{"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/get-3d-sincos-pos-embed","entry":"get_3d_sincos_pos_embed","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":16,"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":13,"n_samples_ran":5,"n_samples_fingerprinted":1,"n_places":16,"n_places_pointer_only":3,"by_status":{"ran_honours":2,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":1,"ran":2,"unverified":8},"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":"2605.15284","paper":"/paper/arxiv-2605-15284","title":"Tadpole: Autoencoders as Foundation Models for 3D PDEs with Online Learning","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"tum-pbs/tadpole","path":"tadpole/architecture/downstream/llm.py","file_url":"https://github.com/tum-pbs/tadpole/blob/HEAD/tadpole/architecture/downstream/llm.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"a802dd8537cd773f","mcp_get_code":{"code_sha256":"a802dd8537cd773f"}},{"arxiv_id":"2602.02603","paper":"/paper/arxiv-2602-02603","title":"EchoJEPA: A Latent Predictive Foundation Model for Echocardiography","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"bowang-lab/EchoJEPA","path":"src/models/predictor.py","file_url":"https://github.com/bowang-lab/EchoJEPA/blob/HEAD/src/models/predictor.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"e7efdd433b96807d","mcp_get_code":{"code_sha256":"e7efdd433b96807d"}},{"arxiv_id":"2511.16315","paper":"/paper/arxiv-2511-16315","title":"BioBench: A Blueprint to Move Beyond ImageNet for Scientific ML Benchmarks","date":null,"month_inferred_from_arxiv_id":"2025-11","title_source":"syntology","repo":"samuelstevens/biobench","path":"src/biobench/vjepa.py","file_url":"https://github.com/samuelstevens/biobench/blob/HEAD/src/biobench/vjepa.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"40c848b8db7f92c8","mcp_get_code":{"code_sha256":"40c848b8db7f92c8"}},{"arxiv_id":"2506.11777","paper":"/paper/self-supervised-learning-of-echocardiographic","title":"Self-supervised Learning of Echocardiographic Video Representations via Online Cluster Distillation","date":"2025-06-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mdivyanshu97/discovr","path":"models/modeling_pretrain.py","file_url":"https://github.com/mdivyanshu97/discovr/blob/HEAD/models/modeling_pretrain.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a12beb78a97ac9a5","mcp_get_code":{"code_sha256":"a12beb78a97ac9a5"}},{"arxiv_id":"2503.20287","paper":"/paper/insvie-1m-effective-instruction-based-video","title":"InsViE-1M: Effective Instruction-based Video Editing with Elaborate Dataset Construction","date":"2025-03-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"langmanbusi/insvie","path":"CogVideo/sat/dit_video_concat.py","file_url":"https://github.com/langmanbusi/insvie/blob/HEAD/CogVideo/sat/dit_video_concat.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7759e9a501ea471a","mcp_get_code":{"code_sha256":"7759e9a501ea471a"}},{"arxiv_id":"2503.14237","paper":"/paper/make-your-training-flexible-towards","title":"Make Your Training Flexible: Towards Deployment-Efficient Video Models","date":"2025-03-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"OpenGVLab/FluxViT","path":"single_modality/models/pos_embed.py","file_url":"https://github.com/OpenGVLab/FluxViT/blob/HEAD/single_modality/models/pos_embed.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"54f45ddeec14da87","mcp_get_code":{"code_sha256":"54f45ddeec14da87"}},{"arxiv_id":"2502.05179","paper":"/paper/flashvideo-flowing-fidelity-to-detail-for","title":"FlashVideo:Flowing Fidelity to Detail for Efficient High-Resolution Video Generation","date":"2025-02-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"foundationvision/flashvideo","path":"flashvideo/dit_video_concat.py","file_url":"https://github.com/foundationvision/flashvideo/blob/HEAD/flashvideo/dit_video_concat.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":"7759e9a501ea471a","mcp_get_code":{"code_sha256":"7759e9a501ea471a"}},{"arxiv_id":"2412.00733","paper":"/paper/hallo3-highly-dynamic-and-realistic-portrait","title":"Hallo3: Highly Dynamic and Realistic Portrait Image Animation with Video Diffusion Transformer","date":"2024-12-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fudan-generative-vision/hallo3","path":"hallo3/dit_video_concat.py","file_url":"https://github.com/fudan-generative-vision/hallo3/blob/HEAD/hallo3/dit_video_concat.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7759e9a501ea471a","mcp_get_code":{"code_sha256":"7759e9a501ea471a"}},{"arxiv_id":"2410.21264","paper":"/paper/larp-tokenizing-videos-with-a-learned-1","title":"LARP: Tokenizing Videos with a Learned Autoregressive Generative Prior","date":"2024-10-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hywang66/LARP","path":"models/embed.py","file_url":"https://github.com/hywang66/LARP/blob/HEAD/models/embed.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e8364ecd23bce54d","mcp_get_code":{"code_sha256":"e8364ecd23bce54d"}},{"arxiv_id":"2406.18070","paper":"/paper/egovideo-exploring-egocentric-foundation","title":"EgoVideo: Exploring Egocentric Foundation Model and Downstream Adaptation","date":"2024-06-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"opengvlab/egovideo","path":"backbone/model/pos_embed.py","file_url":"https://github.com/opengvlab/egovideo/blob/HEAD/backbone/model/pos_embed.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"54f45ddeec14da87","mcp_get_code":{"code_sha256":"54f45ddeec14da87"}},{"arxiv_id":"2403.05856","paper":"/paper/pov-prompt-oriented-view-agnostic-learning","title":"POV: Prompt-Oriented View-Agnostic Learning for Egocentric Hand-Object Interaction in the Multi-View World","date":"2024-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xuboshen/pov_acmmm2023","path":"slowfast/models/utils.py","file_url":"https://github.com/xuboshen/pov_acmmm2023/blob/HEAD/slowfast/models/utils.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":"14f6b1125cb2fc10","mcp_get_code":{"code_sha256":"14f6b1125cb2fc10"}},{"arxiv_id":"2307.01831","paper":"/paper/dit-3d-exploring-plain-diffusion-transformers-1","title":"DiT-3D: Exploring Plain Diffusion Transformers for 3D Shape Generation","date":"2023-07-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DiT-3D/DiT-3D","path":"models/dit3d.py","file_url":"https://github.com/DiT-3D/DiT-3D/blob/HEAD/models/dit3d.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"a4339312e5467413","mcp_get_code":{"code_sha256":"a4339312e5467413"}},{"arxiv_id":"2212.03229","paper":"/paper/rethinking-video-vits-sparse-video-tubes-for","title":"Rethinking Video ViTs: Sparse Video Tubes for Joint Image and Video Learning","date":"2022-12-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"daniel-code/TubeViT","path":"tubevit/positional_encoding.py","file_url":"https://github.com/daniel-code/TubeViT/blob/HEAD/tubevit/positional_encoding.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"23745849d316e5ae","mcp_get_code":{"code_sha256":"23745849d316e5ae"}},{"arxiv_id":"2111.09887","paper":"/paper/pytorchvideo-a-deep-learning-library-for","title":"PyTorchVideo: A Deep Learning Library for Video Understanding","date":"2021-11-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/pytorchvideo","path":"pytorchvideo/layers/positional_encoding.py","file_url":"https://github.com/facebookresearch/pytorchvideo/blob/HEAD/pytorchvideo/layers/positional_encoding.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":false,"code_sha256_prefix":"11dcb96392d4a3b1","mcp_get_code":{"code_sha256":"11dcb96392d4a3b1"}},{"arxiv_id":"2111.06377","paper":"/paper/masked-autoencoders-are-scalable-vision","title":"Masked Autoencoders Are Scalable Vision Learners","date":"2021-11-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nasa-impact/hls-foundation-os","path":"geospatial_fm/geospatial_fm.py","file_url":"https://github.com/nasa-impact/hls-foundation-os/blob/HEAD/geospatial_fm/geospatial_fm.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":"c6230d72998a4af2","mcp_get_code":{"code_sha256":"c6230d72998a4af2"}},{"arxiv_id":"aaai_29391","paper":null,"title":"arXiv:aaai_29391","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Jiahuiqu/LDS2AE","path":"pos_embed.py","file_url":"https://github.com/Jiahuiqu/LDS2AE/blob/HEAD/pos_embed.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"11bd5b849a3e90a1","mcp_get_code":{"code_sha256":"11bd5b849a3e90a1"}}]}