{"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-accuracy","entry":"compute_accuracy","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":81,"n_papers_ran":48,"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":63,"n_samples_ran":35,"n_samples_fingerprinted":7,"n_places":83,"n_places_pointer_only":28,"by_status":{"ran_honours":3,"ran_violates":1,"ran_draft_wrong":4,"ran_fixture":8,"ran":19,"unverified":28},"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":"2608.13676","paper":"/paper/arxiv-2608-13676","title":"EEG-PRISM: Physiologically-Grounded Interpretability of Predictions by EEG Foundation Models","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"deeksha-ms/EEG-PRISM","path":"EEG_GUI/src/prediction.py","file_url":"https://github.com/deeksha-ms/EEG-PRISM/blob/HEAD/EEG_GUI/src/prediction.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7ffe7e16b0f6c4ed","mcp_get_code":{"code_sha256":"7ffe7e16b0f6c4ed"}},{"arxiv_id":"2605.31500","paper":"/paper/arxiv-2605-31500","title":"On Efficient Scaling of GNNs via IO-Aware Layers Implementations","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"yandex-research/On-Efficient-Scaling-Of-GNNs","path":"src/training/metrics.py","file_url":"https://github.com/yandex-research/On-Efficient-Scaling-Of-GNNs/blob/HEAD/src/training/metrics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"0f2a52d87e993417","mcp_get_code":{"code_sha256":"0f2a52d87e993417"}},{"arxiv_id":"2603.28591","paper":"/paper/arxiv-2603-28591","title":"Universal Approximation Constraints of Narrow ResNets: The Tunnel Effect","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"twoehrer/Narrow_ResNet_Constraints","path":"models/training.py","file_url":"https://github.com/twoehrer/Narrow_ResNet_Constraints/blob/HEAD/models/training.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"76743c3cf042cd5f","mcp_get_code":{"code_sha256":"76743c3cf042cd5f"}},{"arxiv_id":"2601.09385","paper":"/paper/arxiv-2601-09385","title":"SLAM-LLM: A Modular, Open-Source Multimodal Large Language Model Framework and Best Practice for Speech, Language, Audio and Music Processing","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"X-LANCE/SLAM-LLM","path":"src/slam_llm/models/slam_model.py","file_url":"https://github.com/X-LANCE/SLAM-LLM/blob/HEAD/src/slam_llm/models/slam_model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a9723f976415fae3","mcp_get_code":{"code_sha256":"a9723f976415fae3"}},{"arxiv_id":"2510.10100","paper":"/paper/arxiv-2510-10100","title":"Cooperative Pseudo Labeling for Unsupervised Federated Classification","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"krumpguo/FedCoPL","path":"methods/unsupervised_learning_new/fedcopl.py","file_url":"https://github.com/krumpguo/FedCoPL/blob/HEAD/methods/unsupervised_learning_new/fedcopl.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"da48cf51668411a0","mcp_get_code":{"code_sha256":"da48cf51668411a0"}},{"arxiv_id":"2510.06307","paper":"/paper/arxiv-2510-06307","title":"Belief-Calibrated Multi-Agent Consensus Seeking for Complex NLP Tasks","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"dengwentao99/BCCS","path":"MMLU/evaluate.py","file_url":"https://github.com/dengwentao99/BCCS/blob/HEAD/MMLU/evaluate.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c8ac8a7c21aec637","mcp_get_code":{"code_sha256":"c8ac8a7c21aec637"}},{"arxiv_id":"2506.08184","paper":"/paper/2506-08184","title":"Unable to Forget: Proactive lnterference Reveals Working Memory Limits in LLMs Beyond Context Length","date":"2025-06-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhuangziGiantfish/Unable-to-Forget","path":"core/analysis_helper.py","file_url":"https://github.com/zhuangziGiantfish/Unable-to-Forget/blob/HEAD/core/analysis_helper.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"045c872a70e0a465","mcp_get_code":{"code_sha256":"045c872a70e0a465"}},{"arxiv_id":"2411.07979","paper":"/paper/exact-tractable-gauss-newton-optimization-in","title":"Exact, Tractable Gauss-Newton Optimization in Deep Reversible Architectures Reveal Poor Generalization","date":"2024-11-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mtkresearch/exact_GN_revNN","path":"fastbreak/losses/accuracy.py","file_url":"https://github.com/mtkresearch/exact_GN_revNN/blob/HEAD/fastbreak/losses/accuracy.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":"32455d018491dc70","mcp_get_code":{"code_sha256":"32455d018491dc70"}},{"arxiv_id":"2411.01696","paper":"/paper/conformal-risk-minimization-with-variance","title":"Conformal Risk Minimization with Variance Reduction","date":"2024-11-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nooranisima/conformal-risk-minimization-w-variance-reduction-code","path":"evaluation/evaluation.py","file_url":"https://github.com/nooranisima/conformal-risk-minimization-w-variance-reduction-code/blob/HEAD/evaluation/evaluation.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4db0c4d0d9f1dd47","mcp_get_code":{"code_sha256":"4db0c4d0d9f1dd47"}},{"arxiv_id":"2410.22770","paper":"/paper/injecguard-benchmarking-and-mitigating-over","title":"InjecGuard: Benchmarking and Mitigating Over-defense in Prompt Injection Guardrail Models","date":"2024-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"leolee99/injecguard","path":"util.py","file_url":"https://github.com/leolee99/injecguard/blob/HEAD/util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00f2b41a4116f1f4","mcp_get_code":{"code_sha256":"00f2b41a4116f1f4"}},{"arxiv_id":"2410.03782","paper":"/paper/dawin-training-free-dynamic-weight","title":"DaWin: Training-free Dynamic Weight Interpolation for Robust Adaptation","date":"2024-10-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"naver-ai/dawin","path":"dawin_rft/main_dawin.py","file_url":"https://github.com/naver-ai/dawin/blob/HEAD/dawin_rft/main_dawin.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"21d6c99219deff61","mcp_get_code":{"code_sha256":"21d6c99219deff61"}},{"arxiv_id":"2410.03145","paper":"/paper/margin-matching-preference-optimization","title":"Margin Matching Preference Optimization: Enhanced Model Alignment with Granular Feedback","date":"2024-10-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kykim0/margin-matching-pref-opt","path":"src/alignment/trainer/utils.py","file_url":"https://github.com/kykim0/margin-matching-pref-opt/blob/HEAD/src/alignment/trainer/utils.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":"170d1cc526ffc36c","mcp_get_code":{"code_sha256":"170d1cc526ffc36c"}},{"arxiv_id":"2409.03662","paper":"/paper/the-representation-landscape-of-few-shot","title":"The representation landscape of few-shot learning and fine-tuning in large language models","date":"2024-09-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"diegodoimo/geometry_icl_finetuning","path":"finetune.py","file_url":"https://github.com/diegodoimo/geometry_icl_finetuning/blob/HEAD/finetune.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b16d252479c798a0","mcp_get_code":{"code_sha256":"b16d252479c798a0"}},{"arxiv_id":"2408.07832","paper":"/paper/language-driven-slice-discovery-and-error","title":"LADDER: Language Driven Slice Discovery and Error Rectification","date":"2024-07-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"batmanlab/Ladder","path":"src/codebase/metrics.py","file_url":"https://github.com/batmanlab/Ladder/blob/HEAD/src/codebase/metrics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"CC-BY-4.0","inline_ok":false,"code_sha256_prefix":"8f49eb5b19d3ac4e","mcp_get_code":{"code_sha256":"8f49eb5b19d3ac4e"}},{"arxiv_id":"2407.00900","paper":"/paper/mathcamps-fine-grained-synthesis-of","title":"MathCAMPS: Fine-grained Synthesis of Mathematical Problems From Human Curricula","date":"2024-07-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gpoesia/mathcamps","path":"analysis.py","file_url":"https://github.com/gpoesia/mathcamps/blob/HEAD/analysis.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5a79c6682974a9b5","mcp_get_code":{"code_sha256":"5a79c6682974a9b5"}},{"arxiv_id":"2406.16797","paper":"/paper/lottery-ticket-adaptation-mitigating","title":"Lottery Ticket Adaptation: Mitigating Destructive Interference in LLMs","date":"2024-06-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kiddyboots216/lottery-ticket-adaptation","path":"rlaif/eval_model_all.py","file_url":"https://github.com/kiddyboots216/lottery-ticket-adaptation/blob/HEAD/rlaif/eval_model_all.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":"6619147dea2d2ff6","mcp_get_code":{"code_sha256":"6619147dea2d2ff6"}},{"arxiv_id":"2406.14496","paper":"/paper/african-or-european-swallow-benchmarking","title":"African or European Swallow? Benchmarking Large Vision-Language Models for Fine-Grained Object Classification","date":"2024-06-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gregor-ge/FOCI-Benchmark","path":"benchmark/clip_benchmark/evaluate.py","file_url":"https://github.com/gregor-ge/FOCI-Benchmark/blob/HEAD/benchmark/clip_benchmark/evaluate.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"11ec7f195cfd82d8","mcp_get_code":{"code_sha256":"11ec7f195cfd82d8"}},{"arxiv_id":"2406.10815","paper":"/paper/on-the-effectiveness-of-supervision-in","title":"On the Effectiveness of Supervision in Asymmetric Non-Contrastive Learning","date":"2024-06-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jh-oh-23/sup-ancl","path":"transfer.py","file_url":"https://github.com/jh-oh-23/sup-ancl/blob/HEAD/transfer.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":"48afbec326335e94","mcp_get_code":{"code_sha256":"48afbec326335e94"}},{"arxiv_id":"2406.09173","paper":"/paper/potion-towards-poison-unlearning","title":"Potion: Towards Poison Unlearning","date":"2024-06-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"if-loops/towards_poison_unlearning","path":"visualize.py","file_url":"https://github.com/if-loops/towards_poison_unlearning/blob/HEAD/visualize.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"1808b5d76587f897","mcp_get_code":{"code_sha256":"1808b5d76587f897"}},{"arxiv_id":"2406.01512","paper":"/paper/mad-multi-alignment-meg-to-text-decoding","title":"MAD: Multi-Alignment MEG-to-Text Decoding","date":"2024-06-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"neuspeech/mad-meg2text","path":"utils/model_utils.py","file_url":"https://github.com/neuspeech/mad-meg2text/blob/HEAD/utils/model_utils.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":"262947201dc066e9","mcp_get_code":{"code_sha256":"262947201dc066e9"}},{"arxiv_id":"2405.18217","paper":"/paper/understanding-inter-concept-relationships-in","title":"Understanding Inter-Concept Relationships in Concept-Based Models","date":"2024-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"naveenr414/Concept-Learning","path":"cem/cem/models/cbm.py","file_url":"https://github.com/naveenr414/Concept-Learning/blob/HEAD/cem/cem/models/cbm.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4ff918536e7f785c","mcp_get_code":{"code_sha256":"4ff918536e7f785c"}},{"arxiv_id":"2405.18217","paper":"/paper/understanding-inter-concept-relationships-in","title":"Understanding Inter-Concept Relationships in Concept-Based Models","date":"2024-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"naveenr414/Concept-Learning","path":"cem/cem/models/cem.py","file_url":"https://github.com/naveenr414/Concept-Learning/blob/HEAD/cem/cem/models/cem.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"625948c28aa9ff09","mcp_get_code":{"code_sha256":"625948c28aa9ff09"}},{"arxiv_id":"2405.16681","paper":"/paper/triple-preference-optimization-achieving","title":"Triple Preference Optimization: Achieving Better Alignment with Less Data in a Single Step Optimization","date":"2024-05-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sahsaeedi/triple-preference-optimization","path":"utils/utils.py","file_url":"https://github.com/sahsaeedi/triple-preference-optimization/blob/HEAD/utils/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dfc8e2556a70f4f3","mcp_get_code":{"code_sha256":"dfc8e2556a70f4f3"}},{"arxiv_id":"2405.11756","paper":"/paper/erasing-the-bias-fine-tuning-foundation","title":"Erasing the Bias: Fine-Tuning Foundation Models for Semi-Supervised Learning","date":"2024-05-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Gank0078/FineSSL","path":"utils/evaluator.py","file_url":"https://github.com/Gank0078/FineSSL/blob/HEAD/utils/evaluator.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"da48cf51668411a0","mcp_get_code":{"code_sha256":"da48cf51668411a0"}},{"arxiv_id":"2405.06424","paper":"/paper/improving-instruction-following-in-language","title":"Improving Instruction Following in Language Models through Proxy-Based Uncertainty Estimation","date":"2024-05-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"p-b-u/proxy_based_uncertainty","path":"Proxy-Uncertainty-Training-LLaMa-Factory/src/llmtuner/tuner/rm/metric.py","file_url":"https://github.com/p-b-u/proxy_based_uncertainty/blob/HEAD/Proxy-Uncertainty-Training-LLaMa-Factory/src/llmtuner/tuner/rm/metric.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"34c717348b9652b8","mcp_get_code":{"code_sha256":"34c717348b9652b8"}},{"arxiv_id":"2404.15806","paper":"/paper/where-to-mask-structure-guided-masking-for","title":"Where to Mask: Structure-Guided Masking for Graph Masked Autoencoders","date":"2024-04-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LiuChuang0059/StructMAE","path":"StructMAE-L/chem/pretraining.py","file_url":"https://github.com/LiuChuang0059/StructMAE/blob/HEAD/StructMAE-L/chem/pretraining.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1d31c46fec1fe3d1","mcp_get_code":{"code_sha256":"1d31c46fec1fe3d1"}},{"arxiv_id":"2404.07983","paper":"/paper/two-effects-one-trigger-on-the-modality-gap","title":"Two Effects, One Trigger: On the Modality Gap, Object Bias, and Information Imbalance in Contrastive Vision-Language Models","date":"2024-04-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lmb-freiburg/two-effects-one-trigger","path":"analysis/gap_precompute.py","file_url":"https://github.com/lmb-freiburg/two-effects-one-trigger/blob/HEAD/analysis/gap_precompute.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"08e7bba3e22dab87","mcp_get_code":{"code_sha256":"08e7bba3e22dab87"}},{"arxiv_id":"2404.07713","paper":"/paper/progressive-semantic-guided-vision","title":"Progressive Semantic-Guided Vision Transformer for Zero-Shot Learning","date":"2024-04-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shiming-chen/zslvit","path":"train_function.py","file_url":"https://github.com/shiming-chen/zslvit/blob/HEAD/train_function.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"63f355a22c4bfb43","mcp_get_code":{"code_sha256":"63f355a22c4bfb43"}},{"arxiv_id":"2403.01748","paper":"/paper/decode-neural-signal-as-speech","title":"NeuSpeech: Decode Neural signal as Speech","date":"2024-03-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"neuspeech/neuspeech1","path":"utils/model_utils.py","file_url":"https://github.com/neuspeech/neuspeech1/blob/HEAD/utils/model_utils.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":"262947201dc066e9","mcp_get_code":{"code_sha256":"262947201dc066e9"}},{"arxiv_id":"2403.01165","paper":"/paper/star-constraint-lora-with-dynamic-active","title":"STAR: Constraint LoRA with Dynamic Active Learning for Data-Efficient Fine-Tuning of Large Language Models","date":"2024-03-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"callanwu/star","path":"src/utils.py","file_url":"https://github.com/callanwu/star/blob/HEAD/src/utils.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":"6d89eb39c2cd1646","mcp_get_code":{"code_sha256":"6d89eb39c2cd1646"}},{"arxiv_id":"2402.14015","paper":"/paper/corrective-machine-unlearning","title":"Corrective Machine Unlearning","date":"2024-02-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"drimpossible/corrective-unlearning-bench","path":"logs/visualize.py","file_url":"https://github.com/drimpossible/corrective-unlearning-bench/blob/HEAD/logs/visualize.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"1808b5d76587f897","mcp_get_code":{"code_sha256":"1808b5d76587f897"}},{"arxiv_id":"2402.07197","paper":"/paper/graphtranslator-aligning-graph-model-to-large","title":"GraphTranslator: Aligning Graph Model to Large Language Model for Open-ended Tasks","date":"2024-02-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alibaba/graphtranslator","path":"Producer/inference/GraphSAGE.py","file_url":"https://github.com/alibaba/graphtranslator/blob/HEAD/Producer/inference/GraphSAGE.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"eb3a55d5da2240a5","mcp_get_code":{"code_sha256":"eb3a55d5da2240a5"}},{"arxiv_id":"2401.09243","paper":"/paper/diffclone-enhanced-behaviour-cloning-in","title":"DiffClone: Enhanced Behaviour Cloning in Robotics with Diffusion-Driven Policy Learning","date":"2024-01-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sirabas369/DiffClone","path":"toto_benchmark/vision/pvr_model_training.py","file_url":"https://github.com/sirabas369/DiffClone/blob/HEAD/toto_benchmark/vision/pvr_model_training.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"1263c50ebe6172a3","mcp_get_code":{"code_sha256":"1263c50ebe6172a3"}},{"arxiv_id":"2311.14265","paper":"/paper/bursting-spikes-efficient-and-high","title":"Adaptive Calibration: A Unified Conversion Framework of Spiking Neural Networks","date":"2023-11-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bic-l/burst-ann2snn","path":"general_utils/spiking_layer.py","file_url":"https://github.com/bic-l/burst-ann2snn/blob/HEAD/general_utils/spiking_layer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"38b283cfdea44e8b","mcp_get_code":{"code_sha256":"38b283cfdea44e8b"}},{"arxiv_id":"2311.04419","paper":"/paper/pepland-a-large-scale-pre-trained-peptide","title":"PepLand: a large-scale pre-trained peptide representation model for a comprehensive landscape of both canonical and non-canonical amino acids","date":"2023-11-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhangruochi/pepland","path":"model/util.py","file_url":"https://github.com/zhangruochi/pepland/blob/HEAD/model/util.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"1d31c46fec1fe3d1","mcp_get_code":{"code_sha256":"1d31c46fec1fe3d1"}},{"arxiv_id":"2310.18765","paper":"/paper/rethinking-semi-supervised-imbalanced-node-1","title":"Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance Decomposition","date":"2023-10-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yanliang3612/revar","path":"src/utils.py","file_url":"https://github.com/yanliang3612/revar/blob/HEAD/src/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9152905367d843e7","mcp_get_code":{"code_sha256":"9152905367d843e7"}},{"arxiv_id":"2310.16076","paper":"/paper/practical-computational-power-of-linear","title":"Practical Computational Power of Linear Transformers and Their Recurrent and Self-Referential Extensions","date":"2023-10-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IDSIA/fwp-formal-lang","path":"eval_utils.py","file_url":"https://github.com/IDSIA/fwp-formal-lang/blob/HEAD/eval_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"ae43b775b5812cfb","mcp_get_code":{"code_sha256":"ae43b775b5812cfb"}},{"arxiv_id":"2310.08659","paper":"/paper/loftq-lora-fine-tuning-aware-quantization-for","title":"LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models","date":"2023-10-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yxli2123/loftq","path":"test_gsm8k.py","file_url":"https://github.com/yxli2123/loftq/blob/HEAD/test_gsm8k.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6d89eb39c2cd1646","mcp_get_code":{"code_sha256":"6d89eb39c2cd1646"}},{"arxiv_id":"2309.16519","paper":"/paper/atomsurf-surface-representation-for-learning","title":"AtomSurf : Surface Representation for Learning on Protein Structures","date":"2023-09-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vincentx15/atom2d","path":"atom2d/holoprot/pl_module.py","file_url":"https://github.com/vincentx15/atom2d/blob/HEAD/atom2d/holoprot/pl_module.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b67e340958dd4794","mcp_get_code":{"code_sha256":"b67e340958dd4794"}},{"arxiv_id":"2306.16248","paper":"/paper/latent-sdes-on-homogeneous-spaces-1","title":"Latent SDEs on Homogeneous Spaces","date":"2023-06-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"plus-rkwitt/latentsdeonhs","path":"activity_classification.py","file_url":"https://github.com/plus-rkwitt/latentsdeonhs/blob/HEAD/activity_classification.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"75a1b0d78086afa8","mcp_get_code":{"code_sha256":"75a1b0d78086afa8"}},{"arxiv_id":"2306.09299","paper":"/paper/can-language-models-teach-weaker-agents","title":"Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Personalization","date":"2023-06-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"swarnaHub/ExplanationIntervention","path":"src/student_model.py","file_url":"https://github.com/swarnaHub/ExplanationIntervention/blob/HEAD/src/student_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d46ff19844104d81","mcp_get_code":{"code_sha256":"d46ff19844104d81"}},{"arxiv_id":"2306.08658","paper":"/paper/babel-imagenet-massively-multilingual","title":"Babel-ImageNet: Massively Multilingual Evaluation of Vision-and-Language Representations","date":"2023-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gregor-ge/babel-imagenet","path":"benchmark/eval_babel_imagenet.py","file_url":"https://github.com/gregor-ge/babel-imagenet/blob/HEAD/benchmark/eval_babel_imagenet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a64bbb8f73b4b407","mcp_get_code":{"code_sha256":"a64bbb8f73b4b407"}},{"arxiv_id":"2306.00942","paper":"/paper/train-offline-test-online-a-real-robot","title":"Train Offline, Test Online: A Real Robot Learning Benchmark","date":"2023-06-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AGI-Labs/toto_benchmark","path":"toto_benchmark/vision/pvr_model_training.py","file_url":"https://github.com/AGI-Labs/toto_benchmark/blob/HEAD/toto_benchmark/vision/pvr_model_training.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"1263c50ebe6172a3","mcp_get_code":{"code_sha256":"1263c50ebe6172a3"}},{"arxiv_id":"2305.19394","paper":"/paper/synaptic-weight-distributions-depend-on-the","title":"Synaptic Weight Distributions Depend on the Geometry of Plasticity","date":"2023-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"romanpogodin/synaptic-weight-distr","path":"finetuning.py","file_url":"https://github.com/romanpogodin/synaptic-weight-distr/blob/HEAD/finetuning.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"257ce0b1a219166c","mcp_get_code":{"code_sha256":"257ce0b1a219166c"}},{"arxiv_id":"2304.03977","paper":"/paper/emp-ssl-towards-self-supervised-learning-in","title":"EMP-SSL: Towards Self-Supervised Learning in One Training Epoch","date":"2023-04-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tsb0601/emp-ssl","path":"evaluate.py","file_url":"https://github.com/tsb0601/emp-ssl/blob/HEAD/evaluate.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1608268b36d9d53f","mcp_get_code":{"code_sha256":"1608268b36d9d53f"}},{"arxiv_id":"2303.10371","paper":"/paper/unreal-unlabeled-nodes-retrieval-and-labeling","title":"UNREAL:Unlabeled Nodes Retrieval and Labeling for Heavily-imbalanced Node Classification","date":"2023-03-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yanliang3612/UNREAL","path":"models/Summer.py","file_url":"https://github.com/yanliang3612/UNREAL/blob/HEAD/models/Summer.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00a374c740d0efb3","mcp_get_code":{"code_sha256":"00a374c740d0efb3"}},{"arxiv_id":"2303.10158","paper":"/paper/data-centric-artificial-intelligence-a-survey","title":"Data-centric Artificial Intelligence: A Survey","date":"2023-03-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"daochenzha/simtsc","path":"src/simtsc/model.py","file_url":"https://github.com/daochenzha/simtsc/blob/HEAD/src/simtsc/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c54a9f059d35378d","mcp_get_code":{"code_sha256":"c54a9f059d35378d"}},{"arxiv_id":"2302.01190","paper":"/paper/on-the-efficacy-of-differentially-private-few","title":"On the Efficacy of Differentially Private Few-shot Image Classification","date":"2023-02-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cambridge-mlg/dp-few-shot","path":"src/utils.py","file_url":"https://github.com/cambridge-mlg/dp-few-shot/blob/HEAD/src/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"335818a6c090235f","mcp_get_code":{"code_sha256":"335818a6c090235f"}},{"arxiv_id":"2211.04079","paper":"/paper/copen-probing-conceptual-knowledge-in-pre","title":"COPEN: Probing Conceptual Knowledge in Pre-trained Language Models","date":"2022-11-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"THU-KEG/COPEN","path":"code/finetuning/metrics.py","file_url":"https://github.com/THU-KEG/COPEN/blob/HEAD/code/finetuning/metrics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0a98f376f88ee0c4","mcp_get_code":{"code_sha256":"0a98f376f88ee0c4"}},{"arxiv_id":"2210.16484","paper":"/paper/a-systematic-survey-of-molecular-pre-trained","title":"A Systematic Survey of Chemical Pre-trained Models","date":"2022-10-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"junxia97/mole-bert","path":"pretrain.py","file_url":"https://github.com/junxia97/mole-bert/blob/HEAD/pretrain.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1d31c46fec1fe3d1","mcp_get_code":{"code_sha256":"1d31c46fec1fe3d1"}},{"arxiv_id":"2210.14026","paper":"/paper/swift-rapid-decentralized-federated-learning","title":"SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication","date":"2022-10-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"umd-huang-lab/SWIFT","path":"Utils/Misc.py","file_url":"https://github.com/umd-huang-lab/SWIFT/blob/HEAD/Utils/Misc.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6e8adbb131ab347a","mcp_get_code":{"code_sha256":"6e8adbb131ab347a"}},{"arxiv_id":"2210.04206","paper":"/paper/attention-diversification-for-domain","title":"Attention Diversification for Domain Generalization","date":"2022-10-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hikvision-research/DomainGeneralization","path":"dassl/metrics/accuracy.py","file_url":"https://github.com/hikvision-research/DomainGeneralization/blob/HEAD/dassl/metrics/accuracy.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":"da48cf51668411a0","mcp_get_code":{"code_sha256":"da48cf51668411a0"}},{"arxiv_id":"2209.00189","paper":"/paper/federated-learning-with-label-distribution","title":"Federated Learning with Label Distribution Skew via Logits Calibration","date":"2022-09-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bytedance/feddecorr","path":"approach/fedlogitcal.py","file_url":"https://github.com/bytedance/feddecorr/blob/HEAD/approach/fedlogitcal.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e7e5bb878fd22400","mcp_get_code":{"code_sha256":"e7e5bb878fd22400"}},{"arxiv_id":"2201.10777","paper":"/paper/meta-learning-spiking-neural-networks-with","title":"Meta-learning Spiking Neural Networks with Surrogate Gradient Descent","date":"2022-01-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nmi-lab/snn_maml","path":"snn_maml/utils.py","file_url":"https://github.com/nmi-lab/snn_maml/blob/HEAD/snn_maml/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":"58242025465b988c","mcp_get_code":{"code_sha256":"58242025465b988c"}},{"arxiv_id":"2112.03482","paper":"/paper/combining-learning-from-human-feedback-and","title":"Combining Learning from Human Feedback and Knowledge Engineering to Solve Hierarchical Tasks in Minecraft","date":"2021-12-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"viniciusguigo/kairos_minerl_basalt","path":"kairos_minerl/src/kairos_minerl/behavior_cloner.py","file_url":"https://github.com/viniciusguigo/kairos_minerl_basalt/blob/HEAD/kairos_minerl/src/kairos_minerl/behavior_cloner.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"481210e6f5732a32","mcp_get_code":{"code_sha256":"481210e6f5732a32"}},{"arxiv_id":"2112.03482","paper":"/paper/combining-learning-from-human-feedback-and","title":"Combining Learning from Human Feedback and Knowledge Engineering to Solve Hierarchical Tasks in Minecraft","date":"2021-12-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"viniciusguigo/kairos_minerl_basalt","path":"kairos_minerl/src/kairos_minerl/state_classifier.py","file_url":"https://github.com/viniciusguigo/kairos_minerl_basalt/blob/HEAD/kairos_minerl/src/kairos_minerl/state_classifier.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9fd4e3706a238d72","mcp_get_code":{"code_sha256":"9fd4e3706a238d72"}},{"arxiv_id":"2111.09613","paper":"/paper/improving-transferability-of-representations","title":"Improving Transferability of Representations via Augmentation-Aware Self-Supervision","date":"2021-11-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hankook/AugSelf","path":"transfer_linear_eval.py","file_url":"https://github.com/hankook/AugSelf/blob/HEAD/transfer_linear_eval.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":"4c7df3c8287f477f","mcp_get_code":{"code_sha256":"4c7df3c8287f477f"}},{"arxiv_id":"2110.15122","paper":"/paper/cafe-catastrophic-data-leakage-in-vertical","title":"CAFE: Catastrophic Data Leakage in Vertical Federated Learning","date":"2021-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"derafael/cafe","path":"utils.py","file_url":"https://github.com/derafael/cafe/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"778b9ea3a727c31d","mcp_get_code":{"code_sha256":"778b9ea3a727c31d"}},{"arxiv_id":"2110.09192","paper":"/paper/learning-optimal-conformal-classifiers-1","title":"Learning Optimal Conformal Classifiers","date":"2021-10-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"deepmind/conformal_training","path":"evaluation.py","file_url":"https://github.com/deepmind/conformal_training/blob/HEAD/evaluation.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":"2504d3a1635ed126","mcp_get_code":{"code_sha256":"2504d3a1635ed126"}},{"arxiv_id":"2108.11898","paper":"/paper/supervised-compression-for-resource","title":"Supervised Compression for Resource-Constrained Edge Computing Systems","date":"2021-08-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"a427a6169de8f607","mcp_get_code":{"code_sha256":"a427a6169de8f607"}},{"arxiv_id":"2107.01105","paper":"/paper/memory-efficient-meta-learning-with-large","title":"Memory Efficient Meta-Learning with Large Images","date":"2021-07-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cambridge-mlg/LITE","path":"src/utils.py","file_url":"https://github.com/cambridge-mlg/LITE/blob/HEAD/src/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"335818a6c090235f","mcp_get_code":{"code_sha256":"335818a6c090235f"}},{"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/AKT.py","file_url":"https://github.com/bigdata-ustc/EduKTM/blob/HEAD/EduKTM/AKT/AKT.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":"eaf7bb8070068ec5","mcp_get_code":{"code_sha256":"eaf7bb8070068ec5"}},{"arxiv_id":"2010.00515","paper":"/paper/linguistic-structure-guided-context-modeling-1","title":"Linguistic Structure Guided Context Modeling for Referring Image Segmentation","date":"2020-10-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"spyflying/LSCM-Refseg","path":"util/processing_tools.py","file_url":"https://github.com/spyflying/LSCM-Refseg/blob/HEAD/util/processing_tools.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"194457507e72807b","mcp_get_code":{"code_sha256":"194457507e72807b"}},{"arxiv_id":"2010.00352","paper":"/paper/meta-consolidation-for-continual-learning","title":"Meta-Consolidation for Continual Learning","date":"2020-10-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JosephKJ/merlin","path":"lib/consolidate.py","file_url":"https://github.com/JosephKJ/merlin/blob/HEAD/lib/consolidate.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6d1affa42db4b765","mcp_get_code":{"code_sha256":"6d1affa42db4b765"}},{"arxiv_id":"2006.07114","paper":"/paper/knowledge-distillation-meets-self-supervision","title":"Knowledge Distillation Meets Self-Supervision","date":"2020-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"a427a6169de8f607","mcp_get_code":{"code_sha256":"a427a6169de8f607"}},{"arxiv_id":"2006.03806","paper":"/paper/leveraging-the-feature-distribution-in","title":"Leveraging the Feature Distribution in Transfer-based Few-Shot Learning","date":"2020-06-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mbonto/fewshot_neuroimaging_classification","path":"SimpleShot/functions.py","file_url":"https://github.com/mbonto/fewshot_neuroimaging_classification/blob/HEAD/SimpleShot/functions.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a280c947612b8a09","mcp_get_code":{"code_sha256":"a280c947612b8a09"}},{"arxiv_id":"2005.11079","paper":"/paper/graph-random-neural-network","title":"Graph Random Neural Network for Semi-Supervised Learning on Graphs","date":"2020-05-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"junzhuang-code/graphss","path":"graphSS/models_SS.py","file_url":"https://github.com/junzhuang-code/graphss/blob/HEAD/graphSS/models_SS.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d22f6a0cb53e22bc","mcp_get_code":{"code_sha256":"d22f6a0cb53e22bc"}},{"arxiv_id":"2005.00669","paper":"/paper/contrastive-self-supervised-learning-for","title":"Contrastive Self-Supervised Learning for Commonsense Reasoning","date":"2020-05-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SAP-samples/acl2020-commonsense","path":"scorer.py","file_url":"https://github.com/SAP-samples/acl2020-commonsense/blob/HEAD/scorer.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":"ff87d20ae15918c9","mcp_get_code":{"code_sha256":"ff87d20ae15918c9"}},{"arxiv_id":"2004.06025","paper":"/paper/learning-from-rules-generalizing-labeled-1","title":"Learning from Rules Generalizing Labeled Exemplars","date":"2020-04-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"awasthiabhijeet/learning-from-rules","path":"src/hls/run_snorkel.py","file_url":"https://github.com/awasthiabhijeet/learning-from-rules/blob/HEAD/src/hls/run_snorkel.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"8f0ff324b2b8d401","mcp_get_code":{"code_sha256":"8f0ff324b2b8d401"}},{"arxiv_id":"2003.05856","paper":"/paper/online-fast-adaptation-and-knowledge","title":"Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning","date":"2020-03-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ElementAI/osaka","path":"MAML/utils.py","file_url":"https://github.com/ElementAI/osaka/blob/HEAD/MAML/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":"58242025465b988c","mcp_get_code":{"code_sha256":"58242025465b988c"}},{"arxiv_id":"1910.12853","paper":"/paper/a-game-theoretic-approach-to-class-wise","title":"A Game Theoretic Approach to Class-wise Selective Rationalization","date":"2019-10-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"code-terminator/classwise_rationale","path":"core/metric.py","file_url":"https://github.com/code-terminator/classwise_rationale/blob/HEAD/core/metric.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d51408db9fe03765","mcp_get_code":{"code_sha256":"d51408db9fe03765"}},{"arxiv_id":"1907.09682","paper":"/paper/similarity-preserving-knowledge-distillation","title":"Similarity-Preserving Knowledge Distillation","date":"2019-07-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"a427a6169de8f607","mcp_get_code":{"code_sha256":"a427a6169de8f607"}},{"arxiv_id":"1905.12265","paper":"/paper/pre-training-graph-neural-networks","title":"Strategies for Pre-training Graph Neural Networks","date":"2019-05-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"snap-stanford/pretrain-gnns","path":"bio/pretrain_masking.py","file_url":"https://github.com/snap-stanford/pretrain-gnns/blob/HEAD/bio/pretrain_masking.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1d31c46fec1fe3d1","mcp_get_code":{"code_sha256":"1d31c46fec1fe3d1"}},{"arxiv_id":"1904.05068","paper":"/paper/relational-knowledge-distillation","title":"Relational Knowledge Distillation","date":"2019-04-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"a427a6169de8f607","mcp_get_code":{"code_sha256":"a427a6169de8f607"}},{"arxiv_id":"1901.03407","paper":"/paper/deep-learning-for-anomaly-detection-a-survey","title":"Deep Learning for Anomaly Detection: A Survey","date":"2019-01-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fastforwardlabs/deepad","path":"utils/eval_utils.py","file_url":"https://github.com/fastforwardlabs/deepad/blob/HEAD/utils/eval_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":"b259a84b8a375955","mcp_get_code":{"code_sha256":"b259a84b8a375955"}},{"arxiv_id":"1810.10191","paper":"/paper/making-sense-of-vision-and-touch-self","title":"Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks","date":"2018-10-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"stanford-iprl-lab/multimodal_representation","path":"multimodal/utils.py","file_url":"https://github.com/stanford-iprl-lab/multimodal_representation/blob/HEAD/multimodal/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dcf160ba03ce4e37","mcp_get_code":{"code_sha256":"dcf160ba03ce4e37"}},{"arxiv_id":"1710.03077","paper":"/paper/deeper-broader-and-artier-domain","title":"Deeper, Broader and Artier Domain Generalization","date":"2017-10-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xch-liu/geom-tex-dg","path":"Dassl/dassl/metrics/accuracy.py","file_url":"https://github.com/xch-liu/geom-tex-dg/blob/HEAD/Dassl/dassl/metrics/accuracy.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"da48cf51668411a0","mcp_get_code":{"code_sha256":"da48cf51668411a0"}},{"arxiv_id":"1703.05175","paper":"/paper/prototypical-networks-for-few-shot-learning","title":"Prototypical Networks for Few-shot Learning","date":"2017-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jsalbert/prototypical-networks","path":"utils.py","file_url":"https://github.com/jsalbert/prototypical-networks/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d91ab830cd1fc8e4","mcp_get_code":{"code_sha256":"d91ab830cd1fc8e4"}},{"arxiv_id":"1703.03400","paper":"/paper/model-agnostic-meta-learning-for-fast","title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","date":"2017-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SinghJasdeep/Projecting-Conflicting-Gradients","path":"maml/utils.py","file_url":"https://github.com/SinghJasdeep/Projecting-Conflicting-Gradients/blob/HEAD/maml/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"58242025465b988c","mcp_get_code":{"code_sha256":"58242025465b988c"}},{"arxiv_id":"1503.02531","paper":"/paper/distilling-the-knowledge-in-a-neural-network","title":"Distilling the Knowledge in a Neural Network","date":"2015-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yoshitomo-matsubara/torchdistill","path":"examples/torchvision/image_classification.py","file_url":"https://github.com/yoshitomo-matsubara/torchdistill/blob/HEAD/examples/torchvision/image_classification.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a427a6169de8f607","mcp_get_code":{"code_sha256":"a427a6169de8f607"}},{"arxiv_id":"1412.6550","paper":"/paper/fitnets-hints-for-thin-deep-nets","title":"FitNets: Hints for Thin Deep Nets","date":"2014-12-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"a427a6169de8f607","mcp_get_code":{"code_sha256":"a427a6169de8f607"}},{"arxiv_id":"2025.findings-acl.1225","paper":null,"title":"arXiv:2025.findings-acl.1225","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"HieuNT91/attention_pruning","path":"src/gsm8k_utils.py","file_url":"https://github.com/HieuNT91/attention_pruning/blob/HEAD/src/gsm8k_utils.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6d89eb39c2cd1646","mcp_get_code":{"code_sha256":"6d89eb39c2cd1646"}},{"arxiv_id":"2025.acl-long.1468","paper":null,"title":"arXiv:2025.acl-long.1468","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"leolee99/PIGuard","path":"util.py","file_url":"https://github.com/leolee99/PIGuard/blob/HEAD/util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00f2b41a4116f1f4","mcp_get_code":{"code_sha256":"00f2b41a4116f1f4"}}]}