{"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/get-extended-attention-mask","entry":"get_extended_attention_mask","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":1,"ran_fixture":0,"ran":1,"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":"2505.18853","paper":"/paper/smoothie-smoothing-diffusion-on-token","title":"Smoothie: Smoothing Diffusion on Token Embeddings for Text Generation","date":"2025-05-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ashaba1in/smoothie","path":"model/score_estimator.py","file_url":"https://github.com/ashaba1in/smoothie/blob/HEAD/model/score_estimator.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2e80e05908242417","mcp_get_code":{"code_sha256":"2e80e05908242417"}},{"arxiv_id":"2405.12564","paper":"/paper/prott3-protein-to-text-generation-for-text","title":"ProtT3: Protein-to-Text Generation for Text-based Protein Understanding","date":"2024-05-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"acharkq/ProtT3","path":"model/esm_flash_attention.py","file_url":"https://github.com/acharkq/ProtT3/blob/HEAD/model/esm_flash_attention.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"01998e2a237f84ab","mcp_get_code":{"code_sha256":"01998e2a237f84ab"}},{"arxiv_id":"2210.00312","paper":"/paper/multimodal-analogical-reasoning-over","title":"Multimodal Analogical Reasoning over Knowledge Graphs","date":"2022-10-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zjunlp/MKGformer","path":"MKG/models/modeling_unimo.py","file_url":"https://github.com/zjunlp/MKGformer/blob/HEAD/MKG/models/modeling_unimo.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dd48a2143b53be6f","mcp_get_code":{"code_sha256":"dd48a2143b53be6f"}},{"arxiv_id":"2203.01670","paper":"/paper/a-simple-hash-based-early-exiting-approach-1","title":"A Simple Hash-Based Early Exiting Approach For Language Understanding and Generation","date":"2022-03-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"txsun1997/hashee","path":"models/modeling_elasticbert_approx_hashee.py","file_url":"https://github.com/txsun1997/hashee/blob/HEAD/models/modeling_elasticbert_approx_hashee.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"89bec46d4c56090c","mcp_get_code":{"code_sha256":"89bec46d4c56090c"}},{"arxiv_id":"2024.naacl-long.345","paper":null,"title":"arXiv:2024.naacl-long.345","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"arashardakani/SlimFit","path":"BERT/ILSBERT.py","file_url":"https://github.com/arashardakani/SlimFit/blob/HEAD/BERT/ILSBERT.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cf14341207df48c7","mcp_get_code":{"code_sha256":"cf14341207df48c7"}},{"arxiv_id":"2024.emnlp-main.206","paper":null,"title":"arXiv:2024.emnlp-main.206","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"wenlong1019/MTLS","path":"src/mtls.py","file_url":"https://github.com/wenlong1019/MTLS/blob/HEAD/src/mtls.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":"3b270bcae583f3d3","mcp_get_code":{"code_sha256":"3b270bcae583f3d3"}}]}