{"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/causal-conv1d-ref","entry":"causal_conv1d_ref","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":5,"n_papers_ran":4,"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":2,"n_samples_ran":1,"n_samples_fingerprinted":0,"n_places":5,"n_places_pointer_only":3,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"unverified":1},"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":"2605.28769","paper":"/paper/arxiv-2605-28769","title":"Multi-Mixer Models: Flexible Sequence Modeling with Shared Representations","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"Dao-AILab/causal-conv1d","path":"causal_conv1d/causal_conv1d_interface.py","file_url":"https://github.com/Dao-AILab/causal-conv1d/blob/HEAD/causal_conv1d/causal_conv1d_interface.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"1a3f84dd87b88b3e","mcp_get_code":{"code_sha256":"1a3f84dd87b88b3e"}},{"arxiv_id":"2409.09808","paper":"/paper/famba-v-fast-vision-mamba-with-cross-layer","title":"Famba-V: Fast Vision Mamba with Cross-Layer Token Fusion","date":"2024-09-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aiot-mlsys-lab/famba-v","path":"causal-conv1d/causal_conv1d/causal_conv1d_interface.py","file_url":"https://github.com/aiot-mlsys-lab/famba-v/blob/HEAD/causal-conv1d/causal_conv1d/causal_conv1d_interface.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0595d8b8521fd9ca","mcp_get_code":{"code_sha256":"0595d8b8521fd9ca"}},{"arxiv_id":"2407.21773","paper":"/paper/rainmamba-enhanced-locality-learning-with-2","title":"RainMamba: Enhanced Locality Learning with State Space Models for Video Deraining","date":"2024-07-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"TonyHongtaoWu/RainMamba","path":"causal-conv1d/causal_conv1d/causal_conv1d_interface.py","file_url":"https://github.com/TonyHongtaoWu/RainMamba/blob/HEAD/causal-conv1d/causal_conv1d/causal_conv1d_interface.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0595d8b8521fd9ca","mcp_get_code":{"code_sha256":"0595d8b8521fd9ca"}},{"arxiv_id":"2402.05608","paper":"/paper/scalable-diffusion-models-with-state-space","title":"Scalable Diffusion Models with State Space Backbone","date":"2024-02-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"feizc/dis","path":"causal-conv1d/causal_conv1d/causal_conv1d_interface.py","file_url":"https://github.com/feizc/dis/blob/HEAD/causal-conv1d/causal_conv1d/causal_conv1d_interface.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0595d8b8521fd9ca","mcp_get_code":{"code_sha256":"0595d8b8521fd9ca"}},{"arxiv_id":"2401.14168","paper":"/paper/vivim-a-video-vision-mamba-for-medical-video","title":"Vivim: a Video Vision Mamba for Medical Video Segmentation","date":"2024-01-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"scott-yjyang/vivim","path":"causal-conv1d/causal_conv1d/causal_conv1d_interface.py","file_url":"https://github.com/scott-yjyang/vivim/blob/HEAD/causal-conv1d/causal_conv1d/causal_conv1d_interface.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0595d8b8521fd9ca","mcp_get_code":{"code_sha256":"0595d8b8521fd9ca"}}]}