{"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/from-txt","entry":"from_txt","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":5,"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":2,"n_samples_ran":2,"n_samples_fingerprinted":0,"n_places":5,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":1,"unverified":0},"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":"2506.23502","paper":null,"title":"arXiv:2506.23502","date":null,"month_inferred_from_arxiv_id":"2025-06","title_source":null,"repo":"Mengxiao-Tian/LAMP","path":"vocab.py","file_url":"https://github.com/Mengxiao-Tian/LAMP/blob/HEAD/vocab.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ef05dda21c1cc726","mcp_get_code":{"code_sha256":"ef05dda21c1cc726"}},{"arxiv_id":"2310.17468","paper":"/paper/cross-modal-active-complementary-learning-1","title":"Cross-modal Active Complementary Learning with Self-refining Correspondence","date":"2023-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"QinYang79/CRCL","path":"CRCL_NeurIPS23/vocab.py","file_url":"https://github.com/QinYang79/CRCL/blob/HEAD/CRCL_NeurIPS23/vocab.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"414f20e131140e01","mcp_get_code":{"code_sha256":"414f20e131140e01"}},{"arxiv_id":"2010.03403","paper":"/paper/universal-weighting-metric-learning-for-cross-1","title":"Universal Weighting Metric Learning for Cross-Modal Matching","date":"2020-10-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wayne980/PolyLoss","path":"vocab.py","file_url":"https://github.com/wayne980/PolyLoss/blob/HEAD/vocab.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ef05dda21c1cc726","mcp_get_code":{"code_sha256":"ef05dda21c1cc726"}},{"arxiv_id":"1806.10348","paper":"/paper/learning-visually-grounded-semantics-from","title":"Learning Visually-Grounded Semantics from Contrastive Adversarial Samples","date":"2018-06-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ExplorerFreda/VSE-C","path":"VSE_C/vocab.py","file_url":"https://github.com/ExplorerFreda/VSE-C/blob/HEAD/VSE_C/vocab.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ef05dda21c1cc726","mcp_get_code":{"code_sha256":"ef05dda21c1cc726"}},{"arxiv_id":"1803.08024","paper":"/paper/stacked-cross-attention-for-image-text","title":"Stacked Cross Attention for Image-Text Matching","date":"2018-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"abhidipbhattacharyya/srl_aware_ret","path":"vocab.py","file_url":"https://github.com/abhidipbhattacharyya/srl_aware_ret/blob/HEAD/vocab.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ef05dda21c1cc726","mcp_get_code":{"code_sha256":"ef05dda21c1cc726"}}]}