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test_model

Syntologyentry name in harvested coderead from the graph 2026-09-24

test_model appears in the code Syntology harvested for 25 papers, as 30 distinct code bodies found in 30 places (a place is one code body under one paper). At least one of them ran in 10 of the papers; 0 of the code bodies carry a behaviour fingerprint.

What this page is not. Routines are grouped here by the exact string of their function or class name. Nothing asserts that two samples named test_model do the same thing, share code, or are comparable; the name is a string, not an identity. Behaviour outputs (what a fingerprinted sample returned on the shared battery) are not in this export and are not shown here; the graph at syntology.ai holds them. "Ran" means executed on a synthesized fixture, not that the code is correct or reproduces a paper.

Samples Syntology

Syntology ran 11 of the 30 distinct code bodies named test_model; 19 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

4ran · honoured contract
0ran · violated contract
4ran · our draft was wrong
0ran · fixture could not drive it
3ran
19unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 9 of the 30 places are pointer only (Syntology does not serve that copy's text). This site shows no code text for any sample; every row below links to the file in its repository where the record names one.

“Ran” means the sample executed on a synthesized input; it does not mean the output is correct. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code, and those samples did run. The ran count above is every status except unverified, the same rule as each paper page.

Papers

25 papers shown of 25, newest first; 30 places in the table. A paper with no recorded date is placed by the month its arXiv id encodes, shown in the Date column as YYYY-MM (from id). One row per place: a paper whose repository defines the name more than once appears more than once, and the same code body held for several papers appears once under each, with the same status. Titles and dates are the archive's archive 2025-07-28 for papers in the archive. Status and fingerprint are Syntology's record of each code body; licence is recorded for each place. The File cell ends with the code body's code_sha256, Syntology's identity for that exact code: an agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

PaperDateFileStatus SyntologyLicence
ATOM: A Framework of Detecting Query-Based Model Extraction Attacks for Graph Neural Networks 20 Mar 2025 LabRAI/ATOM/evaluation/ppo_eval.py ac42a65528c80bfd ran · our draft was wrong MIT (permissive)
One-shot Federated Learning via Synthetic Distiller-Distillate Communication 6 Dec 2024 carkham/fedsd2c/fedsd2c_main.py 07e49de23e69430c ran · honoured contract Apache-2.0 (permissive)
MetRex: A Benchmark for Verilog Code Metric Reasoning Using LLMs 5 Nov 2024 scale-lab/MetRex/src/model/model.py 8543df2964e03d8e unverified no licence file found · pointer only
Integrating Graph Neural Networks and Many-Body Expansion Theory for Potential Energy Surfaces 3 Nov 2024 lin-group-at-umass/fbgnn-mbe/src/train_energy_staged.py 7ad51071329de776 ran · our draft was wrong MIT (permissive)
Quest: Query-Aware Sparsity for Efficient Long-Context LLM Inference 16 Jun 2024 mit-han-lab/Quest/evaluation/passkey/passkey.py 3bccc0e6004c76a1 unverified MIT (permissive)
Characterizing out-of-distribution generalization of neural networks: application to the disordered Su-Schrieffer-Heeger model 2024-06 (from id) kcybinski/interpreting_nns_for_topological_phases_of_matter/src/utils.py 1dcac578d4759333 unverified no licence file found · pointer only
Invisible Backdoor Attacks on Diffusion Models 2 Jun 2024 linweiii/backdoordm/classifier_models/resnet.py bd3f866cccaf7c8b unverified MIT (permissive)
Nearly Tight Black-Box Auditing of Differentially Private Machine Learning 23 May 2024 spalabucr/bb-audit-dpsgd/audit_model.py 4463a86d730ba758 ran no licence file found · pointer only
SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling 23 Dec 2023 jquesnelle/yarn/eval/passkey.py a7fefb7833cddfa4 ran MIT (permissive)
SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling 23 Dec 2023 jquesnelle/yarn/eval/passkey_hard.py 9fb7bff934e0d12d ran MIT (permissive)
MINDE: Mutual Information Neural Diffusion Estimation 13 Oct 2023 MustaphaBounoua/minde/src/baseline/eval_doe.py 0faaf4c77cd5b5e7 unverified no licence file found · pointer only
Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach 10 Oct 2023 zknus/NeurIPS-2023-HANG-Robustness/run_metattack_rate.py 12cfc91579987423 ran · honoured contract no licence file found · pointer only
CoNMix for Source-free Single and Multi-target Domain Adaptation 7 Nov 2022 vcl-iisc/CoNMix/test_model_acc.py 7f65f61ff29c1cc8 unverified no licence file found · pointer only
Adversarial Style Augmentation for Domain Generalized Urban-Scene Segmentation 11 Jul 2022 harisankar95/AdvStyle/src/advstyle.py c8c7d51278790df9 ran · honoured contract MIT (permissive)
Learning from Label Proportions by Learning with Label Noise 4 Mar 2022 z-jianxin/llpfc/llpfclib/train_fun.py 735ff4fe221938e9 unverified MIT (permissive)
From Deterioration to Acceleration: A Calibration Approach to Rehabilitating Step Asynchronism in Federated Optimization 17 Dec 2021 harliwu/fedagrac/federated_learning/FedaGrac/param_server.py d18d8999c71dc4a7 unverified MIT (permissive)
Gradient representations in ReLU networks as similarity functions 26 Oct 2021 danielracz/gradsim/grad_utils.py 2ccdbf7196e90927 ran · honoured contract no licence file found · pointer only
HumBugDB: A Large-scale Acoustic Mosquito Dataset 14 Oct 2021 HumBug-Mosquito/HumBugDB/lib/PyTorch/runTorch.py 34e93ffee7856b18 unverified MIT (permissive)
Out-of-Distribution Dynamics Detection: RL-Relevant Benchmarks and Results 11 Jul 2021 modanesh/recurrent_implicit_quantile_networks/autoregressive_control.py 7a96153b4427a0a4 ran · our draft was wrong no licence file found · pointer only
Few-shot Image Classification: Just Use a Library of Pre-trained Feature Extractors and a Simple Classifier 3 Jan 2021 arjish/PreTrainedFullLibrary_FewShot/classifier_single.py 9ebffd187c8f57b0 unverified MIT (permissive)
Few-shot Image Classification: Just Use a Library of Pre-trained Feature Extractors and a Simple Classifier 3 Jan 2021 arjish/PreTrainedFullLibrary_FewShot/classifier_full_library.py 1355bc6b5767e40a unverified MIT (permissive)
Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly Data 15 Sep 2020 teo-sl/DPLAN_pytorch/util.py 1683ef1df431e343 unverified MIT (permissive)
Distilling Knowledge from Graph Convolutional Networks 23 Mar 2020 ihollywhy/DistillGCN.PyTorch/utils.py 37a7a4358779ae46 unverified MIT (permissive)
FR-Train: A Mutual Information-Based Approach to Fair and Robust Training 24 Feb 2020 yuji-roh/fr-train/FRTrain_arch.py c84bc36eb64a6a38 ran · our draft was wrong no licence file found · pointer only
Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization 5 Jun 2019 nikvaessen/Rethinking-Binarized-Neural-Network-Optimization/research_seed/theoretical/larq_approximation_viewpoint_cifar.py fe3050fba6a31ee2 unverified Apache-2.0 (permissive)
Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization 5 Jun 2019 nikvaessen/Rethinking-Binarized-Neural-Network-Optimization/research_seed/theoretical/larq_approximation_viewpoint_mnist.py 576d4e99e5399660 unverified Apache-2.0 (permissive)
Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization 5 Jun 2019 nikvaessen/Rethinking-Binarized-Neural-Network-Optimization/research_seed/theoretical/larq_theorem_cifar.py 8a2e32e7dd71db12 unverified Apache-2.0 (permissive)
Robust Text-to-SQL Generation with Execution-Guided Decoding 9 Jul 2018 Microsoft/PointerSQL/model/learn.py 6ce7b508f74964a9 unverified MIT (permissive)
Robust Text-to-SQL Generation with Execution-Guided Decoding 9 Jul 2018 Microsoft/PointerSQL/model/learn_meta.py 2d2a3a813048a37f unverified MIT (permissive)
Jet-Images -- Deep Learning Edition 16 Nov 2015 deepjets/deepjets/deepjets/learning.py 36100b103e1ce4c7 unverified BSD-3-Clause (permissive)

This site shows no code text; each File cell links to the file on GitHub at the repository's current default branch, which may have changed since the harvest. "Pointer only" means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence cell for the reason. Per-sample records for a paper are on its paper page under "Code Syntology ran".

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