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rand_brightness
rand_brightness: 7 implementations from 9 papers ran on one shared input (census 2026-09-22, battery b986f7e04d79); they produced 5 distinct outputs across 2 buckets, one shared input per bucket.
Identical values to six decimals (the recorded digest) on the shared input are agreement on those inputs, not equivalence. Implementations are compared only within one bucket, the positional (rank, kind, dtype) of each array argument; the argument name is not part of the key because the harness draws the shared array from (rank, kind) and casts it to the dtype, whatever the name; each bucket's shared input is fixed by that key, so members of one bucket saw bitwise-identical inputs under their own scalar arguments. A cluster is the set of members whose recorded output digest is identical. Nothing here says which computation a paper's method intended, and nothing reproduces a paper's results.
Not compared, and not in the tables or the counts above:
- Every examined implementation of this name that took an array argument ran on the shared input.
- No implementation of this name ran on its own fixture arguments only: every one that ran took an array argument.
- Every compared output was digested.
Bucket 1 of 2: arg 1: rank 4, kind float, dtype float32
6 implementations from 9 papers share this bucket (rank, kind, dtype of each array argument, positional; each member's recorded signature, argument name included, is shown under it); 4 distinct outputs, largest cluster first. Values are the first 8 of the recorded output, flattened.
| Cluster (same digest to six decimals) | Members | Shared output on this bucket's input |
|---|---|---|
| 2 implementations 5 papers 536312369585 recorded values identical |
src/utils/diffaug.py 011230b2 recorded x:4/float/float32
DiffAugment_pytorch.py fd66a459 recorded x:4/float/float32 |
[0.719361, 0.649336, 1.44234, 1.0948, 1.13355, -0.203671, -0.27523, 0.828236, …] shape [2, 3, 4, 4] · float32 · Tensor |
| 2 implementations 2 papers baa10f0e43ea recorded values identical |
main_DataDAM.py 1cad5517 recorded x:4/float/float32; param=<fx_1cad5517390bb5b5__rand_brightness.Param object at 0xffff distill.py 2381e0ea recorded x:4/float/float32; param=<fx_2381e0ea15f245ea__rand_brightness.Param object at 0xffff |
[0.722356, 0.652331, 1.44534, 1.09779, 1.13655, -0.200676, -0.272236, 0.83123, …] shape [2, 3, 4, 4] · float32 · Tensor |
| 1 implementation 1 paper 88afd26e1e13 |
main_run.py bbcf3d37 recorded x:4/float/float32; param=<fx_bbcf3d37a05e74fa__rand_brightness.ParamConfig object at |
[0.837786, 0.76776, 1.56077, 1.21322, 1.25198, -0.0852465, -0.156806, 0.94666, …] shape [2, 3, 4, 4] · float32 · Tensor |
| 1 implementation 1 paper d7a8dd70b7b9 |
distill.py ff7b23be recorded x:4/float/float32; param=<fx_ff7b23be3efb6c10__rand_brightness.Param object at 0xffff |
[0.837786, 0.76776, 1.56077, 1.21322, 1.25198, -0.0852465, -0.156806, 0.94666, …] shape [2, 3, 4, 4] · float32 · Tensor |
Bucket 2 of 2: arg 1: rank 5, kind float, dtype float32
1 implementation from 1 paper share this bucket (rank, kind, dtype of each array argument, positional; each member's recorded signature, argument name included, is shown under it); 1 distinct output, largest cluster first. Values are the first 8 of the recorded output, flattened.
| Cluster (same digest to six decimals) | Members | Shared output on this bucket's input |
|---|---|---|
| 1 implementation 1 paper 754faf4b7d34 |
diff_augment.py 00543420 recorded x:5/float/float32; scale=0.5 |
[-1.11263, -1.15576, 0.896651, 1.6561, -0.885354, -0.789079, 0.288488, -1.03351, …] shape [2, 2, 3, 4, 4] · float32 · Tensor |
Identical values to six decimals (the recorded digest) on the shared input are agreement on those inputs, not equivalence; where a cluster's members carry recorded values, the largest difference among them is shown under the cluster. Paper titles are the archive's archive 2025-07-28 where the paper is in the archive and the graph's where it was added by Syntology; papers with no page here are shown by their recorded paper id only. A paper count above the implementation count means one implementation (one code sha) is held from several papers' repositories and counts once. Per-sample status, licence and fingerprint records for each paper are on its paper page. JSON twin: /census/rand-brightness.json.
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