{"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/truncated-normal-2","entry":"truncated_normal_","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":3,"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":3,"n_samples_fingerprinted":3,"n_places":8,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":1,"ran":1,"unverified":5},"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":"2603.28743","paper":"/paper/arxiv-2603-28743","title":"Rethinking Language Model Scaling under Transferable Hypersphere Optimization","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"microsoft/ArchScale","path":"lit_gpt/model.py","file_url":"https://github.com/microsoft/ArchScale/blob/HEAD/lit_gpt/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1e4e234055203901","mcp_get_code":{"code_sha256":"1e4e234055203901"}},{"arxiv_id":"2405.10369","paper":"/paper/reinforcement-learning-1","title":"Reinforcement learning","date":"2024-05-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sarodyatawatta/hintrl","path":"model_based/train_dyn.py","file_url":"https://github.com/sarodyatawatta/hintrl/blob/HEAD/model_based/train_dyn.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"1077f3c7e0a41889","mcp_get_code":{"code_sha256":"1077f3c7e0a41889"}},{"arxiv_id":"2405.08322","paper":"/paper/straightpcf-straight-point-cloud-filtering","title":"StraightPCF: Straight Point Cloud Filtering","date":"2024-05-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ddsediri/StraightPCF","path":"models/utils.py","file_url":"https://github.com/ddsediri/StraightPCF/blob/HEAD/models/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b68eb6bc82f68d94","mcp_get_code":{"code_sha256":"b68eb6bc82f68d94"}},{"arxiv_id":"2403.13417","paper":"/paper/diversified-and-personalized-multi-rater","title":"Diversified and Personalized Multi-rater Medical Image Segmentation","date":"2024-03-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ycwu1997/D-Persona","path":"D-Persona/code/lib/DPersona.py","file_url":"https://github.com/ycwu1997/D-Persona/blob/HEAD/D-Persona/code/lib/DPersona.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6f12908079fae859","mcp_get_code":{"code_sha256":"6f12908079fae859"}},{"arxiv_id":"2209.15597","paper":"/paper/meim-multi-partition-embedding-interaction","title":"MEIM: Multi-partition Embedding Interaction Beyond Block Term Format for Efficient and Expressive Link Prediction","date":"2022-09-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tranhungnghiep/MEIM","path":"src/models.py","file_url":"https://github.com/tranhungnghiep/MEIM/blob/HEAD/src/models.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3ca0782c847afb07","mcp_get_code":{"code_sha256":"3ca0782c847afb07"}},{"arxiv_id":"2208.10559","paper":"/paper/transductive-decoupled-variational-inference","title":"Transductive Decoupled Variational Inference for Few-Shot Classification","date":"2022-08-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"anujinho/trident","path":"src/zoo/archs.py","file_url":"https://github.com/anujinho/trident/blob/HEAD/src/zoo/archs.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"11a97a6c9d7fbf5d","mcp_get_code":{"code_sha256":"11a97a6c9d7fbf5d"}},{"arxiv_id":"2004.13912","paper":"/paper/neural-additive-models-interpretable-machine","title":"Neural Additive Models: Interpretable Machine Learning with Neural Nets","date":"2020-04-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kherud/neural-additive-models-pt","path":"nam/model.py","file_url":"https://github.com/kherud/neural-additive-models-pt/blob/HEAD/nam/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9ea962308449a4bb","mcp_get_code":{"code_sha256":"9ea962308449a4bb"}},{"arxiv_id":"1911.02855","paper":"/paper/dice-loss-for-data-imbalanced-nlp-tasks","title":"Dice Loss for Data-imbalanced NLP Tasks","date":"2019-11-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ShannonAI/dice_loss_for_NLP","path":"models/classifier.py","file_url":"https://github.com/ShannonAI/dice_loss_for_NLP/blob/HEAD/models/classifier.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":"db9f3807cf1d3857","mcp_get_code":{"code_sha256":"db9f3807cf1d3857"}}]}