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squash

Syntologyfunction-name censuscensus 2026-09-22battery b986f7e04d79all samples with this name

squash: 8 implementations from 5 papers ran on one shared input (census 2026-09-22, battery b986f7e04d79); they produced 4 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:

Bucket 1 of 2: arg 1: rank 2, kind float, dtype float32

5 implementations from 4 papers share this bucket (rank, kind, dtype of each array argument, positional; each member's recorded signature, argument name included, is shown under it); 3 distinct outputs, largest cluster first. Values are the first 8 of the recorded output, flattened.

Cluster (same digest to six decimals)MembersShared output on this bucket's input
3 implementations
2 papers
2d134f5fe6f0
recorded values identical
one code sha held from 2 papers' repositories
capsnet.py 89d981e0
recorded x:2/float/float32; dim=-1

capsules.py 36782ecd
recorded s:2/float/float32; dim=-1

model/Pretrain_model/GPTST.py c0581450
recorded x:2/float/float32; dim=-1
[0.541986, -0.461151, 0.143132, -0.207725, -0.271454, -0.309826, -0.199268, 0.172247, …]
shape [4, 8] · float32 · Tensor
1 implementation
2 papers
6201e41e7d18
one code sha held from 2 papers' repositories
CapsDiscriminator.py e8e375ca
recorded inputs:2/float/float32; axis=-1
[0.541986, -0.461151, 0.143132, -0.207725, -0.271454, -0.309826, -0.199268, 0.172247, …]
shape [4, 8] · float32 · Tensor
1 implementation
1 paper
bd05891f8911

deepcaps.py 4ac5c90d
recorded s:2/float/float32; dim=-1
[0.541986, -0.461152, 0.143132, -0.207725, -0.271454, -0.309826, -0.199268, 0.172247, …]
shape [4, 8] · float32 · Tensor

Bucket 2 of 2: arg 1: rank 3, kind float, dtype float32

3 implementations from 2 papers 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)MembersShared output on this bucket's input
3 implementations
2 papers
3f19b1a3dddc
recorded values differ by up to 5.96e-08
one code sha held from 2 papers' repositories
layers/cap_layer.py ac7650b0
recorded vec:3/float/float32; manner='paper'

functional.py 43a28458
recorded input:3/float/float32; dim=-1, keepdim=True

net.py 5abcd8c6
recorded x:3/float/float32
[-0.285965, 0.538823, -0.403049, -0.0826734, 0.0483465, -0.459387, -0.108909, -0.12203, …]
shape [2, 4, 8] · 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/squash.json.

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