{"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/apply-label-smoothing","entry":"apply_label_smoothing","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-25T09:33:49+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":1,"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":4,"n_samples_ran":1,"n_samples_fingerprinted":1,"n_places":5,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"ran_violates":1,"ran_draft_wrong":0,"ran_fixture":0,"ran":0,"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":"2601.14154","paper":"/paper/arxiv-2601-14154","title":"LLM Augmented Intervenable Multimodal Adaptor for Post-operative Complication Prediction in Lung Cancer Surgery","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"KNITPhoenix/MIRACLE","path":"loss.py","file_url":"https://github.com/KNITPhoenix/MIRACLE/blob/HEAD/loss.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"33c57d45ee8c08ab","mcp_get_code":{"code_sha256":"33c57d45ee8c08ab"}},{"arxiv_id":"2306.04675","paper":"/paper/exposing-flaws-of-generative-model-evaluation-1","title":"Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models","date":"2023-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"google-research/maskgit","path":"maskgit/libml/losses.py","file_url":"https://github.com/google-research/maskgit/blob/HEAD/maskgit/libml/losses.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":"1b2e0a7656cc76a7","mcp_get_code":{"code_sha256":"1b2e0a7656cc76a7"}},{"arxiv_id":"2302.05496","paper":"/paper/masksketch-unpaired-structure-guided-masked","title":"MaskSketch: Unpaired Structure-guided Masked Image Generation","date":"2023-02-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"google-research/masksketch","path":"masksketch/libml/losses.py","file_url":"https://github.com/google-research/masksketch/blob/HEAD/masksketch/libml/losses.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":"1b2e0a7656cc76a7","mcp_get_code":{"code_sha256":"1b2e0a7656cc76a7"}},{"arxiv_id":"2210.00990","paper":"/paper/visual-prompt-tuning-for-generative-transfer","title":"Visual Prompt Tuning for Generative Transfer Learning","date":"2022-10-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"google-research/generative_transfer","path":"trainer/prompt_trainer.py","file_url":"https://github.com/google-research/generative_transfer/blob/HEAD/trainer/prompt_trainer.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"0ab9187398e2fea2","mcp_get_code":{"code_sha256":"0ab9187398e2fea2"}},{"arxiv_id":"2105.12723","paper":"/paper/aggregating-nested-transformers","title":"Nested Hierarchical Transformer: Towards Accurate, Data-Efficient and Interpretable Visual Understanding","date":"2021-05-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"google-research/nested-transformer","path":"libml/losses.py","file_url":"https://github.com/google-research/nested-transformer/blob/HEAD/libml/losses.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":"08baacad242c29f2","mcp_get_code":{"code_sha256":"08baacad242c29f2"}}]}