{"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/attention-2","entry":"attention","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":144,"n_papers_ran":92,"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":118,"n_samples_ran":70,"n_samples_fingerprinted":12,"n_places":154,"n_places_pointer_only":66,"by_status":{"ran_honours":4,"ran_violates":0,"ran_draft_wrong":42,"ran_fixture":9,"ran":15,"unverified":48},"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":"2607.13103","paper":"/paper/arxiv-2607-13103","title":"Disentangling Knowledge States with Ability and Proficiency Modeling for Knowledge Tracing","date":null,"month_inferred_from_arxiv_id":"2026-07","title_source":"syntology","repo":"ThaliaBee/PAKT","path":"models/pakt.py","file_url":"https://github.com/ThaliaBee/PAKT/blob/HEAD/models/pakt.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":"72814a15da26ba0d","mcp_get_code":{"code_sha256":"72814a15da26ba0d"}},{"arxiv_id":"2606.21447","paper":"/paper/arxiv-2606-21447","title":"Precision Recall Controllable Radiology Report Generation via Hybrid Natural Language and Clinical Reward Learning","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"98lingchen/MICCAI2026","path":"modules/encoder_decoder.py","file_url":"https://github.com/98lingchen/MICCAI2026/blob/HEAD/modules/encoder_decoder.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":"83045338e170467c","mcp_get_code":{"code_sha256":"83045338e170467c"}},{"arxiv_id":"2606.17584","paper":"/paper/arxiv-2606-17584","title":"Root-Selecting Fixed-Point Inversion for Rectified Flows via Trajectory Straightness","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"seminkim/selfix","path":"src/flux/sampling.py","file_url":"https://github.com/seminkim/selfix/blob/HEAD/src/flux/sampling.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":"9060f70adf38d1aa","mcp_get_code":{"code_sha256":"9060f70adf38d1aa"}},{"arxiv_id":"2605.30965","paper":"/paper/arxiv-2605-30965","title":"ImmersiveTTS: Environment-Aware Text-to-Speech with Multimodal Diffusion Transformer and Domain-Specific Representation Alignment","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"black-forest-labs/flux","path":"src/flux/math.py","file_url":"https://github.com/black-forest-labs/flux/blob/HEAD/src/flux/math.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":"b17d74354ecaf7fe","mcp_get_code":{"code_sha256":"b17d74354ecaf7fe"}},{"arxiv_id":"2605.24064","paper":"/paper/arxiv-2605-24064","title":"Generative Representation Learning on Hyper-relational Knowledge Graphs via Masked Discrete Diffusion","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"bdi-lab/KREPE","path":"model.py","file_url":"https://github.com/bdi-lab/KREPE/blob/HEAD/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"b82d07975b90ffc5","mcp_get_code":{"code_sha256":"b82d07975b90ffc5"}},{"arxiv_id":"2605.14654","paper":"/paper/arxiv-2605-14654","title":"Beyond Instance-Level Self-Supervision in 3D Multi-Modal Medical Imaging","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"Ashespt/TACO","path":"pretrain/TACO/losses/superglue.py","file_url":"https://github.com/Ashespt/TACO/blob/HEAD/pretrain/TACO/losses/superglue.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a3f7220c6bc5e56c","mcp_get_code":{"code_sha256":"a3f7220c6bc5e56c"}},{"arxiv_id":"2604.18215","paper":"/paper/arxiv-2604-18215","title":"Memorize When Needed: Decoupled Memory Control for Spatially Consistent Long-Horizon Video Generation","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"iguoyanjun/Memorize-When-Needed","path":"models/wan_modules/attention_utils.py","file_url":"https://github.com/iguoyanjun/Memorize-When-Needed/blob/HEAD/models/wan_modules/attention_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c0487d5566533bde","mcp_get_code":{"code_sha256":"c0487d5566533bde"}},{"arxiv_id":"2603.15774","paper":"/paper/arxiv-2603-15774","title":"Domain Adaptation Without the Compute Burden for Efficient Whole Slide Image Analysis","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"umarikkar/eWSI","path":"models/wsi_models.py","file_url":"https://github.com/umarikkar/eWSI/blob/HEAD/models/wsi_models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"10cb030b7c0ea1e4","mcp_get_code":{"code_sha256":"10cb030b7c0ea1e4"}},{"arxiv_id":"2603.11462","paper":"/paper/arxiv-2603-11462","title":"Bridging Discrete Marks and Continuous Dynamics: Dual-Path Cross-Interaction for Marked Temporal Point Processes","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"AONE-NLP/NEXTPP","path":"tpp/model/baselayer.py","file_url":"https://github.com/AONE-NLP/NEXTPP/blob/HEAD/tpp/model/baselayer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8abbfdabbdf5e708","mcp_get_code":{"code_sha256":"8abbfdabbdf5e708"}},{"arxiv_id":"2603.04805","paper":"/paper/arxiv-2603-04805","title":"Attention's Gravitational Field: A Power-Law Interpretation of Positional Correlation","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"windyrobin/AGF","path":"attention.py","file_url":"https://github.com/windyrobin/AGF/blob/HEAD/attention.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1f873422e04f7352","mcp_get_code":{"code_sha256":"1f873422e04f7352"}},{"arxiv_id":"2602.15922","paper":"/paper/arxiv-2602-15922","title":"World Action Models are Zero-shot Policies","date":"2026-02-17","month_inferred_from_arxiv_id":null,"title_source":"syntology","repo":"dreamzero0/dreamzero","path":"groot/vla/model/dreamzero/modules/attention.py","file_url":"https://github.com/dreamzero0/dreamzero/blob/HEAD/groot/vla/model/dreamzero/modules/attention.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":"6f8f364555e907ac","mcp_get_code":{"code_sha256":"6f8f364555e907ac"}},{"arxiv_id":"2510.08791","paper":"/paper/arxiv-2510-08791","title":"Alignment, Mining and Fusion: Representation Alignment with Hard Negative Mining and Selective Knowledge Fusion for Medical Visual Question Answering","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"AlexCo1d/AMiF","path":"model/submodule/Gated_Cross_Attention.py","file_url":"https://github.com/AlexCo1d/AMiF/blob/HEAD/model/submodule/Gated_Cross_Attention.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":"8162be9dd8cf7f11","mcp_get_code":{"code_sha256":"8162be9dd8cf7f11"}},{"arxiv_id":"2507.06119","paper":"/paper/omni-video-democratizing-unified-video","title":"Omni-Video: Democratizing Unified Video Understanding and Generation","date":"2025-07-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sais-fuxi/omni-video","path":"omnivideo/modules/attention.py","file_url":"https://github.com/sais-fuxi/omni-video/blob/HEAD/omnivideo/modules/attention.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e46ebd89452d4376","mcp_get_code":{"code_sha256":"e46ebd89452d4376"}},{"arxiv_id":"2506.06295","paper":"/paper/dllm-cache-accelerating-diffusion-large","title":"dLLM-Cache: Accelerating Diffusion Large Language Models with Adaptive Caching","date":"2025-05-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"maomaocun/dLLM-cache","path":"dllm_cache/hooks/cache_hook_Dream.py","file_url":"https://github.com/maomaocun/dLLM-cache/blob/HEAD/dllm_cache/hooks/cache_hook_Dream.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":"e23dc4567306b759","mcp_get_code":{"code_sha256":"e23dc4567306b759"}},{"arxiv_id":"2505.23742","paper":"/paper/magref-masked-guidance-for-any-reference","title":"MAGREF: Masked Guidance for Any-Reference Video Generation","date":"2025-05-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"magref-video/magref","path":"magref/modules/attention.py","file_url":"https://github.com/magref-video/magref/blob/HEAD/magref/modules/attention.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":"2659ec11ef2cea0d","mcp_get_code":{"code_sha256":"2659ec11ef2cea0d"}},{"arxiv_id":"2505.13489","paper":"/paper/contrastive-cross-course-knowledge-tracing","title":"Contrastive Cross-Course Knowledge Tracing via Concept Graph Guided Knowledge Transfer","date":"2025-05-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DQYZHWK/TransKT","path":"model/TransKT.py","file_url":"https://github.com/DQYZHWK/TransKT/blob/HEAD/model/TransKT.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"134e95d193454d9c","mcp_get_code":{"code_sha256":"134e95d193454d9c"}},{"arxiv_id":"2504.02160","paper":"/paper/less-to-more-generalization-unlocking-more","title":"Less-to-More Generalization: Unlocking More Controllability by In-Context Generation","date":"2025-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bytedance/UNO","path":"uno/flux/pipeline.py","file_url":"https://github.com/bytedance/UNO/blob/HEAD/uno/flux/pipeline.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":"030be521b5aae819","mcp_get_code":{"code_sha256":"030be521b5aae819"}},{"arxiv_id":"2503.04151","paper":null,"title":"arXiv:2503.04151","date":null,"month_inferred_from_arxiv_id":"2025-03","title_source":null,"repo":"SubmissionsIn/RML","path":"RML+NRCH/RML_network.py","file_url":"https://github.com/SubmissionsIn/RML/blob/HEAD/RML%2BNRCH/RML_network.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"62a5ef486fa1cb33","mcp_get_code":{"code_sha256":"62a5ef486fa1cb33"}},{"arxiv_id":"2502.17363","paper":"/paper/kv-edit-training-free-image-editing-for","title":"KV-Edit: Training-Free Image Editing for Precise Background Preservation","date":"2025-02-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Xilluill/KV-Edit","path":"models/kv_edit.py","file_url":"https://github.com/Xilluill/KV-Edit/blob/HEAD/models/kv_edit.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":"9e0102b5544f4909","mcp_get_code":{"code_sha256":"9e0102b5544f4909"}},{"arxiv_id":"2502.11079","paper":"/paper/phantom-subject-consistent-video-generation","title":"Phantom: Subject-consistent video generation via cross-modal alignment","date":"2025-02-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"phantom-video/phantom","path":"phantom_wan/modules/attention.py","file_url":"https://github.com/phantom-video/phantom/blob/HEAD/phantom_wan/modules/attention.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":"2659ec11ef2cea0d","mcp_get_code":{"code_sha256":"2659ec11ef2cea0d"}},{"arxiv_id":"2502.04320","paper":"/paper/conceptattention-diffusion-transformers-learn","title":"ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features","date":"2025-02-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"helblazer811/ConceptAttention","path":"concept_attention/flux/dit_block.py","file_url":"https://github.com/helblazer811/ConceptAttention/blob/HEAD/concept_attention/flux/dit_block.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6d1fbe9be366c5ae","mcp_get_code":{"code_sha256":"6d1fbe9be366c5ae"}},{"arxiv_id":"2502.01105","paper":"/paper/layertracer-cognitive-aligned-layered-svg","title":"LayerTracer: Cognitive-Aligned Layered SVG Synthesis via Diffusion Transformer","date":"2025-02-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"showlab/LayerTracer","path":"library/flux_models.py","file_url":"https://github.com/showlab/LayerTracer/blob/HEAD/library/flux_models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b4b29f7369dbaf95","mcp_get_code":{"code_sha256":"b4b29f7369dbaf95"}},{"arxiv_id":"2412.15322","paper":"/paper/taming-multimodal-joint-training-for-high","title":"MMAudio: Taming Multimodal Joint Training for High-Quality Video-to-Audio Synthesis","date":"2024-12-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hkchengrex/MMAudio","path":"mmaudio/model/transformer_layers.py","file_url":"https://github.com/hkchengrex/MMAudio/blob/HEAD/mmaudio/model/transformer_layers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"09143760aafd552c","mcp_get_code":{"code_sha256":"09143760aafd552c"}},{"arxiv_id":"2412.07517","paper":"/paper/fireflow-fast-inversion-of-rectified-flow-for","title":"FireFlow: Fast Inversion of Rectified Flow for Image Semantic Editing","date":"2024-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"holmesshuan/fireflow","path":"src/flux/math.py","file_url":"https://github.com/holmesshuan/fireflow/blob/HEAD/src/flux/math.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":"b17d74354ecaf7fe","mcp_get_code":{"code_sha256":"b17d74354ecaf7fe"}},{"arxiv_id":"2412.03603","paper":"/paper/hunyuanvideo-a-systematic-framework-for-large","title":"HunyuanVideo: A Systematic Framework For Large Video Generative Models","date":"2024-12-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tencent/hunyuanvideo","path":"hyvideo/modules/models.py","file_url":"https://github.com/tencent/hunyuanvideo/blob/HEAD/hyvideo/modules/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"7f6ea85164888083","mcp_get_code":{"code_sha256":"7f6ea85164888083"}},{"arxiv_id":"2411.07527","paper":"/paper/prompt-enhanced-network-for-hateful-meme","title":"Prompt-enhanced Network for Hateful Meme Classification","date":"2024-11-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"juszzi/Pen","path":"Pen/rela_encoder.py","file_url":"https://github.com/juszzi/Pen/blob/HEAD/Pen/rela_encoder.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":"a714be7300922d46","mcp_get_code":{"code_sha256":"a714be7300922d46"}},{"arxiv_id":"2411.04746","paper":"/paper/taming-rectified-flow-for-inversion-and","title":"Taming Rectified Flow for Inversion and Editing","date":"2024-11-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wangjiangshan0725/rf-solver-edit","path":"FLUX_Image_Edit/src/flux/math.py","file_url":"https://github.com/wangjiangshan0725/rf-solver-edit/blob/HEAD/FLUX_Image_Edit/src/flux/math.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b17d74354ecaf7fe","mcp_get_code":{"code_sha256":"b17d74354ecaf7fe"}},{"arxiv_id":"2410.17005","paper":"/paper/hybrid-generative-ai-for-de-novo-design-of-co","title":"Hybrid Generative AI for De Novo Design of Co-Crystals with Enhanced Tabletability","date":"2024-10-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ai-chem/GEMCODE","path":"generative_models/TVAE/Sublayers.py","file_url":"https://github.com/ai-chem/GEMCODE/blob/HEAD/generative_models/TVAE/Sublayers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e7d0985e55efb9e7","mcp_get_code":{"code_sha256":"e7d0985e55efb9e7"}},{"arxiv_id":"2410.10524","paper":"/paper/get-rid-of-task-isolation-a-continuous-multi","title":"Get Rid of Isolation: A Continuous Multi-task Spatio-Temporal Learning Framework","date":"2024-10-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DILab-USTCSZ/CMuST","path":"model/layers.py","file_url":"https://github.com/DILab-USTCSZ/CMuST/blob/HEAD/model/layers.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":"86885c66700abb4c","mcp_get_code":{"code_sha256":"86885c66700abb4c"}},{"arxiv_id":"2410.02604","paper":"/paper/long-sequence-recommendation-models-need","title":"Long-Sequence Recommendation Models Need Decoupled Embeddings","date":"2024-10-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thuml/DARE","path":"model/attention_pytorch.py","file_url":"https://github.com/thuml/DARE/blob/HEAD/model/attention_pytorch.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b5459f1f6325b000","mcp_get_code":{"code_sha256":"b5459f1f6325b000"}},{"arxiv_id":"2409.18073","paper":"/paper/infer-human-s-intentions-before-following","title":"Infer Human's Intentions Before Following Natural Language Instructions","date":"2024-09-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"simon-wan/fiser","path":"networks/layers.py","file_url":"https://github.com/simon-wan/fiser/blob/HEAD/networks/layers.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":"ba5496494629bdbe","mcp_get_code":{"code_sha256":"ba5496494629bdbe"}},{"arxiv_id":"2409.06209","paper":"/paper/adaptive-transformer-modelling-of-density","title":"Adaptive Transformer Modelling of Density Function for Nonparametric Survival Analysis","date":"2024-09-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xinz0419/unisurv","path":"modules/MultiHeadedAttention.py","file_url":"https://github.com/xinz0419/unisurv/blob/HEAD/modules/MultiHeadedAttention.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"916dd244b4ead0ac","mcp_get_code":{"code_sha256":"916dd244b4ead0ac"}},{"arxiv_id":"2409.00587","paper":"/paper/flux-that-plays-music","title":"FLUX that Plays Music","date":"2024-09-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"feizc/fluxmusic","path":"modules/layers.py","file_url":"https://github.com/feizc/fluxmusic/blob/HEAD/modules/layers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b17d74354ecaf7fe","mcp_get_code":{"code_sha256":"b17d74354ecaf7fe"}},{"arxiv_id":"2408.16345","paper":"/paper/the-unreasonable-ineffectiveness-of-nucleus","title":"The Unreasonable Ineffectiveness of Nucleus Sampling on Mitigating Text Memorization","date":"2024-08-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lukaborec/memorization-nucleus-sampling","path":"memorization/models/transformer.py","file_url":"https://github.com/lukaborec/memorization-nucleus-sampling/blob/HEAD/memorization/models/transformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"36da153ced354d5d","mcp_get_code":{"code_sha256":"36da153ced354d5d"}},{"arxiv_id":"2407.18525","paper":"/paper/is-larger-always-better-evaluating-and","title":"ClinicRealm: Re-evaluating Large Language Models with Conventional Machine Learning for Non-Generative Clinical Prediction Tasks","date":"2024-07-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yhzhu99/ehr-llm-benchmark","path":"src/structured_ehr/models/aicare.py","file_url":"https://github.com/yhzhu99/ehr-llm-benchmark/blob/HEAD/src/structured_ehr/models/aicare.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"916dd244b4ead0ac","mcp_get_code":{"code_sha256":"916dd244b4ead0ac"}},{"arxiv_id":"2406.12465","paper":"/paper/rigl-a-unified-reciprocal-approach-for","title":"RIGL: A Unified Reciprocal Approach for Tracing the Independent and Group Learning Processes","date":"2024-06-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"labyrinthineleo/rigl","path":"models/RIGL.py","file_url":"https://github.com/labyrinthineleo/rigl/blob/HEAD/models/RIGL.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"826a8351b522c054","mcp_get_code":{"code_sha256":"826a8351b522c054"}},{"arxiv_id":"2405.17394","paper":"/paper/the-expressive-capacity-of-state-space-models","title":"The Expressive Capacity of State Space Models: A Formal Language Perspective","date":"2024-05-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"satwik77/Transformer-Formal-Languages","path":"src/components/self_attention.py","file_url":"https://github.com/satwik77/Transformer-Formal-Languages/blob/HEAD/src/components/self_attention.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6b4d15e6e8d8778f","mcp_get_code":{"code_sha256":"6b4d15e6e8d8778f"}},{"arxiv_id":"2405.10305","paper":"/paper/4d-panoptic-scene-graph-generation-1","title":"4D Panoptic Scene Graph Generation","date":"2024-05-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jingkang50/psg4d","path":"pc_seg/backbone/transformer.py","file_url":"https://github.com/jingkang50/psg4d/blob/HEAD/pc_seg/backbone/transformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6693fd4e3fbcd72f","mcp_get_code":{"code_sha256":"6693fd4e3fbcd72f"}},{"arxiv_id":"2405.09539","paper":"/paper/mmfusion-multi-modality-diffusion-model-for","title":"MMFusion: Multi-modality Diffusion Model for Lymph Node Metastasis Diagnosis in Esophageal Cancer","date":"2024-05-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wuchengyu123/mmfusion","path":"model/cross_relation_atten.py","file_url":"https://github.com/wuchengyu123/mmfusion/blob/HEAD/model/cross_relation_atten.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bb3f06949955297c","mcp_get_code":{"code_sha256":"bb3f06949955297c"}},{"arxiv_id":"2404.15729","paper":"/paper/gradformer-graph-transformer-with-exponential","title":"Gradformer: Graph Transformer with Exponential Decay","date":"2024-04-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LiuChuang0059/Gradformer","path":"model/Attention.py","file_url":"https://github.com/LiuChuang0059/Gradformer/blob/HEAD/model/Attention.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2936d5ee68562846","mcp_get_code":{"code_sha256":"2936d5ee68562846"}},{"arxiv_id":"2404.13478","paper":"/paper/deep-se-3-equivariant-geometric-reasoning-for","title":"Deep SE(3)-Equivariant Geometric Reasoning for Precise Placement Tasks","date":"2024-04-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"70ba9ecc422a6a48","mcp_get_code":{"code_sha256":"70ba9ecc422a6a48"}},{"arxiv_id":"2404.13039","paper":"/paper/lapa-latent-prompt-assist-model-for-medical","title":"LaPA: Latent Prompt Assist Model For Medical Visual Question Answering","date":"2024-04-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"garygutc/lapa_model","path":"m3ae/modules/language_encoders/bert_model.py","file_url":"https://github.com/garygutc/lapa_model/blob/HEAD/m3ae/modules/language_encoders/bert_model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6eef58b15279e099","mcp_get_code":{"code_sha256":"6eef58b15279e099"}},{"arxiv_id":"2403.06801","paper":"/paper/ct2rep-automated-radiology-report-generation","title":"CT2Rep: Automated Radiology Report Generation for 3D Medical Imaging","date":"2024-03-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ibrahimethemhamamci/ct2rep","path":"CT2Rep/modules/encoder_decoder.py","file_url":"https://github.com/ibrahimethemhamamci/ct2rep/blob/HEAD/CT2Rep/modules/encoder_decoder.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":"83045338e170467c","mcp_get_code":{"code_sha256":"83045338e170467c"}},{"arxiv_id":"2403.05396","paper":"/paper/histgen-histopathology-report-generation-via","title":"HistGen: Histopathology Report Generation via Local-Global Feature Encoding and Cross-modal Context Interaction","date":"2024-03-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dddavid4real/HistGen","path":"models/PlainTransformer_Modules.py","file_url":"https://github.com/dddavid4real/HistGen/blob/HEAD/models/PlainTransformer_Modules.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":"269003e7c21d565d","mcp_get_code":{"code_sha256":"269003e7c21d565d"}},{"arxiv_id":"2402.05391","paper":"/paper/knowledge-graphs-meet-multi-modal-learning-a","title":"Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey","date":"2024-02-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zjukg/snag","path":"SNAG_MMEA/model/EVA_tools.py","file_url":"https://github.com/zjukg/snag/blob/HEAD/SNAG_MMEA/model/EVA_tools.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e7d0985e55efb9e7","mcp_get_code":{"code_sha256":"e7d0985e55efb9e7"}},{"arxiv_id":"2402.01401","paper":"/paper/zero-shot-machine-unlearning-at-scale-via","title":"An Information Theoretic Approach to Machine Unlearning","date":"2024-02-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jwf40/zeroshot-unlearning-at-scale","path":"src/gkt.py","file_url":"https://github.com/jwf40/zeroshot-unlearning-at-scale/blob/HEAD/src/gkt.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b817aa66879a905d","mcp_get_code":{"code_sha256":"b817aa66879a905d"}},{"arxiv_id":"2402.01713","paper":"/paper/prompting-large-language-models-for-zero-shot-1","title":"Prompting Large Language Models for Zero-Shot Clinical Prediction with Structured Longitudinal Electronic Health Record Data","date":"2024-01-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yhzhu99/llm4healthcare","path":"AICare-baselines/models/aicare.py","file_url":"https://github.com/yhzhu99/llm4healthcare/blob/HEAD/AICare-baselines/models/aicare.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"20bb0ff4a2d77a03","mcp_get_code":{"code_sha256":"20bb0ff4a2d77a03"}},{"arxiv_id":"2401.15478","paper":"/paper/product-manifold-representations-for-learning","title":"Product Manifold Representations for Learning on Biological Pathways","date":"2024-01-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mcneela/mixed-curvature-gcn","path":"layers/hyp_layers.py","file_url":"https://github.com/mcneela/mixed-curvature-gcn/blob/HEAD/layers/hyp_layers.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"916dd244b4ead0ac","mcp_get_code":{"code_sha256":"916dd244b4ead0ac"}},{"arxiv_id":"2310.07477","paper":"/paper/gmocat-a-graph-enhanced-multi-objective","title":"GMOCAT: A Graph-Enhanced Multi-Objective Method for Computerized Adaptive Testing","date":"2023-10-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"justarter/gmocat","path":"function/GCAT.py","file_url":"https://github.com/justarter/gmocat/blob/HEAD/function/GCAT.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b0414f8c05368b1f","mcp_get_code":{"code_sha256":"b0414f8c05368b1f"}},{"arxiv_id":"2310.07259","paper":"/paper/uncovering-hidden-connections-iterative","title":"Uncovering Hidden Connections: Iterative Search and Reasoning for Video-grounded Dialog","date":"2023-10-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Hyu-Zhang/ISR","path":"model/modules.py","file_url":"https://github.com/Hyu-Zhang/ISR/blob/HEAD/model/modules.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0cf22e1991ad4ef4","mcp_get_code":{"code_sha256":"0cf22e1991ad4ef4"}},{"arxiv_id":"2310.03502","paper":"/paper/kandinsky-an-improved-text-to-image-synthesis","title":"Kandinsky: an Improved Text-to-Image Synthesis with Image Prior and Latent Diffusion","date":"2023-10-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ai-forever/Kandinsky-2","path":"kandinsky2/model/text2im_model2_1.py","file_url":"https://github.com/ai-forever/Kandinsky-2/blob/HEAD/kandinsky2/model/text2im_model2_1.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fe30479834a53868","mcp_get_code":{"code_sha256":"fe30479834a53868"}},{"arxiv_id":"2310.01680","paper":null,"title":"arXiv:2310.01680","date":null,"month_inferred_from_arxiv_id":"2023-10","title_source":null,"repo":"zshyang/kaf","path":"network/superglue.py","file_url":"https://github.com/zshyang/kaf/blob/HEAD/network/superglue.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"70702edb93d9116a","mcp_get_code":{"code_sha256":"70702edb93d9116a"}},{"arxiv_id":"2310.01082","paper":"/paper/linear-attention-is-maybe-all-you-need-to","title":"Linear attention is (maybe) all you need (to understand transformer optimization)","date":"2023-10-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chengxiang/lineartransformer","path":"linear_transformer.py","file_url":"https://github.com/chengxiang/lineartransformer/blob/HEAD/linear_transformer.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4a13c21dc4551a1c","mcp_get_code":{"code_sha256":"4a13c21dc4551a1c"}},{"arxiv_id":"2309.10255","paper":"/paper/rgb-based-category-level-object-pose","title":"RGB-based Category-level Object Pose Estimation via Decoupled Metric Scale Recovery","date":"2023-09-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"goldoak/DMSR","path":"lib/adaptor.py","file_url":"https://github.com/goldoak/DMSR/blob/HEAD/lib/adaptor.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"936b3157dfb41a61","mcp_get_code":{"code_sha256":"936b3157dfb41a61"}},{"arxiv_id":"2308.11072","paper":"/paper/ted-spad-temporal-distinctiveness-for-self","title":"TeD-SPAD: Temporal Distinctiveness for Self-supervised Privacy-preservation for video Anomaly Detection","date":"2023-08-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ucf-crcv/ted-spad","path":"anomaly_detection_mgfn/models/mgfn.py","file_url":"https://github.com/ucf-crcv/ted-spad/blob/HEAD/anomaly_detection_mgfn/models/mgfn.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"03b067c6fb97d661","mcp_get_code":{"code_sha256":"03b067c6fb97d661"}},{"arxiv_id":"2307.08097","paper":"/paper/easytpp-towards-open-benchmarking-the","title":"EasyTPP: Towards Open Benchmarking Temporal Point Processes","date":"2023-07-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ant-research/easytemporalpointprocess","path":"easy_tpp/model/baselayer.py","file_url":"https://github.com/ant-research/easytemporalpointprocess/blob/HEAD/easy_tpp/model/baselayer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"8abbfdabbdf5e708","mcp_get_code":{"code_sha256":"8abbfdabbdf5e708"}},{"arxiv_id":"2305.16646","paper":"/paper/language-models-can-improve-event-prediction-1","title":"Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoning","date":"2023-05-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ant-research/EasyTemporalPointProcess","path":"easy_tpp/model/baselayer.py","file_url":"https://github.com/ant-research/EasyTemporalPointProcess/blob/HEAD/easy_tpp/model/baselayer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"8abbfdabbdf5e708","mcp_get_code":{"code_sha256":"8abbfdabbdf5e708"}},{"arxiv_id":"2305.05610","paper":"/paper/can-point-cloud-networks-learn-statistical","title":"Can point cloud networks learn statistical shape models of anatomies?","date":"2023-05-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jadie1/pointcompletionssm","path":"utils/model_utils.py","file_url":"https://github.com/jadie1/pointcompletionssm/blob/HEAD/utils/model_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"243fb2f0c671bcd0","mcp_get_code":{"code_sha256":"243fb2f0c671bcd0"}},{"arxiv_id":"2304.02008","paper":"/paper/gluestick-robust-image-matching-by-sticking","title":"GlueStick: Robust Image Matching by Sticking Points and Lines Together","date":"2023-04-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Golbstein/GlueStick-tensorflow","path":"gluestick.py","file_url":"https://github.com/Golbstein/GlueStick-tensorflow/blob/HEAD/gluestick.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"35ddab8839071886","mcp_get_code":{"code_sha256":"35ddab8839071886"}},{"arxiv_id":"2304.02008","paper":"/paper/gluestick-robust-image-matching-by-sticking","title":"GlueStick: Robust Image Matching by Sticking Points and Lines Together","date":"2023-04-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cvg/GlueStick","path":"gluestick/models/gluestick.py","file_url":"https://github.com/cvg/GlueStick/blob/HEAD/gluestick/models/gluestick.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5317c011246420e0","mcp_get_code":{"code_sha256":"5317c011246420e0"}},{"arxiv_id":"2303.14348","paper":"/paper/zero-shot-everything-sketch-based-image","title":"Zero-Shot Everything Sketch-Based Image Retrieval, and in Explainable Style","date":"2023-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"buptLinfy/ZSE-SBIR","path":"model/model.py","file_url":"https://github.com/buptLinfy/ZSE-SBIR/blob/HEAD/model/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3eed1a7a6d48923a","mcp_get_code":{"code_sha256":"3eed1a7a6d48923a"}},{"arxiv_id":"2303.10975","paper":"/paper/vimi-vehicle-infrastructure-multi-view","title":"VIMI: Vehicle-Infrastructure Multi-view Intermediate Fusion for Camera-based 3D Object Detection","date":"2023-03-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bosszhe/vimi","path":"mmdet3d/models/detectors/vicfuser_voxel/vimi.py","file_url":"https://github.com/bosszhe/vimi/blob/HEAD/mmdet3d/models/detectors/vicfuser_voxel/vimi.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":"e9e0804910947f94","mcp_get_code":{"code_sha256":"e9e0804910947f94"}},{"arxiv_id":"2303.10323","paper":"/paper/dynamic-graph-enhanced-contrastive-learning","title":"Dynamic Graph Enhanced Contrastive Learning for Chest X-ray Report Generation","date":"2023-03-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mlii0117/dcl","path":"models/tagencoder.py","file_url":"https://github.com/mlii0117/dcl/blob/HEAD/models/tagencoder.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"83045338e170467c","mcp_get_code":{"code_sha256":"83045338e170467c"}},{"arxiv_id":"2303.07180","paper":"/paper/incomplete-multi-view-multi-label-learning","title":"Incomplete Multi-View Multi-Label Learning via Label-Guided Masked View- and Category-Aware Transformers","date":"2023-03-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"justsmart/LMVCAT","path":"model.py","file_url":"https://github.com/justsmart/LMVCAT/blob/HEAD/model.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":"7dbb70f699830878","mcp_get_code":{"code_sha256":"7dbb70f699830878"}},{"arxiv_id":"2303.05376","paper":"/paper/pc-jedi-diffusion-for-particle-cloud","title":"PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physics","date":"2023-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rodem-hep/pc-jedi","path":"src/models/transformers.py","file_url":"https://github.com/rodem-hep/pc-jedi/blob/HEAD/src/models/transformers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"37f26eb1c05ac461","mcp_get_code":{"code_sha256":"37f26eb1c05ac461"}},{"arxiv_id":"2302.06881","paper":"/paper/simplekt-a-simple-but-tough-to-beat-baseline","title":"simpleKT: A Simple But Tough-to-Beat Baseline for Knowledge Tracing","date":"2023-02-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pykt-team/pykt-toolkit","path":"pykt/models/simplekt.py","file_url":"https://github.com/pykt-team/pykt-toolkit/blob/HEAD/pykt/models/simplekt.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5bd62b8707ef0480","mcp_get_code":{"code_sha256":"5bd62b8707ef0480"}},{"arxiv_id":"2302.06881","paper":"/paper/simplekt-a-simple-but-tough-to-beat-baseline","title":"simpleKT: A Simple But Tough-to-Beat Baseline for Knowledge Tracing","date":"2023-02-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pykt-team/pykt-toolkit","path":"pykt/models/simplekt.py","file_url":"https://github.com/pykt-team/pykt-toolkit/blob/HEAD/pykt/models/simplekt.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e05cd8a1686715a5","mcp_get_code":{"code_sha256":"e05cd8a1686715a5"}},{"arxiv_id":"2302.02738","paper":"/paper/increase-inductive-graph-representation","title":"INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Kriging","date":"2023-02-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhengchuanpan/increase","path":"Beijing/INCREASE/model.py","file_url":"https://github.com/zhengchuanpan/increase/blob/HEAD/Beijing/INCREASE/model.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":"8bcde2184940ecb4","mcp_get_code":{"code_sha256":"8bcde2184940ecb4"}},{"arxiv_id":"2210.12748","paper":"/paper/sc-wls-towards-interpretable-feed-forward","title":"SC-wLS: Towards Interpretable Feed-forward Camera Re-localization","date":"2022-10-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"70702edb93d9116a","mcp_get_code":{"code_sha256":"70702edb93d9116a"}},{"arxiv_id":"2210.08196","paper":"/paper/deep-regression-unlearning","title":"Deep Regression Unlearning","date":"2022-10-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ayu987/deep-regression-unlearning","path":"unlearn.py","file_url":"https://github.com/ayu987/deep-regression-unlearning/blob/HEAD/unlearn.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b817aa66879a905d","mcp_get_code":{"code_sha256":"b817aa66879a905d"}},{"arxiv_id":"2210.06609","paper":"/paper/trafficgen-learning-to-generate-diverse-and","title":"TrafficGen: Learning to Generate Diverse and Realistic Traffic Scenarios","date":null,"month_inferred_from_arxiv_id":"2022-10","title_source":"archive","repo":"metadriverse/trafficgen","path":"trafficgen/act/utils/model_utils.py","file_url":"https://github.com/metadriverse/trafficgen/blob/HEAD/trafficgen/act/utils/model_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":"b221aff3b6d88bb6","mcp_get_code":{"code_sha256":"b221aff3b6d88bb6"}},{"arxiv_id":"2210.01753","paper":"/paper/hypro-a-hybridly-normalized-probabilistic","title":"HYPRO: A Hybridly Normalized Probabilistic Model for Long-Horizon Prediction of Event Sequences","date":"2022-10-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alipay/hypro_tpp","path":"hypro_tpp/models/layers.py","file_url":"https://github.com/alipay/hypro_tpp/blob/HEAD/hypro_tpp/models/layers.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"8abbfdabbdf5e708","mcp_get_code":{"code_sha256":"8abbfdabbdf5e708"}},{"arxiv_id":"2208.10449","paper":"/paper/scone-surface-coverage-optimization-in","title":"SCONE: Surface Coverage Optimization in Unknown Environments by Volumetric Integration","date":"2022-08-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Anttwo/SCONE","path":"SCONE/Attention.py","file_url":"https://github.com/Anttwo/SCONE/blob/HEAD/SCONE/Attention.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e55f9687ff391f5e","mcp_get_code":{"code_sha256":"e55f9687ff391f5e"}},{"arxiv_id":"2207.08625","paper":"/paper/unifying-event-detection-and-captioning-as","title":"Unifying Event Detection and Captioning as Sequence Generation via Pre-Training","date":"2022-07-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"QiQAng/UEDVC","path":"modules/transformer.py","file_url":"https://github.com/QiQAng/UEDVC/blob/HEAD/modules/transformer.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":"354006a4908f58bd","mcp_get_code":{"code_sha256":"354006a4908f58bd"}},{"arxiv_id":"2207.08549","paper":"/paper/dense-cross-query-and-support-attention","title":"Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentation","date":"2022-07-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pawn-sxy/DCAMA","path":"model/DCAMA.py","file_url":"https://github.com/pawn-sxy/DCAMA/blob/HEAD/model/DCAMA.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b008136cddaa0337","mcp_get_code":{"code_sha256":"b008136cddaa0337"}},{"arxiv_id":"2207.07372","paper":"/paper/3d-instances-as-1d-kernels","title":"3D Instances as 1D Kernels","date":"2022-07-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"W1zheng/DKNet","path":"backbone/transformer.py","file_url":"https://github.com/W1zheng/DKNet/blob/HEAD/backbone/transformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6693fd4e3fbcd72f","mcp_get_code":{"code_sha256":"6693fd4e3fbcd72f"}},{"arxiv_id":"2207.05500","paper":"/paper/modality-aware-contrastive-instance-learning","title":"Modality-Aware Contrastive Instance Learning with Self-Distillation for Weakly-Supervised Audio-Visual Violence Detection","date":"2022-07-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JustinYuu/MACIL_SD","path":"Transformer.py","file_url":"https://github.com/JustinYuu/MACIL_SD/blob/HEAD/Transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d549060ffa0a0179","mcp_get_code":{"code_sha256":"d549060ffa0a0179"}},{"arxiv_id":"2207.02206","paper":"/paper/segmenting-moving-objects-via-an-object","title":"Segmenting Moving Objects via an Object-Centric Layered Representation","date":"2022-07-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Jyxarthur/OCLR_model","path":"models/oclr.py","file_url":"https://github.com/Jyxarthur/OCLR_model/blob/HEAD/models/oclr.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"86341ad6b5c2c7a4","mcp_get_code":{"code_sha256":"86341ad6b5c2c7a4"}},{"arxiv_id":"2206.07710","paper":"/paper/planarrecon-real-time-3d-plane-detection-and-1","title":"PlanarRecon: Real-time 3D Plane Detection and Reconstruction from Posed Monocular Videos","date":"2022-06-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"neu-vi/PlanarRecon","path":"models/matching.py","file_url":"https://github.com/neu-vi/PlanarRecon/blob/HEAD/models/matching.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"70702edb93d9116a","mcp_get_code":{"code_sha256":"70702edb93d9116a"}},{"arxiv_id":"2204.04046","paper":"/paper/kcd-knowledge-walks-and-textual-cues-enhanced-1","title":"KCD: Knowledge Walks and Textual Cues Enhanced Political Perspective Detection in News Media","date":"2022-04-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wenqian-zhang/kcd","path":"main/allmain/KSD_GatedRGCN.py","file_url":"https://github.com/wenqian-zhang/kcd/blob/HEAD/main/allmain/KSD_GatedRGCN.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"71873696ca5ed64c","mcp_get_code":{"code_sha256":"71873696ca5ed64c"}},{"arxiv_id":"2203.16910","paper":"/paper/end-to-end-trajectory-distribution-prediction","title":"End-to-End Trajectory Distribution Prediction Based on Occupancy Grid Maps","date":"2022-03-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Kguo-cs/TDOR","path":"model/transformer.py","file_url":"https://github.com/Kguo-cs/TDOR/blob/HEAD/model/transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fcdea34a2fb23b2d","mcp_get_code":{"code_sha256":"fcdea34a2fb23b2d"}},{"arxiv_id":"2203.16618","paper":"/paper/end-to-end-document-recognition-and","title":"End-to-end Document Recognition and Understanding with Dessurt","date":"2022-03-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"herobd/dessurt","path":"model/attention.py","file_url":"https://github.com/herobd/dessurt/blob/HEAD/model/attention.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"89ca2234fcb6c0d7","mcp_get_code":{"code_sha256":"89ca2234fcb6c0d7"}},{"arxiv_id":"2203.07628","paper":"/paper/p-stmo-pre-trained-spatial-temporal-many-to","title":"P-STMO: Pre-Trained Spatial Temporal Many-to-One Model for 3D Human Pose Estimation","date":"2022-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"paTRICK-swk/P-STMO","path":"model/stmo_pretrain.py","file_url":"https://github.com/paTRICK-swk/P-STMO/blob/HEAD/model/stmo_pretrain.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cbe07626e47b77c3","mcp_get_code":{"code_sha256":"cbe07626e47b77c3"}},{"arxiv_id":"2203.07216","paper":"/paper/a-novel-perspective-to-look-at-attention-bi","title":"A Novel Perspective to Look At Attention: Bi-level Attention-based Explainable Topic Modeling for News Classification","date":"2022-03-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Ruixinhua/BATM","path":"models/nc_bi_attention.py","file_url":"https://github.com/Ruixinhua/BATM/blob/HEAD/models/nc_bi_attention.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":"f49f69ecc50ef233","mcp_get_code":{"code_sha256":"f49f69ecc50ef233"}},{"arxiv_id":"2202.04298","paper":"/paper/image-difference-captioning-with-pre-training","title":"Image Difference Captioning with Pre-training and Contrastive Learning","date":"2022-02-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"875f05a473ae1687","mcp_get_code":{"code_sha256":"875f05a473ae1687"}},{"arxiv_id":"2201.05629","paper":"/paper/zero-shot-machine-unlearning","title":"Zero-Shot Machine Unlearning","date":"2022-01-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ayu987/zero-shot-unlearning","path":"unlearn.py","file_url":"https://github.com/ayu987/zero-shot-unlearning/blob/HEAD/unlearn.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b817aa66879a905d","mcp_get_code":{"code_sha256":"b817aa66879a905d"}},{"arxiv_id":"2112.12053","paper":"/paper/multi-view-partial-mvp-point-cloud-challenge","title":"Multi-View Partial (MVP) Point Cloud Challenge 2021 on Completion and Registration: Methods and Results","date":"2021-12-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"paul007pl/MVP_Benchmark","path":"completion/model_utils.py","file_url":"https://github.com/paul007pl/MVP_Benchmark/blob/HEAD/completion/model_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":"243fb2f0c671bcd0","mcp_get_code":{"code_sha256":"243fb2f0c671bcd0"}},{"arxiv_id":"2112.12053","paper":"/paper/multi-view-partial-mvp-point-cloud-challenge","title":"Multi-View Partial (MVP) Point Cloud Challenge 2021 on Completion and Registration: Methods and Results","date":"2021-12-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"paul007pl/MVP_Benchmark","path":"registration/model_utils.py","file_url":"https://github.com/paul007pl/MVP_Benchmark/blob/HEAD/registration/model_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":"33f41ccdc70d7e4b","mcp_get_code":{"code_sha256":"33f41ccdc70d7e4b"}},{"arxiv_id":"2112.08171","paper":"/paper/text-gestalt-stroke-aware-scene-text-image","title":"Text Gestalt: Stroke-Aware Scene Text Image Super-Resolution","date":"2021-12-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fudanvi/fudanocr","path":"stroke-level-decomposition/model/transformer.py","file_url":"https://github.com/fudanvi/fudanocr/blob/HEAD/stroke-level-decomposition/model/transformer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"58f1bb86e3563501","mcp_get_code":{"code_sha256":"58f1bb86e3563501"}},{"arxiv_id":"2112.05682","paper":"/paper/self-attention-does-not-need-o-n-2-memory","title":"Self-attention Does Not Need $O(n^2)$ Memory","date":"2021-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lucidrains/memory-efficient-attention-pytorch","path":"memory_efficient_attention_pytorch/memory_efficient_attention.py","file_url":"https://github.com/lucidrains/memory-efficient-attention-pytorch/blob/HEAD/memory_efficient_attention_pytorch/memory_efficient_attention.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"640155bcba1f2f92","mcp_get_code":{"code_sha256":"640155bcba1f2f92"}},{"arxiv_id":"2111.12374","paper":"/paper/mm-pyramid-multimodal-pyramid-attentional","title":"MM-Pyramid: Multimodal Pyramid Attentional Network for Audio-Visual Event Localization and Video Parsing","date":"2021-11-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JustinYuu/MM_Pyramid","path":"nets/Transformer.py","file_url":"https://github.com/JustinYuu/MM_Pyramid/blob/HEAD/nets/Transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d549060ffa0a0179","mcp_get_code":{"code_sha256":"d549060ffa0a0179"}},{"arxiv_id":"2110.09994","paper":"/paper/dpfm-deep-partial-functional-maps","title":"DPFM: Deep Partial Functional Maps","date":"2021-10-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pvnieo/dpfm","path":"dpfm/model.py","file_url":"https://github.com/pvnieo/dpfm/blob/HEAD/dpfm/model.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"926403763962cd8b","mcp_get_code":{"code_sha256":"926403763962cd8b"}},{"arxiv_id":"2110.02210","paper":"/paper/mix3d-out-of-context-data-augmentation-for-3d","title":"Mix3D: Out-of-Context Data Augmentation for 3D Scenes","date":"2021-10-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"TejasAnvekar/Mahalanobis-k-NN","path":"DeepUME/model.py","file_url":"https://github.com/TejasAnvekar/Mahalanobis-k-NN/blob/HEAD/DeepUME/model.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"70ba9ecc422a6a48","mcp_get_code":{"code_sha256":"70ba9ecc422a6a48"}},{"arxiv_id":"2110.01191","paper":"/paper/3d-transformer-molecular-representation-with","title":"Molformer: Motif-based Transformer on 3D Heterogeneous Molecular Graphs","date":"2021-10-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"smiles724/molformer","path":"model/tr_spe.py","file_url":"https://github.com/smiles724/molformer/blob/HEAD/model/tr_spe.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"916dd244b4ead0ac","mcp_get_code":{"code_sha256":"916dd244b4ead0ac"}},{"arxiv_id":"2110.01191","paper":"/paper/3d-transformer-molecular-representation-with","title":"Molformer: Motif-based Transformer on 3D Heterogeneous Molecular Graphs","date":"2021-10-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"smiles724/molformer","path":"model/tr_msa.py","file_url":"https://github.com/smiles724/molformer/blob/HEAD/model/tr_msa.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2c84d508b80f34a0","mcp_get_code":{"code_sha256":"2c84d508b80f34a0"}},{"arxiv_id":"2108.10723","paper":"/paper/improving-3d-object-detection-with-channel","title":"Improving 3D Object Detection with Channel-wise Transformer","date":"2021-08-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hlsheng1/ct3d","path":"pcdet/models/roi_heads/ct3d_head.py","file_url":"https://github.com/hlsheng1/ct3d/blob/HEAD/pcdet/models/roi_heads/ct3d_head.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":"f93d42eb62cfe6e7","mcp_get_code":{"code_sha256":"f93d42eb62cfe6e7"}},{"arxiv_id":"2108.07386","paper":"/paper/bobcat-bilevel-optimization-based","title":"BOBCAT: Bilevel Optimization-Based Computerized Adaptive Testing","date":"2021-08-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"arghosh/NeurIPSEducation2020","path":"model_task_1_2.py","file_url":"https://github.com/arghosh/NeurIPSEducation2020/blob/HEAD/model_task_1_2.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1abee56c1c82b3be","mcp_get_code":{"code_sha256":"1abee56c1c82b3be"}},{"arxiv_id":"2106.01342","paper":"/paper/saint-improved-neural-networks-for-tabular","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","date":"2021-06-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ogunlao/saint","path":"models/transformer.py","file_url":"https://github.com/ogunlao/saint/blob/HEAD/models/transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"525ff13ecf3ecd51","mcp_get_code":{"code_sha256":"525ff13ecf3ecd51"}},{"arxiv_id":"2106.00920","paper":"/paper/dialograph-incorporating-interpretable-1","title":"DialoGraph: Incorporating Interpretable Strategy-Graph Networks into Negotiation Dialogues","date":"2021-06-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rishabhjoshi/DialoGraph_ICLR21","path":"src/bot/transformer_model.py","file_url":"https://github.com/rishabhjoshi/DialoGraph_ICLR21/blob/HEAD/src/bot/transformer_model.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":"f6ec93136ba6135d","mcp_get_code":{"code_sha256":"f6ec93136ba6135d"}},{"arxiv_id":"2105.15106","paper":"/paper/a-survey-of-knowledge-tracing","title":"A Survey of Knowledge Tracing: Models, Variants, and Applications","date":"2021-05-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bigdata-ustc/EduKTM","path":"EduKTM/AKT/AKTNet.py","file_url":"https://github.com/bigdata-ustc/EduKTM/blob/HEAD/EduKTM/AKT/AKTNet.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":"4745dc0811db6c27","mcp_get_code":{"code_sha256":"4745dc0811db6c27"}},{"arxiv_id":"2105.14477","paper":"/paper/towards-diverse-paragraph-captioning-for","title":"Towards Diverse Paragraph Captioning for Untrimmed Videos","date":"2021-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"syuqings/video-paragraph","path":"modules/common.py","file_url":"https://github.com/syuqings/video-paragraph/blob/HEAD/modules/common.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6506293d511b80a3","mcp_get_code":{"code_sha256":"6506293d511b80a3"}},{"arxiv_id":"2104.13897","paper":"/paper/inpainting-transformer-for-anomaly-detection","title":"Inpainting Transformer for Anomaly Detection","date":"2021-04-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jhy12/inpainting-transformer","path":"model.py","file_url":"https://github.com/jhy12/inpainting-transformer/blob/HEAD/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bcec61b27800543d","mcp_get_code":{"code_sha256":"bcec61b27800543d"}},{"arxiv_id":"2104.09770","paper":"/paper/m2tr-multi-modal-multi-scale-transformers-for","title":"M2TR: Multi-modal Multi-scale Transformers for Deepfake Detection","date":"2021-04-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wangjk666/M2TR-Multi-modal-Multi-scale-Transformers-for-Deepfake-Detection","path":"M2TR/models/m2tr.py","file_url":"https://github.com/wangjk666/M2TR-Multi-modal-Multi-scale-Transformers-for-Deepfake-Detection/blob/HEAD/M2TR/models/m2tr.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"087ffc0296348717","mcp_get_code":{"code_sha256":"087ffc0296348717"}},{"arxiv_id":"2104.03589","paper":"/paper/pqa-perceptual-question-answering","title":"PQA: Perceptual Question Answering","date":"2021-04-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qugank/pqa.github.io","path":"code/model/ContextAttention/attention.py","file_url":"https://github.com/qugank/pqa.github.io/blob/HEAD/code/model/ContextAttention/attention.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":"a89502710fa48aa0","mcp_get_code":{"code_sha256":"a89502710fa48aa0"}},{"arxiv_id":"2103.04256","paper":"/paper/robust-point-cloud-registration-framework","title":"Robust Point Cloud Registration Framework Based on Deep Graph Matching","date":"2021-03-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fukexue/RGM","path":"models/Net_noais.py","file_url":"https://github.com/fukexue/RGM/blob/HEAD/models/Net_noais.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":"c032b5c8af4d2299","mcp_get_code":{"code_sha256":"c032b5c8af4d2299"}},{"arxiv_id":"2102.07108","paper":"/paper/cate-computation-aware-neural-architecture","title":"CATE: Computation-aware Neural Architecture Encoding with Transformers","date":"2021-02-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MSU-MLSys-Lab/CATE","path":"layers/transformer.py","file_url":"https://github.com/MSU-MLSys-Lab/CATE/blob/HEAD/layers/transformer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"bbc7c22f9d8732dc","mcp_get_code":{"code_sha256":"bbc7c22f9d8732dc"}},{"arxiv_id":"2010.16056","paper":"/paper/generating-radiology-reports-via-memory","title":"Generating Radiology Reports via Memory-driven Transformer","date":"2020-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cuhksz-nlp/R2Gen","path":"modules/encoder_decoder.py","file_url":"https://github.com/cuhksz-nlp/R2Gen/blob/HEAD/modules/encoder_decoder.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":"83045338e170467c","mcp_get_code":{"code_sha256":"83045338e170467c"}},{"arxiv_id":"2010.13924","paper":"/paper/benchmarking-deep-learning-interpretability","title":"Benchmarking Deep Learning Interpretability in Time Series Predictions","date":"2020-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ayaabdelsalam91/TS-Interpretability-Benchmark","path":"Scripts/Models/Transformer.py","file_url":"https://github.com/ayaabdelsalam91/TS-Interpretability-Benchmark/blob/HEAD/Scripts/Models/Transformer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"94be2b4c3aa07cf9","mcp_get_code":{"code_sha256":"94be2b4c3aa07cf9"}},{"arxiv_id":"2010.11929","paper":"/paper/an-image-is-worth-16x16-words-transformers-1","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","date":"2020-10-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"konstantinos-p/image_classification_SOTA","path":"testing/models/ViTransformer.py","file_url":"https://github.com/konstantinos-p/image_classification_SOTA/blob/HEAD/testing/models/ViTransformer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"98bf46cb18536d19","mcp_get_code":{"code_sha256":"98bf46cb18536d19"}},{"arxiv_id":"2009.13603","paper":"/paper/visual-pivoting-for-unsupervised-entity","title":"Visual Pivoting for (Unsupervised) Entity Alignment","date":"2020-09-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cambridgeltl/eva","path":"src/models.py","file_url":"https://github.com/cambridgeltl/eva/blob/HEAD/src/models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bebe16d3aeca99f3","mcp_get_code":{"code_sha256":"bebe16d3aeca99f3"}},{"arxiv_id":"2008.12736","paper":"/paper/rkt-relation-aware-self-attention-for","title":"RKT : Relation-Aware Self-Attention for Knowledge Tracing","date":"2020-08-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shalini1194/RKT","path":"RKT/model_rkt.py","file_url":"https://github.com/shalini1194/RKT/blob/HEAD/RKT/model_rkt.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f866d7aba76b8872","mcp_get_code":{"code_sha256":"f866d7aba76b8872"}},{"arxiv_id":"2008.02693","paper":"/paper/fashion-captioning-towards-generating","title":"Fashion Captioning: Towards Generating Accurate Descriptions with Semantic Rewards","date":"2020-08-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xuewyang/Fashion-Image-Captioning-Benchmark","path":"captioning/models/TransformerModel.py","file_url":"https://github.com/xuewyang/Fashion-Image-Captioning-Benchmark/blob/HEAD/captioning/models/TransformerModel.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"269003e7c21d565d","mcp_get_code":{"code_sha256":"269003e7c21d565d"}},{"arxiv_id":"2007.12324","paper":"/paper/context-aware-attentive-knowledge-tracing","title":"Context-Aware Attentive Knowledge Tracing","date":"2020-07-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"arghosh/AKT","path":"akt.py","file_url":"https://github.com/arghosh/AKT/blob/HEAD/akt.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fabc269880eb1ab2","mcp_get_code":{"code_sha256":"fabc269880eb1ab2"}},{"arxiv_id":"2006.12469","paper":"/paper/attention-based-quantum-tomography","title":"Attention-based Quantum Tomography","date":"2020-06-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"KimGroup/AQT","path":".ipynb_checkpoints/ann-checkpoint.py","file_url":"https://github.com/KimGroup/AQT/blob/HEAD/.ipynb_checkpoints/ann-checkpoint.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0cf22e1991ad4ef4","mcp_get_code":{"code_sha256":"0cf22e1991ad4ef4"}},{"arxiv_id":"2006.09286","paper":"/paper/on-the-computational-power-of-transformers","title":"On the Computational Power of Transformers and its Implications in Sequence Modeling","date":"2020-06-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"satwik77/Transformer-Computation-Analysis","path":"Transformer/src/components/self_attention.py","file_url":"https://github.com/satwik77/Transformer-Computation-Analysis/blob/HEAD/Transformer/src/components/self_attention.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"20bb0ff4a2d77a03","mcp_get_code":{"code_sha256":"20bb0ff4a2d77a03"}},{"arxiv_id":"2006.04730","paper":"/paper/picket-self-supervised-data-diagnostics-for","title":"Picket: Guarding Against Corrupted Data in Tabular Data during Learning and Inference","date":"2020-06-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rekords-uw/Picket","path":"picket/transformer/PicketNet.py","file_url":"https://github.com/rekords-uw/Picket/blob/HEAD/picket/transformer/PicketNet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b1b1f3c94280b7cd","mcp_get_code":{"code_sha256":"b1b1f3c94280b7cd"}},{"arxiv_id":"2005.08271","paper":"/paper/a-better-use-of-audio-visual-cues-dense-video","title":"A Better Use of Audio-Visual Cues: Dense Video Captioning with Bi-modal Transformer","date":"2020-05-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"v-iashin/BMT","path":"model/multihead_attention.py","file_url":"https://github.com/v-iashin/BMT/blob/HEAD/model/multihead_attention.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c695e53ea4b93d41","mcp_get_code":{"code_sha256":"c695e53ea4b93d41"}},{"arxiv_id":"2005.04560","paper":"/paper/posterior-control-of-blackbox-generation","title":"Posterior Control of Blackbox Generation","date":"2020-05-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"FranxYao/Gumbel-CRF","path":"src/modeling/latent_temp_crf_ar.py","file_url":"https://github.com/FranxYao/Gumbel-CRF/blob/HEAD/src/modeling/latent_temp_crf_ar.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5dc10625fad08447","mcp_get_code":{"code_sha256":"5dc10625fad08447"}},{"arxiv_id":"2003.08111","paper":"/paper/transformer-networks-for-trajectory","title":"Transformer Networks for Trajectory Forecasting","date":"2020-03-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"FGiuliari/Trajectory-Transformer","path":"transformer/functional.py","file_url":"https://github.com/FGiuliari/Trajectory-Transformer/blob/HEAD/transformer/functional.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4398df6341c69e22","mcp_get_code":{"code_sha256":"4398df6341c69e22"}},{"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":"yangalan123/anhp-andtt","path":"thp/thp_testing/model/xfmr.py","file_url":"https://github.com/yangalan123/anhp-andtt/blob/HEAD/thp/thp_testing/model/xfmr.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8abbfdabbdf5e708","mcp_get_code":{"code_sha256":"8abbfdabbdf5e708"}},{"arxiv_id":"2002.08264","paper":"/paper/molecule-attention-transformer","title":"Molecule Attention Transformer","date":"2020-02-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gmum/MAT","path":"src/transformer.py","file_url":"https://github.com/gmum/MAT/blob/HEAD/src/transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f30d2c34a90c3d8a","mcp_get_code":{"code_sha256":"f30d2c34a90c3d8a"}},{"arxiv_id":"2001.01941","paper":"/paper/paraphrase-generation-with-latent-bag-of-1","title":"Paraphrase Generation with Latent Bag of Words","date":"2020-01-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"FranxYao/dgm_latent_bow","path":"src/decoder.py","file_url":"https://github.com/FranxYao/dgm_latent_bow/blob/HEAD/src/decoder.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2754ede201877c8e","mcp_get_code":{"code_sha256":"2754ede201877c8e"}},{"arxiv_id":"1912.10824","paper":"/paper/differentiable-reasoning-on-large-knowledge","title":"Differentiable Reasoning on Large Knowledge Bases and Natural Language","date":"2019-12-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"uclnlp/gntp","path":"gntp/attention.py","file_url":"https://github.com/uclnlp/gntp/blob/HEAD/gntp/attention.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8ff6535b5f8688aa","mcp_get_code":{"code_sha256":"8ff6535b5f8688aa"}},{"arxiv_id":"1912.09363","paper":"/paper/temporal-fusion-transformers-for","title":"Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting","date":"2019-12-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LiamMaclean216/Any-Coin-TFN","path":"utils.py","file_url":"https://github.com/LiamMaclean216/Any-Coin-TFN/blob/HEAD/utils.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"72ce5bea9e3abec1","mcp_get_code":{"code_sha256":"72ce5bea9e3abec1"}},{"arxiv_id":"1910.12240","paper":"/paper/prnet-self-supervised-learning-for-partial-to","title":"PRNet: Self-Supervised Learning for Partial-to-Partial Registration","date":"2019-10-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"WangYueFt/prnet","path":"model.py","file_url":"https://github.com/WangYueFt/prnet/blob/HEAD/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"05553b35b1e87d43","mcp_get_code":{"code_sha256":"05553b35b1e87d43"}},{"arxiv_id":"1909.06639","paper":"/paper/tree-transformer-integrating-tree-structures","title":"Tree Transformer: Integrating Tree Structures into Self-Attention","date":"2019-09-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"itzpankajpanwar/Tree-transform","path":"attention.py","file_url":"https://github.com/itzpankajpanwar/Tree-transform/blob/HEAD/attention.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":"30ac4034117b7c76","mcp_get_code":{"code_sha256":"30ac4034117b7c76"}},{"arxiv_id":"1907.06837","paper":"/paper/a-self-attentive-model-for-knowledge-tracing","title":"A Self-Attentive model for Knowledge Tracing","date":"2019-07-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"theophilegervet/learner-performance-prediction","path":"model_sakt.py","file_url":"https://github.com/theophilegervet/learner-performance-prediction/blob/HEAD/model_sakt.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"47e44e2cbc1d0361","mcp_get_code":{"code_sha256":"47e44e2cbc1d0361"}},{"arxiv_id":"1907.05572","paper":"/paper/r-transformer-recurrent-neural-network","title":"R-Transformer: Recurrent Neural Network Enhanced Transformer","date":"2019-07-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DSE-MSU/R-transformer","path":"models/RTransformer.py","file_url":"https://github.com/DSE-MSU/R-transformer/blob/HEAD/models/RTransformer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"219e29bc22733121","mcp_get_code":{"code_sha256":"219e29bc22733121"}},{"arxiv_id":"1907.01166","paper":"/paper/multimodal-transformer-networks-for-end-to","title":"Multimodal Transformer Networks for End-to-End Video-Grounded Dialogue Systems","date":"2019-07-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"henryhungle/MTN","path":"mtn.py","file_url":"https://github.com/henryhungle/MTN/blob/HEAD/mtn.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0cf22e1991ad4ef4","mcp_get_code":{"code_sha256":"0cf22e1991ad4ef4"}},{"arxiv_id":"1906.07510","paper":"/paper/attention-guided-graph-convolutional-networks","title":"Attention Guided Graph Convolutional Networks for Relation Extraction","date":"2019-06-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Cartus/AGGCN_TACRED","path":"model/aggcn.py","file_url":"https://github.com/Cartus/AGGCN_TACRED/blob/HEAD/model/aggcn.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d29688d33b9aa937","mcp_get_code":{"code_sha256":"d29688d33b9aa937"}},{"arxiv_id":"1906.05963","paper":"/paper/image-captioning-transforming-objects-into","title":"Image Captioning: Transforming Objects into Words","date":"2019-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"20bb0ff4a2d77a03","mcp_get_code":{"code_sha256":"20bb0ff4a2d77a03"}},{"arxiv_id":"1905.12926","paper":"/paper/controllable-unsupervised-text-attribute","title":"Controllable Unsupervised Text Attribute Transfer via Editing Entangled Latent Representation","date":"2019-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nrgeup/controllable-text-attribute-transfer","path":"method/mymodel-amazon/model.py","file_url":"https://github.com/nrgeup/controllable-text-attribute-transfer/blob/HEAD/method/mymodel-amazon/model.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":"7fe78353a2ca448c","mcp_get_code":{"code_sha256":"7fe78353a2ca448c"}},{"arxiv_id":"1905.12794","paper":"/paper/the-fashion-iq-dataset-retrieving-images-by","title":"Fashion IQ: A New Dataset Towards Retrieving Images by Natural Language Feedback","date":"2019-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"XiaoxiaoGuo/fashion-iq","path":"transformer/interactive_retrieval/models.py","file_url":"https://github.com/XiaoxiaoGuo/fashion-iq/blob/HEAD/transformer/interactive_retrieval/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"875f05a473ae1687","mcp_get_code":{"code_sha256":"875f05a473ae1687"}},{"arxiv_id":"1905.09768","paper":"/paper/zero-shot-knowledge-transfer-via-adversarial","title":"Zero-shot Knowledge Transfer via Adversarial Belief Matching","date":"2019-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SZamboni/advanceddeep","path":"Our_code/Pytorch/zero-shot-baseline.py","file_url":"https://github.com/SZamboni/advanceddeep/blob/HEAD/Our_code/Pytorch/zero-shot-baseline.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8031f2b6467987ab","mcp_get_code":{"code_sha256":"8031f2b6467987ab"}},{"arxiv_id":"1905.09768","paper":"/paper/zero-shot-knowledge-transfer-via-adversarial","title":"Zero-shot Knowledge Transfer via Adversarial Belief Matching","date":"2019-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SZamboni/advanceddeep","path":"Our_code/Pytorch/Basic_experiments/exp-16_2-16_1.py","file_url":"https://github.com/SZamboni/advanceddeep/blob/HEAD/Our_code/Pytorch/Basic_experiments/exp-16_2-16_1.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ab5d18fb42f8dcba","mcp_get_code":{"code_sha256":"ab5d18fb42f8dcba"}},{"arxiv_id":"1905.03304","paper":"/paper/190503304","title":"Deep Closest Point: Learning Representations for Point Cloud Registration","date":"2019-05-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"WangYueFt/dcp","path":"model.py","file_url":"https://github.com/WangYueFt/dcp/blob/HEAD/model.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"70ba9ecc422a6a48","mcp_get_code":{"code_sha256":"70ba9ecc422a6a48"}},{"arxiv_id":"1712.07629","paper":"/paper/superpoint-self-supervised-interest-point","title":"SuperPoint: Self-Supervised Interest Point Detection and Description","date":"2017-12-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tzvikif/SuperGlue","path":"models/superglue.py","file_url":"https://github.com/tzvikif/SuperGlue/blob/HEAD/models/superglue.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":"70702edb93d9116a","mcp_get_code":{"code_sha256":"70702edb93d9116a"}},{"arxiv_id":"1707.07998","paper":"/paper/bottom-up-and-top-down-attention-for-image","title":"Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering","date":"2017-07-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ruotianluo/Transformer_Captioning","path":"models/TransformerModel.py","file_url":"https://github.com/ruotianluo/Transformer_Captioning/blob/HEAD/models/TransformerModel.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"20bb0ff4a2d77a03","mcp_get_code":{"code_sha256":"20bb0ff4a2d77a03"}},{"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":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"0cf22e1991ad4ef4","mcp_get_code":{"code_sha256":"0cf22e1991ad4ef4"}},{"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":"tbmoon/LANL_Earthquake_Prediction","path":"models.py","file_url":"https://github.com/tbmoon/LANL_Earthquake_Prediction/blob/HEAD/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a81a763dbea1b617","mcp_get_code":{"code_sha256":"a81a763dbea1b617"}},{"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":"guillaume-chevalier/Linear-Attention-Recurrent-Neural-Network","path":"multi_head_attention.py","file_url":"https://github.com/guillaume-chevalier/Linear-Attention-Recurrent-Neural-Network/blob/HEAD/multi_head_attention.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c43babeff28f9d8d","mcp_get_code":{"code_sha256":"c43babeff28f9d8d"}},{"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":"harvardnlp/annotated-transformer","path":"the_annotated_transformer.py","file_url":"https://github.com/harvardnlp/annotated-transformer/blob/HEAD/the_annotated_transformer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2362ddc1fc1fd1a5","mcp_get_code":{"code_sha256":"2362ddc1fc1fd1a5"}},{"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":"kotu931226/classifier_transformer_pytorch","path":"model.py","file_url":"https://github.com/kotu931226/classifier_transformer_pytorch/blob/HEAD/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"af3158d888a25210","mcp_get_code":{"code_sha256":"af3158d888a25210"}},{"arxiv_id":"1704.04368","paper":"/paper/get-to-the-point-summarization-with-pointer","title":"Get To The Point: Summarization with Pointer-Generator Networks","date":"2017-04-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"NirmalenduPrakash/DocumentSummarizer","path":"summarizer_transformer.py","file_url":"https://github.com/NirmalenduPrakash/DocumentSummarizer/blob/HEAD/summarizer_transformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6d646c72fd411781","mcp_get_code":{"code_sha256":"6d646c72fd411781"}},{"arxiv_id":"1612.00563","paper":"/paper/self-critical-sequence-training-for-image","title":"Self-critical Sequence Training for Image Captioning","date":"2016-12-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"20bb0ff4a2d77a03","mcp_get_code":{"code_sha256":"20bb0ff4a2d77a03"}},{"arxiv_id":"1609.08144","paper":"/paper/googles-neural-machine-translation-system","title":"Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation","date":"2016-09-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"psunlpgroup/xsemplr","path":"model/seq2seqPTR/src/ptrbert.py","file_url":"https://github.com/psunlpgroup/xsemplr/blob/HEAD/model/seq2seqPTR/src/ptrbert.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"51522955ae4c222d","mcp_get_code":{"code_sha256":"51522955ae4c222d"}},{"arxiv_id":"1602.02867","paper":"/paper/value-iteration-networks","title":"Value Iteration Networks","date":"2016-02-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zuoxingdong/VIN_PyTorch_Visdom","path":"VIN.py","file_url":"https://github.com/zuoxingdong/VIN_PyTorch_Visdom/blob/HEAD/VIN.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6a211696e771f81a","mcp_get_code":{"code_sha256":"6a211696e771f81a"}},{"arxiv_id":"1411.4555","paper":"/paper/show-and-tell-a-neural-image-caption","title":"Show and Tell: A Neural Image Caption Generator","date":"2014-11-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Cathy-t/HELLO_image","path":"model.py","file_url":"https://github.com/Cathy-t/HELLO_image/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":"d3feb7c8293a8cfd","mcp_get_code":{"code_sha256":"d3feb7c8293a8cfd"}},{"arxiv_id":"Yuan_StyleSRN_Scene_Text_Image_Super-Resolution_with_Text_Style_Embedding_ICCV_2025_paper","paper":null,"title":"arXiv:Yuan_StyleSRN_Scene_Text_Image_Super-Resolution_with_Text_Style_Embedding_ICCV_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Yuanssr/StyleSRN","path":"model/model_transformer.py","file_url":"https://github.com/Yuanssr/StyleSRN/blob/HEAD/model/model_transformer.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":"9959386283f613a2","mcp_get_code":{"code_sha256":"9959386283f613a2"}},{"arxiv_id":"Yuan_StyleSRN_Scene_Text_Image_Super-Resolution_with_Text_Style_Embedding_ICCV_2025_paper","paper":null,"title":"arXiv:Yuan_StyleSRN_Scene_Text_Image_Super-Resolution_with_Text_Style_Embedding_ICCV_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Yuanssr/StyleSRN","path":"model/tbsrn.py","file_url":"https://github.com/Yuanssr/StyleSRN/blob/HEAD/model/tbsrn.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":"f394414cbf58ce03","mcp_get_code":{"code_sha256":"f394414cbf58ce03"}},{"arxiv_id":"Shen_CocoER_Aligning_Multi-Level_Feature_by__Competition_and_Coordination_for_CVPR_2025_paper","paper":null,"title":"arXiv:Shen_CocoER_Aligning_Multi-Level_Feature_by__Competition_and_Coordination_for_CVPR_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"bisno/CocoER","path":"models_sw.py","file_url":"https://github.com/bisno/CocoER/blob/HEAD/models_sw.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bbcd430fbbd7f7e9","mcp_get_code":{"code_sha256":"bbcd430fbbd7f7e9"}},{"arxiv_id":"2023.emnlp-main.1005","paper":null,"title":"arXiv:2023.emnlp-main.1005","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"YichenZW/Coh-MGT-Detection","path":"custom_LSTM.py","file_url":"https://github.com/YichenZW/Coh-MGT-Detection/blob/HEAD/custom_LSTM.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4cf272ff166fbc20","mcp_get_code":{"code_sha256":"4cf272ff166fbc20"}},{"arxiv_id":"2021.acl-long.459","paper":null,"title":"arXiv:2021.acl-long.459","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"cuhksz-nlp/R2GenCMN","path":"modules/base_cmn.py","file_url":"https://github.com/cuhksz-nlp/R2GenCMN/blob/HEAD/modules/base_cmn.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"8162be9dd8cf7f11","mcp_get_code":{"code_sha256":"8162be9dd8cf7f11"}},{"arxiv_id":"2020.findings-emnlp.234","paper":null,"title":"arXiv:2020.findings-emnlp.234","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"NJUNLP/GTS","path":"code/NNModel/attention_module.py","file_url":"https://github.com/NJUNLP/GTS/blob/HEAD/code/NNModel/attention_module.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"916dd244b4ead0ac","mcp_get_code":{"code_sha256":"916dd244b4ead0ac"}}]}