{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/code/validate-model","entry":"validate_model","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":7,"n_papers_ran":4,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":6,"n_samples_ran":3,"n_samples_fingerprinted":0,"n_places":7,"n_places_pointer_only":2,"by_status":{"ran_honours":1,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":1,"unverified":3},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2503.16693","paper":"/paper/atom-a-framework-of-detecting-query-based","title":"ATOM: A Framework of Detecting Query-Based Model Extraction Attacks for Graph Neural Networks","date":"2025-03-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LabRAI/ATOM","path":"evaluation/ppo_eval.py","file_url":"https://github.com/LabRAI/ATOM/blob/HEAD/evaluation/ppo_eval.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"947112a2f75a6fdb","mcp_get_code":{"code_sha256":"947112a2f75a6fdb"}},{"arxiv_id":"2411.09263","paper":"/paper/rethinking-weight-averaged-model-merging","title":"Rethinking Weight-Averaged Model-merging","date":"2024-11-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"billhhh/rethink-merge","path":"Wt_vs_Ft/cifar10_merge.py","file_url":"https://github.com/billhhh/rethink-merge/blob/HEAD/Wt_vs_Ft/cifar10_merge.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ff3ee610b4ac419c","mcp_get_code":{"code_sha256":"ff3ee610b4ac419c"}},{"arxiv_id":"2407.04616","paper":"/paper/isomorphic-pruning-for-vision-models","title":"Isomorphic Pruning for Vision Models","date":"2024-07-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VainF/Isomorphic-Pruning","path":"evaluate.py","file_url":"https://github.com/VainF/Isomorphic-Pruning/blob/HEAD/evaluate.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4627b6cf43e4005f","mcp_get_code":{"code_sha256":"4627b6cf43e4005f"}},{"arxiv_id":"2106.06984","paper":"/paper/a-free-lunch-from-ann-towards-efficient","title":"A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration","date":"2021-06-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yhhhli/SNN_Calibration","path":"main_cal_imagenet.py","file_url":"https://github.com/yhhhli/SNN_Calibration/blob/HEAD/main_cal_imagenet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a0c40c8deb933e86","mcp_get_code":{"code_sha256":"a0c40c8deb933e86"}},{"arxiv_id":"2102.05426","paper":"/paper/brecq-pushing-the-limit-of-post-training-1","title":"BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction","date":"2021-02-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yhhhli/BRECQ","path":"main_imagenet.py","file_url":"https://github.com/yhhhli/BRECQ/blob/HEAD/main_imagenet.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"605b5163dc46fdad","mcp_get_code":{"code_sha256":"605b5163dc46fdad"}},{"arxiv_id":"1810.03958","paper":"/paper/deterministic-variational-inference-for","title":"Deterministic Variational Inference for Robust Bayesian Neural Networks","date":"2018-10-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"omwright/cov-prop-nn","path":"run_dvi.py","file_url":"https://github.com/omwright/cov-prop-nn/blob/HEAD/run_dvi.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fafc757982dcd649","mcp_get_code":{"code_sha256":"fafc757982dcd649"}},{"arxiv_id":"Liu_PD-Quant_Post-Training_Quantization_Based_on_Prediction_Difference_Metric_CVPR_2023_paper","paper":null,"title":"arXiv:Liu_PD-Quant_Post-Training_Quantization_Based_on_Prediction_Difference_Metric_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"hustvl/PD-Quant","path":"main_imagenet.py","file_url":"https://github.com/hustvl/PD-Quant/blob/HEAD/main_imagenet.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"605b5163dc46fdad","mcp_get_code":{"code_sha256":"605b5163dc46fdad"}}]}