{"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/create-encoder","entry":"create_encoder","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":6,"n_papers_ran":2,"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":2,"n_samples_fingerprinted":0,"n_places":6,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":2,"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":"2608.07713","paper":"/paper/arxiv-2608-07713","title":"Tokenizer-Generator Coupling in Medical Image Generation","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"liamchalcroft/medlatents","path":"src/medlatents/conditioning/encoders.py","file_url":"https://github.com/liamchalcroft/medlatents/blob/HEAD/src/medlatents/conditioning/encoders.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"614326f7bc65a4c4","mcp_get_code":{"code_sha256":"614326f7bc65a4c4"}},{"arxiv_id":"2602.00381","paper":"/paper/arxiv-2602-00381","title":"Modeling Image-Caption Rating from Comparative Judgments","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"hil-se/comparative_image_caption","path":"src/compare_same_image.py","file_url":"https://github.com/hil-se/comparative_image_caption/blob/HEAD/src/compare_same_image.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2144a71f14fd2cc0","mcp_get_code":{"code_sha256":"2144a71f14fd2cc0"}},{"arxiv_id":"2405.18217","paper":"/paper/understanding-inter-concept-relationships-in","title":"Understanding Inter-Concept Relationships in Concept-Based Models","date":"2024-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"naveenr414/Concept-Learning","path":"src/models.py","file_url":"https://github.com/naveenr414/Concept-Learning/blob/HEAD/src/models.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8c5436aaf4f86091","mcp_get_code":{"code_sha256":"8c5436aaf4f86091"}},{"arxiv_id":"2402.13555","paper":"/paper/full-atom-peptide-design-with-geometric","title":"Full-Atom Peptide Design with Geometric Latent Diffusion","date":"2024-02-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thunlp-mt/pepglad","path":"models/autoencoder/model.py","file_url":"https://github.com/thunlp-mt/pepglad/blob/HEAD/models/autoencoder/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5bebb16ab75fb874","mcp_get_code":{"code_sha256":"5bebb16ab75fb874"}},{"arxiv_id":"2312.02843","paper":"/paper/are-vision-transformers-more-data-hungry-than-1","title":"Are Vision Transformers More Data Hungry Than Newborn Visual Systems?","date":"2023-12-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"buildingamind/vit-cot","path":"models/common.py","file_url":"https://github.com/buildingamind/vit-cot/blob/HEAD/models/common.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b4b12ef2736a3dae","mcp_get_code":{"code_sha256":"b4b12ef2736a3dae"}},{"arxiv_id":"2308.02351","paper":"/paper/a-parameter-efficient-multi-subject-model-for","title":"A Parameter-efficient Multi-subject Model for Predicting fMRI Activity","date":"2023-08-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cmi-dair/algonauts23","path":"algonauts23/models/registry.py","file_url":"https://github.com/cmi-dair/algonauts23/blob/HEAD/algonauts23/models/registry.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6da3e9ff065e4f4b","mcp_get_code":{"code_sha256":"6da3e9ff065e4f4b"}}]}