{"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/transformer-timestep-embedding","entry":"transformer_timestep_embedding","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":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":4,"n_samples_ran":4,"n_samples_fingerprinted":4,"n_places":5,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":2,"ran":2,"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":"2605.11125","paper":"/paper/arxiv-2605-11125","title":"Language Modeling with Hyperspherical Flows","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"s-sahoo/duo","path":"models/unet.py","file_url":"https://github.com/s-sahoo/duo/blob/HEAD/models/unet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"c40a24e67f67c90e","mcp_get_code":{"code_sha256":"c40a24e67f67c90e"}},{"arxiv_id":"2601.16249","paper":"/paper/arxiv-2601-16249","title":"Ordering-based Causal Discovery via Generalized Score Matching","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"isVy08/discrete-SCORE","path":"model/ebm.py","file_url":"https://github.com/isVy08/discrete-SCORE/blob/HEAD/model/ebm.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b7754f91689130a8","mcp_get_code":{"code_sha256":"b7754f91689130a8"}},{"arxiv_id":"2412.10193","paper":"/paper/simple-guidance-mechanisms-for-discrete","title":"Simple Guidance Mechanisms for Discrete Diffusion Models","date":"2024-12-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kuleshov-group/discrete-diffusion-guidance","path":"models/unet.py","file_url":"https://github.com/kuleshov-group/discrete-diffusion-guidance/blob/HEAD/models/unet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"c40a24e67f67c90e","mcp_get_code":{"code_sha256":"c40a24e67f67c90e"}},{"arxiv_id":"2410.08709","paper":"/paper/distillation-of-discrete-diffusion-through","title":"Distillation of Discrete Diffusion through Dimensional Correlations","date":"2024-10-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sony/di4c","path":"maskgit-pytorch/Network/transformer.py","file_url":"https://github.com/sony/di4c/blob/HEAD/maskgit-pytorch/Network/transformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d0434d43396aa8fb","mcp_get_code":{"code_sha256":"d0434d43396aa8fb"}},{"arxiv_id":"2410.06264","paper":"/paper/think-while-you-generate-discrete-diffusion","title":"Think While You Generate: Discrete Diffusion with Planned Denoising","date":"2024-10-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liusulin/ddpd","path":"text8/model_planner.py","file_url":"https://github.com/liusulin/ddpd/blob/HEAD/text8/model_planner.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2947e15ec51fe668","mcp_get_code":{"code_sha256":"2947e15ec51fe668"}}]}