 是全局条件，不是 token 级光谱。
+
+读取后至少包含：
+
+- `allowed_material_mask`
+- `allowed_layer_count_mask`
+- `allow_top_metal`
+- `allow_bottom_metal`
+- `max_metal_layers`
+- `substrate_id`
+- `prototype_family_hint`
+
+说明：
+
+- 即使磁盘中是 compact 版本，`dataset.py` 读取时也会自动补齐默认约束
+- 所以后续训练代码统一按完整字段使用即可
+
+## 7. structure_labels 的用法
+
+`structure_labels` 里包含：
+
+- `layer_count`
+- `material_sequence_ids`
+- `material_role_sequence_ids`
+- `coarse_thickness_bin_ids`
+- `continuous_thickness_values`
+
+建议训练用途：
+
+- 离散结构生成器
+  - 用 `material_sequence_ids`
+  - 用 `coarse_thickness_bin_ids`
+- 连续厚度回归/微调头
+  - 用 `continuous_thickness_values`
+- 长度监督
+  - 用 `layer_count`
+
+注意：
+
+- `material_sequence_ids` 和厚度序列是变长的
+- DataLoader 通常需要自定义 `collate_fn` 做 padding
+
+## 8. 条件场编码器怎么接
+
+建议用：
+
+- `coolhouse3_diffusion/new_dataset_maker/encoder.py`
+
+中的：
+
+- `ConditionFieldEncoder`
+
+输入：
+
+- `target_field`
+- `condition_mask`
+- `objective_weights`
+- `window_ids`
+- `channel_ids`
+- `position_ids`
+- `constraint_field`
+
+输出：
+
+- `condition_tokens`
+- `global_condition_embedding`
+
+推荐接法：
+
+- `condition_tokens`
+  - 给 token 级结构生成器做 cross-attention 或 concat conditioning
+- `global_condition_embedding`
+  - 给长度头、全局先验头、或者 decoder 初始态使用
+
+## 9. 最小读取示例
+
+```python
+from torch.utils.data import DataLoader
+
+from new_dataset_maker.dataset import PreparedTrainingDataset
+
+train_ds = PreparedTrainingDataset(
+    dataset_root="./new_data",
+    split="train",
+    condition_mode="random",
+)
+
+sample = train_ds[0]
+print(sample["target_field"].shape)         # (774,)
+print(sample["condition_mask"].shape)       # (774,)
+print(sample["objective_weights"].shape)    # (774,)
+print(sample["structure_labels"]["layer_count"])
+
+loader = DataLoader(
+    train_ds,
+    batch_size=16,
+    shuffle=True,
+    num_workers=4,
+)
+```
+
+## 10. 最小 encoder 接入示例
+
+```python
+import torch
+
+from new_dataset_maker.dataset import PreparedTrainingDataset
+from new_dataset_maker.encoder import ConditionFieldEncoder
+
+ds = PreparedTrainingDataset(
+    dataset_root="./new_data",
+    split="train",
+    condition_mode="vis_ir",
+)
+
+sample = ds[0]
+
+encoder = ConditionFieldEncoder(
+    target_length=sample["target_field"].shape[0],
+    num_window_ids=int(sample["window_ids"].max()) + 1,
+    num_channel_ids=int(sample["channel_ids"].max()) + 1,
+    num_position_ids=int(sample["position_ids"].max()) + 1,
+    num_materials=sample["constraint_field"]["allowed_material_mask"].shape[0],
+    num_layer_count_ids=sample["constraint_field"]["allowed_layer_count_mask"].shape[0],
+    num_substrates=2,
+    num_prototypes=8,
+)
+
+batch = {
+    "target_field": torch.from_numpy(sample["target_field"]).unsqueeze(0),
+    "condition_mask": torch.from_numpy(sample["condition_mask"]).unsqueeze(0),
+    "objective_weights": torch.from_numpy(sample["objective_weights"]).unsqueeze(0),
+    "window_ids": torch.from_numpy(sample["window_ids"]).unsqueeze(0),
+    "channel_ids": torch.from_numpy(sample["channel_ids"]).unsqueeze(0),
+    "position_ids": torch.from_numpy(sample["position_ids"]).unsqueeze(0),
+    "constraint_field": {
+        "allowed_material_mask": torch.from_numpy(sample["constraint_field"]["allowed_material_mask"]).unsqueeze(0),
+        "allowed_layer_count_mask": torch.from_numpy(sample["constraint_field"]["allowed_layer_count_mask"]).unsqueeze(0),
+        "allow_top_metal": torch.tensor([[sample["constraint_field"]["allow_top_metal"]]], dtype=torch.float32),
+        "allow_bottom_metal": torch.tensor([[sample["constraint_field"]["allow_bottom_metal"]]], dtype=torch.float32),
+        "max_metal_layers": torch.tensor([[sample["constraint_field"]["max_metal_layers"]]], dtype=torch.float32),
+        "substrate_id": torch.tensor([sample["constraint_field"]["substrate_id"]], dtype=torch.long),
+        "prototype_family_hint": torch.tensor([sample["constraint_field"]["prototype_family_hint"]], dtype=torch.long),
+    },
+}
+
+condition_tokens, global_condition_embedding = encoder(**batch)
+print(condition_tokens.shape)
+print(global_condition_embedding.shape)
+```
+
+## 11. 后续主模型训练建议
+
+推荐把训练链路分成这几层：
+
+1. `PreparedTrainingDataset`
+   - 负责读取整理后的统一样本协议
+   - 动态采样 condition mode
+
+2. `collate_fn`
+   - 负责对变长 `material_sequence_ids / thickness` 做 padding
+
+3. `ConditionFieldEncoder`
+   - 负责把目标光谱条件和全局约束编码成统一条件表示
+
+4. 主生成器
+   - 读取 `condition_tokens + global_condition_embedding`
+   - 预测 `layer_count / material_sequence / thickness`
+
+## 12. 不建议的用法
+
+不要这样做：
+
+- 不要直接手写解析 `jsonl.gz + npz`
+- 不要把 `condition_mask` 只放到 loss 里而不编码
+- 不要把固定网格重复写进每条样本
+- 不要预先把一个样本展开成多份条件模板写回磁盘
+
+正确做法是：
+
+- 训练时动态采样条件模式
+- 统一通过 `dataset.py` 读取
+- 统一通过 `encoder.py` 做条件编码
+
+## 13. 当前 compact 版本说明
+
+为了节省空间，当前 prepared dataset 采用 compact 记录格式：
+
+- 元数据：`jsonl.gz`
+- target field：`npz(uint16_x1000)`
+- 固定约束：读取时动态补全
+
+所以看到某些字段没有完整重复存储是正常的，不影响训练。
diff --git a/dedup_stats.json b/dedup_stats.json
new file mode 100644
index 0000000000000000000000000000000000000000..453a6b9e79bcea5b678fb955695e71a474b7a1d0
--- /dev/null
+++ b/dedup_stats.json
@@ -0,0 +1,14 @@
+{
+  "dedup_enabled": true,
+  "signature_tolerance_nm": 1.0,
+  "unique_count": 19831633,
+  "duplicate_count": 228367,
+  "source_duplicate_counts": {
+    "src_00_generated_dataset_full": 0,
+    "src_01_generated_dataset_full_2": 2017,
+    "src_02_generated_dataset_full_1": 5113,
+    "src_03_generated_dataset_full_2": 7860,
+    "src_04_generated_dataset_full_3": 13377,
+    "src_05_generated_dataset_full_4": 200000
+  }
+}
\ No newline at end of file
diff --git a/grids.json b/grids.json
new file mode 100644
index 0000000000000000000000000000000000000000..7fb01605939070d5b404281cefe931986d6d7823
--- /dev/null
+++ b/grids.json
@@ -0,0 +1,3827 @@
+{
+  "vis_nm": [
+    380.0,
+    384.0,
+    388.0,
+    392.0,
+    396.0,
+    400.0,
+    404.0,
+    408.0,
+    412.0,
+    416.0,
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+    756.0,
+    760.0,
+    764.0,
+    768.0,
+    772.0,
+    776.0,
+    780.0
+  ],
+  "nir_nm": [
+    780.0,
+    790.0,
+    800.0,
+    810.0,
+    820.0,
+    830.0,
+    840.0,
+    850.0,
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+    870.0,
+    880.0,
+    890.0,
+    900.0,
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+    990.0,
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+    2500.0
+  ],
+  "ir_nm": [
+    2500.0,
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+    3000.0,
+    3100.0,
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+}
\ No newline at end of file
diff --git a/manifest.json b/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..0bd5d09769ce49d7aac8a5b512c92d5f76457643
--- /dev/null
+++ b/manifest.json
@@ -0,0 +1,745 @@
+{
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+  "dataset_version": "hf_parquet_subset_v1",
+  "source_dataset_root": "F:\\PhysOptoFlow_2\\new_data_windows",
+  "source_manifest_path": "F:\\PhysOptoFlow_1\\new_data_windows_v1\\source_manifest.original.json",
+  "source_record_format": "compact_jsonl_gz_plus_target_uint16_x1000",
+  "record_format": "hf_parquet_uint16_target_plus_padded_meta_v1",
+  "created_at": "2026-03-27 14:24:52",
+  "target_field_length": 774,
+  "target_decode_scale": 0.001,
+  "chunk_size": 32768,
+  "max_layers": 24,
+  "sample_id_bytes": 32,
+  "allowed_material_count": 14,
+  "allowed_layer_count_len": 25,
+  "selected_splits": [
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new file mode 100644
index 0000000000000000000000000000000000000000..280302da4ba4146222373bd885983c1c8aa77cb4
--- /dev/null
+++ b/materials_catalog.json
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+}
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diff --git a/merged_stats.json b/merged_stats.json
new file mode 100644
index 0000000000000000000000000000000000000000..767dea16d737abc22f6d73d70ca57e4f5ce21d9f
--- /dev/null
+++ b/merged_stats.json
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+  "material_usage_counts": {},
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+    }
+  },
+  "source_roots": [
+    "C:\\RL_2d\\cool_house\\new_llms3\\coolhouse3_diffusion\\new_dataset_maker\\generated_dataset_full",
+    "C:\\RL_2d\\cool_house\\new_llms3\\coolhouse3_diffusion\\new_dataset_maker\\generated_dataset_full_2",
+    "F:\\generated_dataset_full_1",
+    "F:\\generated_dataset_full_2",
+    "F:\\generated_dataset_full_3",
+    "F:\\generated_dataset_full_4"
+  ]
+}
\ No newline at end of file
diff --git a/source_manifest.original.json b/source_manifest.original.json
new file mode 100644
index 0000000000000000000000000000000000000000..4198c53bdf93ace05ad3141003ca924279df455b
--- /dev/null
+++ b/source_manifest.original.json
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+{
+  "dataset_name": "prepared_training_dataset",
+  "dataset_version": "prepared_v1",
+  "seed": 20260312210644,
+  "output_root": "C:\\RL_2d\\cool_house\\new_llms3\\new_data",
+  "source_dataset_roots": [
+    "C:\\RL_2d\\cool_house\\new_llms3\\coolhouse3_diffusion\\new_dataset_maker\\generated_dataset_full",
+    "C:\\RL_2d\\cool_house\\new_llms3\\coolhouse3_diffusion\\new_dataset_maker\\generated_dataset_full_2",
+    "F:\\generated_dataset_full_1",
+    "F:\\generated_dataset_full_2",
+    "F:\\generated_dataset_full_3",
+    "F:\\generated_dataset_full_4"
+  ],
+  "source_dataset_ids": [
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+    "src_01_generated_dataset_full_2",
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+    "src_04_generated_dataset_full_3",
+    "src_05_generated_dataset_full_4"
+  ],
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