{"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/make-pair","entry":"make_pair","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":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":8,"n_samples_ran":4,"n_samples_fingerprinted":0,"n_places":8,"n_places_pointer_only":3,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":3,"unverified":4},"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":"2606.22020","paper":"/paper/arxiv-2606-22020","title":"What Do Neural Networks Learn for TDOA Estimation? A Cross-Architecture Probing Study","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"york1to/cross-power-is-all-you-need","path":"experiment.py","file_url":"https://github.com/york1to/cross-power-is-all-you-need/blob/HEAD/experiment.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"ff74b3f1821e5134","mcp_get_code":{"code_sha256":"ff74b3f1821e5134"}},{"arxiv_id":"2601.22581","paper":"/paper/arxiv-2601-22581","title":"Cross-Domain Few-Shot Learning for Hyperspectral Image Classification Based on Mixup Foundation Model","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"Naeem-Paeedeh/MIFOMO","path":"shared.py","file_url":"https://github.com/Naeem-Paeedeh/MIFOMO/blob/HEAD/shared.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8b23ac8e9d2fc58a","mcp_get_code":{"code_sha256":"8b23ac8e9d2fc58a"}},{"arxiv_id":"2410.06581","paper":"/paper/enhancing-legal-case-retrieval-via-scaling","title":"Enhancing Legal Case Retrieval via Scaling High-quality Synthetic Query-Candidate Pairs","date":"2024-10-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thunlp/DeepTHULAC","path":"deepthulac/seg/data_format.py","file_url":"https://github.com/thunlp/DeepTHULAC/blob/HEAD/deepthulac/seg/data_format.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"55c0dbc360ed8346","mcp_get_code":{"code_sha256":"55c0dbc360ed8346"}},{"arxiv_id":"2310.18605","paper":"/paper/torchdeq-a-library-for-deep-equilibrium","title":"TorchDEQ: A Library for Deep Equilibrium Models","date":"2023-10-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"locuslab/torchdeq","path":"torchdeq/grad.py","file_url":"https://github.com/locuslab/torchdeq/blob/HEAD/torchdeq/grad.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"41e82f3c5dad5481","mcp_get_code":{"code_sha256":"41e82f3c5dad5481"}},{"arxiv_id":"2107.13435","paper":"/paper/mwp-bert-a-strong-baseline-for-math-word","title":"MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving","date":"2021-07-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lzhenwen/mwp-bert","path":"Fine-tuning/MathQA/run_ft.py","file_url":"https://github.com/lzhenwen/mwp-bert/blob/HEAD/Fine-tuning/MathQA/run_ft.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d8e7137b458da412","mcp_get_code":{"code_sha256":"d8e7137b458da412"}},{"arxiv_id":"1909.01285","paper":"/paper/robust-invisible-video-watermarking-with","title":"Robust Invisible Video Watermarking with Attention","date":"2019-09-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DAI-Lab/RivaGAN","path":"rivagan/rivagan.py","file_url":"https://github.com/DAI-Lab/RivaGAN/blob/HEAD/rivagan/rivagan.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"95bd3e565a16c225","mcp_get_code":{"code_sha256":"95bd3e565a16c225"}},{"arxiv_id":"1807.09341","paper":"/paper/learning-plannable-representations-with","title":"Learning Plannable Representations with Causal InfoGAN","date":"2018-07-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thanard/causal-infogan","path":"dataset.py","file_url":"https://github.com/thanard/causal-infogan/blob/HEAD/dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1f2dd05e9159a937","mcp_get_code":{"code_sha256":"1f2dd05e9159a937"}},{"arxiv_id":"1805.02410","paper":"/paper/mmdenselstm-an-efficient-combination-of","title":"MMDenseLSTM: An efficient combination of convolutional and recurrent neural networks for audio source separation","date":null,"month_inferred_from_arxiv_id":"2018-05","title_source":"archive","repo":"tsurumeso/vocal-remover","path":"lib/dataset.py","file_url":"https://github.com/tsurumeso/vocal-remover/blob/HEAD/lib/dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"be42da4ca7a9e660","mcp_get_code":{"code_sha256":"be42da4ca7a9e660"}}]}