{"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/sirenlayer","entry":"SirenLayer","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":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":5,"n_samples_ran":5,"n_samples_fingerprinted":3,"n_places":5,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":5,"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":"2503.10000","paper":"/paper/metricgrids-arbitrary-nonlinear-approximation","title":"MetricGrids: Arbitrary Nonlinear Approximation with Elementary Metric Grids based Implicit Neural Representation","date":"2025-03-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wangshu31/MetricGrids","path":"img_sdf/ngp.py","file_url":"https://github.com/wangshu31/MetricGrids/blob/HEAD/img_sdf/ngp.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4dd0e1ba6e90b5b7","mcp_get_code":{"code_sha256":"4dd0e1ba6e90b5b7"}},{"arxiv_id":"2503.04835","paper":"/paper/distilling-dataset-into-neural-field","title":"Distilling Dataset into Neural Field","date":"2025-03-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aailab-kaist/ddif","path":"3D_Voxel/DDiF.py","file_url":"https://github.com/aailab-kaist/ddif/blob/HEAD/3D_Voxel/DDiF.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6c4bdbee51e51b61","mcp_get_code":{"code_sha256":"6c4bdbee51e51b61"}},{"arxiv_id":"2405.12398","paper":"/paper/asmr-activation-sharing-multi-resolution","title":"ASMR: Activation-sharing Multi-resolution Coordinate Networks For Efficient Inference","date":"2024-05-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"stevolopolis/asmr","path":"models/asmr.py","file_url":"https://github.com/stevolopolis/asmr/blob/HEAD/models/asmr.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8a12ed11a478da67","mcp_get_code":{"code_sha256":"8a12ed11a478da67"}},{"arxiv_id":"2104.10078","paper":"/paper/unisurf-unifying-neural-implicit-surfaces-and","title":"UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction","date":"2021-04-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ventusff/neurecon","path":"models/frameworks/unisurf.py","file_url":"https://github.com/ventusff/neurecon/blob/HEAD/models/frameworks/unisurf.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9abd512a9f98758c","mcp_get_code":{"code_sha256":"9abd512a9f98758c"}},{"arxiv_id":"2006.09661","paper":"/paper/implicit-neural-representations-with-periodic","title":"Implicit Neural Representations with Periodic Activation Functions","date":"2020-06-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"palombom/sirenmri","path":"siren.py","file_url":"https://github.com/palombom/sirenmri/blob/HEAD/siren.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8ac1c1befb2f7857","mcp_get_code":{"code_sha256":"8ac1c1befb2f7857"}}]}