{"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/dplr","entry":"dplr","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":7,"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":7,"n_samples_ran":3,"n_samples_fingerprinted":0,"n_places":7,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":3,"ran_fixture":0,"ran":0,"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":"2411.04165","paper":"/paper/bio-xlstm-generative-modeling-representation","title":"Bio-xLSTM: Generative modeling, representation and in-context learning of biological and chemical sequences","date":"2024-11-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ml-jku/chem-xlstm","path":"chemxlstm/module_library/dplr.py","file_url":"https://github.com/ml-jku/chem-xlstm/blob/HEAD/chemxlstm/module_library/dplr.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":"7c3fe064733e56b3","mcp_get_code":{"code_sha256":"7c3fe064733e56b3"}},{"arxiv_id":"2407.08751","paper":"/paper/latent-diffusion-for-neural-spiking-data","title":"Latent Diffusion for Neural Spiking Data","date":"2024-06-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mackelab/LDNS","path":"ldns/networks/blocks.py","file_url":"https://github.com/mackelab/LDNS/blob/HEAD/ldns/networks/blocks.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"64f3b90fc3deaa09","mcp_get_code":{"code_sha256":"64f3b90fc3deaa09"}},{"arxiv_id":"2312.00752","paper":"/paper/mamba-linear-time-sequence-modeling-with","title":"Mamba: Linear-Time Sequence Modeling with Selective State Spaces","date":"2023-12-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lab-emi/cleanumamba","path":"src/network/S4/MambaS4.py","file_url":"https://github.com/lab-emi/cleanumamba/blob/HEAD/src/network/S4/MambaS4.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0e3d9394e01f8e81","mcp_get_code":{"code_sha256":"0e3d9394e01f8e81"}},{"arxiv_id":"2212.14052","paper":"/paper/hungry-hungry-hippos-towards-language","title":"Hungry Hungry Hippos: Towards Language Modeling with State Space Models","date":"2022-12-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hazyresearch/safari","path":"src/models/sequence/h3.py","file_url":"https://github.com/hazyresearch/safari/blob/HEAD/src/models/sequence/h3.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":"871ae87aa2c2ffb6","mcp_get_code":{"code_sha256":"871ae87aa2c2ffb6"}},{"arxiv_id":"2111.00396","paper":"/paper/efficiently-modeling-long-sequences-with-1","title":"Efficiently Modeling Long Sequences with Structured State Spaces","date":"2021-10-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"state-spaces/s4","path":"models/s4/s4.py","file_url":"https://github.com/state-spaces/s4/blob/HEAD/models/s4/s4.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":"9eb8abe7e31d874d","mcp_get_code":{"code_sha256":"9eb8abe7e31d874d"}},{"arxiv_id":"2009.09761","paper":"/paper/diffwave-a-versatile-diffusion-model-for","title":"DiffWave: A Versatile Diffusion Model for Audio Synthesis","date":"2020-09-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"albertfgu/diffwave-sashimi","path":"models/sashimi.py","file_url":"https://github.com/albertfgu/diffwave-sashimi/blob/HEAD/models/sashimi.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"45091dfbbeb536a4","mcp_get_code":{"code_sha256":"45091dfbbeb536a4"}},{"arxiv_id":"1312.5602","paper":"/paper/playing-atari-with-deep-reinforcement","title":"Playing Atari with Deep Reinforcement Learning","date":"2013-12-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"proroklab/popgym","path":"popgym/baselines/models/s4d.py","file_url":"https://github.com/proroklab/popgym/blob/HEAD/popgym/baselines/models/s4d.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"391c3adc43a9fb2d","mcp_get_code":{"code_sha256":"391c3adc43a9fb2d"}}]}