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to_numpy

Syntologyfunction-name censuscensus 2026-09-22battery b986f7e04d79all samples with this name

to_numpy: 24 implementations from 29 papers ran on one shared input (census 2026-09-22, battery b986f7e04d79); they produced 2 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.
1 more implementation from 1 paper took no array argument and ran only on its own fixture arguments; no shared input existed for it, so it is not compared (on 1 the recorded shared-output digest is the own-fixture digest).
Every compared output was digested.

Bucket 1 of 2: arg 1: rank 2, kind float, dtype float32

23 implementations from 28 papers 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
23 implementations 28 papers d7bd5ceec8a6 recorded values identical	QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs Massive Activations in Large Language Models DFRot: Achieving Outlier-Free and Massive... one code sha held from 3 papers' repositories scripts/optimize_procrustes_alter.py 962843b3 recorded data:2/float/float32 BACON: Band-limited Coordinate Networks for Multiscale... A Demographic-Conditioned Variational Autoencoder for... one code sha held from 2 papers' repositories pip/src/demovae/model.py 4589e45c recorded x:2/float/float32 Meta-Learning with Implicit Gradients Test-Time Adaptation for Unsupervised Combinatorial Optimization added by Syntology one code sha held from 2 papers' repositories implicit_maml/learner_model.py 4e11fa37 recorded x:2/float/float32 Semantics Meets Temporal Correspondence: Self-supervised... Intra-Inter Camera Similarity for Unsupervised Person... one code sha held from 2 papers' repositories reid/cluster_utils/cluster.py b4766237 recorded tensor:2/float/float32 Bilevel Optimization with a Lower-level Contraction:... source/DEQs.py 206093cc recorded tensor:2/float/float32 SkillRise: Agentic Reinforcement Learning for Cross-Task... added by Syntology agent_system/environments/skillrise_alfworld/env_manager.py 21853363 recorded data:2/float/float32 Post-training 4-bit quantization of convolution networks... pytorch_quantizer/quantization/qtypes/int_quantizer.py 2a066b9a recorded tensor:2/float/float32 Learning to Discover Cross-Domain Relations with... discogan/discogan.py 334c0b12 recorded data:2/float/float32 Influencing Long-Term Behavior in Multiagent... algorithm/further/agent.py 4f00b7f6 recorded tensor:2/float/float32; dtype=<class 'numpy.float32'> arXiv:2502.15147 alfworld/linear_corex.py 5e6ec5e5 recorded x:2/float/float32 Combining Static Word Embeddings and Contextual... bliss/models/models.py 6fda5218 recorded t:2/float/float32; gpu=False Diffusion Models for Counterfactual Generation and... scripts/saliency_maps.py 71438f30 recorded z:2/float/float32 Distributionally Robust Neural Networks for Group... real_datasets/isr.py 78b0af73 recorded tensor:2/float/float32 Learning De-biased Representations with Biased Representations criterions/hsic.py 913b7c90 recorded x:2/float/float32 Singh_Combining_Semantic_Guidance_and_Deep_Reinforcement_Learning_for_Generating_Human_CVPR_2021_paper semantic_guidance/env_ins.py 941c9927 recorded var:2/float/float32 R3PM-Net: Real-time, Robust, Real-world Point Matching Network added by Syntology r3pm_net/model.py a2a91d3e recorded tensor:2/float/float32 Unsupervised Kinematic Motion Detection for... src/config.py b3b18dfd recorded item:2/float/float32 SAM-Med2D scripts/export_onnx_model.py d6895179 recorded tensor:2/float/float32 Achieving Long-Term Fairness in Sequential Decision Making src/fair_model.py dcc242b0 recorded x:2/float/float32 Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks maml_rl/metalearners/maml_trpo.py e329883d recorded tensor:2/float/float32 Fine-Grained Causal Dynamics Learning with Quantization... fcdl/model/inference_ours_base.py e8a1a77d recorded tensor:2/float/float32 "Why Should I Trust You?": Explaining the Predictions of... src/maxi/lib/loss/lime_loss.py eca7268d recorded data:2/float/float32 Causal Dynamics Learning for Task-Independent State Abstraction cdl/model/inference_cmi.py fb4d1734 recorded tensor:2/float/float32	[1.80263, -1.53378, 0.476053, -0.690887, -0.902849, -1.03047, -0.662761, 0.572889, …] shape [4, 8] · float32 · ndarray

Bucket 2 of 2: arg 1: 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 d7bd5ceec8a6	TabEBM: A Tabular Data Augmentation Method with Distinct... src/tabebm/TabEBM.py d7484a73 recorded X:2/float/float64	[1.80263, -1.53378, 0.476053, -0.690887, -0.902849, -1.03047, -0.662761, 0.572889, …] 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/to-numpy.json.

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