identity
identity: 11 implementations from 39 papers ran on one shared input (census 2026-09-22, battery b986f7e04d79); they produced 3 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 2, kind float, dtype float32
10 implementations from 38 papers share this bucket (rank, kind, dtype of each array argument, positional; each member's recorded signature, argument name included, is shown under it); 2 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 |
|---|---|---|
| 9 implementations 37 papers d7bd5ceec8a6 recorded values identical |
recurrent_memory_transformer_pytorch/recurrent_memory_transformer.py 7f1040f5 recorded t:2/float/float32; args=(tensor([[-0.7193, -0.4033, -0.5966, 0.1820, -0.8567], , kwargs={'key1': 42, 'key2': [1, 2, 3]}
sampling.py 9910e2fc recorded x:2/float/float32
memorizing_transformers_pytorch/memorizing_transformers_pytorch.py f3232418 recorded t:2/float/float32
marge_pytorch/marge_pytorch.py caeb28d3 recorded x:2/float/float32; args=(tensor([[-0.7193, -0.4033, -0.5966, 0.1820, -0.8567], , kwargs={'key1': tensor([[ 0.7748, 0.1919, 1.2638], [-1.29 genienlp/tasks/almond_task.py 159ce993 recorded x:2/float/float32; kw={'key1': 'some_value', 'key2': 42}
DSTPP/DiffusionModel.py 5e613c56 recorded t:2/float/float32; args=(1, 2, 'test'), kwargs={'key1': 'value1', 'key2': 42} lightning_uq_box/uq_methods/raps.py 9adf3e5a recorded x:2/float/float32; dim=None dawin_rft/main_dawin.py c5bb6591 recorded x:2/float/float32; device='cpu' Models/interpretable_diffusion/gaussian_diffusion.py d6b55d23 recorded t:2/float/float32; args=(), kwargs={} |
[1.80263, -1.53378, 0.476053, -0.690887, -0.902849, -1.03047, -0.662761, 0.572889, …] shape [4, 8] · float32 · Tensor |
| 1 implementation 1 paper 4327b6dd66c2 |
learn_embedding/embedding.py 2ac41ae2 recorded y:2/float/float32 |
[1, 0, 0, 0, 0, 0, 0, 0, …] shape [4, 8, 8] · float32 · Tensor |
Bucket 2 of 2: arg 1: rank 3, 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 fe5b7d530940 |
wiener_loss/wiener_loss.py 0e4da82a recorded mesh:3/float/float32; val=1 |
[1, 1, 1, 1, 1, 1, 1, 1] shape [2, 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/identity.json.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections