{"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/stringify","entry":"stringify","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":8,"n_papers_ran":5,"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":8,"n_samples_ran":5,"n_samples_fingerprinted":1,"n_places":8,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":4,"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":"2602.22431","paper":"/paper/arxiv-2602-22431","title":"mmWave Radar Aware Dual-Conditioned GAN for Speech Reconstruction of Signals With Low SNR","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"chitadi/RADGAN","path":"src/utils.py","file_url":"https://github.com/chitadi/RADGAN/blob/HEAD/src/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a8d4dc10bebb05df","mcp_get_code":{"code_sha256":"a8d4dc10bebb05df"}},{"arxiv_id":"2404.00473","paper":"/paper/privacy-backdoors-stealing-data-with","title":"Privacy Backdoors: Stealing Data with Corrupted Pretrained Models","date":"2024-03-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shanglunfengatethz/privacybackdoor","path":"src/tools.py","file_url":"https://github.com/shanglunfengatethz/privacybackdoor/blob/HEAD/src/tools.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bb8089c8172faa52","mcp_get_code":{"code_sha256":"bb8089c8172faa52"}},{"arxiv_id":"2311.09312","paper":"/paper/h-packer-holographic-rotationally-equivariant","title":"H-Packer: Holographic Rotationally Equivariant Convolutional Neural Network for Protein Side-Chain Packing","date":"2023-11-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gvisani/hpacker","path":"hpacker/src/preprocessing/__tests.py","file_url":"https://github.com/gvisani/hpacker/blob/HEAD/hpacker/src/preprocessing/__tests.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e5900b9a9569def3","mcp_get_code":{"code_sha256":"e5900b9a9569def3"}},{"arxiv_id":"2310.16755","paper":"/paper/hi-tom-a-benchmark-for-evaluating-higher","title":"HI-TOM: A Benchmark for Evaluating Higher-Order Theory of Mind Reasoning in Large Language Models","date":"2023-10-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ying-hui-he/hi-tom_dataset","path":"stringify.py","file_url":"https://github.com/ying-hui-he/hi-tom_dataset/blob/HEAD/stringify.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"dd84b48987756b62","mcp_get_code":{"code_sha256":"dd84b48987756b62"}},{"arxiv_id":"2310.02065","paper":"/paper/venom-a-vectorized-n-m-format-for-unleashing","title":"VENOM: A Vectorized N:M Format for Unleashing the Power of Sparse Tensor Cores","date":"2023-10-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"udc-gac/venom","path":"benchmark/native_scripting.py","file_url":"https://github.com/udc-gac/venom/blob/HEAD/benchmark/native_scripting.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"721bc89c148723fd","mcp_get_code":{"code_sha256":"721bc89c148723fd"}},{"arxiv_id":"2111.02549","paper":"/paper/vortex-physics-driven-data-augmentations-for-1","title":"VORTEX: Physics-Driven Data Augmentations Using Consistency Training for Robust Accelerated MRI Reconstruction","date":"2021-11-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ad12/meddlr","path":"meddlr/config/util.py","file_url":"https://github.com/ad12/meddlr/blob/HEAD/meddlr/config/util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"40379a2e3f19a2af","mcp_get_code":{"code_sha256":"40379a2e3f19a2af"}},{"arxiv_id":"1902.00249","paper":"/paper/proteinnet-a-standardized-data-set-for","title":"ProteinNet: a standardized data set for machine learning of protein structure","date":"2019-02-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"EricAlcaide/MiniFold","path":"models/distance_pipeline/pipeline_caller.py","file_url":"https://github.com/EricAlcaide/MiniFold/blob/HEAD/models/distance_pipeline/pipeline_caller.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f79dd2a29757aa88","mcp_get_code":{"code_sha256":"f79dd2a29757aa88"}},{"arxiv_id":"1705.10829","paper":"/paper/accuracy-first-selecting-a-differential-1","title":"Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM","date":"2017-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"steven7woo/Accuracy-First-Differential-Privacy","path":"code/run_ridge.py","file_url":"https://github.com/steven7woo/Accuracy-First-Differential-Privacy/blob/HEAD/code/run_ridge.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"80f0aa50ad0ee664","mcp_get_code":{"code_sha256":"80f0aa50ad0ee664"}}]}