Record the gate correction in the concept document

The user-facing statement still carried the retired correlation
threshold. Adds the correction, the joint condition that replaces it,
the width diagnosis, and the closure of the shrink-N route.

Co-Authored-By: Claude <noreply@anthropic.com>

7 files changed, 204 insertions, 1 deletions

diff --git a/PROJECT_CONCEPT.md b/PROJECT_CONCEPT.md
index d5c0265..d9232e6 100644
--- a/PROJECT_CONCEPT.md
+++ b/PROJECT_CONCEPT.md
@@ -297,6 +297,53 @@ E(\text{permuted / semantically wrong configurations}),
 唯一、低能的一致解释。视图级探针给出了这个方向的第一个正信号：
 以粗对齐为参照系，细粒度状态在种群上下文中变得跨模态可比。
 
+## 2026-08-01 更正：我们一直在看错的那个统计量
+
+过去几个月的判据是「真配对处的场相关 ρ 要到 0.9」，低于就不值得跑
+搜索。**这条判据是错的**，而且是被我们自己的对照实验推翻的：把那个
+能恢复到 95.3% 的合成世界的场截断到秩 r，ρ 几乎不动（0.902 / 0.906
+/ 0.928），恢复率却横跨整个量程（6.2% / 12.9% / **95.6%**，秩 4 / 8
+/ 16）。一个 ρ=0.906、已经越过所谓门槛的场，只恢复 13%。
+
+理论其实早就说了，是我们读错了。相关匹配的门槛是为**满秩可交换噪声**
+推导的，那里 N²/2 个矩阵元每一个都独立约束配对；而秩 r 的共享成分只
+提供约 rN 个。256 个场景、r≈10 时，这是门槛公式所假设的八分之一。
+
+新的判据是**二元条件**：场相关，加上**共享谱宽度**——两侧关系场主
+特征子空间之间主角余弦大于 0.7 的方向数，以打乱场景顺序的 null 为
+基线（null=1.0）。两者缺一不可：18 个共享方向配 ρ=0.508 会失败，
+11 个配 ρ=0.902 也会失败。
+
+旧判据一撤，「自然数据不值得跑搜索」的理由也没了，于是跑了：
+**0.0000，chance 是 0.0039**。结论没变，但现在它是测量结果，而不是
+从一个并不支配它的统计量推出来的。
+
+诊断比「特征不够好」精确得多：照片上两个模态**各自都很丰富**（视觉
+有效秩 40，文本 48），但只在 15 个方向上一致——**各自丰富的东西不
+是同一批东西**。而且这个交集对视觉侧几乎一切可调项免疫：分割数 6→16
+掉 1 个方向，直接给标注框只买到 1 个，集合核加三阶矩把视觉自身的秩
+抬高 7 而共享只 +1。文本侧能推动但很快饱和（10→17）。场景数从 256
+加到 1024 反而从 23 降到 19。
+
+于是**语料选择变成一等变量**：合成世界能到 26 个共享方向，是因为它
+的 caption 恰好陈述了完整世界状态；VG 只有 15，是因为一句 region
+description 和一个 patch descriptor 大概就只在这么多个方面重叠。下一
+步要找的不是更好的特征，而是**天然重叠就宽的语料对**——稠密描述、
+带完整规格的商品图文、截图配可访问性树。
+
+顺带的免费收益：判别方向按自身特征值加权、保留更宽的基底，再把文本
+向量提到 128 维，把 ρ 从 0.656 推到 0.716，共享方向从 10 到 16；
+短语按中心词/修饰词分开编码再到 0.725。hubness 假设被证伪（度模型只
+占场方差 1–5%，去掉反而让 ρ 略升）。
+
+另外**「缩小 N」这条路彻底死了，而且方向是反的**：信息论门槛
+ρ_IT ≈ √(4 log N / N) 随 N 减小而**升高**（256 时 0.29，64 时 0.51，
+16 时 0.83）。闸门直接证实：N = 16 至 96 的每个尺寸上，强搜索器找到
+的最深状态都比真值更深，3/3 复现。真值都不是最优解时，算法类别就不
+重要了——这也顺带回答了「接受指数级代价」为什么没用。
+
+详见 `RANK_RESULTS.md`。
+
 ## 合作者应避免的误解
 
 - 这不是“LLM 通过文本已经获得了视觉知觉”。语言模型拥有的可能是可被
diff --git a/RANK_RESULTS.md b/RANK_RESULTS.md
index e1c4343..0e481b9 100644
--- a/RANK_RESULTS.md
+++ b/RANK_RESULTS.md
@@ -91,6 +91,18 @@ segmentation and nothing over using fewer segments. This confirms from a new
 angle what the earlier oracle-box comparison found: **segmentation is not the
 constraint on photographs.**
 
+The correlation column in that table should be read against a noise floor.
+Moving the segmentation eigendecomposition from CPU to GPU changes nothing in
+the recipe, yet re-deriving segments through it takes the baseline correlation
+from 0.6559 to 0.6767 — the eigenvectors differ in sign and, where eigenvalues
+are near-degenerate, in rotation, so the clustering that follows lands
+differently. **Segmentation reseeding is worth about 0.02 in correlation**, so
+the 6-versus-16-segment and oracle-box differences in that table are inside
+the noise and only the shared counts distinguish them. The session's headline
+movements, 0.656 to 0.716 to 0.725, are three times the floor. The pipeline
+itself is unchanged: run against the original CPU-derived segments it
+reproduces 0.6559 exactly.
+
 The text side does move it, and saturates:
 
 | Text vector dimension | text eff. rank | ρ | shared |
diff --git a/logs/obj_base_gpu.log b/logs/obj_base_gpu.log
index c3ea786..3fccb88 100644
--- a/logs/obj_base_gpu.log
+++ b/logs/obj_base_gpu.log
@@ -1,3 +1,5 @@
 Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.
 `torch_dtype` is deprecated! Use `dtype` instead!
-
segment:   0%|          | 0/250 [00:00<?, ?it/s]
segment:   0%|          | 1/250 [00:02<10:41,  2.58s/it]
segment:   1%|          | 2/250 [00:04<08:49,  2.13s/it]
segment:   1%|          | 3/250 [00:06<08:11,  1.99s/it]
segment:   2%|▏         | 4/250 [00:08<07:52,  1.92s/it]
segment:   2%|▏         | 5/250 [00:09<07:41,  1.88s/it]
segment:   2%|▏         | 6/250 [00:11<07:02,  1.73s/it]
segment:   3%|▎         | 7/250 [00:12<06:28,  1.60s/it]
segment:   3%|▎         | 8/250 [00:13<06:06,  1.52s/it]
segment:   4%|▎         | 9/250 [00:15<05:50,  1.45s/it]
segment:   4%|▍         | 10/250 [00:16<05:40,  1.42s/it]
segment:   4%|▍         | 11/250 [00:17<05:31,  1.39s/it]
segment:   5%|▍         | 12/250 [00:19<05:26,  1.37s/it]
segment:   5%|▌         | 13/250 [00:20<05:23,  1.37s/it]
segment:   6%|▌         | 14/250 [00:21<05:20,  1.36s/it]
segment:   6%|▌         | 15/250 [00:23<05:16,  1.35s/it]
segment:   6%|▋         | 16/250 [00:24<05:14,  1.34s/it]
segment:   7%|▋         | 17/250 [00:25<05:12,  1.34s/it]
segment:   7%|▋         | 18/250 [00:27<05:10,  1.34s/it]
segment:   8%|▊         | 19/250 [00:28<05:07,  1.33s/it]
segment:   8%|▊         | 20/250 [00:29<05:06,  1.33s/it]
segment:   8%|▊         | 21/250 [00:31<05:04,  1.33s/it]
segment:   9%|▉         | 22/250 [00:32<05:01,  1.32s/it]
segment:   9%|▉         | 23/250 [00:33<04:59,  1.32s/it]
segment:  10%|▉         | 24/250 [00:35<04:58,  1.32s/it]
segment:  10%|█         | 25/250 [00:36<04:57,  1.32s/it]
segment:  10%|█         | 26/250 [00:37<04:56,  1.32s/it]
segment:  11%|█         | 27/250 [00:39<04:56,  1.33s/it]
segment:  11%|█         | 28/250 [00:40<04:55,  1.33s/it]
segment:  12%|█▏        | 29/250 [00:41<04:54,  1.33s/it]
segment:  12%|█▏        | 30/250 [00:43<04:53,  1.33s/it]
segment:  12%|█▏        | 31/250 [00:44<04:51,  1.33s/it]
segment:  13%|█▎        | 32/250 [00:45<04:48,  1.32s/it]
segment:  13%|█▎        | 33/250 [00:47<04:46,  1.32s/it]
segment:  14%|█▎        | 34/250 [00:48<04:44,  1.32s/it]
segment:  14%|█▍        | 35/250 [00:49<04:43,  1.32s/it]
segment:  14%|█▍        | 36/250 [00:51<04:43,  1.32s/it]
segment:  15%|█▍        | 37/250 [00:52<04:41,  1.32s/it]
segment:  15%|█▌        | 38/250 [00:53<04:40,  1.32s/it]
segment:  16%|█▌        | 39/250 [00:55<04:39,  1.32s/it]
segment:  16%|█▌        | 40/250 [00:56<04:36,  1.32s/it]
segment:  16%|█▋        | 41/250 [00:57<04:34,  1.31s/it]
segment:  17%|█▋        | 42/250 [00:59<04:33,  1.32s/it]
segment:  17%|█▋        | 43/250 [01:00<04:32,  1.32s/it]
segment:  18%|█▊        | 44/250 [01:01<04:31,  1.32s/it]
segment:  18%|█▊        | 45/250 [01:03<04:34,  1.34s/it]
segment:  18%|█▊        | 46/250 [01:04<04:31,  1.33s/it]
segment:  19%|█▉        | 47/250 [01:05<04:27,  1.32s/it]
segment:  19%|█▉        | 48/250 [01:06<04:25,  1.32s/it]
segment:  20%|█▉        | 49/250 [01:08<04:23,  1.31s/it]
segment:  20%|██        | 50/250 [01:09<04:21,  1.31s/it]
segment:  20%|██        | 51/250 [01:10<04:21,  1.31s/it]
segment:  21%|██        | 52/250 [01:12<04:20,  1.31s/it]
segment:  21%|██        | 53/250 [01:13<04:19,  1.32s/it]
segment:  22%|██▏       | 54/250 [01:14<04:17,  1.32s/it]
segment:  22%|██▏       | 55/250 [01:16<04:16,  1.32s/it]
segment:  22%|██▏       | 56/250 [01:17<04:15,  1.32s/it]
segment:  23%|██▎       | 57/250 [01:18<04:14,  1.32s/it]
segment:  23%|██▎       | 58/250 [01:20<04:13,  1.32s/it]
segment:  24%|██▎       | 59/250 [01:21<04:11,  1.32s/it]
segment:  24%|██▍       | 60/250 [01:22<04:10,  1.32s/it]
segment:  24%|██▍       | 61/250 [01:24<04:08,  1.32s/it]
segment:  25%|██▍       | 62/250 [01:25<04:07,  1.32s/it]
segment:  25%|██▌       | 63/250 [01:26<04:06,  1.32s/it]
segment:  26%|██▌       | 64/250 [01:27<04:04,  1.31s/it]
segment:  26%|██▌       | 65/250 [01:29<04:03,  1.32s/it]
segment:  26%|██▋       | 66/250 [01:30<04:01,  1.32s/it]
segment:  27%|██▋       | 67/250 [01:31<04:01,  1.32s/it]
segment:  27%|██▋       | 68/250 [01:33<03:58,  1.31s/it]
segment:  28%|██▊       | 69/250 [01:34<03:57,  1.31s/it]
segment:  28%|██▊       | 70/250 [01:35<03:56,  1.31s/it]
segment:  28%|██▊       | 71/250 [01:37<03:56,  1.32s/it]
segment:  29%|██▉       | 72/250 [01:38<03:54,  1.32s/it]
segment:  29%|██▉       | 73/250 [01:39<03:52,  1.31s/it]
segment:  30%|██▉       | 74/250 [01:41<03:52,  1.32s/it]
segment:  30%|███       | 75/250 [01:42<03:51,  1.32s/it]
segment:  30%|███       | 76/250 [01:43<03:49,  1.32s/it]
segment:  31%|███       | 77/250 [01:45<03:47,  1.32s/it]
segment:  31%|███       | 78/250 [01:46<03:47,  1.32s/it]
segment:  32%|███▏      | 79/250 [01:47<03:44,  1.31s/it]
segment:  32%|███▏      | 80/250 [01:49<03:43,  1.32s/it]
segment:  32%|███▏      | 81/250 [01:50<03:42,  1.32s/it]
segment:  33%|███▎      | 82/250 [01:51<03:41,  1.32s/it]
segment:  33%|███▎      | 83/250 [01:53<03:39,  1.31s/it]
segment:  34%|███▎      | 84/250 [01:54<03:37,  1.31s/it]
segment:  34%|███▍      | 85/250 [01:55<03:37,  1.32s/it]
segment:  34%|███▍      | 86/250 [01:56<03:35,  1.31s/it]
segment:  35%|███▍      | 87/250 [01:58<03:34,  1.31s/it]
segment:  35%|███▌      | 88/250 [01:59<03:33,  1.32s/it]
segment:  36%|███▌      | 89/250 [02:00<03:32,  1.32s/it]
segment:  36%|███▌      | 90/250 [02:02<03:30,  1.32s/it]
segment:  36%|███▋      | 91/250 [02:03<03:30,  1.32s/it]
segment:  37%|███▋      | 92/250 [02:04<03:29,  1.32s/it]
segment:  37%|███▋      | 93/250 [02:06<03:28,  1.32s/it]
segment:  38%|███▊      | 94/250 [02:07<03:26,  1.33s/it]
segment:  38%|███▊      | 95/250 [02:08<03:24,  1.32s/it]
segment:  38%|███▊      | 96/250 [02:10<03:23,  1.32s/it]
segment:  39%|███▉      | 97/250 [02:11<03:21,  1.32s/it]
segment:  39%|███▉      | 98/250 [02:12<03:20,  1.32s/it]
segment:  40%|███▉      | 99/250 [02:14<03:18,  1.32s/it]
segment:  40%|████      | 100/250 [02:15<03:16,  1.31s/it]
segment:  40%|████      | 101/250 [02:16<03:15,  1.31s/it]
segment:  41%|████      | 102/250 [02:18<03:14,  1.31s/it]
segment:  41%|████      | 103/250 [02:19<03:13,  1.31s/it]
segment:  42%|████▏     | 104/250 [02:20<03:11,  1.31s/it]
segment:  42%|████▏     | 105/250 [02:21<03:10,  1.31s/it]
segment:  42%|████▏     | 106/250 [02:23<03:09,  1.32s/it]
segment:  43%|████▎     | 107/250 [02:24<03:08,  1.32s/it]
segment:  43%|████▎     | 108/250 [02:25<03:06,  1.31s/it]
segment:  44%|████▎     | 109/250 [02:27<03:04,  1.31s/it]
segment:  44%|████▍     | 110/250 [02:28<03:03,  1.31s/it]
segment:  44%|████▍     | 111/250 [02:29<03:03,  1.32s/it]
segment:  45%|████▍     | 112/250 [02:31<03:02,  1.32s/it]
segment:  45%|████▌     | 113/250 [02:32<03:00,  1.32s/it]
segment:  46%|████▌     | 114/250 [02:33<02:59,  1.32s/it]
segment:  46%|████▌     | 115/250 [02:35<02:57,  1.31s/it]
segment:  46%|████▋     | 116/250 [02:36<02:56,  1.32s/it]
segment:  47%|████▋     | 117/250 [02:37<02:55,  1.32s/it]
segment:  47%|████▋     | 118/250 [02:39<02:54,  1.32s/it]
segment:  48%|████▊     | 119/250 [02:40<02:52,  1.32s/it]
segment:  48%|████▊     | 120/250 [02:41<02:50,  1.31s/it]
segment:  48%|████▊     | 121/250 [02:43<02:48,  1.31s/it]
segment:  49%|████▉     | 122/250 [02:44<02:47,  1.31s/it]
segment:  49%|████▉     | 123/250 [02:45<02:46,  1.31s/it]
segment:  50%|████▉     | 124/250 [02:46<02:45,  1.32s/it]
segment:  50%|█████     | 125/250 [02:48<02:43,  1.31s/it]
segment:  50%|█████     | 126/250 [02:49<02:42,  1.31s/it]
segment:  51%|█████     | 127/250 [02:50<02:40,  1.31s/it]
segment:  51%|█████     | 128/250 [02:52<02:39,  1.31s/it]
segment:  52%|█████▏    | 129/250 [02:53<02:38,  1.31s/it]
segment:  52%|█████▏    | 130/250 [02:54<02:37,  1.31s/it]
segment:  52%|█████▏    | 131/250 [02:56<02:36,  1.32s/it]
segment:  53%|█████▎    | 132/250 [02:57<02:35,  1.32s/it]
segment:  53%|█████▎    | 133/250 [02:58<02:33,  1.31s/it]
segment:  54%|█████▎    | 134/250 [03:00<02:31,  1.31s/it]
segment:  54%|█████▍    | 135/250 [03:01<02:30,  1.31s/it]
segment:  54%|█████▍    | 136/250 [03:02<02:28,  1.31s/it]
segment:  55%|█████▍    | 137/250 [03:03<02:28,  1.31s/it]
segment:  55%|█████▌    | 138/250 [03:05<02:26,  1.31s/it]
segment:  56%|█████▌    | 139/250 [03:06<02:25,  1.31s/it]
segment:  56%|█████▌    | 140/250 [03:07<02:23,  1.31s/it]
segment:  56%|█████▋    | 141/250 [03:09<02:22,  1.31s/it]
segment:  57%|█████▋    | 142/250 [03:10<02:21,  1.31s/it]
segment:  57%|█████▋    | 143/250 [03:11<02:20,  1.31s/it]
segment:  58%|█████▊    | 144/250 [03:13<02:19,  1.32s/it]
segment:  58%|█████▊    | 145/250 [03:14<02:18,  1.32s/it]
segment:  58%|█████▊    | 146/250 [03:15<02:18,  1.33s/it]
segment:  59%|█████▉    | 147/250 [03:17<02:15,  1.31s/it]
segment:  59%|█████▉    | 148/250 [03:18<02:13,  1.31s/it]
segment:  60%|█████▉    | 149/250 [03:19<02:12,  1.31s/it]
segment:  60%|██████    | 150/250 [03:21<02:11,  1.32s/it]
segment:  60%|██████    | 151/250 [03:22<02:10,  1.32s/it]
segment:  61%|██████    | 152/250 [03:23<02:09,  1.32s/it]
segment:  61%|██████    | 153/250 [03:25<02:08,  1.32s/it]
segment:  62%|██████▏   | 154/250 [03:26<02:07,  1.33s/it]
segment:  62%|██████▏   | 155/250 [03:27<02:05,  1.32s/it]
segment:  62%|██████▏   | 156/250 [03:29<02:04,  1.33s/it]
segment:  63%|██████▎   | 157/250 [03:30<02:02,  1.32s/it]
segment:  63%|██████▎   | 158/250 [03:31<02:01,  1.32s/it]
segment:  64%|██████▎   | 159/250 [03:33<02:02,  1.35s/it]
segment:  64%|██████▍   | 160/250 [03:34<02:00,  1.34s/it]
segment:  64%|██████▍   | 161/250 [03:35<01:58,  1.34s/it]
segment:  65%|██████▍   | 162/250 [03:37<01:57,  1.33s/it]
segment:  65%|██████▌   | 163/250 [03:38<01:55,  1.33s/it]
segment:  66%|██████▌   | 164/250 [03:39<02:00,  1.40s/it]
segment:  66%|██████▌   | 165/250 [03:41<02:09,  1.53s/it]
segment:  66%|██████▋   | 166/250 [03:43<02:16,  1.62s/it]
segment:  67%|██████▋   | 167/250 [03:45<02:19,  1.68s/it]
segment:  67%|██████▋   | 168/250 [03:47<02:21,  1.73s/it]
segment:  68%|██████▊   | 169/250 [03:49<02:22,  1.76s/it]
segment:  68%|██████▊   | 170/250 [03:50<02:22,  1.79s/it]
segment:  68%|██████▊   | 171/250 [03:52<02:22,  1.80s/it]
segment:  69%|██████▉   | 172/250 [03:54<02:21,  1.82s/it]
segment:  69%|██████▉   | 173/250 [03:56<02:20,  1.82s/it]
segment:  70%|██████▉   | 174/250 [03:58<02:19,  1.83s/it]
segment:  70%|███████   | 175/250 [04:00<02:19,  1.86s/it]
segment:  70%|███████   | 176/250 [04:02<02:17,  1.85s/it]
segment:  71%|███████   | 177/250 [04:03<02:15,  1.85s/it]
segment:  71%|███████   | 178/250 [04:05<02:13,  1.86s/it]
segment:  72%|███████▏  | 179/250 [04:07<02:11,  1.86s/it]
segment:  72%|███████▏  | 180/250 [04:09<02:09,  1.85s/it]
segment:  72%|███████▏  | 181/250 [04:11<02:07,  1.84s/it]
segment:  73%|███████▎  | 182/250 [04:13<02:05,  1.84s/it]
segment:  73%|███████▎  | 183/250 [04:15<02:03,  1.84s/it]
segment:  74%|███████▎  | 184/250 [04:16<02:01,  1.84s/it]
segment:  74%|███████▍  | 185/250 [04:18<01:59,  1.84s/it]
segment:  74%|███████▍  | 186/250 [04:20<01:58,  1.85s/it]
segment:  75%|███████▍  | 187/250 [04:22<01:56,  1.85s/it]
segment:  75%|███████▌  | 188/250 [04:24<01:54,  1.85s/it]
segment:  76%|███████▌  | 189/250 [04:26<01:52,  1.85s/it]
segment:  76%|███████▌  | 190/250 [04:27<01:50,  1.85s/it]
segment:  76%|███████▋  | 191/250 [04:29<01:49,  1.86s/it]
segment:  77%|███████▋  | 192/250 [04:31<01:47,  1.86s/it]
segment:  77%|███████▋  | 193/250 [04:33<01:45,  1.85s/it]
segment:  78%|███████▊  | 194/250 [04:35<01:43,  1.85s/it]
segment:  78%|███████▊  | 195/250 [04:37<01:41,  1.85s/it]
segment:  78%|███████▊  | 196/250 [04:39<01:40,  1.86s/it]
segment:  79%|███████▉  | 197/250 [04:40<01:38,  1.86s/it]
segment:  79%|███████▉  | 198/250 [04:42<01:36,  1.85s/it]
segment:  80%|███████▉  | 199/250 [04:44<01:34,  1.85s/it]
segment:  80%|████████  | 200/250 [04:46<01:32,  1.85s/it]
segment:  80%|████████  | 201/250 [04:48<01:30,  1.85s/it]
segment:  81%|████████  | 202/250 [04:50<01:28,  1.85s/it]
segment:  81%|████████  | 203/250 [04:52<01:26,  1.85s/it]
segment:  82%|████████▏ | 204/250 [04:53<01:25,  1.85s/it]
segment:  82%|████████▏ | 205/250 [04:55<01:23,  1.85s/it]
segment:  82%|████████▏ | 206/250 [04:57<01:22,  1.87s/it]
segment:  83%|████████▎ | 207/250 [04:59<01:20,  1.86s/it]
segment:  83%|████████▎ | 208/250 [05:01<01:18,  1.86s/it]
segment:  84%|████████▎ | 209/250 [05:03<01:16,  1.86s/it]
segment:  84%|████████▍ | 210/250 [05:05<01:15,  1.88s/it]
segment:  84%|████████▍ | 211/250 [05:06<01:12,  1.87s/it]
segment:  85%|████████▍ | 212/250 [05:08<01:10,  1.86s/it]
segment:  85%|████████▌ | 213/250 [05:10<01:08,  1.85s/it]
segment:  86%|████████▌ | 214/250 [05:12<01:06,  1.86s/it]
segment:  86%|████████▌ | 215/250 [05:14<01:04,  1.85s/it]
segment:  86%|████████▋ | 216/250 [05:16<01:02,  1.85s/it]
segment:  87%|████████▋ | 217/250 [05:18<01:01,  1.85s/it]
segment:  87%|████████▋ | 218/250 [05:19<00:59,  1.86s/it]
segment:  88%|████████▊ | 219/250 [05:21<00:57,  1.85s/it]
segment:  88%|████████▊ | 220/250 [05:23<00:55,  1.85s/it]
segment:  88%|████████▊ | 221/250 [05:25<00:53,  1.85s/it]
segment:  89%|████████▉ | 222/250 [05:27<00:51,  1.85s/it]
segment:  89%|████████▉ | 223/250 [05:29<00:49,  1.85s/it]
segment:  90%|████████▉ | 224/250 [05:31<00:48,  1.85s/it]
segment:  90%|█████████ | 225/250 [05:32<00:46,  1.84s/it]
segment:  90%|█████████ | 226/250 [05:34<00:44,  1.84s/it]
segment:  91%|█████████ | 227/250 [05:36<00:42,  1.84s/it]
segment:  91%|█████████ | 228/250 [05:38<00:40,  1.85s/it]
segment:  92%|█████████▏| 229/250 [05:40<00:38,  1.85s/it]
segment:  92%|█████████▏| 230/250 [05:42<00:36,  1.84s/it]
segment:  92%|█████████▏| 231/250 [05:43<00:34,  1.84s/it]
segment:  93%|█████████▎| 232/250 [05:45<00:33,  1.84s/it]
segment:  93%|█████████▎| 233/250 [05:47<00:31,  1.85s/it]
segment:  94%|█████████▎| 234/250 [05:49<00:29,  1.86s/it]
segment:  94%|█████████▍| 235/250 [05:51<00:28,  1.88s/it]
segment:  94%|█████████▍| 236/250 [05:53<00:26,  1.87s/it]
segment:  95%|█████████▍| 237/250 [05:56<00:30,  2.35s/it]
segment:  95%|█████████▌| 238/250 [05:58<00:26,  2.20s/it]
segment:  96%|█████████▌| 239/250 [06:00<00:22,  2.09s/it]
segment:  96%|█████████▌| 240/250 [06:02<00:20,  2.01s/it]
segment:  96%|█████████▋| 241/250 [06:04<00:17,  1.96s/it]
segment:  97%|█████████▋| 242/250 [06:05<00:15,  1.92s/it]
segment:  97%|█████████▋| 243/250 [06:07<00:13,  1.90s/it]
segment:  98%|█████████▊| 244/250 [06:09<00:11,  1.88s/it]
segment:  98%|█████████▊| 245/250 [06:11<00:09,  1.87s/it]
segment:  98%|█████████▊| 246/250 [06:13<00:07,  1.86s/it]
segment:  99%|█████████▉| 247/250 [06:15<00:05,  1.86s/it]
\ No newline at end of file
+
segment:   0%|          | 0/250 [00:00<?, ?it/s]
segment:   0%|          | 1/250 [00:02<10:41,  2.58s/it]
segment:   1%|          | 2/250 [00:04<08:49,  2.13s/it]
segment:   1%|          | 3/250 [00:06<08:11,  1.99s/it]
segment:   2%|▏         | 4/250 [00:08<07:52,  1.92s/it]
segment:   2%|▏         | 5/250 [00:09<07:41,  1.88s/it]
segment:   2%|▏         | 6/250 [00:11<07:02,  1.73s/it]
segment:   3%|▎         | 7/250 [00:12<06:28,  1.60s/it]
segment:   3%|▎         | 8/250 [00:13<06:06,  1.52s/it]
segment:   4%|▎         | 9/250 [00:15<05:50,  1.45s/it]
segment:   4%|▍         | 10/250 [00:16<05:40,  1.42s/it]
segment:   4%|▍         | 11/250 [00:17<05:31,  1.39s/it]
segment:   5%|▍         | 12/250 [00:19<05:26,  1.37s/it]
segment:   5%|▌         | 13/250 [00:20<05:23,  1.37s/it]
segment:   6%|▌         | 14/250 [00:21<05:20,  1.36s/it]
segment:   6%|▌         | 15/250 [00:23<05:16,  1.35s/it]
segment:   6%|▋         | 16/250 [00:24<05:14,  1.34s/it]
segment:   7%|▋         | 17/250 [00:25<05:12,  1.34s/it]
segment:   7%|▋         | 18/250 [00:27<05:10,  1.34s/it]
segment:   8%|▊         | 19/250 [00:28<05:07,  1.33s/it]
segment:   8%|▊         | 20/250 [00:29<05:06,  1.33s/it]
segment:   8%|▊         | 21/250 [00:31<05:04,  1.33s/it]
segment:   9%|▉         | 22/250 [00:32<05:01,  1.32s/it]
segment:   9%|▉         | 23/250 [00:33<04:59,  1.32s/it]
segment:  10%|▉         | 24/250 [00:35<04:58,  1.32s/it]
segment:  10%|█         | 25/250 [00:36<04:57,  1.32s/it]
segment:  10%|█         | 26/250 [00:37<04:56,  1.32s/it]
segment:  11%|█         | 27/250 [00:39<04:56,  1.33s/it]
segment:  11%|█         | 28/250 [00:40<04:55,  1.33s/it]
segment:  12%|█▏        | 29/250 [00:41<04:54,  1.33s/it]
segment:  12%|█▏        | 30/250 [00:43<04:53,  1.33s/it]
segment:  12%|█▏        | 31/250 [00:44<04:51,  1.33s/it]
segment:  13%|█▎        | 32/250 [00:45<04:48,  1.32s/it]
segment:  13%|█▎        | 33/250 [00:47<04:46,  1.32s/it]
segment:  14%|█▎        | 34/250 [00:48<04:44,  1.32s/it]
segment:  14%|█▍        | 35/250 [00:49<04:43,  1.32s/it]
segment:  14%|█▍        | 36/250 [00:51<04:43,  1.32s/it]
segment:  15%|█▍        | 37/250 [00:52<04:41,  1.32s/it]
segment:  15%|█▌        | 38/250 [00:53<04:40,  1.32s/it]
segment:  16%|█▌        | 39/250 [00:55<04:39,  1.32s/it]
segment:  16%|█▌        | 40/250 [00:56<04:36,  1.32s/it]
segment:  16%|█▋        | 41/250 [00:57<04:34,  1.31s/it]
segment:  17%|█▋        | 42/250 [00:59<04:33,  1.32s/it]
segment:  17%|█▋        | 43/250 [01:00<04:32,  1.32s/it]
segment:  18%|█▊        | 44/250 [01:01<04:31,  1.32s/it]
segment:  18%|█▊        | 45/250 [01:03<04:34,  1.34s/it]
segment:  18%|█▊        | 46/250 [01:04<04:31,  1.33s/it]
segment:  19%|█▉        | 47/250 [01:05<04:27,  1.32s/it]
segment:  19%|█▉        | 48/250 [01:06<04:25,  1.32s/it]
segment:  20%|█▉        | 49/250 [01:08<04:23,  1.31s/it]
segment:  20%|██        | 50/250 [01:09<04:21,  1.31s/it]
segment:  20%|██        | 51/250 [01:10<04:21,  1.31s/it]
segment:  21%|██        | 52/250 [01:12<04:20,  1.31s/it]
segment:  21%|██        | 53/250 [01:13<04:19,  1.32s/it]
segment:  22%|██▏       | 54/250 [01:14<04:17,  1.32s/it]
segment:  22%|██▏       | 55/250 [01:16<04:16,  1.32s/it]
segment:  22%|██▏       | 56/250 [01:17<04:15,  1.32s/it]
segment:  23%|██▎       | 57/250 [01:18<04:14,  1.32s/it]
segment:  23%|██▎       | 58/250 [01:20<04:13,  1.32s/it]
segment:  24%|██▎       | 59/250 [01:21<04:11,  1.32s/it]
segment:  24%|██▍       | 60/250 [01:22<04:10,  1.32s/it]
segment:  24%|██▍       | 61/250 [01:24<04:08,  1.32s/it]
segment:  25%|██▍       | 62/250 [01:25<04:07,  1.32s/it]
segment:  25%|██▌       | 63/250 [01:26<04:06,  1.32s/it]
segment:  26%|██▌       | 64/250 [01:27<04:04,  1.31s/it]
segment:  26%|██▌       | 65/250 [01:29<04:03,  1.32s/it]
segment:  26%|██▋       | 66/250 [01:30<04:01,  1.32s/it]
segment:  27%|██▋       | 67/250 [01:31<04:01,  1.32s/it]
segment:  27%|██▋       | 68/250 [01:33<03:58,  1.31s/it]
segment:  28%|██▊       | 69/250 [01:34<03:57,  1.31s/it]
segment:  28%|██▊       | 70/250 [01:35<03:56,  1.31s/it]
segment:  28%|██▊       | 71/250 [01:37<03:56,  1.32s/it]
segment:  29%|██▉       | 72/250 [01:38<03:54,  1.32s/it]
segment:  29%|██▉       | 73/250 [01:39<03:52,  1.31s/it]
segment:  30%|██▉       | 74/250 [01:41<03:52,  1.32s/it]
segment:  30%|███       | 75/250 [01:42<03:51,  1.32s/it]
segment:  30%|███       | 76/250 [01:43<03:49,  1.32s/it]
segment:  31%|███       | 77/250 [01:45<03:47,  1.32s/it]
segment:  31%|███       | 78/250 [01:46<03:47,  1.32s/it]
segment:  32%|███▏      | 79/250 [01:47<03:44,  1.31s/it]
segment:  32%|███▏      | 80/250 [01:49<03:43,  1.32s/it]
segment:  32%|███▏      | 81/250 [01:50<03:42,  1.32s/it]
segment:  33%|███▎      | 82/250 [01:51<03:41,  1.32s/it]
segment:  33%|███▎      | 83/250 [01:53<03:39,  1.31s/it]
segment:  34%|███▎      | 84/250 [01:54<03:37,  1.31s/it]
segment:  34%|███▍      | 85/250 [01:55<03:37,  1.32s/it]
segment:  34%|███▍      | 86/250 [01:56<03:35,  1.31s/it]
segment:  35%|███▍      | 87/250 [01:58<03:34,  1.31s/it]
segment:  35%|███▌      | 88/250 [01:59<03:33,  1.32s/it]
segment:  36%|███▌      | 89/250 [02:00<03:32,  1.32s/it]
segment:  36%|███▌      | 90/250 [02:02<03:30,  1.32s/it]
segment:  36%|███▋      | 91/250 [02:03<03:30,  1.32s/it]
segment:  37%|███▋      | 92/250 [02:04<03:29,  1.32s/it]
segment:  37%|███▋      | 93/250 [02:06<03:28,  1.32s/it]
segment:  38%|███▊      | 94/250 [02:07<03:26,  1.33s/it]
segment:  38%|███▊      | 95/250 [02:08<03:24,  1.32s/it]
segment:  38%|███▊      | 96/250 [02:10<03:23,  1.32s/it]
segment:  39%|███▉      | 97/250 [02:11<03:21,  1.32s/it]
segment:  39%|███▉      | 98/250 [02:12<03:20,  1.32s/it]
segment:  40%|███▉      | 99/250 [02:14<03:18,  1.32s/it]
segment:  40%|████      | 100/250 [02:15<03:16,  1.31s/it]
segment:  40%|████      | 101/250 [02:16<03:15,  1.31s/it]
segment:  41%|████      | 102/250 [02:18<03:14,  1.31s/it]
segment:  41%|████      | 103/250 [02:19<03:13,  1.31s/it]
segment:  42%|████▏     | 104/250 [02:20<03:11,  1.31s/it]
segment:  42%|████▏     | 105/250 [02:21<03:10,  1.31s/it]
segment:  42%|████▏     | 106/250 [02:23<03:09,  1.32s/it]
segment:  43%|████▎     | 107/250 [02:24<03:08,  1.32s/it]
segment:  43%|████▎     | 108/250 [02:25<03:06,  1.31s/it]
segment:  44%|████▎     | 109/250 [02:27<03:04,  1.31s/it]
segment:  44%|████▍     | 110/250 [02:28<03:03,  1.31s/it]
segment:  44%|████▍     | 111/250 [02:29<03:03,  1.32s/it]
segment:  45%|████▍     | 112/250 [02:31<03:02,  1.32s/it]
segment:  45%|████▌     | 113/250 [02:32<03:00,  1.32s/it]
segment:  46%|████▌     | 114/250 [02:33<02:59,  1.32s/it]
segment:  46%|████▌     | 115/250 [02:35<02:57,  1.31s/it]
segment:  46%|████▋     | 116/250 [02:36<02:56,  1.32s/it]
segment:  47%|████▋     | 117/250 [02:37<02:55,  1.32s/it]
segment:  47%|████▋     | 118/250 [02:39<02:54,  1.32s/it]
segment:  48%|████▊     | 119/250 [02:40<02:52,  1.32s/it]
segment:  48%|████▊     | 120/250 [02:41<02:50,  1.31s/it]
segment:  48%|████▊     | 121/250 [02:43<02:48,  1.31s/it]
segment:  49%|████▉     | 122/250 [02:44<02:47,  1.31s/it]
segment:  49%|████▉     | 123/250 [02:45<02:46,  1.31s/it]
segment:  50%|████▉     | 124/250 [02:46<02:45,  1.32s/it]
segment:  50%|█████     | 125/250 [02:48<02:43,  1.31s/it]
segment:  50%|█████     | 126/250 [02:49<02:42,  1.31s/it]
segment:  51%|█████     | 127/250 [02:50<02:40,  1.31s/it]
segment:  51%|█████     | 128/250 [02:52<02:39,  1.31s/it]
segment:  52%|█████▏    | 129/250 [02:53<02:38,  1.31s/it]
segment:  52%|█████▏    | 130/250 [02:54<02:37,  1.31s/it]
segment:  52%|█████▏    | 131/250 [02:56<02:36,  1.32s/it]
segment:  53%|█████▎    | 132/250 [02:57<02:35,  1.32s/it]
segment:  53%|█████▎    | 133/250 [02:58<02:33,  1.31s/it]
segment:  54%|█████▎    | 134/250 [03:00<02:31,  1.31s/it]
segment:  54%|█████▍    | 135/250 [03:01<02:30,  1.31s/it]
segment:  54%|█████▍    | 136/250 [03:02<02:28,  1.31s/it]
segment:  55%|█████▍    | 137/250 [03:03<02:28,  1.31s/it]
segment:  55%|█████▌    | 138/250 [03:05<02:26,  1.31s/it]
segment:  56%|█████▌    | 139/250 [03:06<02:25,  1.31s/it]
segment:  56%|█████▌    | 140/250 [03:07<02:23,  1.31s/it]
segment:  56%|█████▋    | 141/250 [03:09<02:22,  1.31s/it]
segment:  57%|█████▋    | 142/250 [03:10<02:21,  1.31s/it]
segment:  57%|█████▋    | 143/250 [03:11<02:20,  1.31s/it]
segment:  58%|█████▊    | 144/250 [03:13<02:19,  1.32s/it]
segment:  58%|█████▊    | 145/250 [03:14<02:18,  1.32s/it]
segment:  58%|█████▊    | 146/250 [03:15<02:18,  1.33s/it]
segment:  59%|█████▉    | 147/250 [03:17<02:15,  1.31s/it]
segment:  59%|█████▉    | 148/250 [03:18<02:13,  1.31s/it]
segment:  60%|█████▉    | 149/250 [03:19<02:12,  1.31s/it]
segment:  60%|██████    | 150/250 [03:21<02:11,  1.32s/it]
segment:  60%|██████    | 151/250 [03:22<02:10,  1.32s/it]
segment:  61%|██████    | 152/250 [03:23<02:09,  1.32s/it]
segment:  61%|██████    | 153/250 [03:25<02:08,  1.32s/it]
segment:  62%|██████▏   | 154/250 [03:26<02:07,  1.33s/it]
segment:  62%|██████▏   | 155/250 [03:27<02:05,  1.32s/it]
segment:  62%|██████▏   | 156/250 [03:29<02:04,  1.33s/it]
segment:  63%|██████▎   | 157/250 [03:30<02:02,  1.32s/it]
segment:  63%|██████▎   | 158/250 [03:31<02:01,  1.32s/it]
segment:  64%|██████▎   | 159/250 [03:33<02:02,  1.35s/it]
segment:  64%|██████▍   | 160/250 [03:34<02:00,  1.34s/it]
segment:  64%|██████▍   | 161/250 [03:35<01:58,  1.34s/it]
segment:  65%|██████▍   | 162/250 [03:37<01:57,  1.33s/it]
segment:  65%|██████▌   | 163/250 [03:38<01:55,  1.33s/it]
segment:  66%|██████▌   | 164/250 [03:39<02:00,  1.40s/it]
segment:  66%|██████▌   | 165/250 [03:41<02:09,  1.53s/it]
segment:  66%|██████▋   | 166/250 [03:43<02:16,  1.62s/it]
segment:  67%|██████▋   | 167/250 [03:45<02:19,  1.68s/it]
segment:  67%|██████▋   | 168/250 [03:47<02:21,  1.73s/it]
segment:  68%|██████▊   | 169/250 [03:49<02:22,  1.76s/it]
segment:  68%|██████▊   | 170/250 [03:50<02:22,  1.79s/it]
segment:  68%|██████▊   | 171/250 [03:52<02:22,  1.80s/it]
segment:  69%|██████▉   | 172/250 [03:54<02:21,  1.82s/it]
segment:  69%|██████▉   | 173/250 [03:56<02:20,  1.82s/it]
segment:  70%|██████▉   | 174/250 [03:58<02:19,  1.83s/it]
segment:  70%|███████   | 175/250 [04:00<02:19,  1.86s/it]
segment:  70%|███████   | 176/250 [04:02<02:17,  1.85s/it]
segment:  71%|███████   | 177/250 [04:03<02:15,  1.85s/it]
segment:  71%|███████   | 178/250 [04:05<02:13,  1.86s/it]
segment:  72%|███████▏  | 179/250 [04:07<02:11,  1.86s/it]
segment:  72%|███████▏  | 180/250 [04:09<02:09,  1.85s/it]
segment:  72%|███████▏  | 181/250 [04:11<02:07,  1.84s/it]
segment:  73%|███████▎  | 182/250 [04:13<02:05,  1.84s/it]
segment:  73%|███████▎  | 183/250 [04:15<02:03,  1.84s/it]
segment:  74%|███████▎  | 184/250 [04:16<02:01,  1.84s/it]
segment:  74%|███████▍  | 185/250 [04:18<01:59,  1.84s/it]
segment:  74%|███████▍  | 186/250 [04:20<01:58,  1.85s/it]
segment:  75%|███████▍  | 187/250 [04:22<01:56,  1.85s/it]
segment:  75%|███████▌  | 188/250 [04:24<01:54,  1.85s/it]
segment:  76%|███████▌  | 189/250 [04:26<01:52,  1.85s/it]
segment:  76%|███████▌  | 190/250 [04:27<01:50,  1.85s/it]
segment:  76%|███████▋  | 191/250 [04:29<01:49,  1.86s/it]
segment:  77%|███████▋  | 192/250 [04:31<01:47,  1.86s/it]
segment:  77%|███████▋  | 193/250 [04:33<01:45,  1.85s/it]
segment:  78%|███████▊  | 194/250 [04:35<01:43,  1.85s/it]
segment:  78%|███████▊  | 195/250 [04:37<01:41,  1.85s/it]
segment:  78%|███████▊  | 196/250 [04:39<01:40,  1.86s/it]
segment:  79%|███████▉  | 197/250 [04:40<01:38,  1.86s/it]
segment:  79%|███████▉  | 198/250 [04:42<01:36,  1.85s/it]
segment:  80%|███████▉  | 199/250 [04:44<01:34,  1.85s/it]
segment:  80%|████████  | 200/250 [04:46<01:32,  1.85s/it]
segment:  80%|████████  | 201/250 [04:48<01:30,  1.85s/it]
segment:  81%|████████  | 202/250 [04:50<01:28,  1.85s/it]
segment:  81%|████████  | 203/250 [04:52<01:26,  1.85s/it]
segment:  82%|████████▏ | 204/250 [04:53<01:25,  1.85s/it]
segment:  82%|████████▏ | 205/250 [04:55<01:23,  1.85s/it]
segment:  82%|████████▏ | 206/250 [04:57<01:22,  1.87s/it]
segment:  83%|████████▎ | 207/250 [04:59<01:20,  1.86s/it]
segment:  83%|████████▎ | 208/250 [05:01<01:18,  1.86s/it]
segment:  84%|████████▎ | 209/250 [05:03<01:16,  1.86s/it]
segment:  84%|████████▍ | 210/250 [05:05<01:15,  1.88s/it]
segment:  84%|████████▍ | 211/250 [05:06<01:12,  1.87s/it]
segment:  85%|████████▍ | 212/250 [05:08<01:10,  1.86s/it]
segment:  85%|████████▌ | 213/250 [05:10<01:08,  1.85s/it]
segment:  86%|████████▌ | 214/250 [05:12<01:06,  1.86s/it]
segment:  86%|████████▌ | 215/250 [05:14<01:04,  1.85s/it]
segment:  86%|████████▋ | 216/250 [05:16<01:02,  1.85s/it]
segment:  87%|████████▋ | 217/250 [05:18<01:01,  1.85s/it]
segment:  87%|████████▋ | 218/250 [05:19<00:59,  1.86s/it]
segment:  88%|████████▊ | 219/250 [05:21<00:57,  1.85s/it]
segment:  88%|████████▊ | 220/250 [05:23<00:55,  1.85s/it]
segment:  88%|████████▊ | 221/250 [05:25<00:53,  1.85s/it]
segment:  89%|████████▉ | 222/250 [05:27<00:51,  1.85s/it]
segment:  89%|████████▉ | 223/250 [05:29<00:49,  1.85s/it]
segment:  90%|████████▉ | 224/250 [05:31<00:48,  1.85s/it]
segment:  90%|█████████ | 225/250 [05:32<00:46,  1.84s/it]
segment:  90%|█████████ | 226/250 [05:34<00:44,  1.84s/it]
segment:  91%|█████████ | 227/250 [05:36<00:42,  1.84s/it]
segment:  91%|█████████ | 228/250 [05:38<00:40,  1.85s/it]
segment:  92%|█████████▏| 229/250 [05:40<00:38,  1.85s/it]
segment:  92%|█████████▏| 230/250 [05:42<00:36,  1.84s/it]
segment:  92%|█████████▏| 231/250 [05:43<00:34,  1.84s/it]
segment:  93%|█████████▎| 232/250 [05:45<00:33,  1.84s/it]
segment:  93%|█████████▎| 233/250 [05:47<00:31,  1.85s/it]
segment:  94%|█████████▎| 234/250 [05:49<00:29,  1.86s/it]
segment:  94%|█████████▍| 235/250 [05:51<00:28,  1.88s/it]
segment:  94%|█████████▍| 236/250 [05:53<00:26,  1.87s/it]
segment:  95%|█████████▍| 237/250 [05:56<00:30,  2.35s/it]
segment:  95%|█████████▌| 238/250 [05:58<00:26,  2.20s/it]
segment:  96%|█████████▌| 239/250 [06:00<00:22,  2.09s/it]
segment:  96%|█████████▌| 240/250 [06:02<00:20,  2.01s/it]
segment:  96%|█████████▋| 241/250 [06:04<00:17,  1.96s/it]
segment:  97%|█████████▋| 242/250 [06:05<00:15,  1.92s/it]
segment:  97%|█████████▋| 243/250 [06:07<00:13,  1.90s/it]
segment:  98%|█████████▊| 244/250 [06:09<00:11,  1.88s/it]
segment:  98%|█████████▊| 245/250 [06:11<00:09,  1.87s/it]
segment:  98%|█████████▊| 246/250 [06:13<00:07,  1.86s/it]
segment:  99%|█████████▉| 247/250 [06:15<00:05,  1.86s/it]
segment:  99%|█████████▉| 248/250 [06:16<00:03,  1.86s/it]
segment: 100%|█████████▉| 249/250 [06:18<00:01,  1.85s/it]
segment: 100%|██████████| 250/250 [06:20<00:00,  1.85s/it]
segment: 100%|██████████| 250/250 [06:20<00:00,  1.52s/it]
+{"nodes": 2000, "mean_segments": 6.0, "min_segments": 6}
+Wrote artifacts/vg_5k/nobj_base_gpu.pt
diff --git a/logs/obj_large_gpu.log b/logs/obj_large_gpu.log
new file mode 100644
index 0000000..0ba1ad7
--- /dev/null
+++ b/logs/obj_large_gpu.log
@@ -0,0 +1,3 @@
+Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.
+`torch_dtype` is deprecated! Use `dtype` instead!
+
segment:   0%|          | 0/250 [00:00<?, ?it/s]
segment:   0%|          | 1/250 [00:03<14:05,  3.39s/it]
segment:   1%|          | 2/250 [00:06<12:26,  3.01s/it]
segment:   1%|          | 3/250 [00:08<11:50,  2.88s/it]
segment:   2%|▏         | 4/250 [00:11<11:34,  2.82s/it]
segment:   2%|▏         | 5/250 [00:14<11:25,  2.80s/it]
segment:   2%|▏         | 6/250 [00:17<11:16,  2.77s/it]
segment:   3%|▎         | 7/250 [00:19<11:09,  2.76s/it]
segment:   3%|▎         | 8/250 [00:22<11:08,  2.76s/it]
segment:   4%|▎         | 9/250 [00:25<11:02,  2.75s/it]
segment:   4%|▍         | 10/250 [00:28<10:59,  2.75s/it]
segment:   4%|▍         | 11/250 [00:30<10:55,  2.74s/it]
segment:   5%|▍         | 12/250 [00:33<10:52,  2.74s/it]
segment:   5%|▌         | 13/250 [00:36<10:49,  2.74s/it]
segment:   6%|▌         | 14/250 [00:38<10:47,  2.74s/it]
segment:   6%|▌         | 15/250 [00:41<10:43,  2.74s/it]
segment:   6%|▋         | 16/250 [00:44<10:40,  2.74s/it]
segment:   7%|▋         | 17/250 [00:47<10:39,  2.74s/it]
segment:   7%|▋         | 18/250 [00:49<10:35,  2.74s/it]
segment:   8%|▊         | 19/250 [00:52<10:31,  2.73s/it]
segment:   8%|▊         | 20/250 [00:55<10:29,  2.74s/it]
segment:   8%|▊         | 21/250 [00:58<10:26,  2.74s/it]
segment:   9%|▉         | 22/250 [01:00<10:24,  2.74s/it]
segment:   9%|▉         | 23/250 [01:03<10:21,  2.74s/it]
segment:  10%|▉         | 24/250 [01:06<10:20,  2.75s/it]
segment:  10%|█         | 25/250 [01:09<10:16,  2.74s/it]
segment:  10%|█         | 26/250 [01:11<10:11,  2.73s/it]
segment:  11%|█         | 27/250 [01:14<10:09,  2.73s/it]
segment:  11%|█         | 28/250 [01:17<10:07,  2.74s/it]
segment:  12%|█▏        | 29/250 [01:20<10:05,  2.74s/it]
segment:  12%|█▏        | 30/250 [01:22<10:02,  2.74s/it]
segment:  12%|█▏        | 31/250 [01:25<10:00,  2.74s/it]
segment:  13%|█▎        | 32/250 [01:28<09:56,  2.74s/it]
segment:  13%|█▎        | 33/250 [01:30<09:53,  2.74s/it]
segment:  14%|█▎        | 34/250 [01:33<09:50,  2.73s/it]
segment:  14%|█▍        | 35/250 [01:36<09:47,  2.73s/it]
segment:  14%|█▍        | 36/250 [01:39<10:36,  2.97s/it]
segment:  15%|█▍        | 37/250 [01:43<11:11,  3.15s/it]
segment:  15%|█▌        | 38/250 [01:47<11:35,  3.28s/it]
segment:  16%|█▌        | 39/250 [01:50<11:50,  3.37s/it]
segment:  16%|█▌        | 40/250 [01:54<11:58,  3.42s/it]
segment:  16%|█▋        | 41/250 [01:57<12:03,  3.46s/it]
segment:  17%|█▋        | 42/250 [02:01<12:07,  3.50s/it]
segment:  17%|█▋        | 43/250 [02:04<12:06,  3.51s/it]
segment:  18%|█▊        | 44/250 [02:08<12:05,  3.52s/it]
segment:  18%|█▊        | 45/250 [02:12<12:05,  3.54s/it]
segment:  18%|█▊        | 46/250 [02:15<12:00,  3.53s/it]
segment:  19%|█▉        | 47/250 [02:19<11:58,  3.54s/it]
segment:  19%|█▉        | 48/250 [02:22<11:56,  3.55s/it]
segment:  20%|█▉        | 49/250 [02:26<11:54,  3.55s/it]
segment:  20%|██        | 50/250 [02:29<11:51,  3.56s/it]
\ No newline at end of file
diff --git a/logs/rank_gate.log b/logs/rank_gate.log
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/logs/rank_gate.log
diff --git a/logs/rho_full_width.log b/logs/rho_full_width.log
index e69de29..f0255ac 100644
--- a/logs/rho_full_width.log
+++ b/logs/rho_full_width.log
@@ -0,0 +1 @@
+noise=0.0   rho=0.928  shared= 21.0  recovery=0.961
diff --git a/worldalign/rank_gate.py b/worldalign/rank_gate.py
new file mode 100644
index 0000000..d55f12e
--- /dev/null
+++ b/worldalign/rank_gate.py
@@ -0,0 +1,138 @@
+"""Is low-rank failure an information limit or a solver limit?
+
+The rank ladder is the result that retires the correlation gate, so it has to
+survive the objection the project has fallen for three times before: a limit
+that looks intrinsic and turns out to belong to the search operator. At rank
+eight the composed solver reaches 13%, and that alone does not say whether the
+truth is unreachable or merely unreached.
+
+The gate answers it. If the deepest state a strong searcher finds is deeper
+than the truth, the truth is not the optimum and no solver of any cost
+recovers it -- the width really is an information limit. If the truth is
+deepest and the solver still misses it, the ladder measures search difficulty
+instead and the conclusion has to be rewritten.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+
+import numpy as np
+import torch
+
+from .common import write_json
+from .spectral_match import grampa
+from .synth_fast_gate import ClosedFormEnergy, all_swaps, steepest_descent
+from .synth_triangle_gate import standardized
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--fields", default="artifacts/synth_v1/fields_tier0_ws_256.pt")
+    parser.add_argument("--ranks", type=int, nargs="+", default=[4, 8, 16, 256])
+    parser.add_argument("--restarts", type=int, default=60)
+    parser.add_argument("--trials", type=int, default=3)
+    parser.add_argument("--device", default="cuda:3")
+    parser.add_argument("--output", default="artifacts/synth_v1/rank_gate.json")
+    return parser.parse_args()
+
+
+def offdiagonal(matrix: np.ndarray) -> np.ndarray:
+    return matrix[~np.eye(len(matrix), dtype=bool)]
+
+
+def normalise(matrix: np.ndarray) -> np.ndarray:
+    values = offdiagonal(matrix)
+    out = (matrix - values.mean()) / values.std()
+    np.fill_diagonal(out, 0.0)
+    return out
+
+
+def truncate(matrix: np.ndarray, rank: int) -> np.ndarray:
+    symmetric = (matrix + matrix.T) / 2.0
+    values, vectors = np.linalg.eigh(symmetric)
+    order = np.argsort(np.abs(values))[::-1][:rank]
+    return (vectors[:, order] * values[order]) @ vectors[:, order].T
+
+
+def main() -> None:
+    args = parse_args()
+    state = torch.load(args.fields, map_location="cpu", weights_only=False)
+    visual_full = state["visual_field"].double().numpy()
+    text_full = state["text_field"].double().numpy()
+    size = len(visual_full)
+    device = torch.device(args.device)
+    swaps = all_swaps(size, device)
+
+    rows = []
+    for rank in args.ranks:
+        visual = normalise(truncate(visual_full, rank) if rank < size else visual_full)
+        text = normalise(truncate(text_full, rank) if rank < size else text_full)
+        verdicts, gaps, accuracies = [], [], []
+        for trial in range(args.trials):
+            generator = np.random.default_rng(trial)
+            hidden = generator.permutation(size)
+            shuffled = text[np.ix_(hidden, hidden)]
+            energy = ClosedFormEnergy(
+                standardized(torch.from_numpy(shuffled).to(device)).float(),
+                standardized(torch.from_numpy(visual).to(device)).float(),
+                1.0, 1.0, 256,
+            )
+            truth = torch.from_numpy(np.argsort(hidden).copy()).to(device)
+            truth_energy = float(energy.energy(truth[None])[0])
+
+            starts = [torch.from_numpy(grampa(visual, shuffled, 1.0).copy()).to(device)]
+            starts += [
+                torch.from_numpy(generator.permutation(size).copy()).to(device)
+                for _ in range(args.restarts)
+            ]
+            best_energy, best_accuracy = np.inf, 0.0
+            for start in starts:
+                final, _ = steepest_descent(energy, start, swaps, 4000)
+                value = float(energy.energy(final[None])[0])
+                if value < best_energy:
+                    best_energy = value
+                    best_accuracy = float(
+                        (hidden[final.cpu().numpy()] == np.arange(size)).mean()
+                    )
+            verdicts.append(truth_energy <= best_energy + 1e-6)
+            gaps.append(best_energy - truth_energy)
+            accuracies.append(best_accuracy)
+
+        row = {
+            "rank": rank,
+            "truth_is_deepest": f"{sum(verdicts)}/{args.trials}",
+            "mean_energy_gap_best_minus_truth": float(np.mean(gaps)),
+            "best_accuracy": float(np.mean(accuracies)),
+            "reading": (
+                "information limit" if sum(verdicts) == 0 else
+                "truth is optimal; failure is search" if sum(verdicts) == args.trials
+                else "mixed"
+            ),
+        }
+        rows.append(row)
+        print(
+            f"rank={rank:<5} truth deepest {row['truth_is_deepest']}  "
+            f"gap(best-truth)={row['mean_energy_gap_best_minus_truth']:+.4f}  "
+            f"best acc={row['best_accuracy']:.3f}  -> {row['reading']}",
+            flush=True,
+        )
+
+    summary = {
+        "protocol": (
+            "Spectral start plus random restarts, each run to a local optimum "
+            "under exact steepest descent. A negative gap means the searcher "
+            "found a state deeper than the truth, so the truth is not the "
+            "optimum and the limit is information rather than search."
+        ),
+        "size": size,
+        "restarts": args.restarts,
+        "rows": rows,
+    }
+    print(json.dumps({"done": True}))
+    write_json(args.output, summary)
+
+
+if __name__ == "__main__":
+    main()