Release scaling-v1.0: classified SpaceVista and proportion-matched Small/Large

README.md

@@ -8,27 +8,27 @@ configs:
 - config_name: small
   data_files:
   - split: train
-    path: video_v1/small/train/*.parquet
+    path: scaling_v1/small/train/*.parquet
   - split: validation
-    path: video_v1/small/validation/*.parquet
+    path: scaling_v1/small/validation/*.parquet
   - split: test
-    path: video_v1/small/test/*.parquet
+    path: scaling_v1/small/test/*.parquet
   - split: video_validation
-    path: video_v1/small/video_validation/*.parquet
+    path: scaling_v1/small/video_validation/*.parquet
   - split: video_test
-    path: video_v1/small/video_test/*.parquet
+    path: scaling_v1/small/video_test/*.parquet
 - config_name: large
   data_files:
   - split: train
-    path: video_v1/large/train/*.parquet
+    path: scaling_v1/large/train/*.parquet
   - split: validation
-    path: video_v1/small/validation/*.parquet
+    path: scaling_v1/small/validation/*.parquet
   - split: test
-    path: video_v1/small/test/*.parquet
+    path: scaling_v1/small/test/*.parquet
   - split: video_validation
-    path: video_v1/large/video_validation/*.parquet
+    path: scaling_v1/small/video_validation/*.parquet
   - split: video_test
-    path: video_v1/large/video_test/*.parquet
+    path: scaling_v1/small/video_test/*.parquet
 - config_name: media_complete
   data_files:
   - split: train
@@ -43,122 +43,157 @@ configs:
     path: annotations/spacevista/all-measurement.parquet
 ---
 
-# Anchor Spatial Reasoning — Stage 3 · video-v1.0
+# Anchor Spatial Reasoning — Stage 3 · scaling-v1.0
 
-## 先选择 Small 或 Large
+## 最新入口：比例匹配的 Small / Large
 
-| 版本 | 训练题数 | 用途 | HF config |
+| 版本 | 训练 QA | 设计用途 | 加载 config |
 |---|---:|---|---|
-| Small | 1,000,000 | 验证 Stage3 答案监督能否恢复空间度量先验 | `small` |
-| Large | 89,828,269 | 全量合格训练题，包含全部 Small | `large` |
+| Small | 1,000,000 | 与 Large 配比对齐的 scaling / 数据量对照实验 | `small` |
+| Large | 89,828,269 | 全量训练，包含新版 Small 全部题目 | `large` |
+| 历史均衡 Small | 1,000,000 | 原先的任务均衡、视频强化与先验恢复实验 | `small`，固定旧 revision=`video-v1.0` |
 
-两版共用原 `validation` / `test`，各 50,000 条；新增独立 `video_validation`
-1,585 条、`video_test` 1,725 条。
-**不要拼接 Small 与 Large，也不要把原始池 `media_complete` 当作隔离后的训练集。**
-
-旧版本固定在 `large-v1.0`；[原始池筛选统计及旧版说明](video_v1/manifests/baseline-large-v1-card.md)保留。
-本版 Small 替换 100,000 条 HiSpatial 距离题，总量不变；Large 保留全部原训练题，
-新增 141,268 条视频题。不改变旧主评测题内容。
+**新版 Small 已重新抽样，不能与旧 Small 的实验结果当作仅数据量变化直接比较。**
+Large 不增删题目；修订 SpaceVista 模态标签。原问题、训练问题、答案、图片／视频路径及顺序不变。
+不添加“最后一帧是参考帧”的提示；CA-VQA 保留原顺序和全部输入帧，单帧样例仍是图片。
+旧 `video-v1.0`、`large-v1.0` 等标签不移动；既有训练媒体 TAR 全部复用，已缓存的相同媒体无需重下。
+只缓存过旧 Small 部分媒体的使用者，新抽样引用到的其余素材仍需按需获取。
 
 ```python
 from datasets import load_dataset
-ds = load_dataset('AnchorSR/TrainingData_Stage3', 'small',  # 或 large
-                  revision='video-v1.0', streaming=True)
-# train, validation, test, video_validation, video_test
+ds = load_dataset('AnchorSR/TrainingData_Stage3', 'small',  # 或 'large'
+                  revision='scaling-v1.0', streaming=True)
 ```
 
-这一步仅加载标注，媒体位于外置 TAR。请见 [Small 指南](small/README.md) /
-[Large 指南](large/README.md)，不要在 300 GiB 环境下载整个媒体目录。
+标注加载不等于媒体加载。媒体使用 [统一读取器](tools/load_stage3_data.py)，
+配套 [small_training.py](tools/small_training.py) 与 [input_sequences.py](tools/input_sequences.py)。
+指南：[Small](small/README.md) / [Large](large/README.md)。
+旧版本复现时，标注、索引和读取工具都应固定到同一旧 revision。
+
+两版共用 `validation` / `test` 各50,000题，`video_validation` 1,585题、`video_test` 1,725题。
+评测成员不变。不要拼接 Small 与 Large，也不要把 `media_complete` 原始池当作隔离后的训练集。
+
+## 配比设计与 scaling 边界
+
+按 **来源 × 任务类型 × 语义模态** 联合分层，以 Large 实际频率分配100万条配额，
+固定种子、无放回抽样；每个非空层至少保留1题。整数取整和稀有层保留会造成微小比例偏差。
+Small 与 Large 的相同 sample_id 内容完全一致，验收检查了原字段与评测 ID 隔离。
+配额与选中 ID：[small-allocation.json](scaling_v1/manifests/small-allocation.json)、
+[small-selected-ids.parquet](scaling_v1/manifests/small-selected-ids.parquet)。
+
+视频题占比：Small **22.6621%**，Large **22.6623%**。
+两者配比对齐有助于控制比较变量，但**不自动保证 scaling 的因果结论**；
+实验仍需固定模型、优化方法、帧采样／分辨率策略，并说明训练步数、epoch 和 token/帧预算。
+Parquet 物理行序不是推荐训练顺序：训练时使用相同种子的跨 shard 随机打乱／shuffle buffer，
+不要按数据源依次完整训练；数据量对照时两版采用一致的混合与采样策略。
+不再维持旧 Small 的新视频题10万条配额；专项均衡实验请使用旧固定版。
+
+## Video / Image 分布
+
+按训练 QA 数统计，不是媒体文件数。`video` 包括 MP4 和有来源依据的视频帧序列，
+其中 CA-VQA 是参考帧型稀疏视频输入；`image` 包括单图和真正的多视角图片组。
+多视角来源可能也是扫描视频，但其题目并不按原时间序列组织，因此不计入视频题。
+文件后缀、`<video>` 占位符或“多张图片”均不是单独的判定依据。
 
-## 视频口径：不能只统计 MP4 文件
+| 类别 | Small 数量 | Small 占比 | Large 数量 | Large 占比 |
+|---|---:|---:|---:|---:|
+| `image` | 773,379 | 77.3379% | 69,471,074 | 77.3377% |
+| `video` | 226,621 | 22.6621% | 20,357,195 | 22.6623% |
 
-`input_modality` 保留原存储口径；新增 `semantic_modality` 表示实际输入语义。
-`video_file` 是视频文件；`video_frame_sequence` / `multiple_video_frame_sequences`
-是有源顺序证据的帧序列；`multi_image_unresolved` 不算已确认视频。
-CA-VQA 保留支持帧顺序和最后的参考帧；SpaceVista 使用原始分组，保留重复帧。
-未知帧率／时间戳不伪造，也不把多视角图片自动归为视频。
+细分模态：
 
 | 类别 | Small 数量 | Small 占比 | Large 数量 | Large 占比 |
 |---|---:|---:|---:|---:|
-| `multi_image_unresolved` | 29,849 | 2.9849% | 49,027 | 0.0546% |
-| `single_image` | 383,871 | 38.3871% | 69,423,352 | 77.2845% |
-| `video_file` | 176,441 | 17.6441% | 234,784 | 0.2614% |
-| `video_frame_sequence` | 409,839 | 40.9839% | 20,121,106 | 22.3995% |
+| `multi_view_images` | 531 | 0.0531% | 47,722 | 0.0531% |
+| `single_image` | 772,848 | 77.2848% | 69,423,352 | 77.2845% |
+| `video_file` | 2,614 | 0.2614% | 234,784 | 0.2614% |
+| `video_frame_sequence` | 224,007 | 22.4007% | 20,122,411 | 22.4010% |
 
-## 全部训练任务类型、数量与占比
+## 数据来源分布
 
-以下是实际 Small / Large **训练集**统计，不是原始池或评测集统计。
+| 类别 | Small 数量 | Small 占比 | Large 数量 | Large 占比 |
+|---|---:|---:|---:|---:|
+| `cavqa` | 220,697 | 22.0697% | 19,825,020 | 22.0699% |
+| `hispatial` | 771,459 | 77.1459% | 69,298,561 | 77.1456% |
+| `sims_vsi` | 245 | 0.0245% | 21,980 | 0.0245% |
+| `spacevista` | 4,771 | 0.4771% | 428,623 | 0.4772% |
+| `vica322k` | 1,328 | 0.1328% | 119,288 | 0.1328% |
+| `vsi590k` | 1,500 | 0.1500% | 134,797 | 0.1501% |
+
+## 全部任务类型、数量与占比
+
+以下均为当前正式训练集，不含评测和原始候选池。
 
 | 类别 | Small 数量 | Small 占比 | Large 数量 | Large 占比 |
 |---|---:|---:|---:|---:|
-| `aabb_3d` | 99,999 | 9.9999% | 4,512,009 | 5.0229% |
-| `angle` | 55,547 | 5.5547% | 217,411 | 0.2420% |
-| `area` | 10,104 | 1.0104% | 15,708 | 0.0175% |
-| `camera_angle` | 5,314 | 0.5314% | 6,654 | 0.0074% |
-| `camera_depth` | 112,497 | 11.2497% | 4,634,330 | 5.1591% |
-| `camera_position` | 8 | 0.0008% | 8 | 0.0000% |
-| `center_depth` | 3,099 | 0.3099% | 3,099 | 0.0034% |
-| `center_distance` | 3,905 | 0.3905% | 3,932 | 0.0044% |
-| `closest_surface` | 44,036 | 4.4036% | 54,248 | 0.0604% |
-| `dimension` | 10,399 | 1.0399% | 19,305 | 0.0215% |
-| `distance` | 100,808 | 10.0808% | 51,919,844 | 57.7990% |
-| `height` | 117,809 | 11.7809% | 9,655,019 | 10.7483% |
-| `image_coordinate_depth` | 7,269 | 0.7269% | 9,035 | 0.0101% |
-| `length` | 40,470 | 4.0470% | 2,457,927 | 2.7363% |
-| `longest_side` | 57,756 | 5.7756% | 69,600 | 0.0775% |
-| `nearest_surface_distance` | 14,113 | 1.4113% | 41,753 | 0.0465% |
-| `object_angle` | 9,139 | 0.9139% | 19,699 | 0.0219% |
-| `object_dimensions` | 1,362 | 0.1362% | 23,230 | 0.0259% |
-| `object_distance` | 119,583 | 11.9583% | 8,838,789 | 9.8397% |
+| `aabb_3d` | 50,229 | 5.0229% | 4,512,009 | 5.0229% |
+| `angle` | 2,420 | 0.2420% | 217,411 | 0.2420% |
+| `area` | 175 | 0.0175% | 15,708 | 0.0175% |
+| `camera_angle` | 74 | 0.0074% | 6,654 | 0.0074% |
+| `camera_depth` | 51,591 | 5.1591% | 4,634,330 | 5.1591% |
+| `camera_position` | 1 | 0.0001% | 8 | 0.0000% |
+| `center_depth` | 34 | 0.0034% | 3,099 | 0.0034% |
+| `center_distance` | 45 | 0.0045% | 3,932 | 0.0044% |
+| `closest_surface` | 604 | 0.0604% | 54,248 | 0.0604% |
+| `dimension` | 215 | 0.0215% | 19,305 | 0.0215% |
+| `distance` | 577,989 | 57.7989% | 51,919,844 | 57.7990% |
+| `height` | 107,483 | 10.7483% | 9,655,019 | 10.7483% |
+| `image_coordinate_depth` | 101 | 0.0101% | 9,035 | 0.0101% |
+| `length` | 27,362 | 2.7362% | 2,457,927 | 2.7363% |
+| `longest_side` | 775 | 0.0775% | 69,600 | 0.0775% |
+| `nearest_surface_distance` | 465 | 0.0465% | 41,753 | 0.0465% |
+| `object_angle` | 219 | 0.0219% | 19,699 | 0.0219% |
+| `object_dimensions` | 259 | 0.0259% | 23,230 | 0.0259% |
+| `object_distance` | 98,396 | 9.8396% | 8,838,789 | 9.8397% |
 | `position_coordinates` | 1 | 0.0001% | 4 | 0.0000% |
-| `surface_distance` | 74,193 | 7.4193% | 110,999 | 0.1236% |
-| `translation_rotation` | 7,723 | 0.7723% | 10,195 | 0.0113% |
-| `triangle_lengths_angles` | 5,000 | 0.5000% | 19,507 | 0.0217% |
-| `volume` | 42 | 0.0042% | 723 | 0.0008% |
-| `width` | 99,824 | 9.9824% | 7,185,241 | 7.9989% |
+| `surface_distance` | 1,236 | 0.1236% | 110,999 | 0.1236% |
+| `translation_rotation` | 113 | 0.0113% | 10,195 | 0.0113% |
+| `triangle_lengths_angles` | 217 | 0.0217% | 19,507 | 0.0217% |
+| `volume` | 8 | 0.0008% | 723 | 0.0008% |
+| `width` | 79,988 | 7.9988% | 7,185,241 | 7.9989% |
+
+## SpaceVista 视频 / 多视角划分
 
-## 各来源的训练数量
+此表以各版 **SpaceVista 训练题数** 为分母。
 
 | 类别 | Small 数量 | Small 占比 | Large 数量 | Large 占比 |
 |---|---:|---:|---:|---:|
-| `cavqa` | 300,000 | 30.0000% | 19,825,020 | 22.0699% |
-| `hispatial` | 350,000 | 35.0000% | 69,298,561 | 77.1456% |
-| `sims_vsi` | 21,980 | 2.1980% | 21,980 | 0.0245% |
-| `spacevista` | 150,000 | 15.0000% | 428,623 | 0.4772% |
-| `vica322k` | 78,020 | 7.8020% | 119,288 | 0.1328% |
-| `vsi590k` | 100,000 | 10.0000% | 134,797 | 0.1501% |
-
-## 新增来源的筛选过程
-
-下表“读取”仅指本次选定的度量任务标注，不是整个源仓库全部问答数量。
-
-| 来源 | 读取的目标任务题数 | 数值规则通过 | 训练保留 | 新视频验证 | 新视频测试 |
-|---|---:|---:|---:|---:|---:|
-| sims_vsi | 27,522 | 27,522 | 21,980 | 935 | 1,167 |
-| vica322k | 207,084 | 207,066 | 119,288 | 650 | 558 |
-
-视频完整性检查另外隔离了 2 个源文件，排除关联的 16 条候选题；上表已是排除后的训练／评测数量。Small 从其余合格题中补齐，不用缺失或无法解码的视频训练。详情见 `video_v1/manifests/source-video-quarantine.json`。
-
-SIMS 为模拟室内导航视频；ViCA 含真实室内扫描视频。核心任务为距离、最长边、房间面积；
-三角形边长／角度是单独的复合度量任务，保留对象对应原文，不把多个数压成单个答案。
-ViCA 18 条退化三角形候选被隔离；85,828 条命中已知评测场景的候选不进入本版训练。
-去重是可识别场景内规范化问题和答案的精确匹配，不保证识别所有语义改写。
-
-## 许可与局限
-
-- [SIMS-VSI](https://huggingface.co/datasets/ellisbrown/SIMS-VSI)：Apache-2.0。
-- [ViCA-322K](https://huggingface.co/datasets/nkkbr/ViCA-322K)：CC-BY-NC-4.0；
-  **包含该来源的混合集不能视为可自由商用**，原始场景数据条件仍适用。
-- 原四来源许可和出处见[基线数据卡](video_v1/manifests/baseline-large-v1-card.md)；不重新授权上游素材。
-- 新增视频构建时隔离了当时可识别的 Stage3 和 Stage1/2 评测场景，但这不代表整个混合集完全场景隔离。
-- **后续路径级审计发现：Small/Large 的既有 VSI 子集中，202 个 Hypersim 源场景跨训练与原验证/测试集。**
-  涉及验证 1,296 条、测试 1,507 条，共 2,803 条，占原验证/测试合计 100,000 条的 2.803%。
-  本次核查中同一媒体路径跨训练/评测为 0；这是同场景不同帧风险，不是已证实的相同图片泄漏或答案错误。
-  因此相关题不能直接支撑严格“未见场景泛化”的结论。审计限于已识别的 VSI/ViCA 路径，非所有来源的穷尽证明。
-  详见[路径级场景审计](video_v1/manifests/path-scene-overlap-audit.json)。此说明未改变样本、划分、计数或 `video-v1.0` 标签。
-- CA-VQA 哈希路径没有完整物理场景身份，不能声称彻底排除隐藏跨源重叠；新视频评测也不代表预训练从未见过的场景。
-- 新视频逐文件检查源哈希并全帧解码；这不是逐题人工视觉真值审核。
-  已做媒体读取／提示渲染抽检，**未运行具体模型训练或测量 GPU 吞吐**。
-- SenseNova-SI-8M 尚未纳入本版本。
-
-构建与检查证据见 `video_v1/manifests/`。原始问题、回答、单位及来源版本保留于每条记录。
+| `multi_view_images` | 531 | 11.1297% | 47,722 | 11.1338% |
+| `single_image` | 121 | 2.5362% | 10,763 | 2.5111% |
+| `video_frame_sequence` | 4,119 | 86.3341% | 370,138 | 86.3551% |
+
+原有保守分类未认定的多图已追溯到 SPAR 原始任务：`_mv`、双图位姿／视角推理归多视角，
+视频来源序列及 `room_size` 视频任务归视频；单帧独立列出。
+缺失标签的57,133条已收录原池记录通过 **场景＋规范化问题正文＋兼容帧数** 匹配到唯一任务类型；
+不使用可能重复的源 ID 单独判断。
+该匹配恢复的是任务分类，不声称完成了重命名媒体的逐像素身份核验或逐题答案真值审核。
+已收录原池499,525题分类结果：视频435,191、多视角51,996、单图12,338，未分类0。
+证据：[分类统计](scaling_v1/manifests/spacevista-classification.json)、
+[逐题源任务匹配](scaling_v1/manifests/spar-task-matches.parquet)、
+[完整帧分组契约](scaling_v1/manifests/spacevista-sequences.parquet)。
+上游：[SpaceVista 格式](https://huggingface.co/datasets/SpaceVista/SpaceVista-Full)、
+[SPAR 原始任务](https://huggingface.co/datasets/jasonzhango/SPAR-7M/tree/0fe664cbada1e7c1173fd743e0f781882eebf777)。
+
+## 评测风险与验收边界
+
+**既有 Hypersim 同场景跨训练／评测风险仍然存在，本版没有重划分数据。**
+Large 中仍有202个 Hypersim 场景跨训练和主评测；原 validation 1,296题、test 1,507题涉及这些场景
+（共2,803 / 100,000题，2.803%）。Large 涉及32,997条训练题。
+新 Small 的路径复核结果：117个跨训练／评测场景，
+其中 Hypersim 训练题366条、validation 1,076条、test 1,324条。
+这是同场景不同画面风险，不等于已证实的同图泄漏或错误答案；仅覆盖可识别 VSI/ViCA 路径。
+其他来源及隐藏场景身份未穷尽，因此不能声称评测完全场景独立。
+[本版路径审计](scaling_v1/manifests/path-scene-overlap-audit.json)。
+
+本版验收包括分类覆盖、联合配额、Small/Large 内容一致、媒体引用、真实读取与远端固定版本校验；
+**没有运行模型训练，也没有新增逐题视觉真值审核。**
+SenseNova-SI-8M 未纳入正式训练。
+
+## 来源许可与历史记录
+
+不重新授权任何上游素材：CA-VQA/CubifyAnything 为 CC-BY-NC-ND-4.0；
+SpaceVista 为 CC-BY-4.0；VSI-590K、SIMS-VSI 为 Apache-2.0；ViCA-322K 为 CC-BY-NC-4.0；
+HiSpatial 为 MIT，底层场景／媒体条件另行适用。混合集不能视为可自由商用。
+原始池筛选统计见 [基线数据卡](video_v1/manifests/baseline-large-v1-card.md)，
+上一版视频来源筛选与坏文件隔离记录保留在 `video_v1/manifests/`。
+本次发布审计见 [build-audit.json](scaling_v1/manifests/build-audit.json)。

large/README.md

@@ -1,36 +1,23 @@
-# Large · video-v1.0
+# Large · scaling-v1.0
 
-训练题数：89,828,269。Small 用于先验恢复实验，Large 用于扩量；两者不能直接拼接。
-完整来源、任务与视频占比见[数据卡](../README.md)。主验证／测试保持原题不变，新增视频评测单独报告。
-
-## 媒体读取
-
-下载 `tools/load_stage3_data.py`、`tools/small_training.py`、`tools/input_sequences.py` 到同一目录。
-安装 `huggingface_hub pyarrow requests pillow numpy av`，然后：
+训练题数：89,828,269。新版 Small 按 Large 来源、任务和模态配比抽样。
+历史均衡 Small 请固定 `revision='video-v1.0'`，不要混用新旧索引与标注。
 
 ```python
 from load_stage3_data import Stage3Dataset
-data = Stage3Dataset('/scratch/my-stage3-cache', config='large',
-                     revision='video-v1.0', max_cache_gib=40)
-row = next(data.rows('train'))
-sample = data.sample(row)
-visual = sample['visual_inputs']
-# visual['images']: 普通图片，已渲染定位提示
-# visual['video_frame_sequences']: list[list[PIL.Image]]，每组独立且有序
-# visual['video_files']: 本地视频路径，交给模型的视频解码器
-# sample['question'], sample['answer']: 答案监督文本
+data = Stage3Dataset('/scratch/your-stage3-cache', revision='scaling-v1.0', config='large', max_cache_gib=40)
+for row in data.rows('train'):
+    sample = data.sample(row)
+    question, answer = sample['question'], sample['answer']
+    visual = sample['visual_inputs']
+    # visual['video_files']: actual video files; choose your training frame sampler.
+    # visual['video_frame_sequences']: existing ordered frame groups; preserve them.
+    # visual['images']: single-image / multi-view inputs; preserve input order and markers.
+    break
 ```
 
-把 `visual` 的三种输入分别交给所用模型处理器。不要再次拼接旧的 `sample['images']`，
-否则帧会重复输入。各序列独立，不要把多个视频合并为一个；未知时间戳不能自行伪造。
-框／点／mask 先在原尺寸图像上渲染，再由模型处理器统一缩放。
-若需抽帧，必须评估是否遗漏题目所需证据；默认读取器不会擅自丢帧。
-
-训练时按可复现随机种子混合打乱，不按来源整块依次训练。Small/Large 的配比见数据卡；
-若训练器另行重采样，记录实际曝光比例。验证和测试不参与训练抽样。
-
-TAR 用于批量传输，读取器按索引进行 Range 读取并校验媒体哈希，无需解包全库。
-大量离线训练可按 `media-manifest.json` 下载所需 TAR；300 GiB 空间应使用有界缓存。
-额外视频评测入口为 `data.rows('video_validation')` / `data.rows('video_test')`。
-
-此读取器不是特定模型的完整训练器；仍需按模型多模态接口构建输入张量和 loss mask。
+读取器按需取媒体 TAR 字节范围，不必下载整个媒体目录。约300GiB环境请限制缓存。
+CA-VQA 不新增参考帧提示；保留原顺序和全部帧。SpaceVista 使用本版分组契约，保留重复帧和标注。
+时间戳未知时不伪造原始 FPS。不要同时输入 legacy images/videos 和 visual_inputs，避免重复送入。
+上面的循环仅演示读取；正式训练需要跨 shard shuffle，不要直接沿物理行序按来源连续训练。
+原 QA、答案不变；共享评测存在的 Hypersim 同场景风险详见仓库主 README。

large/media-manifest.json

@@ -2,14 +2,14 @@
   "input_revision": "3de316b518343519f0b7f8877eaabb20b8221627",
   "baseline_revision": "b93f98b81a18b15dc0cdd97734fa6237fa0c3764",
   "split_paths": {
-    "train": "video_v1/large/train/",
-    "video_validation": "video_v1/large/video_validation/",
-    "video_test": "video_v1/large/video_test/",
-    "validation": "video_v1/small/validation/",
-    "test": "video_v1/small/test/"
+    "validation": "scaling_v1/small/validation/",
+    "test": "scaling_v1/small/test/",
+    "video_validation": "scaling_v1/small/video_validation/",
+    "video_test": "scaling_v1/small/video_test/",
+    "train": "scaling_v1/large/train/"
   },
   "missing_media_references": 0,
-  "sequence_contract_path": "video_v1/manifests/spacevista-sequences.parquet",
+  "sequence_contract_path": "scaling_v1/manifests/spacevista-sequences.parquet",
   "sequence_contract_required": true,
   "assets_by_source": [
     {

scaling_v1/manifests/candidate-acceptance.json

@@ -0,0 +1,1165 @@
+{
+  "status": "passed_real_media_reader",
+  "revision": "c1690b6a6bb4e61fb271fb9db3d06ab68db2b26a",
+  "cases": [
+    {
+      "sample_id": "cavqa:00042-of-01024:130:92",
+      "source": "cavqa",
+      "task_type": "camera_depth",
+      "semantic_modality": "single_image",
+      "sequence_lengths": [],
+      "rendered_rgb_sha256": [
+        "118c5a22f03915167446c2ad4a6ba2fe6794ab32d4af71a2de65df932fa22126"
+      ],
+      "video_first_frames": [],
+      "qa_preserved": true
+    },
+    {
+      "sample_id": "cavqa:00000-of-01024:117:164",
+      "source": "cavqa",
+      "task_type": "camera_depth",
+      "semantic_modality": "video_frame_sequence",
+      "sequence_lengths": [
+        5
+      ],
+      "rendered_rgb_sha256": [
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+        "b968a1f3a97978bb52f80197b94ce50a37407d7021615841c2ae0115b5a8a8ae",
+        "50d5ff1318f6967979a6ab2dfec527c86b9d21fd7e52ab09bdfbbfea2e1e76b2",
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+        "d2968be004e996bda66732cec685f3365be12061e55a04803bcfb2231489593a"
+      ],
+      "video_first_frames": [],
+      "qa_preserved": true
+    },
+    {
+      "sample_id": "cavqa:00003-of-01024:149:18",
+      "source": "cavqa",
+      "task_type": "height",
+      "semantic_modality": "single_image",
+      "sequence_lengths": [],
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+    {
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+      "source": "cavqa",
+      "task_type": "height",
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+      "source": "hispatial",
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+    {
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+      "semantic_modality": "video_file",
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+      "rendered_rgb_sha256": [],
+      "video_first_frames": [
+        [
+          668,
+          384
+        ]
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+    },
+    {
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+      "source": "sims_vsi",
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+      "sequence_lengths": [],
+      "rendered_rgb_sha256": [],
+      "video_first_frames": [
+        [
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+          384
+        ]
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+      "qa_preserved": true
+    },
+    {
+      "sample_id": "sims_vsi:qas/vsi_obj_abs_distance_oe_seed_0.jsonl:1001",
+      "source": "sims_vsi",
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+      "semantic_modality": "video_file",
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+      "rendered_rgb_sha256": [],
+      "video_first_frames": [
+        [
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+          384
+        ]
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+      "qa_preserved": true
+    },
+    {
+      "sample_id": "spacevista:10343",
+      "source": "spacevista",
+      "task_type": "area",
+      "semantic_modality": "video_frame_sequence",
+      "sequence_lengths": [
+        32
+      ],
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+      "semantic_modality": "video_frame_sequence",
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+    },
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+      "source": "vica322k",
+      "task_type": "area",
+      "semantic_modality": "video_file",
+      "sequence_lengths": [],
+      "rendered_rgb_sha256": [],
+      "video_first_frames": [
+        [
+          1920,
+          1440
+        ]
+      ],
+      "qa_preserved": true
+    },
+    {
+      "sample_id": "vica322k:arkitscenes/base/object_size_estimation.json:10043",
+      "source": "vica322k",
+      "task_type": "longest_side",
+      "semantic_modality": "video_file",
+      "sequence_lengths": [],
+      "rendered_rgb_sha256": [],
+      "video_first_frames": [
+        [
+          1920,
+          1440
+        ]
+      ],
+      "qa_preserved": true
+    },
+    {
+      "sample_id": "vica322k:arkitscenes/base/object_abs_distance.json:1003",
+      "source": "vica322k",
+      "task_type": "surface_distance",
+      "semantic_modality": "video_file",
+      "sequence_lengths": [],
+      "rendered_rgb_sha256": [],
+      "video_first_frames": [
+        [
+          1920,
+          1440
+        ]
+      ],
+      "qa_preserved": true
+    },
+    {
+      "sample_id": "vica322k:arkitscenes/base/triangular_positional_relationship.json:10197",
+      "source": "vica322k",
+      "task_type": "triangle_lengths_angles",
+      "semantic_modality": "video_file",
+      "sequence_lengths": [],
+      "rendered_rgb_sha256": [],
+      "video_first_frames": [
+        [
+          1920,
+          1440
+        ]
+      ],
+      "qa_preserved": true
+    },
+    {
+      "sample_id": "vsi590k:288776",
+      "source": "vsi590k",
+      "task_type": "angle",
+      "semantic_modality": "single_image",
+      "sequence_lengths": [],
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+      "source": "vsi590k",
+      "task_type": "angle",
+      "semantic_modality": "video_file",
+      "sequence_lengths": [],
+      "rendered_rgb_sha256": [],
+      "video_first_frames": [
+        [
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+          480
+        ]
+      ],
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+    },
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+      "sample_id": "vsi590k:178076",
+      "source": "vsi590k",
+      "task_type": "area",
+      "semantic_modality": "video_file",
+      "sequence_lengths": [],
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+        ]
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+      "source": "vsi590k",
+      "task_type": "closest_surface",
+      "semantic_modality": "video_file",
+      "sequence_lengths": [],
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+      "video_first_frames": [
+        [
+          640,
+          480
+        ]
+      ],
+      "qa_preserved": true
+    },
+    {
+      "sample_id": "vsi590k:379919",
+      "source": "vsi590k",
+      "task_type": "longest_side",
+      "semantic_modality": "single_image",
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+      "source": "vsi590k",
+      "task_type": "longest_side",
+      "semantic_modality": "video_file",
+      "sequence_lengths": [],
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+      "video_first_frames": [
+        [
+          640,
+          480
+        ]
+      ],
+      "qa_preserved": true
+    },
+    {
+      "sample_id": "spacevista:10424",
+      "source": "spacevista",
+      "task_type": "area",
+      "semantic_modality": "video_frame_sequence",
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+        16
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+      ],
+      "video_first_frames": [],
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+  ],
+  "reuses_previously_verified_media_cache": true,
+  "reader_sha256": "9b1417966d0fd3e489a2ecbcd0bccf19e3b484a879cd23e4d7f42c1f4d61ceca",
+  "scope": "one case per nonempty source/task/modality stratum plus recovered room_size regression; real TAR ranges, frame order, decode and QA parity; not model training"
+}

small/README.md

@@ -1,36 +1,23 @@
-# Small · video-v1.0
+# Small · scaling-v1.0
 
-训练题数：1,000,000。Small 用于先验恢复实验，Large 用于扩量；两者不能直接拼接。
-完整来源、任务与视频占比见[数据卡](../README.md)。主验证／测试保持原题不变，新增视频评测单独报告。
-
-## 媒体读取
-
-下载 `tools/load_stage3_data.py`、`tools/small_training.py`、`tools/input_sequences.py` 到同一目录。
-安装 `huggingface_hub pyarrow requests pillow numpy av`，然后：
+训练题数：1,000,000。新版 Small 按 Large 来源、任务和模态配比抽样。
+历史均衡 Small 请固定 `revision='video-v1.0'`，不要混用新旧索引与标注。
 
 ```python
 from load_stage3_data import Stage3Dataset
-data = Stage3Dataset('/scratch/my-stage3-cache', config='small',
-                     revision='video-v1.0', max_cache_gib=40)
-row = next(data.rows('train'))
-sample = data.sample(row)
-visual = sample['visual_inputs']
-# visual['images']: 普通图片，已渲染定位提示
-# visual['video_frame_sequences']: list[list[PIL.Image]]，每组独立且有序
-# visual['video_files']: 本地视频路径，交给模型的视频解码器
-# sample['question'], sample['answer']: 答案监督文本
+data = Stage3Dataset('/scratch/your-stage3-cache', revision='scaling-v1.0', config='small', max_cache_gib=40)
+for row in data.rows('train'):
+    sample = data.sample(row)
+    question, answer = sample['question'], sample['answer']
+    visual = sample['visual_inputs']
+    # visual['video_files']: actual video files; choose your training frame sampler.
+    # visual['video_frame_sequences']: existing ordered frame groups; preserve them.
+    # visual['images']: single-image / multi-view inputs; preserve input order and markers.
+    break
 ```
 
-把 `visual` 的三种输入分别交给所用模型处理器。不要再次拼接旧的 `sample['images']`，
-否则帧会重复输入。各序列独立，不要把多个视频合并为一个；未知时间戳不能自行伪造。
-框／点／mask 先在原尺寸图像上渲染，再由模型处理器统一缩放。
-若需抽帧，必须评估是否遗漏题目所需证据；默认读取器不会擅自丢帧。
-
-训练时按可复现随机种子混合打乱，不按来源整块依次训练。Small/Large 的配比见数据卡；
-若训练器另行重采样，记录实际曝光比例。验证和测试不参与训练抽样。
-
-TAR 用于批量传输，读取器按索引进行 Range 读取并校验媒体哈希，无需解包全库。
-大量离线训练可按 `media-manifest.json` 下载所需 TAR；300 GiB 空间应使用有界缓存。
-额外视频评测入口为 `data.rows('video_validation')` / `data.rows('video_test')`。
-
-此读取器不是特定模型的完整训练器；仍需按模型多模态接口构建输入张量和 loss mask。
+读取器按需取媒体 TAR 字节范围，不必下载整个媒体目录。约300GiB环境请限制缓存。
+CA-VQA 不新增参考帧提示；保留原顺序和全部帧。SpaceVista 使用本版分组契约，保留重复帧和标注。
+时间戳未知时不伪造原始 FPS。不要同时输入 legacy images/videos 和 visual_inputs，避免重复送入。
+上面的循环仅演示读取；正式训练需要跨 shard shuffle，不要直接沿物理行序按来源连续训练。
+原 QA、答案不变；共享评测存在的 Hypersim 同场景风险详见仓库主 README。

small/asset-index.parquet

@@ -1,3 +1,3 @@
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-oid sha256:e6bc0f319ebf53e240d694d73baae4abea759741035ef390b3f755bc6cd1a21c
-size 83294272
+oid sha256:7c0fca917cb1a0ee83b70d1eb119f516629bd8cbfe7a79591d5735f9bfb24336
+size 47347667

small/media-manifest.json

@@ -2,45 +2,45 @@
   "input_revision": "3de316b518343519f0b7f8877eaabb20b8221627",
   "baseline_revision": "b93f98b81a18b15dc0cdd97734fa6237fa0c3764",
   "split_paths": {
-    "train": "video_v1/small/train/",
-    "video_validation": "video_v1/small/video_validation/",
-    "video_test": "video_v1/small/video_test/",
-    "validation": "video_v1/small/validation/",
-    "test": "video_v1/small/test/"
+    "validation": "scaling_v1/small/validation/",
+    "test": "scaling_v1/small/test/",
+    "video_validation": "scaling_v1/small/video_validation/",
+    "video_test": "scaling_v1/small/video_test/",
+    "train": "scaling_v1/small/train/"
   },
   "missing_media_references": 0,
-  "sequence_contract_path": "video_v1/manifests/spacevista-sequences.parquet",
+  "sequence_contract_path": "scaling_v1/manifests/spacevista-sequences.parquet",
   "sequence_contract_required": true,
   "assets_by_source": [
     {
       "source": "cavqa",
-      "assets": 252812,
-      "selected_bytes": 351502580372
+      "assets": 233170,
+      "selected_bytes": 327361511022
     },
     {
       "source": "hispatial",
-      "assets": 345503,
-      "selected_bytes": 40215294037
+      "assets": 601632,
+      "selected_bytes": 69266919660
     },
     {
       "source": "sims_vsi",
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-      "selected_bytes": 6334925479
+      "assets": 446,
+      "selected_bytes": 1104870400
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     {
       "source": "spacevista",
-      "assets": 1032515,
-      "selected_bytes": 163371344921
+      "assets": 213831,
+      "selected_bytes": 33244705907
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     {
       "source": "vica322k",
-      "assets": 5400,
-      "selected_bytes": 151394323578
+      "assets": 1022,
+      "selected_bytes": 22247019233
     },
     {
       "source": "vsi590k",
-      "assets": 10037,
-      "selected_bytes": 156674196090
+      "assets": 2097,
+      "selected_bytes": 43861603881
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   "shards": [
@@ -2884,11 +2884,6 @@
       "size": 4282296320,
       "sha256": "e99976ae3f4250ddb62b78c84d752aae700a8ded226e1b9fb34cecc9a75a9029"
     },
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-      "size": 99706880,
-      "sha256": "e9391afc7c0680b8017a763819abaa266a56d4edc8e0da020ef8b91a3c159c11"
-    },
     {
       "path": "media/vsi590k/vsi590k-scannetppv2-00000.tar",
       "size": 4287068160,