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normalize_vector
normalize_vector: 6 implementations from 7 papers ran on one shared input (census 2026-09-22, battery b986f7e04d79); they produced 3 distinct outputs.
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 1: arg 1: rank 2, kind float, dtype float32
6 implementations from 7 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) | Members | Shared output on this bucket's input |
|---|---|---|
| 4 implementations 4 papers 2803cfee2d9a recorded values identical |
models/m_dconv.py 1b2ca5cf recorded v:2/float/float32 models/resnet.py 344dc855 recorded v:2/float/float32 model/ist_net.py 6fb9de09 recorded v:2/float/float32; dim=1, return_mag=False event_pose_estimation/model.py 7bea3b3b recorded v:2/float/float32; return_mag=False |
[0.60254, -0.512675, 0.159124, -0.230934, -0.301783, -0.344442, -0.221532, 0.191492, …] shape [4, 8] · float32 · Tensor |
| 1 implementation 2 papers 58eaef3f9b5e |
evaluation/ddg/models/attention.py d26ff18f recorded v:2/float/float32; dim=1, eps=1e-06 |
[0.60254, -0.512675, 0.159124, -0.230933, -0.301783, -0.344442, -0.221532, 0.191492, …] shape [4, 8] · float32 · Tensor |
| 1 implementation 1 paper bc9e7bc34a68 |
model.py 73b4d210 recorded x:2/float/float32; eps=0.0001 |
[0.60252, -0.512658, 0.159119, -0.230926, -0.301773, -0.34443, -0.221525, 0.191486, …] shape [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/normalize-vector.json.
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