{"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/generate-mask","entry":"generate_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":24,"n_papers_ran":15,"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":23,"n_samples_ran":14,"n_samples_fingerprinted":2,"n_places":25,"n_places_pointer_only":7,"by_status":{"ran_honours":1,"ran_violates":1,"ran_draft_wrong":3,"ran_fixture":2,"ran":7,"unverified":9},"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":"2605.08519","paper":"/paper/arxiv-2605-08519","title":"SeBA: Semi-supervised few-shot learning via Separated-at-Birth Alignment for tabular data","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"kacper3615/SeBA","path":"trainers/pretrainer.py","file_url":"https://github.com/kacper3615/SeBA/blob/HEAD/trainers/pretrainer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4b51c14f1bcd8b4c","mcp_get_code":{"code_sha256":"4b51c14f1bcd8b4c"}},{"arxiv_id":"2602.07063","paper":"/paper/arxiv-2602-07063","title":"Video-based Music Generation","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"serkansulun/midi-emotion","path":"src/models/music_continuous_token.py","file_url":"https://github.com/serkansulun/midi-emotion/blob/HEAD/src/models/music_continuous_token.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"b70d86b4cc18c96c","mcp_get_code":{"code_sha256":"b70d86b4cc18c96c"}},{"arxiv_id":"2505.22107","paper":"/paper/curse-of-high-dimensionality-issue-in","title":"Curse of High Dimensionality Issue in Transformer for Long-context Modeling","date":"2025-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bolixinyu/dynamicgroupattention","path":"codebase/core/replace_llama_attn.py","file_url":"https://github.com/bolixinyu/dynamicgroupattention/blob/HEAD/codebase/core/replace_llama_attn.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2b175d1d753da1d7","mcp_get_code":{"code_sha256":"2b175d1d753da1d7"}},{"arxiv_id":"2503.03519","paper":"/paper/do-imagenet-trained-models-learn-shortcuts","title":"Do ImageNet-trained models learn shortcuts? The impact of frequency shortcuts on generalization","date":"2025-03-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nis-research/hfss","path":"hfss/LSI.py","file_url":"https://github.com/nis-research/hfss/blob/HEAD/hfss/LSI.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2e0170b8a1504ca2","mcp_get_code":{"code_sha256":"2e0170b8a1504ca2"}},{"arxiv_id":"2406.18146","paper":"/paper/a-refer-and-ground-multimodal-large-language","title":"A Refer-and-Ground Multimodal Large Language Model for Biomedicine","date":"2024-06-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shawnhuang497/bird","path":"applications/gradio_autolable.py","file_url":"https://github.com/shawnhuang497/bird/blob/HEAD/applications/gradio_autolable.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":"17bafa39d1e2efb8","mcp_get_code":{"code_sha256":"17bafa39d1e2efb8"}},{"arxiv_id":"2406.16148","paper":"/paper/towards-open-respiratory-acoustic-foundation","title":"Towards Open Respiratory Acoustic Foundation Models: Pretraining and Benchmarking","date":"2024-06-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"evelyn0414/OPERA","path":"res_analysis/visualize_masked_spec.py","file_url":"https://github.com/evelyn0414/OPERA/blob/HEAD/res_analysis/visualize_masked_spec.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"119b5fe3e8b4754c","mcp_get_code":{"code_sha256":"119b5fe3e8b4754c"}},{"arxiv_id":"2402.04291","paper":"/paper/billm-pushing-the-limit-of-post-training","title":"BiLLM: Pushing the Limit of Post-Training Quantization for LLMs","date":"2024-02-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aaronhuang-778/billm","path":"utils/mask.py","file_url":"https://github.com/aaronhuang-778/billm/blob/HEAD/utils/mask.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fc283fd7e228ed38","mcp_get_code":{"code_sha256":"fc283fd7e228ed38"}},{"arxiv_id":"2312.14223","paper":"/paper/fast-diffusion-based-counterfactuals-for","title":"Fast Diffusion-Based Counterfactuals for Shortcut Removal and Generation","date":"2023-12-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nina-weng/fastdime_med","path":"models/diffusion.py","file_url":"https://github.com/nina-weng/fastdime_med/blob/HEAD/models/diffusion.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b67301bd34cd84fa","mcp_get_code":{"code_sha256":"b67301bd34cd84fa"}},{"arxiv_id":"2311.15941","paper":"/paper/tell2design-a-dataset-for-language-guided","title":"Tell2Design: A Dataset for Language-Guided Floor Plan Generation","date":"2023-11-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lengsicong/tell2design","path":"CogView/calculate_iou.py","file_url":"https://github.com/lengsicong/tell2design/blob/HEAD/CogView/calculate_iou.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"30f6580c44acf787","mcp_get_code":{"code_sha256":"30f6580c44acf787"}},{"arxiv_id":"2310.19321","paper":"/paper/d4explainer-in-distribution-gnn-explanations","title":"D4Explainer: In-Distribution GNN Explanations via Discrete Denoising Diffusion","date":"2023-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"graph-and-geometric-learning/d4explainer","path":"explainers/diff_explainer.py","file_url":"https://github.com/graph-and-geometric-learning/d4explainer/blob/HEAD/explainers/diff_explainer.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d59e27b5fae8a2be","mcp_get_code":{"code_sha256":"d59e27b5fae8a2be"}},{"arxiv_id":"2310.05797","paper":"/paper/are-large-language-models-post-hoc-explainers","title":"In-Context Explainers: Harnessing LLMs for Explaining Black Box Models","date":"2023-10-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AI4LIFE-GROUP/LLM_Explainer","path":"faithulness_util.py","file_url":"https://github.com/AI4LIFE-GROUP/LLM_Explainer/blob/HEAD/faithulness_util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f4a2f984558ae1e9","mcp_get_code":{"code_sha256":"f4a2f984558ae1e9"}},{"arxiv_id":"2308.01850","paper":"/paper/synthesizing-long-term-human-motions-with","title":"Synthesizing Long-Term Human Motions with Diffusion Models via Coherent Sampling","date":"2023-08-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yangzhao1230/pcmdm","path":"generate_3.py","file_url":"https://github.com/yangzhao1230/pcmdm/blob/HEAD/generate_3.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"14b4b780a575af4d","mcp_get_code":{"code_sha256":"14b4b780a575af4d"}},{"arxiv_id":"2307.09306","paper":"/paper/eigentrajectory-low-rank-descriptors-for","title":"EigenTrajectory: Low-Rank Descriptors for Multi-Modal Trajectory Forecasting","date":"2023-07-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"inhwanbae/EigenTrajectory","path":"baseline/agentformer/model.py","file_url":"https://github.com/inhwanbae/EigenTrajectory/blob/HEAD/baseline/agentformer/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"66d9c97d20cb3bfd","mcp_get_code":{"code_sha256":"66d9c97d20cb3bfd"}},{"arxiv_id":"2305.10825","paper":"/paper/diffute-universal-text-editing-diffusion-1","title":"DiffUTE: Universal Text Editing Diffusion Model","date":"2023-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chenhaoxing/DiffUTE","path":"train_diffute_v1.py","file_url":"https://github.com/chenhaoxing/DiffUTE/blob/HEAD/train_diffute_v1.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"65ca128ea9c454b4","mcp_get_code":{"code_sha256":"65ca128ea9c454b4"}},{"arxiv_id":"2209.14855","paper":"/paper/continuous-pde-dynamics-forecasting-with","title":"Continuous PDE Dynamics Forecasting with Implicit Neural Representations","date":"2022-09-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mkirchmeyer/DINo","path":"utils.py","file_url":"https://github.com/mkirchmeyer/DINo/blob/HEAD/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":"02a0972b1b74d1d4","mcp_get_code":{"code_sha256":"02a0972b1b74d1d4"}},{"arxiv_id":"2207.08409","paper":"/paper/tokenmix-rethinking-image-mixing-for-data","title":"TokenMix: Rethinking Image Mixing for Data Augmentation in Vision Transformers","date":"2022-07-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sense-x/tokenmix","path":"tokenmix.py","file_url":"https://github.com/sense-x/tokenmix/blob/HEAD/tokenmix.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1a893ea1848a97cd","mcp_get_code":{"code_sha256":"1a893ea1848a97cd"}},{"arxiv_id":"2203.05556","paper":"/paper/on-embeddings-for-numerical-features-in","title":"On Embeddings for Numerical Features in Tabular Deep Learning","date":"2022-03-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aruberts/TabTransformerTF","path":"tabtransformertf/utils/helper.py","file_url":"https://github.com/aruberts/TabTransformerTF/blob/HEAD/tabtransformertf/utils/helper.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":"d049416624d7d820","mcp_get_code":{"code_sha256":"d049416624d7d820"}},{"arxiv_id":"2201.11349","paper":"/paper/confidence-may-cheat-self-training-on-graph","title":"Confidence May Cheat: Self-Training on Graph Neural Networks under Distribution Shift","date":"2022-01-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bupt-gamma/dr-gst","path":"utils.py","file_url":"https://github.com/bupt-gamma/dr-gst/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"44e0c4604dfd69e9","mcp_get_code":{"code_sha256":"44e0c4604dfd69e9"}},{"arxiv_id":"2106.06989","paper":"/paper/the-deformer-an-order-agnostic-distribution","title":"The DEformer: An Order-Agnostic Distribution Estimating Transformer","date":"2021-06-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"airalcorn2/deformer","path":"deformer.py","file_url":"https://github.com/airalcorn2/deformer/blob/HEAD/deformer.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3237bc793452728d","mcp_get_code":{"code_sha256":"3237bc793452728d"}},{"arxiv_id":"2106.06989","paper":"/paper/the-deformer-an-order-agnostic-distribution","title":"The DEformer: An Order-Agnostic Distribution Estimating Transformer","date":"2021-06-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"airalcorn2/deformer","path":"deformer.py","file_url":"https://github.com/airalcorn2/deformer/blob/HEAD/deformer.py","status":"unverified","verification_level":0,"contract_check":"VIOLATES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b23e01cf590819cb","mcp_get_code":{"code_sha256":"b23e01cf590819cb"}},{"arxiv_id":"2103.14023","paper":"/paper/agentformer-agent-aware-transformers-for","title":"AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting","date":"2021-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Khrylx/AgentFormer","path":"model/agentformer.py","file_url":"https://github.com/Khrylx/AgentFormer/blob/HEAD/model/agentformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"66d9c97d20cb3bfd","mcp_get_code":{"code_sha256":"66d9c97d20cb3bfd"}},{"arxiv_id":"2009.00725","paper":"/paper/conditional-constrained-graph-variational","title":"Conditional Constrained Graph Variational Autoencoders for Molecule Design","date":"2020-09-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"drigoni/ConditionalCGVAE","path":"model/data_augmentation.py","file_url":"https://github.com/drigoni/ConditionalCGVAE/blob/HEAD/model/data_augmentation.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"87fb0cab6105ca64","mcp_get_code":{"code_sha256":"87fb0cab6105ca64"}},{"arxiv_id":"2007.04583","paper":"/paper/graph-convolutional-networks-for-graphs","title":"Graph Convolutional Networks for Graphs Containing Missing Features","date":"2020-07-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"marblet/GCNmf","path":"utils/missing.py","file_url":"https://github.com/marblet/GCNmf/blob/HEAD/utils/missing.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"42592ec530b479d2","mcp_get_code":{"code_sha256":"42592ec530b479d2"}},{"arxiv_id":"2001.01565","paper":"/paper/stance-detection-benchmark-how-robust-is-your","title":"Stance Detection Benchmark: How Robust Is Your Stance Detection?","date":"2020-01-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"UKPLab/mdl-stance-robustness","path":"module/san.py","file_url":"https://github.com/UKPLab/mdl-stance-robustness/blob/HEAD/module/san.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"809cfdb94234ea27","mcp_get_code":{"code_sha256":"809cfdb94234ea27"}},{"arxiv_id":"1809.02630","paper":"/paper/constrained-generation-of-semantically-valid","title":"Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders","date":"2018-09-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Microsoft/constrained-graph-variational-autoencoder","path":"data_augmentation.py","file_url":"https://github.com/Microsoft/constrained-graph-variational-autoencoder/blob/HEAD/data_augmentation.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"87fb0cab6105ca64","mcp_get_code":{"code_sha256":"87fb0cab6105ca64"}}]}