{"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/linearattention","entry":"LinearAttention","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":20,"n_papers_ran":16,"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":21,"n_samples_ran":17,"n_samples_fingerprinted":3,"n_places":21,"n_places_pointer_only":5,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":17,"unverified":4},"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":"2606.31574","paper":"/paper/arxiv-2606-31574","title":"Temperature Field Reconstruction of Tungsten Monoblock Divertor on EAST using Physics-aware Neural Operator Transformer","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"Event-AHU/OpenFusion","path":"PNOT/src/HeatOperator/pnot_heat/model.py","file_url":"https://github.com/Event-AHU/OpenFusion/blob/HEAD/PNOT/src/HeatOperator/pnot_heat/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"04541b9375b614f4","mcp_get_code":{"code_sha256":"04541b9375b614f4"}},{"arxiv_id":"2509.24332","paper":"/paper/arxiv-2509-24332","title":"Towards Generalizable PDE Dynamics Forecasting via Physics-Guided Invariant Learning","date":null,"month_inferred_from_arxiv_id":"2025-09","title_source":"syntology","repo":"LSY-Cython/iMOOE","path":"models/framework.py","file_url":"https://github.com/LSY-Cython/iMOOE/blob/HEAD/models/framework.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"db3be4d916ec9724","mcp_get_code":{"code_sha256":"db3be4d916ec9724"}},{"arxiv_id":"2506.05584","paper":null,"title":"arXiv:2506.05584","date":null,"month_inferred_from_arxiv_id":"2025-06","title_source":null,"repo":"microsoft/ticl","path":"ticl/models/tabflex.py","file_url":"https://github.com/microsoft/ticl/blob/HEAD/ticl/models/tabflex.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":"906b8e2e76724a87","mcp_get_code":{"code_sha256":"906b8e2e76724a87"}},{"arxiv_id":"2502.07244","paper":"/paper/linear-transformers-as-var-models-aligning","title":"Linear Transformers as VAR Models: Aligning Autoregressive Attention Mechanisms with Autoregressive Forecasting","date":"2025-02-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ljc-fvnr/structural-aligned-mixture-of-var","path":"models/AutoregressiveAlignment.py","file_url":"https://github.com/ljc-fvnr/structural-aligned-mixture-of-var/blob/HEAD/models/AutoregressiveAlignment.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fa3be66911755893","mcp_get_code":{"code_sha256":"fa3be66911755893"}},{"arxiv_id":"2407.16448","paper":"/paper/monowad-weather-adaptive-diffusion-model-for","title":"MonoWAD: Weather-Adaptive Diffusion Model for Robust Monocular 3D Object Detection","date":"2024-07-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VisualAIKHU/MonoWAD","path":"visualDet3D/networks/detectors/MonoWAD.py","file_url":"https://github.com/VisualAIKHU/MonoWAD/blob/HEAD/visualDet3D/networks/detectors/MonoWAD.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e483acb4ed97bccf","mcp_get_code":{"code_sha256":"e483acb4ed97bccf"}},{"arxiv_id":"2407.16171","paper":"/paper/learning-trimodal-relation-for-avqa-with","title":"Learning Trimodal Relation for AVQA with Missing Modality","date":"2024-07-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VisualAIKHU/Missing-AVQA","path":"net_grd_avst/net_avst.py","file_url":"https://github.com/VisualAIKHU/Missing-AVQA/blob/HEAD/net_grd_avst/net_avst.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"49dea4e895a85ad5","mcp_get_code":{"code_sha256":"49dea4e895a85ad5"}},{"arxiv_id":"2406.03919","paper":"/paper/vectorized-conditional-neural-fields-a","title":"Vectorized Conditional Neural Fields: A Framework for Solving Time-dependent Parametric Partial Differential Equations","date":"2024-06-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jhagnberger/vcnef","path":"vcnef/vcnef_1d.py","file_url":"https://github.com/jhagnberger/vcnef/blob/HEAD/vcnef/vcnef_1d.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ccb3e8eb10824cdd","mcp_get_code":{"code_sha256":"ccb3e8eb10824cdd"}},{"arxiv_id":"2405.17398","paper":"/paper/vista-a-generalizable-driving-world-model","title":"Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllability","date":"2024-05-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"opendrivelab/vista","path":"vwm/models/diffusion.py","file_url":"https://github.com/opendrivelab/vista/blob/HEAD/vwm/models/diffusion.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"b7508055b9e0c4d0","mcp_get_code":{"code_sha256":"b7508055b9e0c4d0"}},{"arxiv_id":"2405.16605","paper":"/paper/demystify-mamba-in-vision-a-linear-attention","title":"Demystify Mamba in Vision: A Linear Attention Perspective","date":"2024-05-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LeapLabTHU/MLLA","path":"models/mlla.py","file_url":"https://github.com/LeapLabTHU/MLLA/blob/HEAD/models/mlla.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"145a5aeec8a513b8","mcp_get_code":{"code_sha256":"145a5aeec8a513b8"}},{"arxiv_id":"2404.00815","paper":"/paper/towards-realistic-scene-generation-with-lidar","title":"Towards Realistic Scene Generation with LiDAR Diffusion Models","date":"2024-03-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hancyran/lidar-diffusion","path":"lidm/modules/diffusion/model_lidm.py","file_url":"https://github.com/hancyran/lidar-diffusion/blob/HEAD/lidm/modules/diffusion/model_lidm.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"65eaa953ffaffaad","mcp_get_code":{"code_sha256":"65eaa953ffaffaad"}},{"arxiv_id":"2403.00939","paper":"/paper/g3dr-generative-3d-reconstruction-in-imagenet","title":"G3DR: Generative 3D Reconstruction in ImageNet","date":"2024-03-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"preddy5/G3DR","path":"src/unet.py","file_url":"https://github.com/preddy5/G3DR/blob/HEAD/src/unet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"55b2c2cf73849d61","mcp_get_code":{"code_sha256":"55b2c2cf73849d61"}},{"arxiv_id":"2309.03729","paper":"/paper/phasic-content-fusing-diffusion-model-with","title":"Phasic Content Fusing Diffusion Model with Directional Distribution Consistency for Few-Shot Model Adaption","date":"2023-09-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sjtuplayer/few-shot-diffusion","path":"model/big_unet.py","file_url":"https://github.com/sjtuplayer/few-shot-diffusion/blob/HEAD/model/big_unet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c71125f439d1d5ea","mcp_get_code":{"code_sha256":"c71125f439d1d5ea"}},{"arxiv_id":"2307.08122","paper":"/paper/tangent-transformers-for-composition-privacy","title":"Tangent Transformers for Composition, Privacy and Removal","date":"2023-07-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tianyu139/tangent-model-composition","path":"models/linear_attention_layers.py","file_url":"https://github.com/tianyu139/tangent-model-composition/blob/HEAD/models/linear_attention_layers.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7e5129a3c76b00c8","mcp_get_code":{"code_sha256":"7e5129a3c76b00c8"}},{"arxiv_id":"2212.11972","paper":"/paper/scalable-adaptive-computation-for-iterative","title":"Scalable Adaptive Computation for Iterative Generation","date":"2022-12-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lucidrains/recurrent-interface-network-pytorch","path":"rin_pytorch/rin_pytorch.py","file_url":"https://github.com/lucidrains/recurrent-interface-network-pytorch/blob/HEAD/rin_pytorch/rin_pytorch.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f8753f538bdf8090","mcp_get_code":{"code_sha256":"f8753f538bdf8090"}},{"arxiv_id":"2209.14977","paper":"/paper/transformer-meets-boundary-value-inverse","title":"Transformer Meets Boundary Value Inverse Problems","date":"2022-09-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"scaomath/eit-transformer","path":"libs/hut.py","file_url":"https://github.com/scaomath/eit-transformer/blob/HEAD/libs/hut.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"083b5d5af368508a","mcp_get_code":{"code_sha256":"083b5d5af368508a"}},{"arxiv_id":"2203.11483","paper":"/paper/practical-stereo-matching-via-cascaded","title":"Practical Stereo Matching via Cascaded Recurrent Network with Adaptive Correlation","date":"2022-03-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ibaiGorordo/CREStereo-Pytorch","path":"nets/crestereo.py","file_url":"https://github.com/ibaiGorordo/CREStereo-Pytorch/blob/HEAD/nets/crestereo.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3afd7dc763d222f2","mcp_get_code":{"code_sha256":"3afd7dc763d222f2"}},{"arxiv_id":"2104.00680","paper":"/paper/loftr-detector-free-local-feature-matching","title":"LoFTR: Detector-Free Local Feature Matching with Transformers","date":"2021-04-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kolkir/coarse_loftr_trt","path":"loftr/loftr.py","file_url":"https://github.com/kolkir/coarse_loftr_trt/blob/HEAD/loftr/loftr.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":"48e8896da53462be","mcp_get_code":{"code_sha256":"48e8896da53462be"}},{"arxiv_id":"2006.11239","paper":"/paper/denoising-diffusion-probabilistic-models","title":"Denoising Diffusion Probabilistic Models","date":"2020-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yiyixuxu/denoising-diffusion-flax","path":"denoising_diffusion_flax/unet.py","file_url":"https://github.com/yiyixuxu/denoising-diffusion-flax/blob/HEAD/denoising_diffusion_flax/unet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"48203972e3caa111","mcp_get_code":{"code_sha256":"48203972e3caa111"}},{"arxiv_id":"2006.11239","paper":"/paper/denoising-diffusion-probabilistic-models","title":"Denoising Diffusion Probabilistic Models","date":"2020-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cjfghk5697/Pytorch-Research-Paper-Implementations","path":"Diffusion/DDPM/models/model.py","file_url":"https://github.com/cjfghk5697/Pytorch-Research-Paper-Implementations/blob/HEAD/Diffusion/DDPM/models/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e66e5ee5660ade9e","mcp_get_code":{"code_sha256":"e66e5ee5660ade9e"}},{"arxiv_id":"2004.12362","paper":"/paper/relational-graph-attention-network-for-aspect","title":"Relational Graph Attention Network for Aspect-based Sentiment Analysis","date":"2020-04-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shenwzh3/RGAT-ABSA","path":"model.py","file_url":"https://github.com/shenwzh3/RGAT-ABSA/blob/HEAD/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"76ab310ac3cbd178","mcp_get_code":{"code_sha256":"76ab310ac3cbd178"}},{"arxiv_id":"ijcai2025_0890","paper":null,"title":"arXiv:ijcai2025_0890","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Auguuust/DiffEC","path":"Diffusion_based/DiffusionModels/noisePredictModels/Unet/_1DUNet.py","file_url":"https://github.com/Auguuust/DiffEC/blob/HEAD/Diffusion_based/DiffusionModels/noisePredictModels/Unet/_1DUNet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2f2928c1feb64d12","mcp_get_code":{"code_sha256":"2f2928c1feb64d12"}}]}