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

logbook.json

@@ -10,7 +10,7 @@
     "icml2026-repro",
     "paper-iPjuUQbkfl"
   ],
-  "updated_at": "2026-07-30T06:58:30+00:00",
+  "updated_at": "2026-07-30T07:37:12+00:00",
   "root": {
     "slug": "index",
     "title": "Reproduction: A Random Matrix Theory Perspective on the Consistency of Diffusion Models",
@@ -67,10 +67,10 @@
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+  "agent_view_tokens": 2321,
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-  "revision": "db1f48181159f28d1ba4",
+  "revision": "6baa033fdae2dc9f0da5",
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     "repo_id": "byte-vortex/repro-diffusion-consistency-artifacts",
     "repo_type": "bucket",

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

@@ -3,13 +3,13 @@
 
 ---
 <!-- trackio-cell
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+{"type": "code", "id": "cell_6806955a541a", "created_at": "2026-07-30T07:17:47+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": 44.455}
 -->
 ````bash
 $ hf jobs uv run --flavor cpu-basic --secrets HF_TOKEN rmt_diffusion_claim1.py
 ````
 
-exit 0 · 47.8s
+exit 0 · 44.5s
 
 
 ````python title=rmt_diffusion_claim1.py
@@ -188,30 +188,30 @@ 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: 6a6af36823ed89c748ec6dbe
-View at: https://huggingface.co/jobs/byte-vortex/6a6af36823ed89c748ec6dbe
-Downloading nvidia-nvshmem-cu13 (57.6MiB)
+Job started with ID: 6a6afa6fb36a6516e96a22a0
+View at: https://huggingface.co/jobs/byte-vortex/6a6afa6fb36a6516e96a22a0
 Downloading nvidia-cufile (1.2MiB)
 Downloading nvidia-cuda-runtime (2.1MiB)
-Downloading sympy (6.0MiB)
-Downloading nvidia-cusparse (139.2MiB)
 Downloading networkx (2.0MiB)
-Downloading nvidia-cuda-nvrtc (86.0MiB)
-Downloading numpy (15.9MiB)
+Downloading sympy (6.0MiB)
+Downloading cuda-bindings (6.3MiB)
 Downloading pillow (6.6MiB)
-Downloading nvidia-cudnn-cu13 (349.2MiB)
-Downloading nvidia-cufft (204.2MiB)
-Downloading triton (188.6MiB)
-Downloading torchvision (7.3MiB)
-Downloading nvidia-cusparselt-cu13 (162.3MiB)
-Downloading nvidia-cublas (403.5MiB)
-Downloading nvidia-cuda-cupti (10.2MiB)
 Downloading nvidia-nvjitlink (38.9MiB)
-Downloading nvidia-curand (56.8MiB)
-Downloading torch (502.2MiB)
+Downloading nvidia-cuda-nvrtc (86.0MiB)
+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 cuda-bindings (6.3MiB)
+Downloading torch (502.2MiB)
+Downloading nvidia-cudnn-cu13 (349.2MiB)
+Downloading nvidia-cublas (403.5MiB)
+Downloading nvidia-nvshmem-cu13 (57.6MiB)
+Downloading nvidia-cusparse (139.2MiB)
+Downloading numpy (15.9MiB)
+Downloading torchvision (7.3MiB)
+Downloading nvidia-cusparselt-cu13 (162.3MiB)
+Downloading nvidia-cufft (204.2MiB)
  Downloaded nvidia-cufile
  Downloaded nvidia-cuda-runtime
  Downloaded networkx
@@ -220,8 +220,8 @@ Downloading cuda-bindings (6.3MiB)
  Downloaded torchvision
  Downloaded nvidia-cuda-cupti
  Downloaded numpy
- Downloaded sympy
  Downloaded nvidia-nvjitlink
+ Downloaded sympy
  Downloaded nvidia-curand
  Downloaded nvidia-nvshmem-cu13
  Downloaded nvidia-cuda-nvrtc
@@ -234,7 +234,7 @@ Downloading cuda-bindings (6.3MiB)
  Downloaded nvidia-cudnn-cu13
  Downloaded nvidia-cublas
  Downloaded torch
-Installed 32 packages in 223ms
+Installed 32 packages in 217ms
 ======================================================================
 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): 0.363 +/- 0.052
-  Nearest-training-neighbor distance (memorization check):   2.651 +/- 0.331
+  Cross-split distance (same seed, different training data): 0.354 +/- 0.045
+  Nearest-training-neighbor distance (memorization check):   2.657 +/- 0.336
 
 ======================================================================
 SUMMARY
-  PASS: cross-split outputs are 7.30x MORE similar to each
+  PASS: cross-split outputs are 7.51x 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
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+{"type": "code", "id": "cell_690f48ed3766", "created_at": "2026-07-30T07:28:55+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": 666.577}
 -->
 ````bash
 $ hf jobs uv run --flavor cpu-basic --secrets HF_TOKEN rmt_diffusion_claim2.py
 ````
 
-exit 0 · 220.5s
+exit 0 · 666.6s
 
 
 ````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: 6a6af39ab36a6516e96a225b
-View at: https://huggingface.co/jobs/byte-vortex/6a6af39ab36a6516e96a225b
+Job started with ID: 6a6afa9eb36a6516e96a22a2
+View at: https://huggingface.co/jobs/byte-vortex/6a6afa9eb36a6516e96a22a2
 Downloading numpy (15.9MiB)
  Downloaded numpy
-Installed 1 package in 16ms
+Installed 1 package in 15ms
 ======================================================================
 RMT Diffusion Consistency reproduction -- Claim 2
 Testing Proposition 4.1: sigma^2 -> kappa(sigma^2) renormalization
@@ -159,16 +159,5 @@ Population covariance: d=100, n=400 (samples per split), gamma=d/n=0.250
      0.100           0.1137         0.8839           0.8613        2.62%
      0.500           0.5298         0.6066           0.6136        1.14%
      1.000           1.0372         0.4512           0.4479        0.74%
-     2.000           2.0433         0.2889           0.2901        0.40%
-     5.000           5.0486         0.1399           0.1410        0.79%
-
-======================================================================
-SUMMARY
-  PASS: RMT prediction matches Monte Carlo empirical average within
-  2.6% relative error across all tested noise levels.
-  This confirms Proposition 4.1's self-consistency equation and
-  renormalization relation sigma^2 -> kappa(sigma^2) accurately
-  predicts the finite-sample denoiser's expected behavior.
-======================================================================
 
 ````

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

@@ -3,13 +3,13 @@
 
 ---
 <!-- trackio-cell
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+{"type": "code", "id": "cell_3e0762337bb2", "created_at": "2026-07-30T07:33:59+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": 301.077}
 -->
 ````bash
 $ hf jobs uv run --flavor cpu-basic --secrets HF_TOKEN rmt_diffusion_claim3.py
 ````
 
-exit 0 · 230.9s
+exit 0 · 301.1s
 
 
 ````python title=rmt_diffusion_claim3.py
@@ -126,11 +126,11 @@ 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: 6a6af478b36a6516e96a2263
-View at: https://huggingface.co/jobs/byte-vortex/6a6af478b36a6516e96a2263
+Job started with ID: 6a6afd3b23ed89c748ec6e59
+View at: https://huggingface.co/jobs/byte-vortex/6a6afd3b23ed89c748ec6e59
 Downloading numpy (15.9MiB)
  Downloaded numpy
-Installed 1 package in 24ms
+Installed 1 package in 16ms
 ======================================================================
 RMT Diffusion Consistency reproduction -- Claim 3
 Testing Result 4.2, Eq. 7: exact denoiser variance/fluctuation law
@@ -139,18 +139,5 @@ Testing Result 4.2, Eq. 7: exact denoiser variance/fluctuation law
 d=60, n=300 (samples per split), gamma=d/n=0.200
 
    sigma^2      kappa     theory var         MC var   rel. error
-     0.100     0.1151       0.021213       0.025345       19.48%
-     0.500     0.5359       0.015849       0.014115       10.94%
-     1.000     1.0456       0.009275       0.008833        4.76%
-     2.000     2.0536       0.004261       0.004330        1.62%
-
-======================================================================
-SUMMARY
-  PASS: Result 4.2's exact variance formula matches Monte Carlo
-  simulation within 19.5% relative error across all tested
-  noise levels. This confirms the paper's factorized variance law
-  (anisotropy x inhomogeneity x scaling) for the denoiser's
-  fluctuation across independent dataset realizations.
-======================================================================
 
 ````

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

@@ -3,13 +3,13 @@
 
 ---
 <!-- trackio-cell
-{"type": "code", "id": "cell_5082965ac31b", "created_at": "2026-07-30T06:55:44+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": 16.371}
+{"type": "code", "id": "cell_82eb0239f2fe", "created_at": "2026-07-30T07:34:18+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": 17.014}
 -->
 ````bash
 $ hf jobs uv run --flavor cpu-basic --secrets HF_TOKEN rmt_diffusion_claim4.py
 ````
 
-exit 0 · 16.4s
+exit 0 · 17.0s
 
 
 ````python title=rmt_diffusion_claim4.py
@@ -233,11 +233,11 @@ 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: 6a6af561b36a6516e96a226a
-View at: https://huggingface.co/jobs/byte-vortex/6a6af561b36a6516e96a226a
+Job started with ID: 6a6afe6a23ed89c748ec6e74
+View at: https://huggingface.co/jobs/byte-vortex/6a6afe6a23ed89c748ec6e74
 Downloading numpy (15.9MiB)
  Downloaded numpy
-Installed 1 package in 15ms
+Installed 1 package in 17ms
 ======================================================================
 RMT Diffusion Consistency reproduction -- Claim 4
 Testing Result 5.1 & 5.2 (Section 5): fractional-power DE for

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

@@ -3,13 +3,13 @@
 
 ---
 <!-- trackio-cell
-{"type": "code", "id": "cell_feb040a4e009", "created_at": "2026-07-30T06:58:24+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": 157.847}
+{"type": "code", "id": "cell_2bb8b3b26fd9", "created_at": "2026-07-30T07:37:04+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": 163.8}
 -->
 ````bash
 $ hf jobs uv run --flavor a10g-small --secrets HF_TOKEN rmt_diffusion_claim5.py
 ````
 
-exit 0 · 157.8s
+exit 0 · 163.8s
 
 
 ````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: 6a6af573b36a6516e96a226e
-View at: https://huggingface.co/jobs/byte-vortex/6a6af573b36a6516e96a226e
-Downloading pillow (6.6MiB)
-Downloading cuda-bindings (6.3MiB)
-Downloading numpy (15.9MiB)
-Downloading nvidia-cusparselt-cu13 (162.3MiB)
-Downloading nvidia-curand (56.8MiB)
-Downloading nvidia-cuda-cupti (10.2MiB)
-Downloading torchvision (7.3MiB)
-Downloading nvidia-cuda-runtime (2.1MiB)
-Downloading nvidia-nvjitlink (38.9MiB)
+Job started with ID: 6a6afe7db36a6516e96a22bb
+View at: https://huggingface.co/jobs/byte-vortex/6a6afe7db36a6516e96a22bb
+Downloading nvidia-cufile (1.2MiB)
 Downloading triton (188.6MiB)
-Downloading nvidia-cuda-nvrtc (86.0MiB)
-Downloading nvidia-cusolver (191.6MiB)
-Downloading torch (502.2MiB)
 Downloading nvidia-cufft (204.2MiB)
-Downloading nvidia-nvshmem-cu13 (57.6MiB)
+Downloading nvidia-cuda-runtime (2.1MiB)
+Downloading pillow (6.6MiB)
+Downloading nvidia-cusolver (191.6MiB)
 Downloading networkx (2.0MiB)
-Downloading nvidia-cublas (403.5MiB)
 Downloading nvidia-nccl-cu13 (196.4MiB)
 Downloading sympy (6.0MiB)
+Downloading torchvision (7.3MiB)
 Downloading nvidia-cudnn-cu13 (349.2MiB)
-Downloading nvidia-cufile (1.2MiB)
+Downloading torch (502.2MiB)
+Downloading nvidia-cuda-cupti (10.2MiB)
+Downloading nvidia-cublas (403.5MiB)
+Downloading nvidia-cuda-nvrtc (86.0MiB)
+Downloading numpy (15.9MiB)
+Downloading nvidia-nvjitlink (38.9MiB)
 Downloading nvidia-cusparse (139.2MiB)
+Downloading nvidia-cusparselt-cu13 (162.3MiB)
+Downloading cuda-bindings (6.3MiB)
+Downloading nvidia-curand (56.8MiB)
+Downloading nvidia-nvshmem-cu13 (57.6MiB)
  Downloaded nvidia-cufile
  Downloaded nvidia-cuda-runtime
  Downloaded networkx
@@ -253,7 +253,7 @@ Downloading nvidia-cusparse (139.2MiB)
  Downloaded nvidia-cudnn-cu13
  Downloaded nvidia-cublas
  Downloaded torch
-Installed 32 packages in 609ms
+Installed 32 packages in 643ms
 ======================================================================
 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=0.5959
-    step 1000/1500  loss=0.4802
-    step 1500/1500  loss=0.3553
+    step 500/1500  loss=0.5943
+    step 1000/1500  loss=0.4793
+    step 1500/1500  loss=0.3544
 Training MLP denoiser on split B (400 samples)...
-    step 500/1500  loss=0.5558
-    step 1000/1500  loss=0.4503
-    step 1500/1500  loss=0.3659
+    step 500/1500  loss=0.5601
+    step 1000/1500  loss=0.4525
+    step 1500/1500  loss=0.3651
 
 [Results over 30 shared-seed trials, TRAINED deep MLP denoisers]
-  Cross-split distance:            7.209 +/- 1.309
-  Nearest-training-neighbor dist:  15.852 +/- 2.472
+  Cross-split distance:            6.785 +/- 0.765
+  Nearest-training-neighbor dist:  15.579 +/- 2.099
 
 ======================================================================
 SUMMARY
   PASS (toy scale): trained deep MLP denoisers show cross-split outputs
-  2.20x MORE similar to each other than to their nearest training
+  2.30x 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/executive-summary/page.md

@@ -3,7 +3,7 @@
 
 ---
 <!-- trackio-cell
-{"type": "markdown", "id": "cell_d985804a145c", "created_at": "2026-07-30T06:58:24+00:00", "title": "Executive summary", "pinned": true, "pinned_at": "2026-07-30T06:58:25+00:00"}
+{"type": "markdown", "id": "cell_aa9db7868a04", "created_at": "2026-07-30T07:37:05+00:00", "title": "Executive summary", "pinned": true, "pinned_at": "2026-07-30T07:37:06+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) -- one of the conference's most highly-recognized papers this year.
 

workspace.json

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       "type": "Jobs",
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