{"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/buffered-future-mask","entry":"buffered_future_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":11,"n_papers_ran":8,"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":5,"n_samples_ran":2,"n_samples_fingerprinted":2,"n_places":11,"n_places_pointer_only":4,"by_status":{"ran_honours":0,"ran_violates":1,"ran_draft_wrong":1,"ran_fixture":0,"ran":0,"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":"2606.06249","paper":"/paper/arxiv-2606-06249","title":"GRAMformer: Any-Order Modality Interactions via Volumetric Multimodal Cross-Attention","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"ispamm/GRAMformer","path":"gramformer.py.py","file_url":"https://github.com/ispamm/GRAMformer/blob/HEAD/gramformer.py.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7c2fd3e00f462b1d","mcp_get_code":{"code_sha256":"7c2fd3e00f462b1d"}},{"arxiv_id":"2508.05164","paper":"/paper/arxiv-2508-05164","title":"S 2 M-Former: Spiking Symmetric Mixing Branchformer for Brain Auditory Attention Detection","date":null,"month_inferred_from_arxiv_id":"2025-08","title_source":"syntology","repo":"JackieWang9811/S2M-Former","path":"model_zoo/DBPNet.py","file_url":"https://github.com/JackieWang9811/S2M-Former/blob/HEAD/model_zoo/DBPNet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e384b3ecda7c6ac8","mcp_get_code":{"code_sha256":"e384b3ecda7c6ac8"}},{"arxiv_id":"2412.08979","paper":"/paper/a-wander-through-the-multimodal-landscape","title":"A Wander Through the Multimodal Landscape: Efficient Transfer Learning via Low-rank Sequence Multimodal Adapter","date":"2024-12-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zrguo/Wander","path":"models/transformer.py","file_url":"https://github.com/zrguo/Wander/blob/HEAD/models/transformer.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"184a5eddedaf6a42","mcp_get_code":{"code_sha256":"184a5eddedaf6a42"}},{"arxiv_id":"2412.07121","paper":"/paper/bridging-the-gap-for-test-time-multimodal","title":"Bridging the Gap for Test-Time Multimodal Sentiment Analysis","date":"2024-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zrguo/casp","path":"modules/transformer.py","file_url":"https://github.com/zrguo/casp/blob/HEAD/modules/transformer.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"184a5eddedaf6a42","mcp_get_code":{"code_sha256":"184a5eddedaf6a42"}},{"arxiv_id":"2411.01409","paper":"/paper/classifier-guided-gradient-modulation-for","title":"Classifier-guided Gradient Modulation for Enhanced Multimodal Learning","date":"2024-11-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zrguo/CGGM","path":"modules/transformer.py","file_url":"https://github.com/zrguo/CGGM/blob/HEAD/modules/transformer.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"184a5eddedaf6a42","mcp_get_code":{"code_sha256":"184a5eddedaf6a42"}},{"arxiv_id":"2410.18373","paper":"/paper/ugotme-an-embodied-system-for-affective-human","title":"UGotMe: An Embodied System for Affective Human-Robot Interaction","date":null,"month_inferred_from_arxiv_id":"2024-10","title_source":"archive","repo":"lipzh5/amecavle","path":"models/modules/cross_modal_transformer.py","file_url":"https://github.com/lipzh5/amecavle/blob/HEAD/models/modules/cross_modal_transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9dffdd45cd69f6e0","mcp_get_code":{"code_sha256":"9dffdd45cd69f6e0"}},{"arxiv_id":"2312.14667","paper":"/paper/token-level-contrastive-learning-with","title":"Token-Level Contrastive Learning with Modality-Aware Prompting for Multimodal Intent Recognition","date":"2023-12-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thuiar/TCL-MAP","path":"methods/TCL_MAP/SubNets/transformers_encoder/transformer.py","file_url":"https://github.com/thuiar/TCL-MAP/blob/HEAD/methods/TCL_MAP/SubNets/transformers_encoder/transformer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d13fbe5e7e90947f","mcp_get_code":{"code_sha256":"d13fbe5e7e90947f"}},{"arxiv_id":"2303.13802","paper":"/paper/decoupled-multimodal-distilling-for-emotion","title":"Decoupled Multimodal Distilling for Emotion Recognition","date":"2023-03-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mdswyz/DMD","path":"trains/singleTask/model/dmd.py","file_url":"https://github.com/mdswyz/DMD/blob/HEAD/trains/singleTask/model/dmd.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d13fbe5e7e90947f","mcp_get_code":{"code_sha256":"d13fbe5e7e90947f"}},{"arxiv_id":"2210.17444","paper":"/paper/multimodal-information-bottleneck-learning","title":"Multimodal Information Bottleneck: Learning Minimal Sufficient Unimodal and Multimodal Representations","date":"2022-10-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tmacmai/multimodal-information-bottleneck","path":"MIB_github/modules/transformer.py","file_url":"https://github.com/tmacmai/multimodal-information-bottleneck/blob/HEAD/MIB_github/modules/transformer.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"184a5eddedaf6a42","mcp_get_code":{"code_sha256":"184a5eddedaf6a42"}},{"arxiv_id":"1906.00295","paper":"/paper/190600295","title":"Multimodal Transformer for Unaligned Multimodal Language Sequences","date":"2019-06-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kenford953/graphcage","path":"src/CrossmodalTransformer.py","file_url":"https://github.com/kenford953/graphcage/blob/HEAD/src/CrossmodalTransformer.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"184a5eddedaf6a42","mcp_get_code":{"code_sha256":"184a5eddedaf6a42"}},{"arxiv_id":"Pan_Wnet_Audio-Guided_Video_Object_Segmentation_via_Wavelet-Based_Cross-Modal_Denoising_Networks_CVPR_2022_paper","paper":null,"title":"arXiv:Pan_Wnet_Audio-Guided_Video_Object_Segmentation_via_Wavelet-Based_Cross-Modal_Denoising_Networks_CVPR_2022_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"asudahkzj/Wnet","path":"models/mult_transformer.py","file_url":"https://github.com/asudahkzj/Wnet/blob/HEAD/models/mult_transformer.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"184a5eddedaf6a42","mcp_get_code":{"code_sha256":"184a5eddedaf6a42"}}]}