Update logbook: Reproduction: A Random Matrix Perspective on the Consistency of Diffusion Models

logbook.json

@@ -10,7 +10,7 @@
     "icml2026-repro",
     "paper-iPjuUQbkfl"
   ],
-  "updated_at": "2026-08-01T06:41:22+00:00",
+  "updated_at": "2026-08-01T08:05:55+00:00",
   "root": {
     "slug": "index",
     "title": "Reproduction: A Random Matrix Perspective on the Consistency of Diffusion Models",
@@ -73,15 +73,8 @@
     "total_size": 0,
     "bucket_id": null
   },
-  "agent_view_tokens": 2879,
+  "agent_view_tokens": 3152,
   "trace_view_tokens": 10,
   "workspace_view_tokens": 8,
-  "revision": "cf4585ba83f748b5b086",
-  "workspace_ref": {
-    "repo_id": "byte-vortex/repro-diffusion-consistency-artifacts",
-    "repo_type": "bucket",
-    "repo_url": "https://huggingface.co/buckets/byte-vortex/repro-diffusion-consistency-artifacts",
-    "private": true
-  },
-  "workspace_bucket": "https://huggingface.co/buckets/byte-vortex/repro-diffusion-consistency-artifacts"
+  "revision": "77632d03392161420de1"
 }

pages/claim-1-linear-model-predicts-cross-split-consistency/page.md

@@ -3,13 +3,13 @@
 
 ---
 <!-- trackio-cell
-{"type": "code", "id": "cell_f81cc8015daf", "created_at": "2026-08-01T06:21:15+00:00", "title": "Run: hf rmt_diffusion_claim1.py (exit 0)", "command": ["hf", "jobs", "uv", "run", "--flavor", "cpu-basic", "--secrets", "HF_TOKEN", "rmt_diffusion_claim1.py"], "exit_code": 0, "duration_s": 44.927}
+{"type": "code", "id": "cell_8e22053883dd", "created_at": "2026-08-01T07:56:48+00:00", "title": "Run: hf rmt_diffusion_claim1.py (exit 0)", "command": ["hf", "jobs", "uv", "run", "--flavor", "cpu-basic", "--secrets", "HF_TOKEN", "rmt_diffusion_claim1.py"], "exit_code": 0, "duration_s": 48.005}
 -->
 ````bash
 $ hf jobs uv run --flavor cpu-basic --secrets HF_TOKEN rmt_diffusion_claim1.py
 ````
 
-exit 0 · 44.9s
+exit 0 · 48.0s
 
 
 ````python title=rmt_diffusion_claim1.py
@@ -188,29 +188,29 @@ if __name__ == "__main__":
 ````output
 /usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_experimental.py:59: UserWarning: 'HfApi.run_uv_job' is experimental and might be subject to breaking changes in the future without prior notice. You can disable this warning by setting `HF_HUB_DISABLE_EXPERIMENTAL_WARNING=1` as environment variable.
   warnings.warn(
-Job started with ID: 6a6d902f6b79c09949c1e1a8
-View at: https://huggingface.co/jobs/byte-vortex/6a6d902f6b79c09949c1e1a8
-Downloading pillow (6.6MiB)
-Downloading cuda-bindings (6.3MiB)
+Job started with ID: 6a6da690a00abefd4b28b0db
+View at: https://huggingface.co/jobs/byte-vortex/6a6da690a00abefd4b28b0db
+Downloading nvidia-nvjitlink (38.9MiB)
+Downloading nvidia-cuda-cupti (10.2MiB)
+Downloading networkx (2.0MiB)
+Downloading nvidia-cufft (204.2MiB)
 Downloading sympy (6.0MiB)
+Downloading cuda-bindings (6.3MiB)
+Downloading nvidia-nccl-cu13 (196.4MiB)
 Downloading nvidia-cuda-runtime (2.1MiB)
 Downloading nvidia-cufile (1.2MiB)
-Downloading nvidia-cuda-cupti (10.2MiB)
-Downloading nvidia-nccl-cu13 (196.4MiB)
-Downloading nvidia-curand (56.8MiB)
-Downloading triton (188.6MiB)
 Downloading nvidia-cusolver (191.6MiB)
-Downloading nvidia-nvshmem-cu13 (57.6MiB)
-Downloading nvidia-cublas (403.5MiB)
-Downloading nvidia-cuda-nvrtc (86.0MiB)
-Downloading nvidia-cusparselt-cu13 (162.3MiB)
-Downloading nvidia-nvjitlink (38.9MiB)
-Downloading nvidia-cudnn-cu13 (349.2MiB)
-Downloading nvidia-cufft (204.2MiB)
 Downloading numpy (15.9MiB)
+Downloading triton (188.6MiB)
+Downloading nvidia-cusparselt-cu13 (162.3MiB)
 Downloading nvidia-cusparse (139.2MiB)
-Downloading networkx (2.0MiB)
+Downloading nvidia-cuda-nvrtc (86.0MiB)
 Downloading torch (502.2MiB)
+Downloading pillow (6.6MiB)
+Downloading nvidia-cudnn-cu13 (349.2MiB)
+Downloading nvidia-curand (56.8MiB)
+Downloading nvidia-nvshmem-cu13 (57.6MiB)
+Downloading nvidia-cublas (403.5MiB)
 Downloading torchvision (7.3MiB)
  Downloaded nvidia-cufile
  Downloaded nvidia-cuda-runtime
@@ -220,10 +220,10 @@ Downloading torchvision (7.3MiB)
  Downloaded torchvision
  Downloaded nvidia-cuda-cupti
  Downloaded numpy
- Downloaded nvidia-nvjitlink
  Downloaded sympy
- Downloaded nvidia-nvshmem-cu13
+ Downloaded nvidia-nvjitlink
  Downloaded nvidia-curand
+ Downloaded nvidia-nvshmem-cu13
  Downloaded nvidia-cuda-nvrtc
  Downloaded nvidia-cusparse
  Downloaded nvidia-cusparselt-cu13
@@ -234,7 +234,7 @@ Downloading torchvision (7.3MiB)
  Downloaded nvidia-cudnn-cu13
  Downloaded nvidia-cublas
  Downloaded torch
-Installed 32 packages in 231ms
+Installed 32 packages in 239ms
 ======================================================================
 RMT Diffusion Consistency reproduction -- Claim 1
 Testing: linear closed-form denoiser predicts cross-split
@@ -606,12 +606,12 @@ Using REAL MNIST data (16x16 downsampled, flattened, d=256).
 Dataset: 4000 samples, 256 dims, split into 2000 / 2000
 
 [Results over 100 shared-seed trials]
-  Cross-split distance (same seed, different training data): 0.364 +/- 0.047
-  Nearest-training-neighbor distance (memorization check):   2.649 +/- 0.335
+  Cross-split distance (same seed, different training data): 0.359 +/- 0.047
+  Nearest-training-neighbor distance (memorization check):   2.646 +/- 0.332
 
 ======================================================================
 SUMMARY
-  PASS: cross-split outputs are 7.28x MORE similar to each
+  PASS: cross-split outputs are 7.36x MORE similar to each
   other than to their own nearest training example.
   This supports the paper's Claim 1: a purely linear, closed-form
   model already predicts strong cross-split consistency, without

pages/claim-2-self-consistency-equation-and-renormalized-noise/page.md

@@ -3,13 +3,13 @@
 
 ---
 <!-- trackio-cell
-{"type": "code", "id": "cell_5a2952f192d8", "created_at": "2026-08-01T06:22:29+00:00", "title": "Run: hf rmt_diffusion_claim2.py (exit 0)", "command": ["hf", "jobs", "uv", "run", "--flavor", "cpu-basic", "--secrets", "HF_TOKEN", "rmt_diffusion_claim2.py"], "exit_code": 0, "duration_s": 71.804}
+{"type": "code", "id": "cell_9c5d73d46cf1", "created_at": "2026-08-01T07:58:40+00:00", "title": "Run: hf rmt_diffusion_claim2.py (exit 0)", "command": ["hf", "jobs", "uv", "run", "--flavor", "cpu-basic", "--secrets", "HF_TOKEN", "rmt_diffusion_claim2.py"], "exit_code": 0, "duration_s": 110.454}
 -->
 ````bash
 $ hf jobs uv run --flavor cpu-basic --secrets HF_TOKEN rmt_diffusion_claim2.py
 ````
 
-exit 0 · 71.8s
+exit 0 · 110.5s
 
 
 ````python title=rmt_diffusion_claim2.py
@@ -142,11 +142,11 @@ if __name__ == "__main__":
 ````output
 /usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_experimental.py:59: UserWarning: 'HfApi.run_uv_job' is experimental and might be subject to breaking changes in the future without prior notice. You can disable this warning by setting `HF_HUB_DISABLE_EXPERIMENTAL_WARNING=1` as environment variable.
   warnings.warn(
-Job started with ID: 6a6d905e6b79c09949c1e1ae
-View at: https://huggingface.co/jobs/byte-vortex/6a6d905e6b79c09949c1e1ae
+Job started with ID: 6a6da6c26b79c09949c1e2b4
+View at: https://huggingface.co/jobs/byte-vortex/6a6da6c26b79c09949c1e2b4
 Downloading numpy (15.9MiB)
  Downloaded numpy
-Installed 1 package in 17ms
+Installed 1 package in 16ms
 ======================================================================
 RMT Diffusion Consistency reproduction -- Claim 2
 Testing Proposition 4.1: sigma^2 -> kappa(sigma^2) renormalization

pages/claim-3-variance-factorization-anisotropy-x-inhomogeneity/page.md

@@ -3,13 +3,13 @@
 
 ---
 <!-- trackio-cell
-{"type": "code", "id": "cell_56eff7acaf9c", "created_at": "2026-08-01T06:23:45+00:00", "title": "Run: hf rmt_diffusion_claim3.py (exit 0)", "command": ["hf", "jobs", "uv", "run", "--flavor", "cpu-basic", "--secrets", "HF_TOKEN", "rmt_diffusion_claim3.py"], "exit_code": 0, "duration_s": 73.781}
+{"type": "code", "id": "cell_ad69ee368cf6", "created_at": "2026-08-01T07:59:18+00:00", "title": "Run: hf rmt_diffusion_claim3.py (exit 0)", "command": ["hf", "jobs", "uv", "run", "--flavor", "cpu-basic", "--secrets", "HF_TOKEN", "rmt_diffusion_claim3.py"], "exit_code": 0, "duration_s": 36.125}
 -->
 ````bash
 $ hf jobs uv run --flavor cpu-basic --secrets HF_TOKEN rmt_diffusion_claim3.py
 ````
 
-exit 0 · 73.8s
+exit 0 · 36.1s
 
 
 ````python title=rmt_diffusion_claim3.py
@@ -126,8 +126,8 @@ if __name__ == "__main__":
 ````output
 /usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_experimental.py:59: UserWarning: 'HfApi.run_uv_job' is experimental and might be subject to breaking changes in the future without prior notice. You can disable this warning by setting `HF_HUB_DISABLE_EXPERIMENTAL_WARNING=1` as environment variable.
   warnings.warn(
-Job started with ID: 6a6d90a86b79c09949c1e1b0
-View at: https://huggingface.co/jobs/byte-vortex/6a6d90a86b79c09949c1e1b0
+Job started with ID: 6a6da733a00abefd4b28b117
+View at: https://huggingface.co/jobs/byte-vortex/6a6da733a00abefd4b28b117
 Downloading numpy (15.9MiB)
  Downloaded numpy
 Installed 1 package in 16ms

pages/claim-4-fractional-matrix-power-extension-to-sampling-trajectories/page.md

@@ -3,13 +3,13 @@
 
 ---
 <!-- trackio-cell
-{"type": "code", "id": "cell_e02281758d3b", "created_at": "2026-08-01T06:24:04+00:00", "title": "Run: hf rmt_diffusion_claim4.py (exit 0)", "command": ["hf", "jobs", "uv", "run", "--flavor", "cpu-basic", "--secrets", "HF_TOKEN", "rmt_diffusion_claim4.py"], "exit_code": 0, "duration_s": 17.021}
+{"type": "code", "id": "cell_9298eb629b97", "created_at": "2026-08-01T07:59:36+00:00", "title": "Run: hf rmt_diffusion_claim4.py (exit 0)", "command": ["hf", "jobs", "uv", "run", "--flavor", "cpu-basic", "--secrets", "HF_TOKEN", "rmt_diffusion_claim4.py"], "exit_code": 0, "duration_s": 16.064}
 -->
 ````bash
 $ hf jobs uv run --flavor cpu-basic --secrets HF_TOKEN rmt_diffusion_claim4.py
 ````
 
-exit 0 · 17.0s
+exit 0 · 16.1s
 
 
 ````python title=rmt_diffusion_claim4.py
@@ -233,8 +233,8 @@ if __name__ == "__main__":
 ````output
 /usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_experimental.py:59: UserWarning: 'HfApi.run_uv_job' is experimental and might be subject to breaking changes in the future without prior notice. You can disable this warning by setting `HF_HUB_DISABLE_EXPERIMENTAL_WARNING=1` as environment variable.
   warnings.warn(
-Job started with ID: 6a6d90f4a00abefd4b28af1a
-View at: https://huggingface.co/jobs/byte-vortex/6a6d90f4a00abefd4b28af1a
+Job started with ID: 6a6da7596b79c09949c1e2ef
+View at: https://huggingface.co/jobs/byte-vortex/6a6da7596b79c09949c1e2ef
 Downloading numpy (15.9MiB)
  Downloaded numpy
 Installed 1 package in 16ms

pages/claim-5-deep-network-validation-toy-scale/page.md

@@ -3,13 +3,13 @@
 
 ---
 <!-- trackio-cell
-{"type": "code", "id": "cell_881a436b50fe", "created_at": "2026-08-01T06:24:52+00:00", "title": "Run: hf rmt_diffusion_claim5.py (exit 0)", "command": ["hf", "jobs", "uv", "run", "--flavor", "a10g-small", "--secrets", "HF_TOKEN", "rmt_diffusion_claim5.py"], "exit_code": 0, "duration_s": 46.533}
+{"type": "code", "id": "cell_90081f09fa40", "created_at": "2026-08-01T08:00:25+00:00", "title": "Run: hf rmt_diffusion_claim5.py (exit 0)", "command": ["hf", "jobs", "uv", "run", "--flavor", "a10g-small", "--secrets", "HF_TOKEN", "rmt_diffusion_claim5.py"], "exit_code": 0, "duration_s": 47.003}
 -->
 ````bash
 $ hf jobs uv run --flavor a10g-small --secrets HF_TOKEN rmt_diffusion_claim5.py
 ````
 
-exit 0 · 46.5s
+exit 0 · 47.0s
 
 
 ````python title=rmt_diffusion_claim5.py
@@ -207,30 +207,30 @@ if __name__ == "__main__":
 ````output
 /usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_experimental.py:59: UserWarning: 'HfApi.run_uv_job' is experimental and might be subject to breaking changes in the future without prior notice. You can disable this warning by setting `HF_HUB_DISABLE_EXPERIMENTAL_WARNING=1` as environment variable.
   warnings.warn(
-Job started with ID: 6a6d91066b79c09949c1e1b6
-View at: https://huggingface.co/jobs/byte-vortex/6a6d91066b79c09949c1e1b6
-Downloading numpy (15.9MiB)
-Downloading nvidia-cuda-nvrtc (86.0MiB)
-Downloading nvidia-curand (56.8MiB)
+Job started with ID: 6a6da76b6b79c09949c1e2f1
+View at: https://huggingface.co/jobs/byte-vortex/6a6da76b6b79c09949c1e2f1
+Downloading nvidia-cufft (204.2MiB)
+Downloading cuda-bindings (6.3MiB)
 Downloading sympy (6.0MiB)
-Downloading networkx (2.0MiB)
-Downloading nvidia-cudnn-cu13 (349.2MiB)
 Downloading nvidia-cuda-runtime (2.1MiB)
-Downloading nvidia-nvshmem-cu13 (57.6MiB)
-Downloading nvidia-cusparselt-cu13 (162.3MiB)
+Downloading networkx (2.0MiB)
+Downloading nvidia-nvjitlink (38.9MiB)
+Downloading pillow (6.6MiB)
 Downloading nvidia-nccl-cu13 (196.4MiB)
 Downloading nvidia-cufile (1.2MiB)
 Downloading nvidia-cuda-cupti (10.2MiB)
-Downloading nvidia-cufft (204.2MiB)
-Downloading pillow (6.6MiB)
-Downloading torch (502.2MiB)
 Downloading triton (188.6MiB)
-Downloading cuda-bindings (6.3MiB)
-Downloading nvidia-cusparse (139.2MiB)
+Downloading numpy (15.9MiB)
+Downloading torch (502.2MiB)
+Downloading nvidia-nvshmem-cu13 (57.6MiB)
+Downloading nvidia-cudnn-cu13 (349.2MiB)
+Downloading nvidia-cublas (403.5MiB)
+Downloading nvidia-cuda-nvrtc (86.0MiB)
+Downloading nvidia-cusparselt-cu13 (162.3MiB)
 Downloading nvidia-cusolver (191.6MiB)
-Downloading nvidia-nvjitlink (38.9MiB)
+Downloading nvidia-cusparse (139.2MiB)
+Downloading nvidia-curand (56.8MiB)
 Downloading torchvision (7.3MiB)
-Downloading nvidia-cublas (403.5MiB)
  Downloaded nvidia-cufile
  Downloaded nvidia-cuda-runtime
  Downloaded networkx
@@ -239,8 +239,8 @@ Downloading nvidia-cublas (403.5MiB)
  Downloaded torchvision
  Downloaded nvidia-cuda-cupti
  Downloaded numpy
- Downloaded nvidia-nvjitlink
  Downloaded sympy
+ Downloaded nvidia-nvjitlink
  Downloaded nvidia-curand
  Downloaded nvidia-nvshmem-cu13
  Downloaded nvidia-cuda-nvrtc
@@ -253,7 +253,7 @@ Downloading nvidia-cublas (403.5MiB)
  Downloaded nvidia-cudnn-cu13
  Downloaded nvidia-cublas
  Downloaded torch
-Installed 32 packages in 590ms
+Installed 32 packages in 631ms
 ======================================================================
 RMT Diffusion Consistency reproduction -- Claim 5 (toy scale)
 Testing: do REAL TRAINED deep denoisers show the same
@@ -624,22 +624,22 @@ Using REAL MNIST data (14x14 downsampled, d=196).
 Split sizes: 400 / 400
 
 Training MLP denoiser on split A (400 samples)...
-    step 500/1500  loss=0.5907
-    step 1000/1500  loss=0.4770
-    step 1500/1500  loss=0.3592
+    step 500/1500  loss=0.5953
+    step 1000/1500  loss=0.4811
+    step 1500/1500  loss=0.3512
 Training MLP denoiser on split B (400 samples)...
-    step 500/1500  loss=0.5614
-    step 1000/1500  loss=0.4529
-    step 1500/1500  loss=0.3728
+    step 500/1500  loss=0.5589
+    step 1000/1500  loss=0.4563
+    step 1500/1500  loss=0.3696
 
 [Results over 30 shared-seed trials, TRAINED deep MLP denoisers]
-  Cross-split distance:            7.385 +/- 1.139
-  Nearest-training-neighbor dist:  16.151 +/- 2.416
+  Cross-split distance:            7.508 +/- 1.666
+  Nearest-training-neighbor dist:  16.167 +/- 2.684
 
 ======================================================================
 SUMMARY
   PASS (toy scale): trained deep MLP denoisers show cross-split outputs
-  2.19x MORE similar to each other than to their nearest training
+  2.15x MORE similar to each other than to their nearest training
   example -- the same qualitative pattern the linear theory (Claim 1)
   predicts, now confirmed with REAL TRAINED nonlinear networks, not
   just the closed-form linear denoiser. Supports Claim 5 at toy scale.

pages/claim-6-deep-network-validation-scoped-toy-scale-cifar10-only/page.md

@@ -3,552 +3,7 @@
 
 ---
 <!-- trackio-cell
-{"type": "code", "id": "cell_0006576e0298", "created_at": "2026-08-01T06:30:15+00:00", "title": "Run: hf rmt_diffusion_claim6.py (exit 0)", "command": ["hf", "jobs", "uv", "run", "--flavor", "a10g-small", "--secrets", "HF_TOKEN", "rmt_diffusion_claim6.py"], "exit_code": 0, "duration_s": 321.21}
--->
-````bash
-$ hf jobs uv run --flavor a10g-small --secrets HF_TOKEN rmt_diffusion_claim6.py
-````
-
-exit 0 · 321.2s
-
-
-````python title=rmt_diffusion_claim6.py
-
-# /// script
-# dependencies = ["torch", "numpy", "torchvision"]
-# ///
-"""
-Claim 6 reproduction (SCOPED-DOWN toy scale) -- "A Random Matrix Theory
-Perspective on the Consistency of Diffusion Models" (arXiv 2602.02908,
-OpenReview iPjuUQbkfl, ICML 2026 Oral).
-
-REWRITE NOTE: the previous version of this script tested a "memorization
--> generalization phase transition with n" hypothesis, which does not
-appear anywhere in the official challenge's claim list for this paper.
-It was the wrong target. The official Claim 5 this page should address
-is: "UNet and DiT experiments validate the theory's predictions about
-consistency, overshrinkage, and eigenmode-dependent deviations in the
-non-memorization regime (Figure 5)." This rewrite tests exactly that,
-with three sub-checks tied to the paper's actual claims:
-
-  (a) CONSISTENCY vs. linear theory (relates to Claim 1 / Figure 1):
-      is the trained UNet's cross-split output distance comparable to
-      what a purely LINEAR (closed-form Wiener) diffusion sampler run
-      through the identical reverse-diffusion schedule would predict?
-
-  (b) OVERSHRINKAGE (relates to Claim 2 / Figure 2): does the model's
-      output variance along LOW-eigenvalue directions of the data
-      covariance shrink MORE (relative to the true variance in that
-      direction) than along high-eigenvalue directions -- the
-      signature of finite-sample noise-scale renormalization?
-
-  (c) EIGENMODE-DEPENDENT / ANISOTROPIC cross-split deviation, decaying
-      with n (relates to Result 4.2 / Claim 3): does the cross-split
-      output deviation vary systematically across covariance
-      eigenmodes (not flat/isotropic), and shrink as dataset size n
-      grows?
-
-HONESTY NOTE (unchanged from before): the paper's real Section 6 uses
-UNet AND DiT on seven datasets at five sizes each with 50k training
-steps per run. This is a drastically scoped-down, single-dataset,
-single-architecture, short-training qualitative check -- not a
-quantitative reproduction of the paper's actual figures. But it now at
-least tests the RIGHT hypotheses, using the same UNet training/sampling
-infrastructure as before.
-"""
-
-import sys
-import torch
-import torch.nn as nn
-import numpy as np
-
-# FIX (kept from the previous version): force line-buffered stdout so
-# output isn't lost to a buffering race against the job runner's log
-# capture at process exit.
-sys.stdout.reconfigure(line_buffering=True)
-
-
-# --------------------------------------------------------------------------
-# Small convolutional UNet denoiser (unchanged from the previous version)
-# --------------------------------------------------------------------------
-
-class TimeEmbedding(nn.Module):
-    def __init__(self, dim):
-        super().__init__()
-        self.dim = dim
-        self.mlp = nn.Sequential(
-            nn.Linear(dim, dim * 4),
-            nn.SiLU(),
-            nn.Linear(dim * 4, dim * 4),
-        )
-
-    def forward(self, t, T):
-        half = self.dim // 2
-        freqs = torch.exp(-np.log(10000) * torch.arange(half, device=t.device) / half)
-        args = t.float().unsqueeze(-1) * freqs.unsqueeze(0) / T
-        emb = torch.cat([torch.sin(args), torch.cos(args)], dim=-1)
-        return self.mlp(emb)
-
-
-class ResBlock(nn.Module):
-    def __init__(self, in_ch, out_ch, temb_dim):
-        super().__init__()
-        self.norm1 = nn.GroupNorm(8, in_ch)
-        self.conv1 = nn.Conv2d(in_ch, out_ch, 3, padding=1)
-        self.temb_proj = nn.Linear(temb_dim, out_ch)
-        self.norm2 = nn.GroupNorm(8, out_ch)
-        self.conv2 = nn.Conv2d(out_ch, out_ch, 3, padding=1)
-        self.skip = nn.Conv2d(in_ch, out_ch, 1) if in_ch != out_ch else nn.Identity()
-
-    def forward(self, x, temb):
-        h = self.conv1(torch.nn.functional.silu(self.norm1(x)))
-        h = h + self.temb_proj(temb)[:, :, None, None]
-        h = self.conv2(torch.nn.functional.silu(self.norm2(h)))
-        return h + self.skip(x)
-
-
-class SmallUNet(nn.Module):
-    """Small UNet-CNN denoiser: 32x32 -> 16x16 -> 8x8 -> 16x16 -> 32x32,
-    with skip connections. Matches the paper's "UNet" family in spirit
-    (convolutional, skip connections, sinusoidal time embedding) at a
-    fraction of the channel width / depth."""
-
-    def __init__(self, channels=3, base_ch=32, time_emb_dim=32):
-        super().__init__()
-        temb_dim = time_emb_dim * 4
-        self.time_embed = TimeEmbedding(time_emb_dim)
-
-        self.in_conv = nn.Conv2d(channels, base_ch, 3, padding=1)
-
-        self.down1 = ResBlock(base_ch, base_ch, temb_dim)
-        self.pool1 = nn.Conv2d(base_ch, base_ch, 4, stride=2, padding=1)  # 32->16
-
-        self.down2 = ResBlock(base_ch, base_ch * 2, temb_dim)
-        self.pool2 = nn.Conv2d(base_ch * 2, base_ch * 2, 4, stride=2, padding=1)  # 16->8
-
-        self.mid = ResBlock(base_ch * 2, base_ch * 2, temb_dim)
-
-        self.up2 = nn.ConvTranspose2d(base_ch * 2, base_ch * 2, 4, stride=2, padding=1)  # 8->16
-        self.dec2 = ResBlock(base_ch * 2 + base_ch * 2, base_ch, temb_dim)
-
-        self.up1 = nn.ConvTranspose2d(base_ch, base_ch, 4, stride=2, padding=1)  # 16->32
-        self.dec1 = ResBlock(base_ch + base_ch, base_ch, temb_dim)
-
-        self.out_norm = nn.GroupNorm(8, base_ch)
-        self.out_conv = nn.Conv2d(base_ch, channels, 3, padding=1)
-
-    def forward(self, x, t, T):
-        temb = self.time_embed(t, T)
-
-        h0 = self.in_conv(x)
-        h1 = self.down1(h0, temb)
-        h1p = self.pool1(h1)
-
-        h2 = self.down2(h1p, temb)
-        h2p = self.pool2(h2)
-
-        hm = self.mid(h2p, temb)
-
-        u2 = self.up2(hm)
-        u2 = self.dec2(torch.cat([u2, h2], dim=1), temb)
-
-        u1 = self.up1(u2)
-        u1 = self.dec1(torch.cat([u1, h1], dim=1), temb)
-
-        out = self.out_conv(torch.nn.functional.silu(self.out_norm(u1)))
-        return out
-
-
-# --------------------------------------------------------------------------
-# Data loading (real CIFAR10; synthetic fallback if no internet access)
-# --------------------------------------------------------------------------
-
-def load_real_data(n_samples, seed=0):
-    import torchvision
-    import torchvision.transforms as T
-
-    transform = T.Compose([T.ToTensor()])
-    dataset = torchvision.datasets.CIFAR10(root="./data", train=True, download=True, transform=transform)
-
-    rng = np.random.default_rng(seed)
-    indices = rng.choice(len(dataset), size=n_samples, replace=False)
-    images = torch.stack([dataset[i][0] for i in indices])  # (N, 3, 32, 32) in [0,1]
-    return images * 2.0 - 1.0  # -> [-1, 1]
-
-
-def generate_synthetic_data(n_samples, seed=0):
-    """Fallback: synthetic structured-covariance 'images' (3x32x32) with a
-    decaying eigenvalue spectrum, used only if CIFAR10 download is
-    unavailable (e.g. offline sandbox testing)."""
-    d = 3 * 32 * 32
-    rng = np.random.default_rng(seed)
-    eigenvalues = np.exp(-0.01 * np.arange(d)) + 0.01
-    # Use a random sparse-ish orthonormal basis via QR on a smaller block
-    # for tractability, then embed -- keeps this fast for smoke tests.
-    Q, _ = np.linalg.qr(rng.standard_normal((d, d)) if d <= 200 else rng.standard_normal((d, min(d, 400))))
-    if Q.shape[1] < d:
-        # pad orthonormal columns is not exact; fall back to diagonal cov for speed
-        samples = rng.standard_normal((n_samples, d)) * np.sqrt(eigenvalues)
-    else:
-        cov_sqrt = Q @ np.diag(np.sqrt(eigenvalues))
-        samples = rng.standard_normal((n_samples, d)) @ cov_sqrt.T
-    samples = samples.reshape(n_samples, 3, 32, 32)
-    samples = np.clip(samples, -3, 3)
-    return torch.tensor(samples, dtype=torch.float32)
-
-
-# --------------------------------------------------------------------------
-# Linear (Wiener/MMSE) predictor, for the "approach to linear theory" check
-# --------------------------------------------------------------------------
-
-class LinearDenoiserFlat:
-    """Closed-form Wiener denoiser on flattened images, used only as a
-    reference point (does the trained UNet's output get closer to the
-    linear predictor as n grows, as the paper reports in App B.4.3?)."""
-
-    def __init__(self, data_flat):
-        self.mu = data_flat.mean(dim=0)
-        centered = data_flat - self.mu
-        n = data_flat.shape[0]
-        self.cov = (centered.T @ centered) / max(n - 1, 1)
-        d = self.cov.shape[0]
-        self.cov = self.cov + 1e-3 * torch.eye(d)
-
-    def denoise(self, y_flat, sigma):
-        d = self.cov.shape[0]
-        gain = self.cov @ torch.linalg.inv(self.cov + (sigma ** 2) * torch.eye(d))
-        return self.mu + (gain @ (y_flat - self.mu).T).T
-
-
-# --------------------------------------------------------------------------
-# Training and sampling
-# --------------------------------------------------------------------------
-
-def train_denoiser(data, T=30, n_steps=3000, lr=2e-4, batch_size=64, seed=0, device="cpu"):
-    torch.manual_seed(seed)
-    model = SmallUNet().to(device)
-    opt = torch.optim.Adam(model.parameters(), lr=lr)
-
-    betas = torch.linspace(1e-4, 0.02, T, device=device)
-    alphas = 1.0 - betas
-    alpha_bars = torch.cumprod(alphas, dim=0)
-
-    n_samples = data.shape[0]
-    data = data.to(device)
-
-    for step in range(n_steps):
-        idx = torch.randint(0, n_samples, (min(batch_size, n_samples),))
-        x0 = data[idx]
-        t = torch.randint(0, T, (x0.shape[0],), device=device)
-        noise = torch.randn_like(x0)
-        alpha_bar_t = alpha_bars[t].view(-1, 1, 1, 1)
-        x_t = torch.sqrt(alpha_bar_t) * x0 + torch.sqrt(1 - alpha_bar_t) * noise
-
-        pred_noise = model(x_t, t, T)
-        loss = ((pred_noise - noise) ** 2).mean()
-
-        opt.zero_grad()
-        loss.backward()
-        opt.step()
-
-        if (step + 1) % max(1, n_steps // 5) == 0:
-            print(f"      step {step + 1}/{n_steps}  loss={loss.item():.4f}")
-
-    return model, betas, alphas, alpha_bars
-
-
-@torch.no_grad()
-def ddpm_sample_batch(model, betas, alphas, alpha_bars, z_init_batch, T, device="cpu"):
-    """Batched version: z_init_batch has shape (B, 3, 32, 32)."""
-    x = z_init_batch.clone().to(device)
-    B = x.shape[0]
-    for t in reversed(range(T)):
-        t_tensor = torch.full((B,), t, device=device, dtype=torch.long)
-        pred_noise = model(x, t_tensor, T)
-        alpha_t, alpha_bar_t, beta_t = alphas[t], alpha_bars[t], betas[t]
-        coef = beta_t / torch.sqrt(1 - alpha_bar_t)
-        mean = (1 / torch.sqrt(alpha_t)) * (x - coef * pred_noise)
-        if t > 0:
-            x = mean + torch.sqrt(beta_t) * torch.randn_like(x)
-        else:
-            x = mean
-    return x.cpu()
-
-
-@torch.no_grad()
-def linear_ddpm_sample_batch(lin_model, betas, alphas, alpha_bars, z_init_batch, T):
-    """The paper's 'linear diffusion' comparison point: run the SAME
-    reverse-diffusion schedule and update rule as the UNet sampler, but
-    replace the learned noise predictor with the noise implied by the
-    closed-form Wiener denoiser's x0 estimate at each step. This gives a
-    genuine linear-diffusion sample under the identical process, so its
-    cross-split distance is a fair, principled comparison point for
-    "how much of the UNet's consistency is explained by shared Gaussian
-    statistics alone" (Claim 1 / Figure 1), not just an informal
-    single-step denoiser distance check.
-
-    Tweedie / Wiener step: at reverse step t, x_t = sqrt(abar_t)*x0 +
-    sqrt(1-abar_t)*eps. Rescaling, y = x_t/sqrt(abar_t) = x0 +
-    eps*sqrt((1-abar_t)/abar_t), i.e. y is x0 observed through Gaussian
-    noise of std sigma_eff = sqrt((1-abar_t)/abar_t). Denoise y with the
-    closed-form Wiener filter to get x0_hat, then back out the implied
-    noise estimate eps_hat and plug it into the identical DDPM update
-    used for the UNet, so both samplers are compared under exactly the
-    same process.
-    """
-    x = z_init_batch.clone()
-    B, C, H, W = x.shape
-    d = C * H * W
-    for t in reversed(range(T)):
-        alpha_t, alpha_bar_t, beta_t = alphas[t].item(), alpha_bars[t].item(), betas[t].item()
-        sigma_eff = ((1 - alpha_bar_t) / alpha_bar_t) ** 0.5
-        y_flat = (x / (alpha_bar_t ** 0.5)).reshape(B, d)
-        x0_hat_flat = lin_model.denoise(y_flat, sigma_eff)
-        x0_hat = x0_hat_flat.reshape(B, C, H, W)
-        eps_hat = (x - (alpha_bar_t ** 0.5) * x0_hat) / max((1 - alpha_bar_t) ** 0.5, 1e-8)
-        coef = beta_t / max((1 - alpha_bar_t) ** 0.5, 1e-8)
-        mean = (1 / (alpha_t ** 0.5)) * (x - coef * eps_hat)
-        if t > 0:
-            x = mean + (beta_t ** 0.5) * torch.randn_like(x)
-        else:
-            x = mean
-    return x
-
-
-# --------------------------------------------------------------------------
-# Eigenbasis analysis: overshrinkage and eigenmode-dependent deviation
-# --------------------------------------------------------------------------
-
-def compute_reference_eigenbasis(pool, n_ref=4000, seed=999):
-    """Fixed reference eigenbasis (from a large, independent slice of the
-    pool -- NOT any of the small training splits) so overshrinkage and
-    anisotropy are measured against a stable, consistent set of axes
-    across every dataset size n. Returns eigenvectors (d, d) sorted by
-    DESCENDING eigenvalue, eigenvalues (d,), and the mean (d,)."""
-    rng = np.random.default_rng(seed)
-    idx = rng.choice(pool.shape[0], size=min(n_ref, pool.shape[0]), replace=False)
-    ref = pool[idx].reshape(len(idx), -1)
-    mu = ref.mean(dim=0)
-    centered = ref - mu
-    cov = (centered.T @ centered) / (centered.shape[0] - 1)
-    eigvals, eigvecs = torch.linalg.eigh(cov)  # ascending order
-    order = torch.argsort(eigvals, descending=True)
-    return eigvecs[:, order], eigvals[order].clamp(min=1e-8), mu
-
-
-def project_variance_per_mode(x_batch, eigvecs, mu, n_modes):
-    """x_batch: (B, C, H, W). Returns (n_modes,) variance of the batch's
-    projection onto the top n_modes reference eigenvectors."""
-    B = x_batch.shape[0]
-    x_flat = x_batch.reshape(B, -1) - mu
-    proj = x_flat @ eigvecs[:, :n_modes]  # (B, n_modes)
-    return proj.var(dim=0)
-
-
-def main():
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    print("=" * 70)
-    print("RMT Diffusion Consistency reproduction -- Claim 6 (SCOPED TOY SCALE)")
-    print("Testing official Claim 5: consistency vs linear theory,")
-    print("overshrinkage, and eigenmode-dependent cross-split deviation")
-    print(f"Device: {device}")
-    print("=" * 70)
-
-    dataset_sizes = [100, 500, 3000, 20000]
-    T = 30
-    steps_by_n = {100: 1500, 500: 2000, 3000: 3000, 20000: 4000}
-    n_trials = 12
-    n_modes = 40  # top eigenmodes examined for overshrinkage / anisotropy
-
-    try:
-        max_n = max(dataset_sizes) * 2
-        pool = load_real_data(max_n + 4000, seed=42)  # extra for the reference eigenbasis
-        using_real = True
-        print(f"\nUsing REAL CIFAR10 data (32x32x3). Pool size: {pool.shape[0]}")
-    except Exception as e:
-        print(f"\nCould not load real CIFAR10 ({e}), falling back to synthetic.")
-        max_n = max(dataset_sizes) * 2
-        pool = generate_synthetic_data(max_n + 4000, seed=42)
-        using_real = False
-
-    print("\nComputing reference eigenbasis (independent 4000-image slice)...")
-    eigvecs, eigvals, ref_mu = compute_reference_eigenbasis(pool, n_ref=4000, seed=999)
-    print(f"  Top eigenvalue: {eigvals[0]:.4f}  |  mode-{n_modes} eigenvalue: {eigvals[n_modes-1]:.4f}")
-
-    # remainder of pool (excluding the reference slice) used for splits
-    train_pool = pool[4000:]
-
-    results = []
-    for n in dataset_sizes:
-        print(f"\n{'-' * 70}")
-        print(f"Dataset size n = {n} per split (gamma = d/n = {3*32*32/n:.3f})")
-        print(f"{'-' * 70}")
-
-        perm = torch.randperm(train_pool.shape[0])[: 2 * n]
-        split_a = train_pool[perm[:n]]
-        split_b = train_pool[perm[n:2 * n]]
-        split_a_flat = split_a.reshape(n, -1)
-        split_b_flat = split_b.reshape(n, -1)
-
-        n_steps = steps_by_n[n]
-        print(f"  Training UNet denoiser on split A ({n} samples, {n_steps} steps)...")
-        model_a, betas, alphas, alpha_bars = train_denoiser(split_a, T=T, n_steps=n_steps, seed=100 + n, device=device)
-        print(f"  Training UNet denoiser on split B ({n} samples, {n_steps} steps)...")
-        model_b, _, _, _ = train_denoiser(split_b, T=T, n_steps=n_steps, seed=200 + n, device=device)
-
-        lin_a = LinearDenoiserFlat(split_a_flat)
-        lin_b = LinearDenoiserFlat(split_b_flat)
-
-        torch.manual_seed(9000 + n)
-        z_init_batch = torch.randn(n_trials, 3, 32, 32)
-
-        x_a_unet = ddpm_sample_batch(model_a, betas, alphas, alpha_bars, z_init_batch, T, device=device)
-        x_b_unet = ddpm_sample_batch(model_b, betas, alphas, alpha_bars, z_init_batch, T, device=device)
-        x_a_lin = linear_ddpm_sample_batch(lin_a, betas.cpu(), alphas.cpu(), alpha_bars.cpu(), z_init_batch, T)
-        x_b_lin = linear_ddpm_sample_batch(lin_b, betas.cpu(), alphas.cpu(), alpha_bars.cpu(), z_init_batch, T)
-
-        # (a) Consistency vs linear theory: compare UNet cross-split
-        # distance to the linear sampler's cross-split distance under the
-        # SAME noise seeds and SAME reverse process.
-        unet_cross_mse = float(((x_a_unet - x_b_unet) ** 2).mean())
-        lin_cross_mse = float(((x_a_lin - x_b_lin) ** 2).mean())
-        consistency_ratio = unet_cross_mse / max(lin_cross_mse, 1e-8)
-        print(f"\n  [Consistency check: UNet vs linear-theory cross-split MSE]")
-        print(f"    UNet cross-split MSE:               {unet_cross_mse:.5f}")
-        print(f"    Linear-theory cross-split MSE:       {lin_cross_mse:.5f}")
-        print(f"    Ratio (UNet / linear):                {consistency_ratio:.3f}")
-
-        # (b) Overshrinkage: per-mode output variance vs reference true
-        # variance, checked for a trend with eigenvalue rank.
-        out_var = project_variance_per_mode(torch.cat([x_a_unet, x_b_unet]), eigvecs, ref_mu, n_modes)
-        true_var = eigvals[:n_modes]
-        shrink_ratio = (out_var / true_var).numpy()
-        ranks = np.arange(n_modes)
-        # Spearman-style: correlation between eigenvalue rank (0=highest
-        # variance direction) and shrink ratio. POSITIVE correlation means
-        # low-variance (high-rank) directions are shrunk MORE (lower
-        # ratio for higher rank number would be negative corr with rank;
-        # define overshrink_corr so positive = overshrinkage signature).
-        overshrink_corr = float(np.corrcoef(ranks, shrink_ratio)[0, 1])
-        print(f"\n  [Overshrinkage check: output variance / true variance per eigenmode]")
-        print(f"    Shrink ratio, top-5 modes (high variance):   {np.array2string(shrink_ratio[:5], precision=3)}")
-        print(f"    Shrink ratio, bottom-5 modes (low variance): {np.array2string(shrink_ratio[-5:], precision=3)}")
-        print(f"    Corr(eigenvalue rank, shrink ratio):          {overshrink_corr:.3f}")
-        print(f"    (positive = low-variance modes shrunk more -> overshrinkage signature)")
-
-        # (c) Eigenmode-dependent, anisotropic cross-split deviation
-        diff_a = (x_a_unet.reshape(n_trials, -1) - ref_mu) @ eigvecs[:, :n_modes]
-        diff_b = (x_b_unet.reshape(n_trials, -1) - ref_mu) @ eigvecs[:, :n_modes]
-        per_mode_dev = ((diff_a - diff_b) ** 2).mean(dim=0).numpy()
-        aniso_corr = float(np.corrcoef(np.arange(n_modes), per_mode_dev)[0, 1])
-        mean_dev = float(per_mode_dev.mean())
-        print(f"\n  [Anisotropy check: per-eigenmode cross-split deviation]")
-        print(f"    Mean deviation across top-{n_modes} modes:    {mean_dev:.5f}")
-        print(f"    Corr(eigenvalue rank, deviation):             {aniso_corr:.3f}")
-        print(f"    (nonzero |corr| = anisotropic; deviation should also DECAY with n)")
-
-        results.append(dict(n=n, unet_cross_mse=unet_cross_mse, lin_cross_mse=lin_cross_mse,
-                             consistency_ratio=consistency_ratio, overshrink_corr=overshrink_corr,
-                             aniso_corr=aniso_corr, mean_dev=mean_dev))
-
-    print("\n" + "=" * 70)
-    print("SUMMARY")
-    print("=" * 70)
-    print(f"{'n':>6} {'UNet MSE':>10} {'Linear MSE':>11} {'ratio':>7} {'overshrink_corr':>16} {'aniso_corr':>11} {'mean_dev':>10}")
-    for r in results:
-        print(f"{r['n']:>6} {r['unet_cross_mse']:>10.5f} {r['lin_cross_mse']:>11.5f} {r['consistency_ratio']:>7.2f} "
-              f"{r['overshrink_corr']:>16.3f} {r['aniso_corr']:>11.3f} {r['mean_dev']:>10.5f}")
-
-    dev_decays = results[0]["mean_dev"] > results[-1]["mean_dev"]
-    overshrink_present = np.mean([r["overshrink_corr"] for r in results]) > 0.1
-    anisotropic = np.mean([abs(r["aniso_corr"]) for r in results]) > 0.1
-    ratios_bounded = all(0.1 < r["consistency_ratio"] < 10 for r in results)
-
-    print(f"\n  Cross-split deviation decays with n:        {dev_decays}")
-    print(f"  Overshrinkage signature present (avg corr>0.1): {overshrink_present}")
-    print(f"  Eigenmode-dependent (anisotropic) deviation:  {anisotropic}")
-    print(f"  UNet/linear consistency ratio stays bounded (0.1-10x): {ratios_bounded}")
-
-    n_checks_passed = sum([dev_decays, overshrink_present, anisotropic, ratios_bounded])
-    print(f"\n  {n_checks_passed}/4 sub-checks consistent with the paper's Claim 5 predictions.")
-    if n_checks_passed >= 3:
-        print("  DIRECTIONALLY SUPPORTED at this toy scale.")
-    else:
-        print("  MIXED/INCONCLUSIVE at this toy scale -- see per-n numbers above.")
-    print(f"\n  (Using {'REAL CIFAR10' if using_real else 'SYNTHETIC fallback'} data.)")
-    print("=" * 70)
-    sys.stdout.flush()
-
-
-if __name__ == "__main__":
-    main()
-
-````
-
-
-````output
-/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_experimental.py:59: UserWarning: 'HfApi.run_uv_job' is experimental and might be subject to breaking changes in the future without prior notice. You can disable this warning by setting `HF_HUB_DISABLE_EXPERIMENTAL_WARNING=1` as environment variable.
-  warnings.warn(
-Job started with ID: 6a6d91376b79c09949c1e1b8
-View at: https://huggingface.co/jobs/byte-vortex/6a6d91376b79c09949c1e1b8
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-Downloading nvidia-cuda-runtime (2.1MiB)
-Downloading nvidia-nccl-cu13 (196.4MiB)
-Downloading numpy (15.9MiB)
-Downloading torchvision (7.3MiB)
- Downloaded nvidia-cufile
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-Installed 32 packages in 616ms
-======================================================================
-RMT Diffusion Consistency reproduction -- Claim 6 (SCOPED TOY SCALE)
-Testing official Claim 5: consistency vs linear theory,
-overshrinkage, and eigenmode-dependent cross-split deviation
-Device: cuda
-======================================================================
-
-````
-
-
----
-<!-- trackio-cell
-{"type": "code", "id": "cell_019e449156c2", "created_at": "2026-08-01T06:41:17+00:00", "title": "Run: hf rmt_diffusion_claim6.py (exit 0)", "command": ["hf", "jobs", "uv", "run", "--flavor", "a10g-small", "--timeout", "3600", "--secrets", "HF_TOKEN", "rmt_diffusion_claim6.py"], "exit_code": 0, "duration_s": 321.624}
+{"type": "code", "id": "cell_f05089612d51", "created_at": "2026-08-01T08:05:48+00:00", "title": "Run: hf rmt_diffusion_claim6.py (exit 0)", "command": ["hf", "jobs", "uv", "run", "--flavor", "a10g-small", "--timeout", "3600", "--secrets", "HF_TOKEN", "rmt_diffusion_claim6.py"], "exit_code": 0, "duration_s": 321.594}
 -->
 ````bash
 $ hf jobs uv run --flavor a10g-small --timeout 3600 --secrets HF_TOKEN rmt_diffusion_claim6.py
@@ -1034,30 +489,30 @@ if __name__ == "__main__":
 ````output
 /usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_experimental.py:59: UserWarning: 'HfApi.run_uv_job' is experimental and might be subject to breaking changes in the future without prior notice. You can disable this warning by setting `HF_HUB_DISABLE_EXPERIMENTAL_WARNING=1` as environment variable.
   warnings.warn(
-Job started with ID: 6a6d93cc6b79c09949c1e1cf
-View at: https://huggingface.co/jobs/byte-vortex/6a6d93cc6b79c09949c1e1cf
-Downloading nvidia-cuda-cupti (10.2MiB)
-Downloading nvidia-cuda-runtime (2.1MiB)
-Downloading nvidia-cusparse (139.2MiB)
-Downloading sympy (6.0MiB)
-Downloading networkx (2.0MiB)
-Downloading pillow (6.6MiB)
+Job started with ID: 6a6da79ba00abefd4b28b143
+View at: https://huggingface.co/jobs/byte-vortex/6a6da79ba00abefd4b28b143
 Downloading nvidia-curand (56.8MiB)
-Downloading nvidia-cusolver (191.6MiB)
+Downloading numpy (15.9MiB)
 Downloading nvidia-cufile (1.2MiB)
-Downloading nvidia-nccl-cu13 (196.4MiB)
-Downloading nvidia-cudnn-cu13 (349.2MiB)
-Downloading nvidia-nvjitlink (38.9MiB)
-Downloading cuda-bindings (6.3MiB)
-Downloading nvidia-nvshmem-cu13 (57.6MiB)
 Downloading triton (188.6MiB)
-Downloading nvidia-cufft (204.2MiB)
-Downloading numpy (15.9MiB)
+Downloading nvidia-cusolver (191.6MiB)
+Downloading nvidia-cuda-runtime (2.1MiB)
+Downloading nvidia-cudnn-cu13 (349.2MiB)
+Downloading nvidia-cusparse (139.2MiB)
+Downloading pillow (6.6MiB)
 Downloading nvidia-cuda-nvrtc (86.0MiB)
+Downloading nvidia-nvjitlink (38.9MiB)
 Downloading nvidia-cusparselt-cu13 (162.3MiB)
+Downloading nvidia-nvshmem-cu13 (57.6MiB)
+Downloading networkx (2.0MiB)
+Downloading torchvision (7.3MiB)
+Downloading cuda-bindings (6.3MiB)
+Downloading sympy (6.0MiB)
 Downloading nvidia-cublas (403.5MiB)
+Downloading nvidia-nccl-cu13 (196.4MiB)
+Downloading nvidia-cufft (204.2MiB)
 Downloading torch (502.2MiB)
-Downloading torchvision (7.3MiB)
+Downloading nvidia-cuda-cupti (10.2MiB)
  Downloaded nvidia-cufile
  Downloaded nvidia-cuda-runtime
  Downloaded networkx
@@ -1066,8 +521,8 @@ Downloading torchvision (7.3MiB)
  Downloaded torchvision
  Downloaded nvidia-cuda-cupti
  Downloaded numpy
- Downloaded nvidia-nvjitlink
  Downloaded sympy
+ Downloaded nvidia-nvjitlink
  Downloaded nvidia-curand
  Downloaded nvidia-nvshmem-cu13
  Downloaded nvidia-cuda-nvrtc
@@ -1080,7 +535,7 @@ Downloading torchvision (7.3MiB)
  Downloaded nvidia-cudnn-cu13
  Downloaded nvidia-cublas
  Downloaded torch
-Installed 32 packages in 628ms
+Installed 32 packages in 588ms
 ======================================================================
 RMT Diffusion Consistency reproduction -- Claim 6 (SCOPED TOY SCALE)
 Testing official Claim 5: consistency vs linear theory,

pages/conclusion/page.md

@@ -3,8 +3,12 @@
 
 ---
 <!-- trackio-cell
-{"type": "artifact", "id": "cell_80c251c0de59", "created_at": "2026-08-01T06:30:18+00:00", "title": "Reproduction bundle", "artifact": "repro-diffusion-consistency/repro-bundle:v0", "artifact_type": "dataset", "pinned": true, "pinned_at": "2026-08-01T06:30:20+00:00"}
+{"type": "markdown", "id": "cell_37e322ab8acc", "created_at": "2026-08-01T08:05:53+00:00", "title": "Conclusion", "pinned": true, "pinned_at": "2026-08-01T08:05:53+00:00"}
 -->
-**📦 Artifact** `repro-diffusion-consistency/repro-bundle:v0` · dataset
+This reproduction verified all 5 claims from the official challenge for "A Random Matrix Perspective on the Consistency of Diffusion Models" (arXiv 2602.02908, ICML 2026 Oral).
 
-trackio-artifact://repro-diffusion-consistency/repro-bundle:v0
+Claims 1-4 (the paper's closed-form theory: linear consistency prediction, self-consistency renormalization, factorized variance law, and fractional matrix power extension to sampling trajectories) were verified numerically against Monte Carlo simulation, with Claim 2 additionally cross-checked against the authors' own released code.
+
+Claim 5 (deep network validation) was tested at two scales: a small MLP on real MNIST confirmed the qualitative consistency pattern, and a more thorough UNet trained on real CIFAR10 across four dataset sizes tested all three of the paper's stated predictions directly -- consistency vs. a genuine linear-diffusion baseline, overshrinkage of low-variance eigenmodes, and eigenmode-dependent anisotropic cross-split deviation. All four sub-checks were directionally consistent with the paper's predictions.
+
+Reproducibility notes: every empirical claim was run as a real, independently-verifiable Hugging Face Job with public execution logs rather than local/offline runs. The main scope reduction versus the paper is Claim 5, which uses far less compute than the paper's full setup (7 datasets, UNet+DiT, 50k training steps per run) -- this reproduction used 1 dataset, 1 architecture, and up to 4000 steps per run.

pages/executive-summary/page.md

@@ -3,7 +3,7 @@
 
 ---
 <!-- trackio-cell
-{"type": "markdown", "id": "cell_bf8c0883e67a", "created_at": "2026-08-01T06:30:16+00:00", "title": "Executive summary", "pinned": true, "pinned_at": "2026-08-01T06:30:17+00:00"}
+{"type": "markdown", "id": "cell_7a10108fd65b", "created_at": "2026-08-01T08:05:49+00:00", "title": "Executive summary", "pinned": true, "pinned_at": "2026-08-01T08:05:50+00:00"}
 -->
 Reproduction of "A Random Matrix Theory Perspective on the Consistency of Diffusion Models" (arXiv 2602.02908), an ICML 2026 ORAL presentation (Wang, Zavatone-Veth, Pehlevan, Harvard University).
 
@@ -29,7 +29,7 @@ All empirical claims were run as real, independently-verifiable Hugging Face Job
 
 ---
 <!-- trackio-cell
-{"type": "figure", "id": "cell_ba8728491c4a", "created_at": "2026-08-01T06:41:18+00:00", "title": "Reproduction poster", "pinned": true, "pinned_at": "2026-08-01T06:41:19+00:00"}
+{"type": "figure", "id": "cell_5e61302dfcd0", "created_at": "2026-08-01T08:05:51+00:00", "title": "Reproduction poster", "pinned": true, "pinned_at": "2026-08-01T08:05:52+00:00"}
 -->
 ````html
 <div style="font-family: -apple-system, sans-serif; max-width: 900px; margin: 0 auto; padding: 24px; border: 1px solid #ddd; border-radius: 8px;">

workspace.json

@@ -5,39 +5,34 @@
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+      "url": "https://huggingface.co/jobs/byte-vortex/6a6da690a00abefd4b28b0db",
       "type": "Jobs",
-      "label": "byte-vortex/6a6d902f6b79c09949c1e1a8"
+      "label": "byte-vortex/6a6da690a00abefd4b28b0db"
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-      "url": "https://huggingface.co/jobs/byte-vortex/6a6d905e6b79c09949c1e1ae",
+      "url": "https://huggingface.co/jobs/byte-vortex/6a6da6c26b79c09949c1e2b4",
       "type": "Jobs",
-      "label": "byte-vortex/6a6d905e6b79c09949c1e1ae"
+      "label": "byte-vortex/6a6da6c26b79c09949c1e2b4"
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-      "url": "https://huggingface.co/jobs/byte-vortex/6a6d90a86b79c09949c1e1b0",
+      "url": "https://huggingface.co/jobs/byte-vortex/6a6da733a00abefd4b28b117",
       "type": "Jobs",
-      "label": "byte-vortex/6a6d90a86b79c09949c1e1b0"
+      "label": "byte-vortex/6a6da733a00abefd4b28b117"
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-      "url": "https://huggingface.co/jobs/byte-vortex/6a6d90f4a00abefd4b28af1a",
+      "url": "https://huggingface.co/jobs/byte-vortex/6a6da7596b79c09949c1e2ef",
       "type": "Jobs",
-      "label": "byte-vortex/6a6d90f4a00abefd4b28af1a"
+      "label": "byte-vortex/6a6da7596b79c09949c1e2ef"
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-      "url": "https://huggingface.co/jobs/byte-vortex/6a6d91066b79c09949c1e1b6",
+      "url": "https://huggingface.co/jobs/byte-vortex/6a6da76b6b79c09949c1e2f1",
       "type": "Jobs",
-      "label": "byte-vortex/6a6d91066b79c09949c1e1b6"
+      "label": "byte-vortex/6a6da76b6b79c09949c1e2f1"
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-      "url": "https://huggingface.co/jobs/byte-vortex/6a6d91376b79c09949c1e1b8",
+      "url": "https://huggingface.co/jobs/byte-vortex/6a6da79ba00abefd4b28b143",
       "type": "Jobs",
-      "label": "byte-vortex/6a6d91376b79c09949c1e1b8"
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-      "type": "Jobs",
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