{"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/compute-acc","entry":"compute_acc","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":61,"n_papers_ran":50,"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":28,"n_samples_ran":16,"n_samples_fingerprinted":7,"n_places":62,"n_places_pointer_only":28,"by_status":{"ran_honours":2,"ran_violates":1,"ran_draft_wrong":3,"ran_fixture":3,"ran":7,"unverified":12},"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":"2608.23936","paper":"/paper/arxiv-2608-23936","title":"MnemoDyn: Learning Resting State Dynamics from 40K FMRI sequences","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"vsingh-group/mnemodyn","path":"code/light/abide_classification.py","file_url":"https://github.com/vsingh-group/mnemodyn/blob/HEAD/code/light/abide_classification.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"76ae6692cf6c83ff","mcp_get_code":{"code_sha256":"76ae6692cf6c83ff"}},{"arxiv_id":"2607.10783","paper":"/paper/arxiv-2607-10783","title":"Toward Efficient Weakly Supervised Semantic Segmentation Using Only Low-Magnification Histopathological Images","date":null,"month_inferred_from_arxiv_id":"2026-07","title_source":"syntology","repo":"Dung-Dx/LowMagWSS","path":"SegMethod/WSSS-Tissue/1_train_stage1.py","file_url":"https://github.com/Dung-Dx/LowMagWSS/blob/HEAD/SegMethod/WSSS-Tissue/1_train_stage1.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"16bf0081d1b66da9","mcp_get_code":{"code_sha256":"16bf0081d1b66da9"}},{"arxiv_id":"2606.20673","paper":"/paper/arxiv-2606-20673","title":"NeuroShield: A Device-Agnostic Foundation Model for EEG Authentication","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"snow1/transformer","path":"model_ACGAN.py","file_url":"https://github.com/snow1/transformer/blob/HEAD/model_ACGAN.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9eb62f1192f93669","mcp_get_code":{"code_sha256":"9eb62f1192f93669"}},{"arxiv_id":"2506.05890","paper":"/paper/unleashing-the-potential-of-consistency-1","title":"Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation","date":"2025-06-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liyih/CSCL","path":"code/MultiModal-DeepFake-main/utils.py","file_url":"https://github.com/liyih/CSCL/blob/HEAD/code/MultiModal-DeepFake-main/utils.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aa81d03f07735ab8","mcp_get_code":{"code_sha256":"aa81d03f07735ab8"}},{"arxiv_id":"2506.03355","paper":"/paper/robustness-in-both-domains-clip-needs-a","title":"Robustness in Both Domains: CLIP Needs a Robust Text Encoder","date":"2025-06-03","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":"1f2be2216dc3f7cd","mcp_get_code":{"code_sha256":"1f2be2216dc3f7cd"}},{"arxiv_id":"2412.13647","paper":"/paper/g-veval-a-versatile-metric-for-evaluating","title":"G-VEval: A Versatile Metric for Evaluating Image and Video Captions Using GPT-4o","date":"2024-12-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ztangaj/gveval","path":"compute_foil.py","file_url":"https://github.com/ztangaj/gveval/blob/HEAD/compute_foil.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e1a9a306489a61f0","mcp_get_code":{"code_sha256":"e1a9a306489a61f0"}},{"arxiv_id":"2411.19290","paper":"/paper/sadg-segment-any-dynamic-gaussian-without","title":"SADG: Segment Any Dynamic Gaussian Without Object Trackers","date":"2024-11-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yunjinli/SADG-SegmentAnyDynamicGaussian","path":"metrics_segmentation.py","file_url":"https://github.com/yunjinli/SADG-SegmentAnyDynamicGaussian/blob/HEAD/metrics_segmentation.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"86791b215b923f7f","mcp_get_code":{"code_sha256":"86791b215b923f7f"}},{"arxiv_id":"2411.02669","paper":"/paper/semantic-aligned-adversarial-evolution","title":"Semantic-Aligned Adversarial Evolution Triangle for High-Transferability Vision-Language Attack","date":"2024-11-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jiaxiaojunqaq/sa-aet","path":"utils.py","file_url":"https://github.com/jiaxiaojunqaq/sa-aet/blob/HEAD/utils.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aa81d03f07735ab8","mcp_get_code":{"code_sha256":"aa81d03f07735ab8"}},{"arxiv_id":"2410.23091","paper":"/paper/causaldiff-causality-inspired-disentanglement","title":"CausalDiff: Causality-Inspired Disentanglement via Diffusion Model for Adversarial Defense","date":"2024-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tmlresearchgroup-cas/causaldiff","path":"run_eval.py","file_url":"https://github.com/tmlresearchgroup-cas/causaldiff/blob/HEAD/run_eval.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3f55f85544860186","mcp_get_code":{"code_sha256":"3f55f85544860186"}},{"arxiv_id":"2410.23091","paper":"/paper/causaldiff-causality-inspired-disentanglement","title":"CausalDiff: Causality-Inspired Disentanglement via Diffusion Model for Adversarial Defense","date":"2024-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"TMLResearchGroup-CAS/CausalDiff","path":"experiment.py","file_url":"https://github.com/TMLResearchGroup-CAS/CausalDiff/blob/HEAD/experiment.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dd8d3dcf271eb1a8","mcp_get_code":{"code_sha256":"dd8d3dcf271eb1a8"}},{"arxiv_id":"2409.17663","paper":"/paper/explanation-bottleneck-models","title":"Explanation Bottleneck Models","date":"2024-09-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yshinya6/xbm","path":"xbm-llava/utils.py","file_url":"https://github.com/yshinya6/xbm/blob/HEAD/xbm-llava/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":"aa81d03f07735ab8","mcp_get_code":{"code_sha256":"aa81d03f07735ab8"}},{"arxiv_id":"2409.14500","paper":"/paper/tabgraphs-a-benchmark-and-strong-baselines","title":"TabGraphs: A Benchmark and Strong Baselines for Learning on Graphs with Tabular Node Features","date":"2024-09-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yandex-research/tabgraphs","path":"source/ebbs/AG.py","file_url":"https://github.com/yandex-research/tabgraphs/blob/HEAD/source/ebbs/AG.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d0c5add65632f394","mcp_get_code":{"code_sha256":"d0c5add65632f394"}},{"arxiv_id":"2409.03142","paper":"/paper/causal-temporal-representation-learning-with","title":"Causal Temporal Representation Learning with Nonstationary Sparse Transition","date":"2024-09-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xiangchensong/ctrlns","path":"models/metrics/hmm_metrics.py","file_url":"https://github.com/xiangchensong/ctrlns/blob/HEAD/models/metrics/hmm_metrics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1faa8b8bebc32849","mcp_get_code":{"code_sha256":"1faa8b8bebc32849"}},{"arxiv_id":"2407.21633","paper":"/paper/2407-21633","title":"Zero-Shot Cross-Domain Dialogue State Tracking via Dual Low-Rank Adaptation","date":"2024-07-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"suntea233/DualLoRA","path":"evaluate.py","file_url":"https://github.com/suntea233/DualLoRA/blob/HEAD/evaluate.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3a497dd8883949a9","mcp_get_code":{"code_sha256":"3a497dd8883949a9"}},{"arxiv_id":"2406.05491","paper":"/paper/one-perturbation-is-enough-on-generating","title":"One Perturbation is Enough: On Generating Universal Adversarial Perturbations against Vision-Language Pre-training Models","date":"2024-06-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ffhibnese/cpgc_vlp_universal_attacks","path":"utils.py","file_url":"https://github.com/ffhibnese/cpgc_vlp_universal_attacks/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":"aa81d03f07735ab8","mcp_get_code":{"code_sha256":"aa81d03f07735ab8"}},{"arxiv_id":"2404.19335","paper":"/paper/stablept-towards-stable-prompting-for-few","title":"StablePT: Towards Stable Prompting for Few-shot Learning via Input Separation","date":"2024-04-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lccc0528/stable","path":"utils.py","file_url":"https://github.com/lccc0528/stable/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6bdf532917bee2b8","mcp_get_code":{"code_sha256":"6bdf532917bee2b8"}},{"arxiv_id":"2404.19287","paper":"/paper/revisiting-the-adversarial-robustness-of","title":"Revisiting the Adversarial Robustness of Vision Language Models: a Multimodal Perspective","date":"2024-04-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ellezwq/mmcoa","path":"utils_2.py","file_url":"https://github.com/ellezwq/mmcoa/blob/HEAD/utils_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":"aa81d03f07735ab8","mcp_get_code":{"code_sha256":"aa81d03f07735ab8"}},{"arxiv_id":"2404.04575","paper":"/paper/to-cool-or-not-to-cool-temperature-network","title":"To Cool or not to Cool? Temperature Network Meets Large Foundation Models via DRO","date":"2024-04-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhqiu/tempnet","path":"Bimodal_CL/utils.py","file_url":"https://github.com/zhqiu/tempnet/blob/HEAD/Bimodal_CL/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":"aa81d03f07735ab8","mcp_get_code":{"code_sha256":"aa81d03f07735ab8"}},{"arxiv_id":"2403.19928","paper":"/paper/dijiang-efficient-large-language-models","title":"DiJiang: Efficient Large Language Models through Compact Kernelization","date":"2024-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yuchuantian/dijiang","path":"train_pythia.py","file_url":"https://github.com/yuchuantian/dijiang/blob/HEAD/train_pythia.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"095a8b81ca7acd65","mcp_get_code":{"code_sha256":"095a8b81ca7acd65"}},{"arxiv_id":"2403.07636","paper":"/paper/decomposing-disease-descriptions-for-enhanced","title":"Decomposing Disease Descriptions for Enhanced Pathology Detection: A Multi-Aspect Vision-Language Pre-training Framework","date":"2024-03-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hieuphan33/mavl","path":"Finetuning/classification/utils.py","file_url":"https://github.com/hieuphan33/mavl/blob/HEAD/Finetuning/classification/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":"aa81d03f07735ab8","mcp_get_code":{"code_sha256":"aa81d03f07735ab8"}},{"arxiv_id":"2403.06407","paper":"/paper/can-llms-tuning-methods-work-in-medical","title":"Can LLMs' Tuning Methods Work in Medical Multimodal Domain?","date":"2024-03-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"timmy-chan/miss","path":"utils.py","file_url":"https://github.com/timmy-chan/miss/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":"aa81d03f07735ab8","mcp_get_code":{"code_sha256":"aa81d03f07735ab8"}},{"arxiv_id":"2402.19248","paper":"/paper/let-llms-take-on-the-latest-challenges-a","title":"Let LLMs Take on the Latest Challenges! A Chinese Dynamic Question Answering Benchmark","date":"2024-02-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alibaba-nlp/cdqa","path":"cdqa_eval.py","file_url":"https://github.com/alibaba-nlp/cdqa/blob/HEAD/cdqa_eval.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7756c7928a5f4188","mcp_get_code":{"code_sha256":"7756c7928a5f4188"}},{"arxiv_id":"2402.12336","paper":"/paper/robust-clip-unsupervised-adversarial-fine","title":"Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models","date":"2024-02-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chs20/robustvlm","path":"train/adversarial_training_clip.py","file_url":"https://github.com/chs20/robustvlm/blob/HEAD/train/adversarial_training_clip.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":"1f2be2216dc3f7cd","mcp_get_code":{"code_sha256":"1f2be2216dc3f7cd"}},{"arxiv_id":"2312.11523","paper":"/paper/tovilag-your-visual-language-generative-model","title":"ToViLaG: Your Visual-Language Generative Model is Also An Evildoer","date":"2023-12-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"victorup/ToViLaG","path":"method/BLIP/utils.py","file_url":"https://github.com/victorup/ToViLaG/blob/HEAD/method/BLIP/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":"aa81d03f07735ab8","mcp_get_code":{"code_sha256":"aa81d03f07735ab8"}},{"arxiv_id":"2312.11038","paper":"/paper/unichest-conquer-and-divide-pre-training-for","title":"UniChest: Conquer-and-Divide Pre-training for Multi-Source Chest X-Ray Classification","date":"2023-12-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"elfenreigen/unichest","path":"factory/utils.py","file_url":"https://github.com/elfenreigen/unichest/blob/HEAD/factory/utils.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aa81d03f07735ab8","mcp_get_code":{"code_sha256":"aa81d03f07735ab8"}},{"arxiv_id":"2312.04076","paper":"/paper/large-language-models-are-good-prompt","title":"Large Language Models are Good Prompt Learners for Low-Shot Image Classification","date":"2023-12-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhaohengz/llamp","path":"utils/utils.py","file_url":"https://github.com/zhaohengz/llamp/blob/HEAD/utils/utils.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aa81d03f07735ab8","mcp_get_code":{"code_sha256":"aa81d03f07735ab8"}},{"arxiv_id":"2312.00081","paper":"/paper/synthesize-diagnose-and-optimize-towards-fine","title":"Synthesize, Diagnose, and Optimize: Towards Fine-Grained Vision-Language Understanding","date":"2023-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wjpoom/spec","path":"spec/models/blip_utils/utils.py","file_url":"https://github.com/wjpoom/spec/blob/HEAD/spec/models/blip_utils/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":"aa81d03f07735ab8","mcp_get_code":{"code_sha256":"aa81d03f07735ab8"}},{"arxiv_id":"2311.18815","paper":"/paper/imma-immunizing-text-to-image-models-against","title":"IMMA: Immunizing text-to-image Models against Malicious Adaptation","date":"2023-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhengyjzoe/imma","path":"eval/imgnet_cls.py","file_url":"https://github.com/zhengyjzoe/imma/blob/HEAD/eval/imgnet_cls.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":"9c1fd5d9c8bc4f4e","mcp_get_code":{"code_sha256":"9c1fd5d9c8bc4f4e"}},{"arxiv_id":"2311.08718","paper":"/paper/decomposing-uncertainty-for-large-language","title":"Decomposing Uncertainty for Large Language Models through Input Clarification Ensembling","date":"2023-11-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ucsb-nlp-chang/llm_uncertainty","path":"evaluate_uq_qa.py","file_url":"https://github.com/ucsb-nlp-chang/llm_uncertainty/blob/HEAD/evaluate_uq_qa.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"79fbe372a240d8bd","mcp_get_code":{"code_sha256":"79fbe372a240d8bd"}},{"arxiv_id":"2311.04354","paper":"/paper/uncovering-causal-variables-in-transformers","title":"Uncovering Intermediate Variables in Transformers using Circuit Probing","date":"2023-11-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mlepori1/circuit_probing","path":"src/train_algorithmic_models.py","file_url":"https://github.com/mlepori1/circuit_probing/blob/HEAD/src/train_algorithmic_models.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"76eabbb99306f788","mcp_get_code":{"code_sha256":"76eabbb99306f788"}},{"arxiv_id":"2310.18615","paper":"/paper/temporally-disentangled-representation-2","title":"Temporally Disentangled Representation Learning under Unknown Nonstationarity","date":"2023-10-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xiangchensong/nctrl","path":"models/simulation.py","file_url":"https://github.com/xiangchensong/nctrl/blob/HEAD/models/simulation.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1faa8b8bebc32849","mcp_get_code":{"code_sha256":"1faa8b8bebc32849"}},{"arxiv_id":"2310.15200","paper":"/paper/inject-semantic-concepts-into-image-tagging","title":"Open-Set Image Tagging with Multi-Grained Text Supervision","date":"2023-10-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xinyu1205/Recognize_Anything-Tag2Text","path":"utils.py","file_url":"https://github.com/xinyu1205/Recognize_Anything-Tag2Text/blob/HEAD/utils.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"aa81d03f07735ab8","mcp_get_code":{"code_sha256":"aa81d03f07735ab8"}},{"arxiv_id":"2310.15061","paper":"/paper/the-bla-benchmark-investigating-basic","title":"The BLA Benchmark: Investigating Basic Language Abilities of Pre-Trained Multimodal Models","date":"2023-10-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shin-ee-chen/BLA","path":"evaluation_models/blip2/utils.py","file_url":"https://github.com/shin-ee-chen/BLA/blob/HEAD/evaluation_models/blip2/utils.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aa81d03f07735ab8","mcp_get_code":{"code_sha256":"aa81d03f07735ab8"}},{"arxiv_id":"2309.14203","paper":"/paper/detecting-and-grounding-multi-modal-media-1","title":"Detecting and Grounding Multi-Modal Media Manipulation and Beyond","date":"2023-09-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rshaojimmy/multimodal-deepfake","path":"utils.py","file_url":"https://github.com/rshaojimmy/multimodal-deepfake/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":"aa81d03f07735ab8","mcp_get_code":{"code_sha256":"aa81d03f07735ab8"}},{"arxiv_id":"2308.12898","paper":"/paper/can-linguistic-knowledge-improve-multimodal","title":"Can Linguistic Knowledge Improve Multimodal Alignment in Vision-Language Pretraining?","date":"2023-08-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wangfei-2019/snare","path":"snare/models/utils.py","file_url":"https://github.com/wangfei-2019/snare/blob/HEAD/snare/models/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":"aa81d03f07735ab8","mcp_get_code":{"code_sha256":"aa81d03f07735ab8"}},{"arxiv_id":"2308.10741","paper":"/paper/on-the-adversarial-robustness-of-multi-modal","title":"On the Adversarial Robustness of Multi-Modal Foundation Models","date":"2023-08-21","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":"1f2be2216dc3f7cd","mcp_get_code":{"code_sha256":"1f2be2216dc3f7cd"}},{"arxiv_id":"2308.07146","paper":"/paper/ctp-towards-vision-language-continual","title":"CTP: Towards Vision-Language Continual Pretraining via Compatible Momentum Contrast and Topology Preservation","date":"2023-08-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kevinlight831/ctp","path":"utils.py","file_url":"https://github.com/kevinlight831/ctp/blob/HEAD/utils.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aa81d03f07735ab8","mcp_get_code":{"code_sha256":"aa81d03f07735ab8"}},{"arxiv_id":"2307.14061","paper":"/paper/set-level-guidance-attack-boosting","title":"Set-level Guidance Attack: Boosting Adversarial Transferability of Vision-Language Pre-training Models","date":"2023-07-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Zoky-2020/Set-level_Guidance_Attack","path":"utils.py","file_url":"https://github.com/Zoky-2020/Set-level_Guidance_Attack/blob/HEAD/utils.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aa81d03f07735ab8","mcp_get_code":{"code_sha256":"aa81d03f07735ab8"}},{"arxiv_id":"2303.11403","paper":"/paper/ep-alm-efficient-perceptual-augmentation-of","title":"eP-ALM: Efficient Perceptual Augmentation of Language Models","date":"2023-03-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mshukor/eP-ALM","path":"utils.py","file_url":"https://github.com/mshukor/eP-ALM/blob/HEAD/utils.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aa81d03f07735ab8","mcp_get_code":{"code_sha256":"aa81d03f07735ab8"}},{"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":"utils.py","file_url":"https://github.com/mlii0117/dcl/blob/HEAD/utils.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aa81d03f07735ab8","mcp_get_code":{"code_sha256":"aa81d03f07735ab8"}},{"arxiv_id":"2302.06605","paper":"/paper/uniadapter-unified-parameter-efficient","title":"UniAdapter: Unified Parameter-Efficient Transfer Learning for Cross-modal Modeling","date":"2023-02-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rerv/uniadapter","path":"utils.py","file_url":"https://github.com/rerv/uniadapter/blob/HEAD/utils.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"BSD-3-Clause","inline_ok":false,"code_sha256_prefix":"aa81d03f07735ab8","mcp_get_code":{"code_sha256":"aa81d03f07735ab8"}},{"arxiv_id":"2302.05185","paper":"/paper/on-penalty-based-bilevel-gradient-descent","title":"On Penalty-based Bilevel Gradient Descent Method","date":"2023-02-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hanshen95/penalized-bilevel-gradient-descent","path":"V-PBGD/data-hyper-cleaning/data_hyper_clean.py","file_url":"https://github.com/hanshen95/penalized-bilevel-gradient-descent/blob/HEAD/V-PBGD/data-hyper-cleaning/data_hyper_clean.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0e0f3de9c07cf33d","mcp_get_code":{"code_sha256":"0e0f3de9c07cf33d"}},{"arxiv_id":"2302.00402","paper":"/paper/mplug-2-a-modularized-multi-modal-foundation","title":"mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video","date":"2023-02-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"X-PLUG/mPLUG-2","path":"utils.py","file_url":"https://github.com/X-PLUG/mPLUG-2/blob/HEAD/utils.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"aa81d03f07735ab8","mcp_get_code":{"code_sha256":"aa81d03f07735ab8"}},{"arxiv_id":"2301.13622","paper":"/paper/learning-data-representations-with-joint","title":"Learning Data Representations with Joint Diffusion Models","date":"2023-01-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kamildeja/joint_diffusion","path":"scripts/classifier_train.py","file_url":"https://github.com/kamildeja/joint_diffusion/blob/HEAD/scripts/classifier_train.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"230cc0dd62fe0d64","mcp_get_code":{"code_sha256":"230cc0dd62fe0d64"}},{"arxiv_id":"2212.09737","paper":"/paper/position-guided-text-prompt-for-vision","title":"Position-guided Text Prompt for Vision-Language Pre-training","date":"2022-12-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sail-sg/ptp","path":"src/blip_src/utils.py","file_url":"https://github.com/sail-sg/ptp/blob/HEAD/src/blip_src/utils.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"aa81d03f07735ab8","mcp_get_code":{"code_sha256":"aa81d03f07735ab8"}},{"arxiv_id":"2212.03191","paper":"/paper/internvideo-general-video-foundation-models","title":"InternVideo: General Video Foundation Models via Generative and Discriminative Learning","date":"2022-12-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"opengvlab/internvideo","path":"Data/InternVid/utils/basic_utils.py","file_url":"https://github.com/opengvlab/internvideo/blob/HEAD/Data/InternVid/utils/basic_utils.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"aa81d03f07735ab8","mcp_get_code":{"code_sha256":"aa81d03f07735ab8"}},{"arxiv_id":"2211.13594","paper":"/paper/self-supervised-vision-language-pretraining","title":"Self-supervised vision-language pretraining for Medical visual question answering","date":"2022-11-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pengfeiliheu/m2i2","path":"utils.py","file_url":"https://github.com/pengfeiliheu/m2i2/blob/HEAD/utils.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aa81d03f07735ab8","mcp_get_code":{"code_sha256":"aa81d03f07735ab8"}},{"arxiv_id":"2211.09790","paper":"/paper/construct-vl-data-free-continual-structured","title":"ConStruct-VL: Data-Free Continual Structured VL Concepts Learning","date":"2022-11-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jamessealesmith/construct-vl","path":"utils.py","file_url":"https://github.com/jamessealesmith/construct-vl/blob/HEAD/utils.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"aa81d03f07735ab8","mcp_get_code":{"code_sha256":"aa81d03f07735ab8"}},{"arxiv_id":"2206.06131","paper":"/paper/seeing-the-forest-and-the-tree-building","title":"Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers","date":"2022-06-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nerdslab/EIT","path":"my_transformers/tasks.py","file_url":"https://github.com/nerdslab/EIT/blob/HEAD/my_transformers/tasks.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c6c224933f8c029e","mcp_get_code":{"code_sha256":"c6c224933f8c029e"}},{"arxiv_id":"2111.09452","paper":"/paper/towards-open-vocabulary-object-detection","title":"Open Vocabulary Object Detection with Pseudo Bounding-Box Labels","date":"2021-11-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"salesforce/pb-ovd","path":"ALBEF/utils.py","file_url":"https://github.com/salesforce/pb-ovd/blob/HEAD/ALBEF/utils.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"aa81d03f07735ab8","mcp_get_code":{"code_sha256":"aa81d03f07735ab8"}},{"arxiv_id":"2110.13413","paper":"/paper/convergent-boosted-smoothing-for-modeling-1","title":"Does your graph need a confidence boost? Convergent boosted smoothing on graphs with tabular node features","date":"2021-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JiuhaiChen/EBBS","path":"AG.py","file_url":"https://github.com/JiuhaiChen/EBBS/blob/HEAD/AG.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d0c5add65632f394","mcp_get_code":{"code_sha256":"d0c5add65632f394"}},{"arxiv_id":"2108.01099","paper":"/paper/shift-robust-gnns-overcoming-the-limitations","title":"Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data","date":"2021-08-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gentlezhu/shift-robust-gnns","path":"main_gnn.py","file_url":"https://github.com/gentlezhu/shift-robust-gnns/blob/HEAD/main_gnn.py","status":"ran_draft_wrong","verification_level":2,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a83401ad1b65df8d","mcp_get_code":{"code_sha256":"a83401ad1b65df8d"}},{"arxiv_id":"2104.07858","paper":"/paper/search-oriented-differentiable-product","title":"Matching-oriented Product Quantization For Ad-hoc Retrieval","date":"2021-04-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"microsoft/MoPQ","path":"utils/utils.py","file_url":"https://github.com/microsoft/MoPQ/blob/HEAD/utils/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4702e60a1d1a3d53","mcp_get_code":{"code_sha256":"4702e60a1d1a3d53"}},{"arxiv_id":"2008.01183","paper":"/paper/weakly-supervised-semantic-segmentation-via-1","title":"Weakly-Supervised Semantic Segmentation via Sub-category Exploration","date":"2020-08-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Juliachang/SC-CAM","path":"train_cls.py","file_url":"https://github.com/Juliachang/SC-CAM/blob/HEAD/train_cls.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e7b5ceebedb5efe7","mcp_get_code":{"code_sha256":"e7b5ceebedb5efe7"}},{"arxiv_id":"2006.04292","paper":"/paper/achieving-equalized-odds-by-resampling","title":"Achieving Equalized Odds by Resampling Sensitive Attributes","date":"2020-06-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yromano/fair_dummies","path":"fair_dummies/utility_functions.py","file_url":"https://github.com/yromano/fair_dummies/blob/HEAD/fair_dummies/utility_functions.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"38ec48431e45abd1","mcp_get_code":{"code_sha256":"38ec48431e45abd1"}},{"arxiv_id":"1911.11236","paper":"/paper/191111236","title":"RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds","date":"2019-11-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tsunghan-mama/RandLA-Net-pytorch","path":"utils/metric.py","file_url":"https://github.com/tsunghan-mama/RandLA-Net-pytorch/blob/HEAD/utils/metric.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3bade2b4e07dcaa4","mcp_get_code":{"code_sha256":"3bade2b4e07dcaa4"}},{"arxiv_id":"1902.07473","paper":"/paper/dual-modality-seq2seq-network-for-audio","title":"Dual-modality seq2seq network for audio-visual event localization","date":"2019-02-20","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":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"72739f58746f7a7a","mcp_get_code":{"code_sha256":"72739f58746f7a7a"}},{"arxiv_id":"1803.08842","paper":"/paper/audio-visual-event-localization-in","title":"Audio-Visual Event Localization in Unconstrained Videos","date":"2018-03-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YapengTian/AVE-ECCV18","path":"supervised_main.py","file_url":"https://github.com/YapengTian/AVE-ECCV18/blob/HEAD/supervised_main.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"72739f58746f7a7a","mcp_get_code":{"code_sha256":"72739f58746f7a7a"}},{"arxiv_id":"Zheng_Large_Language_Models_are_Good_Prompt_Learners_for_Low-Shot_Image_CVPR_2024_paper","paper":null,"title":"arXiv:Zheng_Large_Language_Models_are_Good_Prompt_Learners_for_Low-Shot_Image_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"zhaohengz/LLaMP","path":"utils/utils.py","file_url":"https://github.com/zhaohengz/LLaMP/blob/HEAD/utils/utils.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aa81d03f07735ab8","mcp_get_code":{"code_sha256":"aa81d03f07735ab8"}},{"arxiv_id":"Yang_Beyond_Walking_A_Large-Scale_Image-Text_Benchmark_for_Text-based_Person_Anomaly_ICCV_2025_paper","paper":null,"title":"arXiv:Yang_Beyond_Walking_A_Large-Scale_Image-Text_Benchmark_for_Text-based_Person_Anomaly_ICCV_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Shuyu-XJTU/CMP","path":"utils.py","file_url":"https://github.com/Shuyu-XJTU/CMP/blob/HEAD/utils.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aa81d03f07735ab8","mcp_get_code":{"code_sha256":"aa81d03f07735ab8"}},{"arxiv_id":"2022.findings-emnlp.193","paper":null,"title":"arXiv:2022.findings-emnlp.193","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Yushi-Hu/IC-DST","path":"evaluate_metrics.py","file_url":"https://github.com/Yushi-Hu/IC-DST/blob/HEAD/evaluate_metrics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7c658949b3affb4d","mcp_get_code":{"code_sha256":"7c658949b3affb4d"}},{"arxiv_id":"2020.acl-main.53","paper":null,"title":"arXiv:2020.acl-main.53","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"clovaai/som-dst","path":"utils/eval_utils.py","file_url":"https://github.com/clovaai/som-dst/blob/HEAD/utils/eval_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3a497dd8883949a9","mcp_get_code":{"code_sha256":"3a497dd8883949a9"}}]}