{"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/sin","entry":"Sin","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":4,"n_places":6,"n_places_pointer_only":4,"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":"2603.22323","paper":"/paper/arxiv-2603-22323","title":"A Multi-Task Targeted Learning Framework for Lithium-Ion Battery State-of-Health and Remaining Useful Life","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"wang-fujin/PINN4SOH","path":"Model/Model.py","file_url":"https://github.com/wang-fujin/PINN4SOH/blob/HEAD/Model/Model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1c5a0fa75b2a026c","mcp_get_code":{"code_sha256":"1c5a0fa75b2a026c"}},{"arxiv_id":"2310.12109","paper":"/paper/monarch-mixer-a-simple-sub-quadratic-gemm-1","title":"Monarch Mixer: A Simple Sub-Quadratic GEMM-Based Architecture","date":"2023-10-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HazyResearch/m2","path":"bert/src/mm/monarch_mixer_sequence_mixer.py","file_url":"https://github.com/HazyResearch/m2/blob/HEAD/bert/src/mm/monarch_mixer_sequence_mixer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"46738ca5fd203f92","mcp_get_code":{"code_sha256":"46738ca5fd203f92"}},{"arxiv_id":"2306.15794","paper":"/paper/hyenadna-long-range-genomic-sequence-modeling","title":"HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution","date":"2023-06-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"frederikkemarin/bend","path":"bend/models/hyena_dna.py","file_url":"https://github.com/frederikkemarin/bend/blob/HEAD/bend/models/hyena_dna.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"3f162814e9da788b","mcp_get_code":{"code_sha256":"3f162814e9da788b"}},{"arxiv_id":"2304.02633","paper":"/paper/hnerv-a-hybrid-neural-representation-for","title":"HNeRV: A Hybrid Neural Representation for Videos","date":"2023-04-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"haochen-rye/hnerv","path":"model_all.py","file_url":"https://github.com/haochen-rye/hnerv/blob/HEAD/model_all.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6a4b88c02d94376c","mcp_get_code":{"code_sha256":"6a4b88c02d94376c"}},{"arxiv_id":"2207.08132","paper":"/paper/e-nerv-expedite-neural-video-representation","title":"E-NeRV: Expedite Neural Video Representation with Disentangled Spatial-Temporal Context","date":"2022-07-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kyleleey/E-NeRV","path":"model/E_NeRV.py","file_url":"https://github.com/kyleleey/E-NeRV/blob/HEAD/model/E_NeRV.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"aa5c05d08b2acf11","mcp_get_code":{"code_sha256":"aa5c05d08b2acf11"}},{"arxiv_id":"2104.03313","paper":"/paper/scanimate-weakly-supervised-learning-of","title":"SCANimate: Weakly Supervised Learning of Skinned Clothed Avatar Networks","date":"2021-04-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shunsukesaito/SCANimate","path":"lib/model/IGRSDFNet.py","file_url":"https://github.com/shunsukesaito/SCANimate/blob/HEAD/lib/model/IGRSDFNet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"d70da435eb18566c","mcp_get_code":{"code_sha256":"d70da435eb18566c"}}]}