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pairwise_distances
pairwise_distances: 14 implementations from 13 papers ran on one shared input (census 2026-09-22, battery b986f7e04d79); they produced 7 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 · arg 2: rank 2, kind float, dtype float32
10 implementations from 8 papers share this bucket (rank, kind, dtype of each array argument, positional; each member's recorded signature, argument name included, is shown under it); 5 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 |
|---|---|---|
| 3 implementations 3 papers bd5ec630d2df recorded values identical |
run_joint_finetune.py 13c5bcb7 recorded x:2/float/float32, y:2/float/float32 toy_gnn.py 449fa3a7 recorded x:2/float/float32, y:2/float/float32 model/PMF.py 70fa9d2c recorded x:2/float/float32, y:2/float/float32 |
[0, 21.0374, 4.08626, 20.9756, 21.0374, 0, 21.4547, 14.4146, …] shape [4, 4] · float32 · Tensor |
| 3 implementations 2 papers f8b683d411a9 recorded values identical |
few_shot/proto.py 264452c0 recorded x:2/float/float32, y:2/float/float32; matching_fn='l2' Utils/protonet_calculations.py 276c4246 recorded x:2/float/float32, y:2/float/float32 qinco/model/qinco_base.py a34f7531 recorded a:2/float/float32, b:2/float/float32; approx='auto' |
[0, 21.0348, 4.08747, 20.9713, 21.0348, 0, 21.439, 14.4312, …] shape [4, 4] · float32 · Tensor |
| 2 implementations 2 papers 59d3d79b83ca recorded values identical |
GP.py 4e2d3ab4 recorded x:2/float/float32, y:2/float/float32
model_rqmoe.py 53bf28d5 recorded a:2/float/float32, b:2/float/float32 |
[-0.00966263, 21.0374, 4.08626, 20.9756, 21.0374, -0.0782909, 21.4547, 14.4146, …] shape [4, 4] · float32 · Tensor |
| 1 implementation 1 paper cfb0c26ed4e3 |
keyword_detr/keyword_attention.py 732d646a recorded x:2/float/float32, y:2/float/float32; metric='cosine' |
[0.00222206, 1.11759, 0.261728, 1.23741, 1.11759, 0.00250912, 1.39831, 0.808899, …] shape [4, 4] · float32 · Tensor |
| 1 implementation 1 paper db2fd13e171d |
few_shot/proto.py 9c31448b recorded x:2/float/float32, y:2/float/float32; matching_fn='l2', model=MinimalModel() |
[0, 42.0695, 8.17493, 41.9427, 42.0695, 0, 42.878, 28.8625, …] shape [4, 4] · float32 · Tensor |
Bucket 2 of 2: arg 1: rank 2, kind float, dtype float32
4 implementations from 5 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 |
|---|---|---|
| 3 implementations 4 papers 9851ab143a7b recorded values identical |
domainbed/hsic.py 21bf8dee recorded x:2/float/float32 src/RQR-O/losses.py 92723afc recorded x:2/float/float32 conformal.py e39cf783 recorded x:2/float/float32 |
[-0.00966263, 21.0374, 4.08626, 20.9756, 21.0374, -0.0782909, 21.4547, 14.4146, …] shape [4, 4] · float32 · Tensor |
| 1 implementation 1 paper aaf4a4aa87dd |
hcsmoe/merging/clustering.py 881d32a3 recorded X:2/float/float32; method='single' |
[inf, 4.59145, 2.02351, 4.57973, 4.59145, inf, 4.63653, 3.80095, …] shape [4, 4] · float32 · Tensor · non-finite |
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/pairwise-distances.json.
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