{"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/embeddinglayer","entry":"EmbeddingLayer","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":10,"n_papers_ran":7,"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":10,"n_samples_ran":7,"n_samples_fingerprinted":2,"n_places":10,"n_places_pointer_only":6,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":7,"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":"2608.23551","paper":"/paper/arxiv-2608-23551","title":"ConvergeFlow: Language Flow with Provable Convergence to Token Embeddings","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"Na-Li66/ConvergeFlow","path":"model.py","file_url":"https://github.com/Na-Li66/ConvergeFlow/blob/HEAD/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"853e72595e63b242","mcp_get_code":{"code_sha256":"853e72595e63b242"}},{"arxiv_id":"2608.12219","paper":"/paper/arxiv-2608-12219","title":"ScreenShot: A Foundation Model for Few-Shot Combination Drug Screening","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"tansey-lab/screenshot","path":"screenshot/screenshot.py","file_url":"https://github.com/tansey-lab/screenshot/blob/HEAD/screenshot/screenshot.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d811e51c7ba10fb3","mcp_get_code":{"code_sha256":"d811e51c7ba10fb3"}},{"arxiv_id":"2603.22216","paper":"/paper/arxiv-2603-22216","title":"Gumbel Distillation for Parallel Text Generation","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"hxixixh/gumbel-distill","path":"models/dit_gumbel.py","file_url":"https://github.com/hxixixh/gumbel-distill/blob/HEAD/models/dit_gumbel.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a26dfd38a7ca0f3f","mcp_get_code":{"code_sha256":"a26dfd38a7ca0f3f"}},{"arxiv_id":"2508.16112","paper":"/paper/arxiv-2508-16112","title":"IR-Agent: Expert-Inspired LLM Agents for Structure Elucidation from Infrared Spectra","date":null,"month_inferred_from_arxiv_id":"2025-08","title_source":"syntology","repo":"HeewoongNoh/IR-Agent","path":"models/translator.py","file_url":"https://github.com/HeewoongNoh/IR-Agent/blob/HEAD/models/translator.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"adb759950dd2a124","mcp_get_code":{"code_sha256":"adb759950dd2a124"}},{"arxiv_id":"2307.04052","paper":"/paper/learning-to-group-auxiliary-datasets-for","title":"Learning to Group Auxiliary Datasets for Molecule","date":"2023-07-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"graph-and-geometric-learning/molgroup","path":"molgroup/models/metagin.py","file_url":"https://github.com/graph-and-geometric-learning/molgroup/blob/HEAD/molgroup/models/metagin.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5daa83c68e9f2d4c","mcp_get_code":{"code_sha256":"5daa83c68e9f2d4c"}},{"arxiv_id":"2203.15350","paper":"/paper/end-to-end-transformer-based-model-for-image","title":"End-to-End Transformer Based Model for Image Captioning","date":"2022-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jchenghu/expansionnet_v2","path":"models/End_ExpansionNet_v2.py","file_url":"https://github.com/jchenghu/expansionnet_v2/blob/HEAD/models/End_ExpansionNet_v2.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8dc0059690e3667f","mcp_get_code":{"code_sha256":"8dc0059690e3667f"}},{"arxiv_id":"2106.02210","paper":"/paper/nast-a-non-autoregressive-generator-with-word","title":"NAST: A Non-Autoregressive Generator with Word Alignment for Unsupervised Text Style Transfer","date":"2021-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fastnlp/style-transformer","path":"models/transformer.py","file_url":"https://github.com/fastnlp/style-transformer/blob/HEAD/models/transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d8ee2a61c05425ae","mcp_get_code":{"code_sha256":"d8ee2a61c05425ae"}},{"arxiv_id":"2002.03912","paper":"/paper/a-probabilistic-formulation-of-unsupervised-1","title":"A Probabilistic Formulation of Unsupervised Text Style Transfer","date":"2020-02-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thu-coai/NAST","path":"styletransformer/transformer.py","file_url":"https://github.com/thu-coai/NAST/blob/HEAD/styletransformer/transformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fd281e0df749b749","mcp_get_code":{"code_sha256":"fd281e0df749b749"}},{"arxiv_id":"1706.03762","paper":"/paper/attention-is-all-you-need","title":"Attention Is All You Need","date":"2017-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"demelin/nematode","path":"codebase/transformer.py","file_url":"https://github.com/demelin/nematode/blob/HEAD/codebase/transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c76f82b976984e98","mcp_get_code":{"code_sha256":"c76f82b976984e98"}},{"arxiv_id":"1503.05671","paper":"/paper/optimizing-neural-networks-with-kronecker","title":"Optimizing Neural Networks with Kronecker-factored Approximate Curvature","date":"2015-03-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tfjgeorge/nngeometry","path":"nngeometry/object/pspace.py","file_url":"https://github.com/tfjgeorge/nngeometry/blob/HEAD/nngeometry/object/pspace.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"01151fe74bd4e116","mcp_get_code":{"code_sha256":"01151fe74bd4e116"}}]}