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authorYurenHao0426 <Blackhao0426@gmail.com>2026-08-01 16:15:41 -0500
committerYurenHao0426 <Blackhao0426@gmail.com>2026-08-01 16:15:41 -0500
commit08fd63b8fee62ccdc284380c9832900ee83f9ede (patch)
tree53d49406a0b778e25c7604a7ae0fe5d2ecf15367 /logs/obj_base_gpu.log
parent58b9c84dae293359f498fdf6afd533df5c9d3c25 (diff)
Retire the correlation gate: the shared spectrum governs recovery
A controlled truncation refutes the project's central go/no-go rule. Projecting the recovering synthetic fields to rank r holds the field correlation at 0.902-0.929 while recovery moves 6.2% -> 12.9% -> 95.6% across ranks 4, 8, 16. A field past the supposed 0.9 threshold recovers 13%, so correlation neither predicts nor forbids recovery and the width of the shared spectrum is what moves it. The gate becomes a joint condition on correlation and shared width, measured by principal angles against a scene-shuffled null. Neither suffices alone: 18 shared directions at 0.508 fails, 11 at 0.902 fails. With the old gate retired, natural data was finally searched: 0.0000 against 0.0039 chance. The old verdict was right, its reasoning was not. Also closes route D by measurement. rho_IT ~ sqrt(4 log N / N) rises as N falls, and at N = 16 through 96 the deepest state a strong searcher reaches is deeper than the truth in 3/3 replicates at every size. Free gains: eigenvalue-weighted projection over a wide basis with 128-dim text vectors takes the correlation 0.656 -> 0.716 and shared width 10 -> 16. Hubness refuted as an inflation hypothesis. Moving the per-image segmentation eigendecomposition onto the GPU cut batch time from 130s to 1.9s. Co-Authored-By: Claude <noreply@anthropic.com>
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+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!
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