Loading Freckles v40 + CIFAR-10...

======================================================================
  1. FULL ROUND-TRIP — Per-patch reconstruction error
======================================================================

Collecting per-patch reconstruction errors...


Reconstructing:   0%|          | 0/157 [00:00<?, ?it/s]
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Reconstructing:  14%|█▍        | 22/157 [00:00<00:01, 77.91it/s]
Reconstructing:  20%|█▉        | 31/157 [00:00<00:01, 81.98it/s]
Reconstructing:  26%|██▌       | 41/157 [00:00<00:01, 85.54it/s]
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Reconstructing:  39%|███▉      | 61/157 [00:00<00:01, 89.17it/s]
Reconstructing:  45%|████▌     | 71/157 [00:00<00:00, 89.94it/s]
Reconstructing:  52%|█████▏    | 81/157 [00:00<00:00, 90.52it/s]
Reconstructing:  58%|█████▊    | 91/157 [00:01<00:00, 90.89it/s]
Reconstructing:  64%|██████▍   | 101/157 [00:01<00:00, 91.03it/s]
Reconstructing:  71%|███████   | 111/157 [00:01<00:00, 91.05it/s]
Reconstructing:  77%|███████▋  | 121/157 [00:01<00:00, 91.14it/s]
Reconstructing:  83%|████████▎ | 131/157 [00:01<00:00, 91.22it/s]
Reconstructing:  90%|████████▉ | 141/157 [00:01<00:00, 91.26it/s]
Reconstructing: 100%|██████████| 157/157 [00:01<00:00, 86.32it/s]
Collected 10000 images, 2000 individual maps

======================================================================
  1a. SPATIAL STRUCTURE — Does recon error vary across patches?
======================================================================

  Per-image spatial CV of reconstruction error:
    Mean CV:   0.3972
    Median CV: 0.3989
    Min CV:    0.0865
    Max CV:    0.7128
    VERDICT:   HAS SPATIAL STRUCTURE

======================================================================
  1b. PER-CLASS RECONSTRUCTION ERROR
======================================================================

  Class        Mean MSE    Std MSE  Max patch
  ------------------------------------------
  airplane     0.000000   0.000000   0.000000
  auto         0.000000   0.000000   0.000000
  bird         0.000000   0.000000   0.000000
  cat          0.000000   0.000000   0.000000
  deer         0.000000   0.000000   0.000000
  dog          0.000000   0.000000   0.000000
  frog         0.000000   0.000000   0.000000
  horse        0.000000   0.000000   0.000000
  ship         0.000000   0.000000   0.000000
  truck        0.000000   0.000000   0.000000

  Mean inter-class cosine similarity: 0.996998
  Min inter-class cosine similarity:  0.991408
  VERDICT: SIMILAR PATTERNS

======================================================================
  2. CENTER vs EDGE — Where does reconstruction fail?
======================================================================

  Class        Center     Edge   Corner  E/C ratio
  ------------------------------------------------
  airplane   0.000000 0.000000 0.000000     0.9007
  auto       0.000000 0.000000 0.000000     0.9717
  bird       0.000000 0.000000 0.000000     0.9379
  cat        0.000000 0.000000 0.000000     0.9448
  deer       0.000000 0.000000 0.000000     0.9685
  dog        0.000000 0.000000 0.000000     1.0470
  frog       0.000000 0.000000 0.000000     0.9538
  horse      0.000000 0.000000 0.000000     0.9497
  ship       0.000000 0.000000 0.000000     1.0124
  truck      0.000000 0.000000 0.000000     0.9136

======================================================================
  3. PER-MODE RECONSTRUCTION — Ablating SVD modes
======================================================================

Reconstructing with individual modes...

  Per-mode energy fraction (how much each mode contributes):

  Class         Mode0    Mode1    Mode2    Mode3    FullMSE
  --------------------------------------------------
  airplane     0.4242   0.3352   0.1705   0.0701   0.000000
  auto         0.4234   0.3359   0.1704   0.0703   0.000000
  bird         0.4237   0.3361   0.1700   0.0703   0.000000
  cat          0.4232   0.3363   0.1701   0.0704   0.000000
  deer         0.4236   0.3363   0.1700   0.0701   0.000000
  dog          0.4238   0.3358   0.1703   0.0701   0.000000
  frog         0.4229   0.3367   0.1698   0.0706   0.000000
  horse        0.4237   0.3358   0.1703   0.0702   0.000000
  ship         0.4243   0.3353   0.1706   0.0699   0.000000
  truck        0.4237   0.3358   0.1704   0.0701   0.000000

======================================================================
  4. LINEAR PROBE — Reconstruction error maps as features
======================================================================

  Ridge probe comparison:

  Recon error spatial map                  dims=  256  train=51.6%  test=19.0%
    Class         Acc
    ------------------
    airplane   25.9% █████
    auto       19.4% ███
    bird        9.1% █
    cat        12.2% ██
    deer        7.9% █
    dog        31.4% ██████
    frog       22.7% ████
    horse      14.3% ██
    ship       36.6% ███████
    truck      15.6% ███

======================================================================
  5. FULL CONDUIT — Release error + eigenvalues + friction
======================================================================

Full conduit:   0%|          | 0/157 [00:00<?, ?it/s]
Full conduit:   2%|▏         | 3/157 [00:00<00:06, 25.60it/s]
Full conduit:   6%|▌         | 9/157 [00:00<00:03, 42.69it/s]
Full conduit:  10%|▉         | 15/157 [00:00<00:02, 48.80it/s]
Full conduit:  13%|█▎        | 21/157 [00:00<00:02, 51.60it/s]
Full conduit:  20%|██        | 32/157 [00:00<00:02, 47.18it/s]

  Comparative linear probes:

  Release error only                       dims=  256  train=51.6%  test=16.0%
    Class         Acc
    ------------------
    airplane   27.8% █████
    auto       18.2% ███
    bird       14.6% ██
    cat         5.4% █
    deer        8.3% █
    dog        21.6% ████
    frog       18.9% ███
    horse      17.1% ███
    ship       11.6% ██
    truck      20.0% ████

  Eigenvalues (S) only                     dims= 1024  train=91.6%  test=20.7%
    Class         Acc
    ------------------
    airplane   16.7% ███
    auto       24.2% ████
    bird       18.8% ███
    cat        21.6% ████
    deer       14.6% ██
    dog        16.2% ███
    frog       24.3% ████
    horse      17.1% ███
    ship       32.6% ██████
    truck      22.5% ████

  Friction only                            dims= 1024  train=92.4%  test=22.5%
    Class         Acc
    ------------------
    airplane   19.4% ███
    auto       21.2% ████
    bird       18.8% ███
    cat        21.6% ████
    deer       14.6% ██
    dog        16.2% ███
    frog       27.0% █████
    horse      22.0% ████
    ship       34.9% ██████
    truck      30.0% ██████

  Combinations:

  Release + Eigenvalues                    dims= 1280  train=98.4%  test=18.5%
    Class         Acc
    ------------------
    airplane    2.8% 
    auto       21.2% ████
    bird       18.8% ███
    cat         2.7% 
    deer       18.8% ███
    dog        16.2% ███
    frog       21.6% ████
    horse      26.8% █████
    ship       25.6% █████
    truck      27.5% █████
  Release + Friction                       dims= 1280  train=98.3%  test=15.3%
    Class         Acc
    ------------------
    airplane    8.3% █
    auto       18.2% ███
    bird       16.7% ███
    cat         5.4% █
    deer       12.5% ██
    dog        16.2% ███
    frog       13.5% ██
    horse      19.5% ███
    ship       23.3% ████
    truck      17.5% ███
  Release + Eigenvalues + Friction         dims= 2304  train=99.9%  test=17.0%
    Class         Acc
    ------------------
    airplane   13.9% ██
    auto        3.0% 
    bird       16.7% ███
    cat        10.8% ██
    deer       12.5% ██
    dog        21.6% ████
    frog       27.0% █████
    horse      22.0% ████
    ship       23.3% ████
    truck      17.5% ███

======================================================================
  6. HIGH-ERROR PATCHES — Where does reconstruction fail?
======================================================================

  Top error positions per class (patch coordinates):
  Class                    Top 3 positions (row, col)  Error ratio
  ----------------------------------------------------------------
  airplane                      (10,8), (9,8), (10,7)         1.19x
  auto                        (11,14), (11,7), (10,1)         1.12x
  bird                           (8,9), (7,9), (10,8)         1.11x
  cat                             (5,8), (5,7), (6,5)         1.08x
  deer                            (5,7), (5,5), (7,6)         1.06x
  dog                        (15,15), (14,15), (9,15)         1.07x
  frog                            (5,4), (4,7), (4,6)         1.07x
  horse                          (10,9), (9,7), (9,9)         1.11x
  ship                       (14,13), (14,12), (15,9)         1.20x
  truck                      (10,14), (10,13), (10,1)         1.16x

  Overall error map:
    Mean: 0.000000
    Std:  0.000000
    Hot patches (>2σ): 0/256

======================================================================
  THEOREM 3: RELEASE FIDELITY — SUMMARY
======================================================================

  SPATIAL STRUCTURE:
    Recon error spatial CV:     0.3972
    (Friction spatial CV was:   0.0137)

  CLASSIFICATION (ridge probe, test accuracy):
    Chance:                     10.0%
    Friction maps:              24.3% (from Cell 3)
    Eigenvalue (S) maps:        21.0% (from Cell 3)
    Release error maps:         19.0%
    Release + Eigenvalues:      18.5%
    Release + Friction:         15.3%
    FULL CONDUIT (all three):   17.0%

  THE QUESTION ANSWERED:
    Does the release signal carry class-discriminative information
    that eigenvalues and friction do not?
    Lift from release over eigenvalues: -4.7pp
    Lift from full conduit over eigenvalues: -3.7pp