{"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/get-module","entry":"get_module","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":37,"n_papers_ran":18,"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":22,"n_samples_ran":9,"n_samples_fingerprinted":0,"n_places":37,"n_places_pointer_only":14,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":4,"ran_fixture":0,"ran":5,"unverified":13},"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":"2604.11214","paper":"/paper/arxiv-2604-11214","title":"HiEdit: Lifelong Model Editing with Hierarchical Reinforcement Learning","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"yangfanww/hiedit","path":"util.py","file_url":"https://github.com/yangfanww/hiedit/blob/HEAD/util.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ccb869cda9ea80e6","mcp_get_code":{"code_sha256":"ccb869cda9ea80e6"}},{"arxiv_id":"2602.04752","paper":"/paper/arxiv-2602-04752","title":"Decomposing Query-Key Feature Interactions Using Contrastive Covariances","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"ajyl/QK","path":"src/binding/C_lex.py","file_url":"https://github.com/ajyl/QK/blob/HEAD/src/binding/C_lex.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"871dee43b37fa18a","mcp_get_code":{"code_sha256":"871dee43b37fa18a"}},{"arxiv_id":"2602.01267","paper":"/paper/arxiv-2602-01267","title":"Diving into Kronecker Adapters: Component Design Matters","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"rainstonee/CDKA","path":"lora_plus.py","file_url":"https://github.com/rainstonee/CDKA/blob/HEAD/lora_plus.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ef1ba16faaacbb49","mcp_get_code":{"code_sha256":"ef1ba16faaacbb49"}},{"arxiv_id":"2601.11441","paper":"/paper/arxiv-2601-11441","title":"Hierarchical Orthogonal Residual Spread for Precise Massive Editing in Large Language Models","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"XiaojieGu/HORSE","path":"editor/horse.py","file_url":"https://github.com/XiaojieGu/HORSE/blob/HEAD/editor/horse.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ccb869cda9ea80e6","mcp_get_code":{"code_sha256":"ccb869cda9ea80e6"}},{"arxiv_id":"2601.00581","paper":"/paper/arxiv-2601-00581","title":"AceFF: A State-of-the-Art Machine Learning Potential for Small Molecules","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"isayevlab/aimnetcentral","path":"aimnet/models/aimnet2.py","file_url":"https://github.com/isayevlab/aimnetcentral/blob/HEAD/aimnet/models/aimnet2.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9cdb3130232197c6","mcp_get_code":{"code_sha256":"9cdb3130232197c6"}},{"arxiv_id":"2505.18909","paper":"/paper/on-the-role-of-label-noise-in-the-feature","title":"On the Role of Label Noise in the Feature Learning Process","date":"2025-05-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zzp1012/label-noise-theory","path":"src/utils/tools.py","file_url":"https://github.com/zzp1012/label-noise-theory/blob/HEAD/src/utils/tools.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2c6366c32294dadd","mcp_get_code":{"code_sha256":"2c6366c32294dadd"}},{"arxiv_id":"2503.01822","paper":"/paper/projecting-assumptions-the-duality-between","title":"Projecting Assumptions: The Duality Between Sparse Autoencoders and Concept Geometry","date":"2025-03-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"EkdeepSLubana/spadeFormalGrammars","path":"utils/analysis.py","file_url":"https://github.com/EkdeepSLubana/spadeFormalGrammars/blob/HEAD/utils/analysis.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bfa0b6ce30af730f","mcp_get_code":{"code_sha256":"bfa0b6ce30af730f"}},{"arxiv_id":"2412.13341","paper":"/paper/concept-rot-poisoning-concepts-in-large","title":"Concept-ROT: Poisoning Concepts in Large Language Models with Model Editing","date":"2024-12-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"keltin13/concept-rot","path":"rot/compute_v.py","file_url":"https://github.com/keltin13/concept-rot/blob/HEAD/rot/compute_v.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8953f9ef90cf6afd","mcp_get_code":{"code_sha256":"8953f9ef90cf6afd"}},{"arxiv_id":"2411.02853","paper":"/paper/adopt-modified-adam-can-converge-with-any-b-2","title":"ADOPT: Modified Adam Can Converge with Any $β_2$ with the Optimal Rate","date":"2024-11-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"iShohei220/adopt","path":"imagenet/presets.py","file_url":"https://github.com/iShohei220/adopt/blob/HEAD/imagenet/presets.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":"b2ea55703711dd12","mcp_get_code":{"code_sha256":"b2ea55703711dd12"}},{"arxiv_id":"2410.11767","paper":"/paper/analyzing-in-abilities-of-saes-via-formal","title":"Analyzing (In)Abilities of SAEs via Formal Languages","date":"2024-10-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Abhinav271828/pcfg-sae-causal-arr-oct24","path":"utils/analysis.py","file_url":"https://github.com/Abhinav271828/pcfg-sae-causal-arr-oct24/blob/HEAD/utils/analysis.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bfa0b6ce30af730f","mcp_get_code":{"code_sha256":"bfa0b6ce30af730f"}},{"arxiv_id":"2410.07054","paper":"/paper/mitigating-the-language-mismatch-and","title":"Mitigating the Language Mismatch and Repetition Issues in LLM-based Machine Translation via Model Editing","date":"2024-10-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"weichuanw/llm-based-mt-via-model-editing","path":"Model_Editing_Adaptation/fv_task_adaptation/src/utils/intervention_utils.py","file_url":"https://github.com/weichuanw/llm-based-mt-via-model-editing/blob/HEAD/Model_Editing_Adaptation/fv_task_adaptation/src/utils/intervention_utils.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8953f9ef90cf6afd","mcp_get_code":{"code_sha256":"8953f9ef90cf6afd"}},{"arxiv_id":"2410.02247","paper":"/paper/theoretical-insights-into-fine-tuning","title":"Theoretical Insights into Fine-Tuning Attention Mechanism: Generalization and Optimization","date":"2024-10-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chen123CtrlS/LightweightAtt","path":"MyTrainer.py","file_url":"https://github.com/chen123CtrlS/LightweightAtt/blob/HEAD/MyTrainer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ef1ba16faaacbb49","mcp_get_code":{"code_sha256":"ef1ba16faaacbb49"}},{"arxiv_id":"2407.05000","paper":"/paper/lora-ga-low-rank-adaptation-with-gradient","title":"LoRA-GA: Low-Rank Adaptation with Gradient Approximation","date":"2024-07-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"outsider565/lora-ga","path":"reproduce/lora_plus.py","file_url":"https://github.com/outsider565/lora-ga/blob/HEAD/reproduce/lora_plus.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":"ef1ba16faaacbb49","mcp_get_code":{"code_sha256":"ef1ba16faaacbb49"}},{"arxiv_id":"2406.17245","paper":"/paper/unlocking-continual-learning-abilities-in","title":"Unlocking Continual Learning Abilities in Language Models","date":"2024-06-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wenyudu/migu","path":"src/accelerate_local.py","file_url":"https://github.com/wenyudu/migu/blob/HEAD/src/accelerate_local.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"90776fe512a26186","mcp_get_code":{"code_sha256":"90776fe512a26186"}},{"arxiv_id":"2406.10485","paper":"/paper/a-label-is-worth-a-thousand-images-in-dataset","title":"A Label is Worth a Thousand Images in Dataset Distillation","date":"2024-06-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sunnytqin/no-distillation","path":"train_expert/presets.py","file_url":"https://github.com/sunnytqin/no-distillation/blob/HEAD/train_expert/presets.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b2ea55703711dd12","mcp_get_code":{"code_sha256":"b2ea55703711dd12"}},{"arxiv_id":"2405.01008","paper":"/paper/on-mechanistic-knowledge-localization-in-text","title":"On Mechanistic Knowledge Localization in Text-to-Image Generative Models","date":"2024-05-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"samyadeepbasu/locogen","path":"trace_attn_layer.py","file_url":"https://github.com/samyadeepbasu/locogen/blob/HEAD/trace_attn_layer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8953f9ef90cf6afd","mcp_get_code":{"code_sha256":"8953f9ef90cf6afd"}},{"arxiv_id":"2403.10518","paper":"/paper/lodge-a-coarse-to-fine-diffusion-network-for","title":"Lodge: A Coarse to Fine Diffusion Network for Long Dance Generation Guided by the Characteristic Dance Primitives","date":"2024-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"li-ronghui/LODGE","path":"dld/models/get_model.py","file_url":"https://github.com/li-ronghui/LODGE/blob/HEAD/dld/models/get_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1e0ebd96da8d14f9","mcp_get_code":{"code_sha256":"1e0ebd96da8d14f9"}},{"arxiv_id":"2403.10045","paper":"/paper/towards-adversarially-robust-dataset","title":"Towards Adversarially Robust Dataset Distillation by Curvature Regularization","date":"2024-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yumozi/guard","path":"pretrain/presets.py","file_url":"https://github.com/yumozi/guard/blob/HEAD/pretrain/presets.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b2ea55703711dd12","mcp_get_code":{"code_sha256":"b2ea55703711dd12"}},{"arxiv_id":"2402.12354","paper":"/paper/lora-efficient-low-rank-adaptation-of-large","title":"LoRA+: Efficient Low Rank Adaptation of Large Models","date":"2024-02-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nikhil-ghosh-berkeley/loraplus","path":"lora_plus.py","file_url":"https://github.com/nikhil-ghosh-berkeley/loraplus/blob/HEAD/lora_plus.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ef1ba16faaacbb49","mcp_get_code":{"code_sha256":"ef1ba16faaacbb49"}},{"arxiv_id":"2312.09852","paper":"/paper/learning-distributions-on-manifolds-with-free","title":"Learning Distributions on Manifolds with Free-Form Flows","date":"2023-12-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vislearn/FFF","path":"fff/model/utils.py","file_url":"https://github.com/vislearn/FFF/blob/HEAD/fff/model/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"62beb8156e773f1e","mcp_get_code":{"code_sha256":"62beb8156e773f1e"}},{"arxiv_id":"2312.06681","paper":"/paper/steering-llama-2-via-contrastive-activation","title":"Steering Llama 2 via Contrastive Activation Addition","date":"2023-12-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"steering-vectors/steering-vectors","path":"steering_vectors/torch_utils.py","file_url":"https://github.com/steering-vectors/steering-vectors/blob/HEAD/steering_vectors/torch_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f212fc11c8550e27","mcp_get_code":{"code_sha256":"f212fc11c8550e27"}},{"arxiv_id":"2312.00700","paper":"/paper/gift-generative-interpretable-fine-tuning","title":"Generative Parameter-Efficient Fine-Tuning","date":"2023-12-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"savadikarc/gift","path":"language_modeling/math_code_instruct/lora_plus.py","file_url":"https://github.com/savadikarc/gift/blob/HEAD/language_modeling/math_code_instruct/lora_plus.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ef1ba16faaacbb49","mcp_get_code":{"code_sha256":"ef1ba16faaacbb49"}},{"arxiv_id":"2311.04661","paper":"/paper/massive-editing-for-large-language-models-via","title":"Massive Editing for Large Language Models via Meta Learning","date":"2023-11-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chenmientan/malmen","path":"util.py","file_url":"https://github.com/chenmientan/malmen/blob/HEAD/util.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ccb869cda9ea80e6","mcp_get_code":{"code_sha256":"ccb869cda9ea80e6"}},{"arxiv_id":"2311.01015","paper":null,"title":"arXiv:2311.01015","date":null,"month_inferred_from_arxiv_id":"2023-11","title_source":null,"repo":"jpthu17/GraphMotion","path":"GraphMotion/models/get_model.py","file_url":"https://github.com/jpthu17/GraphMotion/blob/HEAD/GraphMotion/models/get_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"526e7520c87e4281","mcp_get_code":{"code_sha256":"526e7520c87e4281"}},{"arxiv_id":"2310.19224","paper":"/paper/chammi-a-benchmark-for-channel-adaptive-1","title":"CHAMMI: A benchmark for channel-adaptive models in microscopy imaging","date":"2023-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chaudatascience/channel_adaptive_models","path":"models/convnext_base_miro.py","file_url":"https://github.com/chaudatascience/channel_adaptive_models/blob/HEAD/models/convnext_base_miro.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c8980fa1301b0ca7","mcp_get_code":{"code_sha256":"c8980fa1301b0ca7"}},{"arxiv_id":"2310.15213","paper":"/paper/function-vectors-in-large-language-models","title":"Function Vectors in Large Language Models","date":"2023-10-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ericwtodd/function_vectors","path":"src/utils/intervention_utils.py","file_url":"https://github.com/ericwtodd/function_vectors/blob/HEAD/src/utils/intervention_utils.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8953f9ef90cf6afd","mcp_get_code":{"code_sha256":"8953f9ef90cf6afd"}},{"arxiv_id":"2310.12978","paper":"/paper/humantomato-text-aligned-whole-body-motion","title":"HumanTOMATO: Text-aligned Whole-body Motion Generation","date":"2023-10-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IDEA-Research/HumanTOMATO","path":"OpenTMA/tma/models/get_model.py","file_url":"https://github.com/IDEA-Research/HumanTOMATO/blob/HEAD/OpenTMA/tma/models/get_model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"5acf2cc2e4bad9a2","mcp_get_code":{"code_sha256":"5acf2cc2e4bad9a2"}},{"arxiv_id":"2309.15098","paper":"/paper/attention-satisfies-a-constraint-satisfaction","title":"Attention Satisfies: A Constraint-Satisfaction Lens on Factual Errors of Language Models","date":"2023-09-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"8953f9ef90cf6afd","mcp_get_code":{"code_sha256":"8953f9ef90cf6afd"}},{"arxiv_id":"2306.07483","paper":"/paper/semi-supervised-learning-made-simple-with-1","title":"Semi-supervised learning made simple with self-supervised clustering","date":"2023-06-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pietroastolfi/suave-daino","path":"suave/main_suave.py","file_url":"https://github.com/pietroastolfi/suave-daino/blob/HEAD/suave/main_suave.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"7d52560c3f93bb3a","mcp_get_code":{"code_sha256":"7d52560c3f93bb3a"}},{"arxiv_id":"2305.12535","paper":"/paper/explaining-how-transformers-use-context-to","title":"Explaining How Transformers Use Context to Build Predictions","date":"2023-05-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mt-upc/logit-explanations","path":"src/contributions.py","file_url":"https://github.com/mt-upc/logit-explanations/blob/HEAD/src/contributions.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":"0b0bac3cd5d8d507","mcp_get_code":{"code_sha256":"0b0bac3cd5d8d507"}},{"arxiv_id":"2303.01233","paper":"/paper/domain-aware-triplet-loss-in-domain","title":"Domain-aware Triplet loss in Domain Generalization","date":"2023-03-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"workerbcd/dct","path":"domainbed/backbones/new_networks.py","file_url":"https://github.com/workerbcd/dct/blob/HEAD/domainbed/backbones/new_networks.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c8980fa1301b0ca7","mcp_get_code":{"code_sha256":"c8980fa1301b0ca7"}},{"arxiv_id":"2301.12246","paper":"/paper/a-closer-look-at-few-shot-classification","title":"A Closer Look at Few-shot Classification Again","date":"2023-01-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Frankluox/Pytorch-MetaDataset","path":"models/build.py","file_url":"https://github.com/Frankluox/Pytorch-MetaDataset/blob/HEAD/models/build.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2685107849023379","mcp_get_code":{"code_sha256":"2685107849023379"}},{"arxiv_id":"2211.12979","paper":"/paper/flair-1-semantic-segmentation-and-domain","title":"FLAIR #1: semantic segmentation and domain adaptation dataset","date":"2022-11-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IGNF/FLAIR-1-AI-Challenge","path":"src/zone_detect/model.py","file_url":"https://github.com/IGNF/FLAIR-1-AI-Challenge/blob/HEAD/src/zone_detect/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"b22bf961966c9438","mcp_get_code":{"code_sha256":"b22bf961966c9438"}},{"arxiv_id":"2103.15798","paper":"/paper/rethinking-neural-operations-for-diverse","title":"Rethinking Neural Operations for Diverse Tasks","date":"2021-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nick11roberts/XD","path":"chrysalis/chrysalis.py","file_url":"https://github.com/nick11roberts/XD/blob/HEAD/chrysalis/chrysalis.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":"e151df3b5424a3fb","mcp_get_code":{"code_sha256":"e151df3b5424a3fb"}},{"arxiv_id":"2103.15619","paper":"/paper/setvae-learning-hierarchical-composition-for","title":"SetVAE: Learning Hierarchical Composition for Generative Modeling of Set-Structured Data","date":"2021-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jw9730/setvae","path":"models/networks.py","file_url":"https://github.com/jw9730/setvae/blob/HEAD/models/networks.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f5ca3e9834167675","mcp_get_code":{"code_sha256":"f5ca3e9834167675"}},{"arxiv_id":"openreview_AAWlum38oE","paper":null,"title":"arXiv:openreview_AAWlum38oE","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"1202kbs/AYT","path":"src/ayt/utils.py","file_url":"https://github.com/1202kbs/AYT/blob/HEAD/src/ayt/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4519681ef95f5e17","mcp_get_code":{"code_sha256":"4519681ef95f5e17"}},{"arxiv_id":"2024.findings-emnlp.379","paper":null,"title":"arXiv:2024.findings-emnlp.379","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"wenyudu/MIGU","path":"src/accelerate_local.py","file_url":"https://github.com/wenyudu/MIGU/blob/HEAD/src/accelerate_local.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"90776fe512a26186","mcp_get_code":{"code_sha256":"90776fe512a26186"}}]}