{"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/cosinesimcodebook","entry":"CosineSimCodebook","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":2,"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":2,"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":2,"unverified":3},"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":"2605.23402","paper":"/paper/arxiv-2605-23402","title":"Parametric Prior Mapping Framework for Non-stationary Probabilistic Time Series Forecasting","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"ljl8336/PPM","path":"layers/SVQ/SVQ_block.py","file_url":"https://github.com/ljl8336/PPM/blob/HEAD/layers/SVQ/SVQ_block.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":"4e1520083d05dd61","mcp_get_code":{"code_sha256":"4e1520083d05dd61"}},{"arxiv_id":"2411.06070","paper":"/paper/gft-graph-foundation-model-with-transferable","title":"GFT: Graph Foundation Model with Transferable Tree Vocabulary","date":"2024-11-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zehong-wang/gft","path":"GFT/model/vq.py","file_url":"https://github.com/zehong-wang/gft/blob/HEAD/GFT/model/vq.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d8a07f536f522526","mcp_get_code":{"code_sha256":"d8a07f536f522526"}},{"arxiv_id":"2402.16321","paper":"/paper/self-supervised-speech-quality-estimation-and","title":"Self-Supervised Speech Quality Estimation and Enhancement Using Only Clean Speech","date":"2024-02-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JasonSWFu/VQscore","path":"models/VQVAE_models.py","file_url":"https://github.com/JasonSWFu/VQscore/blob/HEAD/models/VQVAE_models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f8e325b49ff91a19","mcp_get_code":{"code_sha256":"f8e325b49ff91a19"}},{"arxiv_id":"2311.17245","paper":"/paper/lightgaussian-unbounded-3d-gaussian","title":"LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPS","date":"2023-11-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VITA-Group/LightGaussian","path":"vectree/vectree.py","file_url":"https://github.com/VITA-Group/LightGaussian/blob/HEAD/vectree/vectree.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"b39d64ccdbec91d0","mcp_get_code":{"code_sha256":"b39d64ccdbec91d0"}},{"arxiv_id":"2308.02117","paper":"/paper/vqgraph-graph-vector-quantization-for","title":"VQGraph: Rethinking Graph Representation Space for Bridging GNNs and MLPs","date":"2023-08-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yangling0818/vqgraph","path":"vq.py","file_url":"https://github.com/yangling0818/vqgraph/blob/HEAD/vq.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0b4fc7f49d1b58ba","mcp_get_code":{"code_sha256":"0b4fc7f49d1b58ba"}}]}