{"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-subsequent-mask","entry":"get_subsequent_mask","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":17,"n_papers_ran":11,"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":11,"n_samples_ran":6,"n_samples_fingerprinted":3,"n_places":18,"n_places_pointer_only":4,"by_status":{"ran_honours":1,"ran_violates":1,"ran_draft_wrong":1,"ran_fixture":1,"ran":2,"unverified":5},"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":"2411.01623","paper":"/paper/filternet-harnessing-frequency-filters-for","title":"FilterNet: Harnessing Frequency Filters for Time Series Forecasting","date":"2024-11-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ant-research/Pyraformer","path":"pyraformer/Layers.py","file_url":"https://github.com/ant-research/Pyraformer/blob/HEAD/pyraformer/Layers.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":"d9606e9421f56201","mcp_get_code":{"code_sha256":"d9606e9421f56201"}},{"arxiv_id":"2410.08893","paper":"/paper/drama-mamba-enabled-model-based-reinforcement","title":"Drama: Mamba-Enabled Model-Based Reinforcement Learning Is Sample and Parameter Efficient","date":"2024-10-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"realwenlongwang/Drama","path":"sub_models/attention_blocks.py","file_url":"https://github.com/realwenlongwang/Drama/blob/HEAD/sub_models/attention_blocks.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6ae6a81b313635f6","mcp_get_code":{"code_sha256":"6ae6a81b313635f6"}},{"arxiv_id":"2406.09899","paper":"/paper/learning-solution-aware-transformers-for","title":"Learning Solution-Aware Transformers for Efficiently Solving Quadratic Assignment Problem","date":"2024-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PKUTAN/SAWT","path":"transformer/Models.py","file_url":"https://github.com/PKUTAN/SAWT/blob/HEAD/transformer/Models.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1eedfb10edb08c8a","mcp_get_code":{"code_sha256":"1eedfb10edb08c8a"}},{"arxiv_id":"2312.00063","paper":"/paper/momask-generative-masked-modeling-of-3d-human","title":"MoMask: Generative Masked Modeling of 3D Human Motions","date":"2023-11-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"EricGuo5513/momask-codes","path":"models/mask_transformer/tools.py","file_url":"https://github.com/EricGuo5513/momask-codes/blob/HEAD/models/mask_transformer/tools.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fb6fcde255d6bf72","mcp_get_code":{"code_sha256":"fb6fcde255d6bf72"}},{"arxiv_id":"2310.09615","paper":"/paper/storm-efficient-stochastic-transformer-based-1","title":"STORM: Efficient Stochastic Transformer based World Models for Reinforcement Learning","date":"2023-10-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"weipu-zhang/storm","path":"sub_models/attention_blocks.py","file_url":"https://github.com/weipu-zhang/storm/blob/HEAD/sub_models/attention_blocks.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6ae6a81b313635f6","mcp_get_code":{"code_sha256":"6ae6a81b313635f6"}},{"arxiv_id":"2212.04636","paper":"/paper/ego-body-pose-estimation-via-ego-head-pose","title":"Ego-Body Pose Estimation via Ego-Head Pose Estimation","date":"2022-12-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lijiaman/egoego_release","path":"egoego/model/transformer_module.py","file_url":"https://github.com/lijiaman/egoego_release/blob/HEAD/egoego/model/transformer_module.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"88956bf0120640af","mcp_get_code":{"code_sha256":"88956bf0120640af"}},{"arxiv_id":"2211.01572","paper":"/paper/fedtp-federated-learning-by-transformer","title":"FedTP: Federated Learning by Transformer Personalization","date":"2022-11-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhyczy/fedtp","path":"models/language_transformer.py","file_url":"https://github.com/zhyczy/fedtp/blob/HEAD/models/language_transformer.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1eedfb10edb08c8a","mcp_get_code":{"code_sha256":"1eedfb10edb08c8a"}},{"arxiv_id":"2206.05291","paper":"/paper/proactive-self-attentive-temporal-point","title":"ProActive: Self-Attentive Temporal Point Process Flows for Activity Sequences","date":"2022-06-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"data-iitd/proactive","path":"transformer/Models.py","file_url":"https://github.com/data-iitd/proactive/blob/HEAD/transformer/Models.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":"5e95e556552625a6","mcp_get_code":{"code_sha256":"5e95e556552625a6"}},{"arxiv_id":"2204.01696","paper":"/paper/joint-hand-motion-and-interaction-hotspots","title":"Joint Hand Motion and Interaction Hotspots Prediction from Egocentric Videos","date":"2022-04-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"stevenlsw/hoi-forecast","path":"networks/transformer.py","file_url":"https://github.com/stevenlsw/hoi-forecast/blob/HEAD/networks/transformer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3e17308fbd05134c","mcp_get_code":{"code_sha256":"3e17308fbd05134c"}},{"arxiv_id":"2005.08514","paper":"/paper/spatio-temporal-graph-transformer-networks","title":"Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction","date":"2020-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Majiker/STAR","path":"src/star.py","file_url":"https://github.com/Majiker/STAR/blob/HEAD/src/star.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1eedfb10edb08c8a","mcp_get_code":{"code_sha256":"1eedfb10edb08c8a"}},{"arxiv_id":"2002.09291","paper":"/paper/transformer-hawkes-process","title":"Transformer Hawkes Process","date":"2020-02-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SimiaoZuo/Transformer-Hawkes-Process","path":"transformer/Models.py","file_url":"https://github.com/SimiaoZuo/Transformer-Hawkes-Process/blob/HEAD/transformer/Models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5e95e556552625a6","mcp_get_code":{"code_sha256":"5e95e556552625a6"}},{"arxiv_id":"2001.05295","paper":"/paper/language-models-are-an-effective-patient","title":"Language Models Are An Effective Patient Representation Learning Technique For Electronic Health Record Data","date":"2020-01-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"som-shahlab/ehr_ml","path":"ehr_ml/clmbr/rnn_model.py","file_url":"https://github.com/som-shahlab/ehr_ml/blob/HEAD/ehr_ml/clmbr/rnn_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ca925e93c3ecd2d6","mcp_get_code":{"code_sha256":"ca925e93c3ecd2d6"}},{"arxiv_id":"1905.05460","paper":"/paper/cognitive-graph-for-multi-hop-reading","title":"Cognitive Graph for Multi-Hop Reading Comprehension at Scale","date":"2019-05-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ShaoaAllen/CogQA","path":"transform2_model.py","file_url":"https://github.com/ShaoaAllen/CogQA/blob/HEAD/transform2_model.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1eedfb10edb08c8a","mcp_get_code":{"code_sha256":"1eedfb10edb08c8a"}},{"arxiv_id":"1810.12541","paper":"/paper/robots-learn-social-skills-end-to-end","title":"Robots Learn Social Skills: End-to-End Learning of Co-Speech Gesture Generation for Humanoid Robots","date":null,"month_inferred_from_arxiv_id":"2018-10","title_source":"archive","repo":"ehwa009/Co-Speech_Gesture_Generation","path":"transformer/models.py","file_url":"https://github.com/ehwa009/Co-Speech_Gesture_Generation/blob/HEAD/transformer/models.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"b84290f81844cc1c","mcp_get_code":{"code_sha256":"b84290f81844cc1c"}},{"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":"Matthewdowney18/Transformer_Dialogue","path":"src/transformer/Models.py","file_url":"https://github.com/Matthewdowney18/Transformer_Dialogue/blob/HEAD/src/transformer/Models.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"91c7ac7cc9aef42b","mcp_get_code":{"code_sha256":"91c7ac7cc9aef42b"}},{"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":"Rudedaisy/attention-is-all-you-need-pytorch","path":"transformer/Models.py","file_url":"https://github.com/Rudedaisy/attention-is-all-you-need-pytorch/blob/HEAD/transformer/Models.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1eedfb10edb08c8a","mcp_get_code":{"code_sha256":"1eedfb10edb08c8a"}},{"arxiv_id":"ijcai2024_0790","paper":null,"title":"arXiv:ijcai2024_0790","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"CGCL-codes/CausalNET","path":"model/transformer/Models.py","file_url":"https://github.com/CGCL-codes/CausalNET/blob/HEAD/model/transformer/Models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"688b703d19c01e70","mcp_get_code":{"code_sha256":"688b703d19c01e70"}},{"arxiv_id":"aaai_20341","paper":null,"title":"arXiv:aaai_20341","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"wywyWang/ShuttleNet","path":"ShuttleNet/ShuttleNet.py","file_url":"https://github.com/wywyWang/ShuttleNet/blob/HEAD/ShuttleNet/ShuttleNet.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1eedfb10edb08c8a","mcp_get_code":{"code_sha256":"1eedfb10edb08c8a"}}]}