{"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/samepadconv","entry":"SamePadConv","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":6,"n_papers_ran":6,"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":6,"n_samples_ran":6,"n_samples_fingerprinted":6,"n_places":6,"n_places_pointer_only":3,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":6,"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":"2606.13277","paper":"/paper/arxiv-2606-13277","title":"ProtoX-AD: Self-Explainable Time Series Anomaly Detection and Characterization","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"Aitorzan3/ProtoX-AD","path":"src/models.py","file_url":"https://github.com/Aitorzan3/ProtoX-AD/blob/HEAD/src/models.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a31cbb317f35ad9d","mcp_get_code":{"code_sha256":"a31cbb317f35ad9d"}},{"arxiv_id":"2506.00635","paper":"/paper/learning-with-calibration-exploring-test-time","title":"Learning with Calibration: Exploring Test-Time Computing of Spatio-Temporal Forecasting","date":"2025-05-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"salesforce/fsnet","path":"models/ts2vec/fsnet.py","file_url":"https://github.com/salesforce/fsnet/blob/HEAD/models/ts2vec/fsnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"8c87003809936f8b","mcp_get_code":{"code_sha256":"8c87003809936f8b"}},{"arxiv_id":"2402.10434","paper":"/paper/parametric-augmentation-for-time-series","title":"Parametric Augmentation for Time Series Contrastive Learning","date":"2024-02-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AslanDing/AutoTCL","path":"AutoTCL_CoST.py","file_url":"https://github.com/AslanDing/AutoTCL/blob/HEAD/AutoTCL_CoST.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"dfd9d437685e03a0","mcp_get_code":{"code_sha256":"dfd9d437685e03a0"}},{"arxiv_id":"2311.00519","paper":"/paper/retrieval-based-reconstruction-for-time","title":"REBAR: Retrieval-Based Reconstruction for Time-series Contrastive Learning","date":"2023-11-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"maxxu05/rebar","path":"models/REBAR/REBAR_SSLModel.py","file_url":"https://github.com/maxxu05/rebar/blob/HEAD/models/REBAR/REBAR_SSLModel.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cb26426b6abfe2d3","mcp_get_code":{"code_sha256":"cb26426b6abfe2d3"}},{"arxiv_id":"2309.13378","paper":"/paper/deciphering-spatio-temporal-graph-forecasting","title":"Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatment","date":"2023-09-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yutong-xia/cast","path":"src/models/cast.py","file_url":"https://github.com/yutong-xia/cast/blob/HEAD/src/models/cast.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1a2ed22e016a22d5","mcp_get_code":{"code_sha256":"1a2ed22e016a22d5"}},{"arxiv_id":"2309.12659","paper":"/paper/onenet-enhancing-time-series-forecasting-1","title":"OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling","date":"2023-09-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yfzhang114/onenet","path":"models/ts2vec/fsnet.py","file_url":"https://github.com/yfzhang114/onenet/blob/HEAD/models/ts2vec/fsnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"82ae7ad71d59fe48","mcp_get_code":{"code_sha256":"82ae7ad71d59fe48"}}]}