{"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/positionencoding","entry":"PositionEncoding","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":9,"n_papers_ran":9,"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":9,"n_samples_ran":9,"n_samples_fingerprinted":1,"n_places":9,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":9,"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":"2607.23554","paper":"/paper/arxiv-2607-23554","title":"Neonatal Hypoxic-ischaemic Encephalopathy Classification from the EEG and HRV Signals Using a Conformer based Masked Autoencoder","date":null,"month_inferred_from_arxiv_id":"2026-07","title_source":"syntology","repo":"syu-kylin/MAEConformer","path":"model/MAEConformer.py","file_url":"https://github.com/syu-kylin/MAEConformer/blob/HEAD/model/MAEConformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"5bce251634e2bc7f","mcp_get_code":{"code_sha256":"5bce251634e2bc7f"}},{"arxiv_id":"2605.26190","paper":"/paper/arxiv-2605-26190","title":"HRVConformer: Neonatal Hypoxic-Ischemic Encephalopathy Classification from the Heart Rate signals","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"syu-kylin/HRVConformer","path":"model/ConformerNet.py","file_url":"https://github.com/syu-kylin/HRVConformer/blob/HEAD/model/ConformerNet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"22c63fda47540a8c","mcp_get_code":{"code_sha256":"22c63fda47540a8c"}},{"arxiv_id":"2603.05969","paper":"/paper/arxiv-2603-05969","title":"Imagine How To Change: Explicit Procedure Modeling for Change Captioning","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"BlueberryOreo/ProCap","path":"src/rtransformer/model.py","file_url":"https://github.com/BlueberryOreo/ProCap/blob/HEAD/src/rtransformer/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f92321e2b4118573","mcp_get_code":{"code_sha256":"f92321e2b4118573"}},{"arxiv_id":"2405.20666","paper":"/paper/masa-motion-aware-masked-autoencoder-with","title":"MASA: Motion-aware Masked Autoencoder with Semantic Alignment for Sign Language Recognition","date":"2024-05-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sakura2233565548/masa","path":"moco/GCN_Transformer_mask.py","file_url":"https://github.com/sakura2233565548/masa/blob/HEAD/moco/GCN_Transformer_mask.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b6decf507ce36bfc","mcp_get_code":{"code_sha256":"b6decf507ce36bfc"}},{"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":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bd7b495abeba892f","mcp_get_code":{"code_sha256":"bd7b495abeba892f"}},{"arxiv_id":"2210.08465","paper":"/paper/character-centric-story-visualization-via","title":"Character-Centric Story Visualization via Visual Planning and Token Alignment","date":"2022-10-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"adymaharana/VLCStoryGan","path":"vlcgan/model.py","file_url":"https://github.com/adymaharana/VLCStoryGan/blob/HEAD/vlcgan/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b6f03dfe01742a92","mcp_get_code":{"code_sha256":"b6f03dfe01742a92"}},{"arxiv_id":"2203.14040","paper":"/paper/visual-abductive-reasoning","title":"Visual Abductive Reasoning","date":"2022-03-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"leonnnop/var","path":"src/model/mainmodel.py","file_url":"https://github.com/leonnnop/var/blob/HEAD/src/model/mainmodel.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e4821f9d2b6f5330","mcp_get_code":{"code_sha256":"e4821f9d2b6f5330"}},{"arxiv_id":"2106.10446","paper":"/paper/attend-what-you-need-motion-appearance","title":"Attend What You Need: Motion-Appearance Synergistic Networks for Video Question Answering","date":"2021-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ahjeongseo/MASN-pytorch","path":"model/masn.py","file_url":"https://github.com/ahjeongseo/MASN-pytorch/blob/HEAD/model/masn.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8df81fac54e85c99","mcp_get_code":{"code_sha256":"8df81fac54e85c99"}},{"arxiv_id":"2005.06409","paper":"/paper/dense-caption-matching-and-frame-selection","title":"Dense-Caption Matching and Frame-Selection Gating for Temporal Localization in VideoQA","date":"2020-05-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hyounghk/VideoQADenseCapFrameGate-ACL2020","path":"qanet/tvqanet.py","file_url":"https://github.com/hyounghk/VideoQADenseCapFrameGate-ACL2020/blob/HEAD/qanet/tvqanet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b9683b87afb37df5","mcp_get_code":{"code_sha256":"b9683b87afb37df5"}}]}