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PositionalEmbedding
PositionalEmbedding: 6 implementations from 6 papers ran on one shared input (census 2026-09-22, battery b986f7e04d79); they produced 4 distinct outputs across 3 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:
- 2 more implementations of this name were examined but did not run on the shared input (IndexError 1, RuntimeError 1).
- 2 more implementations from 2 papers took no array argument and ran only on their own fixture arguments; no shared input existed for them, so they are not compared (on 2 the recorded shared-output digest is the own-fixture digest).
- Every compared output was digested.
- 6 compared members are class-bearing (tagged class): the class was constructed with seeded weights in evaluation mode and then called, so its recorded output depends on that initialisation as well as on the forward computation.
Bucket 1 of 3: arg 1: rank 3, kind float, dtype float32
4 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); 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 |
|---|---|---|
| 2 implementations 2 papers 16c3894f2768 recorded values identical |
class models/TimeMixer.py 5da1d16a recorded x:3/float/float32 class model/TDformer.py cc9347d9 recorded x:3/float/float32 |
[0, 1, 0, 1, 0.841471, 0.540302, 0.00999983, 0.99995, …] shape [1, 4, 4] · float32 · Tensor |
| 2 implementations 2 papers 20849e0b851b recorded values identical |
class models/landmark_generator.py a194859b recorded x:3/float/float32 class cvtslr_model.py fd20d6f5 recorded x:3/float/float32 |
[0, 1, 0, 1, 0, 1, 0, 1, …] shape [1, 4, 8] · float32 · Tensor |
Bucket 2 of 3: arg 1: rank 1, 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 d18b5e1f3852 |
src/model_bllip_dep.py e0fa815d recorded pos_seq:1/float/float32; bsz=3 |
[-0.785738, -0.0902659, -0.00903877, -0.000903889, 0.618559, 0.995918, 0.999959, 1, …] shape [8, 3, 8] · float32 · Tensor |
Bucket 3 of 3: arg 1: rank 2, kind int, dtype int64
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 e31e460a230b |
class lib/model/transformer.py 9322dfdb recorded inp:2/int/int64 |
[0, 0.409258, 0.194435, 0, 1.00155, 1.28792, 0.631543, 0.973679, …] shape [4, 8, 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/positionalembedding.json.
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