f
f: 17 implementations from 15 papers ran on one shared input (census 2026-09-22, battery b986f7e04d79); they produced 17 distinct outputs across 7 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:
- 4 more implementations of this name were examined but did not run on the shared input (ValueError 3, 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.
Bucket 1 of 7: arg 1: rank 1, kind float, dtype float64 · arg 2: rank 1, kind float, dtype float64
3 implementations from 3 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 |
|---|---|---|
| 1 implementation 1 paper 6223a00f0477 |
V-PBGD/toy/toy.py 7e299e67 recorded x:1/float/float64, y:1/float/float64 |
[1.74099, 0.352735, 1.26979, 1.64157, 1.07623, 0.631373, 2.1485, 1.07811] shape [8] · float64 · ndarray |
| 1 implementation 1 paper d8253e2acf3d |
autolrs_server.py 3e6c34b5 recorded x:1/float/float64, y:1/float/float64; b=0.5 |
[1.09808] shape [1] · float64 · ndarray |
| 1 implementation 1 paper dc7fc7a423d0 |
contour_plot.py 075afe4d recorded x:1/float/float64, y:1/float/float64 |
[7.18352, 4.56915, 1.99822, 6.87463, 5.26172, 4.61063, 8.47218, 5.26604] shape [8] · float64 · ndarray |
Bucket 2 of 7: arg 1: rank 1, kind float, dtype float64
3 implementations from 3 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 |
|---|---|---|
| 1 implementation 1 paper 0eea27186dfb |
aix360/algorithms/ecertify/ExpCertifyBB.py 6a51d687 recorded x:1/float/float64 |
[0.364865] shape [] · float64 |
| 1 implementation 1 paper 8bdee69ca0d0 |
regression_with_GP.py 9dc92202 recorded x:1/float/float64 |
[0.289069, 0.539227, 0.711264, 0.318751, 0.456081, 0.513258, 0.166696, 0.455764] shape [8] · float64 · ndarray |
| 1 implementation 1 paper c9e4554d6cfc |
bellman.py ed1fb9f8 recorded V:1/float/float64; x=2.0, p=2 |
[-18.5405] shape [] · float64 |
Bucket 3 of 7: arg 1: rank 2, kind float, dtype float32
3 implementations from 3 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 |
|---|---|---|
| 1 implementation 1 paper 54b900409e73 |
demo/base.py c2cbec6b recorded X:2/float/float32 |
[6.05945, 5.24328, 3.92746, 6.34132] shape [4] · float32 · ndarray |
| 1 implementation 1 paper a4f377964998 |
models/operator/mask.py c69509c7 recorded x:2/float/float32; name='softplus' |
[1.95523, 0.195336, 0.959239, 0.406219, 0.340331, 0.305158, 0.415697, 1.02007, …] shape [4, 8] · float32 · Tensor |
| 1 implementation 1 paper d358bbc89d2b |
bidirectional_proof.py 54383f78 recorded v:2/float/float32 |
[1, 0, 1, 0, 0, 0, 0, 1, …] shape [4, 8] · int64 · Tensor |
Bucket 4 of 7: arg 1: rank 2, kind float, dtype float64
3 implementations from 3 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 |
|---|---|---|
| 1 implementation 1 paper 83783416a6e4 |
federated_shap.py a4bc0365 recorded X:2/float/float64 |
[8.97566, 11.52, 8.78497, 10.1926] shape [4] · float64 · ndarray |
| 1 implementation 1 paper d7bd5ceec8a6 |
pyscf_ipu/dft.py 554977c2 recorded x:2/float/float64; float32=False |
[1.80263, -1.53378, 0.476053, -0.690887, -0.902849, -1.03047, -0.662761, 0.572889, …] shape [4, 8] · float64 · ndarray |
| 1 implementation 1 paper d7fdf4321519 |
mcmc_gaussian_nonisotropic_d_exps.py 4ecee136 recorded x:2/float/float64; mean=0.0, sigma=1.0 |
[4.47518, 4.9251, 2.82022, 3.98824] shape [4] · float64 · ndarray |
Bucket 5 of 7: arg 1: rank 1, kind float, dtype float32
2 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); 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 |
|---|---|---|
| 1 implementation 1 paper 5dca9a6d311b |
Proxy-Uncertainty-Training-LLaMa-Factory/src/llmtuner/tuner/rm/trainer.py ffb59402 recorded x:1/float/float32; m=0.0, s=1.0, a=1e-05, b=0.99999, n=10000 |
[nan, 0.734867, 0.411278, nan, nan, 0.124727, nan, nan] shape [8] · float32 · Tensor · non-finite |
| 1 implementation 1 paper ea500555d9ee |
example.py dc7eaed6 recorded x:1/float/float32 |
[0, -0.590994, -0.754603, 0, 0.646551, -0.292257, -0, 0.638095] shape [8] · float32 · ndarray |
Bucket 6 of 7: arg 1: rank 2, kind float, dtype float32 · arg 2: rank 2, kind float, dtype float32
2 implementations 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); 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 |
|---|---|---|
| 1 implementation 1 paper 29b0e89bd145 |
styblinski_tang.py cf69a4ee recorded X:2/float/float32, Y:2/float/float32 |
[-12.9677, -15.9097, -0.47776, -4.34552, -6.7568, -8.40589, -4.05956, -0.911628, …] shape [4, 8] · float32 · Tensor |
| 1 implementation 1 paper 95173d4a9233 |
convex_optim.py 79551e8e recorded X:2/float/float32, Y:2/float/float32 |
[0.659941, 0.345882, 0.00320998, 0.0142399, 0.041528, 0.0704731, 0.0120589, 0.00673229, …] shape [4, 8] · float32 · Tensor |
Bucket 7 of 7: arg 1: rank 2, kind float, dtype float64 · arg 2: rank 2, kind float, dtype float64
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 7ca814d45b89 |
nonconvex_optim.py 7e551776 recorded X:2/float/float64, Y:2/float/float64 |
[-0.120256, 0.947635, -6.52823e-18, 2.10905e-11, 6.36416e-07, 7.25032e-05, 4.05009e-12, -1.17811e-14, …] shape [4, 8] · float64 · ndarray |
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/f.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