{"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/register","entry":"register","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":66,"n_papers_ran":42,"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":35,"n_samples_ran":14,"n_samples_fingerprinted":0,"n_places":71,"n_places_pointer_only":21,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":4,"ran_fixture":0,"ran":10,"unverified":21},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2605.31500","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/models/registry.py","file_url":"https://github.com/yandex-research/On-Efficient-Scaling-Of-GNNs/blob/HEAD/src/models/registry.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":"964dcee138e3fb6a","mcp_get_code":{"code_sha256":"964dcee138e3fb6a"}},{"arxiv_id":"2605.13412","paper":"/paper/arxiv-2605-13412","title":"LLMs as annotators of credibility assessment in Danish asylum decisions: evaluating classification performance and errors beyond aggregated metrics","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"glhr/RAB-Cred","path":"llm_annotation/schemas.py","file_url":"https://github.com/glhr/RAB-Cred/blob/HEAD/llm_annotation/schemas.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"553d68bf1be62460","mcp_get_code":{"code_sha256":"553d68bf1be62460"}},{"arxiv_id":"2603.25976","paper":"/paper/arxiv-2603-25976","title":"Second-Order, First-Class: A Composable Stack for Curvature-Aware Training","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"cor3bit/somax","path":"src/somax/presets.py","file_url":"https://github.com/cor3bit/somax/blob/HEAD/src/somax/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":"bf106c4e3cfa927f","mcp_get_code":{"code_sha256":"bf106c4e3cfa927f"}},{"arxiv_id":"2603.06588","paper":"/paper/arxiv-2603-06588","title":"vLLM Hook v0: A Plug-in for Programming Model Internals on vLLM","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"IBM/vLLM-Hook","path":"vllm_hook_plugins/vllm_hook_plugins/_hook_plugin.py","file_url":"https://github.com/IBM/vLLM-Hook/blob/HEAD/vllm_hook_plugins/vllm_hook_plugins/_hook_plugin.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":"b2bf8210c576741d","mcp_get_code":{"code_sha256":"b2bf8210c576741d"}},{"arxiv_id":"2602.04714","paper":"/paper/arxiv-2602-04714","title":"Bounded-Abstention Multi-horion Time series Forecasting","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"aditya-grover/climate-learn","path":"src/climate_learn/metrics/utils.py","file_url":"https://github.com/aditya-grover/climate-learn/blob/HEAD/src/climate_learn/metrics/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"13710d199a13797d","mcp_get_code":{"code_sha256":"13710d199a13797d"}},{"arxiv_id":"2510.09734","paper":"/paper/arxiv-2510-09734","title":"ARROW: An Adaptive Rollout and Routing Method for Global Weather Forecasting","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"decisionintelligence/ARROW","path":"metrics/utils.py","file_url":"https://github.com/decisionintelligence/ARROW/blob/HEAD/metrics/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"13710d199a13797d","mcp_get_code":{"code_sha256":"13710d199a13797d"}},{"arxiv_id":"2509.26429","paper":"/paper/arxiv-2509-26429","title":"An Orthogonal Learner for Individualized Outcomes in Markov Decision Processes","date":null,"month_inferred_from_arxiv_id":"2025-09","title_source":"syntology","repo":"EmilJavurek/Orthogonal-Q-in-MDPs","path":"algos/registry.py","file_url":"https://github.com/EmilJavurek/Orthogonal-Q-in-MDPs/blob/HEAD/algos/registry.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b7ca1a731038ba64","mcp_get_code":{"code_sha256":"b7ca1a731038ba64"}},{"arxiv_id":"2506.22246","paper":"/paper/eamamba-efficient-all-around-vision-state","title":"EAMamba: Efficient All-Around Vision State Space Model for Image Restoration","date":"2025-06-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"daidaijr/EAMamba","path":"models/models.py","file_url":"https://github.com/daidaijr/EAMamba/blob/HEAD/models/models.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":"44c3aa4c610ea3f7","mcp_get_code":{"code_sha256":"44c3aa4c610ea3f7"}},{"arxiv_id":"2506.22246","paper":"/paper/eamamba-efficient-all-around-vision-state","title":"EAMamba: Efficient All-Around Vision State Space Model for Image Restoration","date":"2025-06-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"daidaijr/EAMamba","path":"datasets/datasets.py","file_url":"https://github.com/daidaijr/EAMamba/blob/HEAD/datasets/datasets.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9687bf7a922f098a","mcp_get_code":{"code_sha256":"9687bf7a922f098a"}},{"arxiv_id":"2411.00715","paper":"/paper/b-cosification-transforming-deep-neural","title":"B-cosification: Transforming Deep Neural Networks to be Inherently Interpretable","date":"2024-11-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shrebox/b-cosification","path":"bcos/models/pretrained.py","file_url":"https://github.com/shrebox/b-cosification/blob/HEAD/bcos/models/pretrained.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":"bd478142f31f9d59","mcp_get_code":{"code_sha256":"bd478142f31f9d59"}},{"arxiv_id":"2410.21264","paper":"/paper/larp-tokenizing-videos-with-a-learned-1","title":"LARP: Tokenizing Videos with a Learned Autoregressive Generative Prior","date":"2024-10-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hywang66/LARP","path":"models/models.py","file_url":"https://github.com/hywang66/LARP/blob/HEAD/models/models.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":"44c3aa4c610ea3f7","mcp_get_code":{"code_sha256":"44c3aa4c610ea3f7"}},{"arxiv_id":"2410.14872","paper":"/paper/how-to-evaluate-reward-models-for-rlhf","title":"How to Evaluate Reward Models for RLHF","date":"2024-10-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lmarena/ppe","path":"utils/core.py","file_url":"https://github.com/lmarena/ppe/blob/HEAD/utils/core.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0289a0e6c43b86d3","mcp_get_code":{"code_sha256":"0289a0e6c43b86d3"}},{"arxiv_id":"2410.13842","paper":"/paper/d-fine-redefine-regression-task-in-detrs-as","title":"D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement","date":"2024-10-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Peterande/D-FINE","path":"src/core/workspace.py","file_url":"https://github.com/Peterande/D-FINE/blob/HEAD/src/core/workspace.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":"2a50c87f96494841","mcp_get_code":{"code_sha256":"2a50c87f96494841"}},{"arxiv_id":"2408.12246","paper":"/paper/ova-detr-open-vocabulary-aerial-object","title":"OVA-DETR: Open Vocabulary Aerial Object Detection Using Image-Text Alignment and Fusion","date":"2024-08-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"GT-Wei/RT-OVAD","path":"src/core/yaml_utils.py","file_url":"https://github.com/GT-Wei/RT-OVAD/blob/HEAD/src/core/yaml_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":"ac0b264881e880f3","mcp_get_code":{"code_sha256":"ac0b264881e880f3"}},{"arxiv_id":"2408.04579","paper":"/paper/sam2-adapter-evaluating-adapting-segment","title":"SAM2-Adapter: Evaluating & Adapting Segment Anything 2 in Downstream Tasks: Camouflage, Shadow, Medical Image Segmentation, and More","date":"2024-08-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tianrun-chen/sam-adapter-pytorch","path":"models/models.py","file_url":"https://github.com/tianrun-chen/sam-adapter-pytorch/blob/HEAD/models/models.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":"44c3aa4c610ea3f7","mcp_get_code":{"code_sha256":"44c3aa4c610ea3f7"}},{"arxiv_id":"2407.21448","paper":"/paper/accelerating-image-super-resolution-networks","title":"Accelerating Image Super-Resolution Networks with Pixel-Level Classification","date":"2024-07-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"3587jjh/PCSR","path":"models/models.py","file_url":"https://github.com/3587jjh/PCSR/blob/HEAD/models/models.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":"44c3aa4c610ea3f7","mcp_get_code":{"code_sha256":"44c3aa4c610ea3f7"}},{"arxiv_id":"2407.17770","paper":"/paper/boteval-facilitating-interactive-human","title":"BotEval: Facilitating Interactive Human Evaluation","date":"2024-07-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"isi-nlp/boteval","path":"boteval/registry.py","file_url":"https://github.com/isi-nlp/boteval/blob/HEAD/boteval/registry.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":"327388c847cf5678","mcp_get_code":{"code_sha256":"327388c847cf5678"}},{"arxiv_id":"2407.15720","paper":"/paper/do-large-language-models-have-compositional","title":"Do Large Language Models Have Compositional Ability? An Investigation into Limitations and Scalability","date":"2024-07-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"oliverxuzy/llm_compose","path":"src/tasks/task.py","file_url":"https://github.com/oliverxuzy/llm_compose/blob/HEAD/src/tasks/task.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"99cbddb25116a0d7","mcp_get_code":{"code_sha256":"99cbddb25116a0d7"}},{"arxiv_id":"2407.09100","paper":"/paper/retrospective-for-the-dynamic-sensorium","title":"Retrospective for the Dynamic Sensorium Competition for predicting large-scale mouse primary visual cortex activity from videos","date":"2024-07-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bryanlimy/ViV1T","path":"src/viv1t/criterions.py","file_url":"https://github.com/bryanlimy/ViV1T/blob/HEAD/src/viv1t/criterions.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3c702d811d125a17","mcp_get_code":{"code_sha256":"3c702d811d125a17"}},{"arxiv_id":"2407.05082","paper":"/paper/dmtg-one-shot-differentiable-multi-task","title":"DMTG: One-Shot Differentiable Multi-Task Grouping","date":"2024-07-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ethanygao/DMTG","path":"models/models.py","file_url":"https://github.com/ethanygao/DMTG/blob/HEAD/models/models.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":"44c3aa4c610ea3f7","mcp_get_code":{"code_sha256":"44c3aa4c610ea3f7"}},{"arxiv_id":"2402.15017","paper":"/paper/towards-few-shot-adaptation-of-foundation","title":"Towards Few-Shot Adaptation of Foundation Models via Multitask Finetuning","date":"2024-02-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"OliverXUZY/Foudation-Model_Multitask","path":"datasets/datasets.py","file_url":"https://github.com/OliverXUZY/Foudation-Model_Multitask/blob/HEAD/datasets/datasets.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9687bf7a922f098a","mcp_get_code":{"code_sha256":"9687bf7a922f098a"}},{"arxiv_id":"2402.03749","paper":"/paper/vision-superalignment-weak-to-strong","title":"Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models","date":"2024-02-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ggjy/vision_weak_to_strong","path":"few_shot_leaerning/models/models.py","file_url":"https://github.com/ggjy/vision_weak_to_strong/blob/HEAD/few_shot_leaerning/models/models.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":"44c3aa4c610ea3f7","mcp_get_code":{"code_sha256":"44c3aa4c610ea3f7"}},{"arxiv_id":"2401.10407","paper":"/paper/learning-high-quality-and-general-purpose","title":"Learning High-Quality and General-Purpose Phrase Representations","date":"2024-01-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tigerchen52/PEARL","path":"source/registry.py","file_url":"https://github.com/tigerchen52/PEARL/blob/HEAD/source/registry.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"CC0-1.0","inline_ok":true,"code_sha256_prefix":"e53465ccba8de2d5","mcp_get_code":{"code_sha256":"e53465ccba8de2d5"}},{"arxiv_id":"2312.07374","paper":"/paper/relax-image-specific-prompt-requirement-in","title":"Relax Image-Specific Prompt Requirement in SAM: A Single Generic Prompt for Segmenting Camouflaged Objects","date":"2023-12-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jyLin8100/GenSAM","path":"datasets/datasets.py","file_url":"https://github.com/jyLin8100/GenSAM/blob/HEAD/datasets/datasets.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9687bf7a922f098a","mcp_get_code":{"code_sha256":"9687bf7a922f098a"}},{"arxiv_id":"2312.01037","paper":"/paper/eliciting-latent-knowledge-from-quirky","title":"Eliciting Latent Knowledge from Quirky Language Models","date":"2023-12-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eleutherai/elk-generalization","path":"elk_generalization/elk/ccs_losses.py","file_url":"https://github.com/eleutherai/elk-generalization/blob/HEAD/elk_generalization/elk/ccs_losses.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9e7337824581dcce","mcp_get_code":{"code_sha256":"9e7337824581dcce"}},{"arxiv_id":"2310.10207","paper":"/paper/bongard-openworld-few-shot-reasoning-for-free","title":"Bongard-OpenWorld: Few-Shot Reasoning for Free-form Visual Concepts in the Real World","date":"2023-10-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"joyjayng/Bongard-OpenWorld","path":"models/model.py","file_url":"https://github.com/joyjayng/Bongard-OpenWorld/blob/HEAD/models/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"44c3aa4c610ea3f7","mcp_get_code":{"code_sha256":"44c3aa4c610ea3f7"}},{"arxiv_id":"2310.10207","paper":"/paper/bongard-openworld-few-shot-reasoning-for-free","title":"Bongard-OpenWorld: Few-Shot Reasoning for Free-form Visual Concepts in the Real World","date":"2023-10-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"joyjayng/Bongard-OpenWorld","path":"datasets/dataset.py","file_url":"https://github.com/joyjayng/Bongard-OpenWorld/blob/HEAD/datasets/dataset.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":"9687bf7a922f098a","mcp_get_code":{"code_sha256":"9687bf7a922f098a"}},{"arxiv_id":"2303.05156","paper":"/paper/local-implicit-normalizing-flow-for-arbitrary","title":"Local Implicit Normalizing Flow for Arbitrary-Scale Image Super-Resolution","date":"2023-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liyuantsao/flowsr-lp","path":"LINF-LP/models/linf.py","file_url":"https://github.com/liyuantsao/flowsr-lp/blob/HEAD/LINF-LP/models/linf.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":"44c3aa4c610ea3f7","mcp_get_code":{"code_sha256":"44c3aa4c610ea3f7"}},{"arxiv_id":"2302.03023","paper":"/paper/v1t-large-scale-mouse-v1-response-prediction","title":"V1T: large-scale mouse V1 response prediction using a Vision Transformer","date":"2023-02-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bryanlimy/V1T","path":"src/v1t/losses.py","file_url":"https://github.com/bryanlimy/V1T/blob/HEAD/src/v1t/losses.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3c702d811d125a17","mcp_get_code":{"code_sha256":"3c702d811d125a17"}},{"arxiv_id":"2302.02299","paper":"/paper/sample-dropout-a-simple-yet-effective","title":"Sample Dropout: A Simple yet Effective Variance Reduction Technique in Deep Policy Optimization","date":"2023-02-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"linzichuan/sdpo","path":"baselines/common/models.py","file_url":"https://github.com/linzichuan/sdpo/blob/HEAD/baselines/common/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"a44574392d168384","mcp_get_code":{"code_sha256":"a44574392d168384"}},{"arxiv_id":"2301.06387","paper":"/paper/pecan-leveraging-policy-ensemble-for-context","title":"PECAN: Leveraging Policy Ensemble for Context-Aware Zero-Shot Human-AI Coordination","date":"2023-01-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LxzGordon/pecan_human_AI_coordination","path":"overcookedgym/human_aware_rl/baselines/baselines/common/models.py","file_url":"https://github.com/LxzGordon/pecan_human_AI_coordination/blob/HEAD/overcookedgym/human_aware_rl/baselines/baselines/common/models.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":"a44574392d168384","mcp_get_code":{"code_sha256":"a44574392d168384"}},{"arxiv_id":"2206.11871","paper":"/paper/offline-rl-for-natural-language-generation","title":"Offline RL for Natural Language Generation with Implicit Language Q Learning","date":"2022-06-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sea-snell/implicit-language-q-learning","path":"src/load_objects.py","file_url":"https://github.com/sea-snell/implicit-language-q-learning/blob/HEAD/src/load_objects.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"792f500fac4f3fba","mcp_get_code":{"code_sha256":"792f500fac4f3fba"}},{"arxiv_id":"2203.01282","paper":"/paper/texttt-py-irt-a-scalable-item-response-theory","title":"py-irt: A Scalable Item Response Theory Library for Python","date":"2022-03-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nd-ball/py-irt","path":"py_irt/initializers.py","file_url":"https://github.com/nd-ball/py-irt/blob/HEAD/py_irt/initializers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"06eee8f34be53db4","mcp_get_code":{"code_sha256":"06eee8f34be53db4"}},{"arxiv_id":"2202.06875","paper":"/paper/visual-acoustic-matching","title":"Visual Acoustic Matching","date":"2022-02-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"see2sound/see2sound","path":"see2sound/codi/models/latent_diffusion/diffusion_unet.py","file_url":"https://github.com/see2sound/see2sound/blob/HEAD/see2sound/codi/models/latent_diffusion/diffusion_unet.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":"c73767b33cb559fa","mcp_get_code":{"code_sha256":"c73767b33cb559fa"}},{"arxiv_id":"2112.11435","paper":"/paper/learned-queries-for-efficient-local-attention","title":"Learned Queries for Efficient Local Attention","date":"2021-12-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"moabarar/qna","path":"models/qna_vit.py","file_url":"https://github.com/moabarar/qna/blob/HEAD/models/qna_vit.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"103f46a75894451d","mcp_get_code":{"code_sha256":"103f46a75894451d"}},{"arxiv_id":"2111.08918","paper":"/paper/local-texture-estimator-for-implicit","title":"Local Texture Estimator for Implicit Representation Function","date":"2021-11-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jaewon-lee-b/lte","path":"models/models.py","file_url":"https://github.com/jaewon-lee-b/lte/blob/HEAD/models/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"44c3aa4c610ea3f7","mcp_get_code":{"code_sha256":"44c3aa4c610ea3f7"}},{"arxiv_id":"2111.08918","paper":"/paper/local-texture-estimator-for-implicit","title":"Local Texture Estimator for Implicit Representation Function","date":"2021-11-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jaewon-lee-b/lte","path":"datasets/datasets.py","file_url":"https://github.com/jaewon-lee-b/lte/blob/HEAD/datasets/datasets.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"9687bf7a922f098a","mcp_get_code":{"code_sha256":"9687bf7a922f098a"}},{"arxiv_id":"2111.00195","paper":"/paper/learning-continuous-representation-of-audio","title":"Learning Continuous Representation of Audio for Arbitrary Scale Super Resolution","date":"2021-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ml-postech/lisa","path":"models/models.py","file_url":"https://github.com/ml-postech/lisa/blob/HEAD/models/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"44c3aa4c610ea3f7","mcp_get_code":{"code_sha256":"44c3aa4c610ea3f7"}},{"arxiv_id":"2106.09146","paper":"/paper/contrastive-reinforcement-learning-of","title":"Contrastive Reinforcement Learning of Symbolic Reasoning Domains","date":"2021-06-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gpoesia/socratic-tutor","path":"agent.py","file_url":"https://github.com/gpoesia/socratic-tutor/blob/HEAD/agent.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":"6a1ad0909c55aa20","mcp_get_code":{"code_sha256":"6a1ad0909c55aa20"}},{"arxiv_id":"2105.12723","paper":"/paper/aggregating-nested-transformers","title":"Nested Hierarchical Transformer: Towards Accurate, Data-Efficient and Interpretable Visual Understanding","date":"2021-05-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"google-research/nested-transformer","path":"models/nest_net.py","file_url":"https://github.com/google-research/nested-transformer/blob/HEAD/models/nest_net.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":"103f46a75894451d","mcp_get_code":{"code_sha256":"103f46a75894451d"}},{"arxiv_id":"2010.07916","paper":"/paper/multi-agent-trust-region-policy-optimization","title":"Multi-Agent Trust Region Policy Optimization","date":"2020-10-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hepengli/matrpo","path":"matrpo/common/models.py","file_url":"https://github.com/hepengli/matrpo/blob/HEAD/matrpo/common/models.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":"a44574392d168384","mcp_get_code":{"code_sha256":"a44574392d168384"}},{"arxiv_id":"2010.00763","paper":"/paper/bongard-logo-a-new-benchmark-for-human-level","title":"Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning","date":"2020-10-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"NVlabs/Bongard-LOGO","path":"Bongard-LOGO_Baselines/models/models.py","file_url":"https://github.com/NVlabs/Bongard-LOGO/blob/HEAD/Bongard-LOGO_Baselines/models/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"44c3aa4c610ea3f7","mcp_get_code":{"code_sha256":"44c3aa4c610ea3f7"}},{"arxiv_id":"2007.08459","paper":"/paper/pc-pg-policy-cover-directed-exploration-for","title":"PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning","date":"2020-07-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mbhenaff/pcpg","path":"baselines/common/models.py","file_url":"https://github.com/mbhenaff/pcpg/blob/HEAD/baselines/common/models.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":"a44574392d168384","mcp_get_code":{"code_sha256":"a44574392d168384"}},{"arxiv_id":"2005.03420","paper":"/paper/curious-hierarchical-actor-critic","title":"Curious Hierarchical Actor-Critic Reinforcement Learning","date":"2020-05-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"knowledgetechnologyuhh/goal_conditioned_RL_baselines","path":"baselines/common/models.py","file_url":"https://github.com/knowledgetechnologyuhh/goal_conditioned_RL_baselines/blob/HEAD/baselines/common/models.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":"a44574392d168384","mcp_get_code":{"code_sha256":"a44574392d168384"}},{"arxiv_id":"2003.04390","paper":"/paper/a-new-meta-baseline-for-few-shot-learning","title":"Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning","date":"2020-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cyvius96/few-shot-meta-baseline","path":"meta-dataset/models/models.py","file_url":"https://github.com/cyvius96/few-shot-meta-baseline/blob/HEAD/meta-dataset/models/models.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":"44c3aa4c610ea3f7","mcp_get_code":{"code_sha256":"44c3aa4c610ea3f7"}},{"arxiv_id":"2002.06673","paper":"/paper/performative-prediction","title":"Performative Prediction","date":"2020-02-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mrtzh/whynot","path":"whynot/gym/envs/registration.py","file_url":"https://github.com/mrtzh/whynot/blob/HEAD/whynot/gym/envs/registration.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2718f2328806addb","mcp_get_code":{"code_sha256":"2718f2328806addb"}},{"arxiv_id":"1912.08664","paper":"/paper/hierarchical-deep-q-network-with-forgetting","title":"Hierarchical Deep Q-Network from Imperfect Demonstrations in Minecraft","date":"2019-12-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cog-isa/forger","path":"policy/models.py","file_url":"https://github.com/cog-isa/forger/blob/HEAD/policy/models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7aeb4ce6909a6810","mcp_get_code":{"code_sha256":"7aeb4ce6909a6810"}},{"arxiv_id":"1910.05512","paper":"/paper/influence-based-multi-agent-exploration","title":"Influence-Based Multi-Agent Exploration","date":"2019-10-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"TonghanWang/EITI-EDTI","path":"baselines/common/models.py","file_url":"https://github.com/TonghanWang/EITI-EDTI/blob/HEAD/baselines/common/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"a44574392d168384","mcp_get_code":{"code_sha256":"a44574392d168384"}},{"arxiv_id":"1910.04376","paper":"/paper/rlcard-a-toolkit-for-reinforcement-learning","title":"RLCard: A Toolkit for Reinforcement Learning in Card Games","date":"2019-10-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Branbados/rlcard","path":"rlcard/models/registration.py","file_url":"https://github.com/Branbados/rlcard/blob/HEAD/rlcard/models/registration.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"502de621e94695b2","mcp_get_code":{"code_sha256":"502de621e94695b2"}},{"arxiv_id":"1910.04376","paper":"/paper/rlcard-a-toolkit-for-reinforcement-learning","title":"RLCard: A Toolkit for Reinforcement Learning in Card Games","date":"2019-10-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cogitoergoread/rlcard3","path":"rlcard3/model_agents/registration.py","file_url":"https://github.com/cogitoergoread/rlcard3/blob/HEAD/rlcard3/model_agents/registration.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"03fa11a9780ef265","mcp_get_code":{"code_sha256":"03fa11a9780ef265"}},{"arxiv_id":"1910.00935","paper":"/paper/difftaichi-differentiable-programming-for","title":"DiffTaichi: Differentiable Programming for Physical Simulation","date":"2019-10-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"taichi-dev/taichi","path":"python/taichi/_main.py","file_url":"https://github.com/taichi-dev/taichi/blob/HEAD/python/taichi/_main.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":"edefab49bfdc7428","mcp_get_code":{"code_sha256":"edefab49bfdc7428"}},{"arxiv_id":"1908.11421","paper":"/paper/learning-latent-parameters-without-human","title":"Learning Latent Parameters without Human Response Patterns: Item Response Theory with Artificial Crowds","date":"2019-08-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jplalor/py-irt","path":"py_irt/initializers.py","file_url":"https://github.com/jplalor/py-irt/blob/HEAD/py_irt/initializers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"06eee8f34be53db4","mcp_get_code":{"code_sha256":"06eee8f34be53db4"}},{"arxiv_id":"1906.05841","paper":"/paper/deep-reinforcement-learning-for-industrial","title":"Deep Reinforcement Learning for Industrial Insertion Tasks with Visual Inputs and Natural Rewards","date":"2019-06-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mizolotu/SmartExcavator","path":"baselines/common/models.py","file_url":"https://github.com/mizolotu/SmartExcavator/blob/HEAD/baselines/common/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"a44574392d168384","mcp_get_code":{"code_sha256":"a44574392d168384"}},{"arxiv_id":"1905.06750","paper":"/paper/random-expert-distillation-imitation-learning","title":"Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation","date":"2019-05-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RuohanW/RED","path":"baselines/common/models.py","file_url":"https://github.com/RuohanW/RED/blob/HEAD/baselines/common/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"a44574392d168384","mcp_get_code":{"code_sha256":"a44574392d168384"}},{"arxiv_id":"1905.02363","paper":"/paper/dimension-wise-importance-sampling-weight","title":"Dimension-Wise Importance Sampling Weight Clipping for Sample-Efficient Reinforcement Learning","date":"2019-05-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"seungyulhan/disc","path":"baselines/common/models.py","file_url":"https://github.com/seungyulhan/disc/blob/HEAD/baselines/common/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"a44574392d168384","mcp_get_code":{"code_sha256":"a44574392d168384"}},{"arxiv_id":"1905.04100","paper":"/paper/190504100","title":"Deep Reinforcement Learning using Genetic Algorithm for Parameter Optimization","date":"2019-02-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aralab-unr/ReinforcementLearningWithGA","path":"baselines/common/models.py","file_url":"https://github.com/aralab-unr/ReinforcementLearningWithGA/blob/HEAD/baselines/common/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"a44574392d168384","mcp_get_code":{"code_sha256":"a44574392d168384"}},{"arxiv_id":"1810.12282","paper":"/paper/assessing-generalization-in-deep","title":"Assessing Generalization in Deep Reinforcement Learning","date":"2018-10-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sunblaze-ucb/rl-generalization","path":"sunblaze_envs/registration.py","file_url":"https://github.com/sunblaze-ucb/rl-generalization/blob/HEAD/sunblaze_envs/registration.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0c4c9f359acd3c78","mcp_get_code":{"code_sha256":"0c4c9f359acd3c78"}},{"arxiv_id":"1810.09538","paper":"/paper/pyro-deep-universal-probabilistic-programming","title":"Pyro: Deep Universal Probabilistic Programming","date":"2018-10-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"uber/pyro","path":"pyro/settings.py","file_url":"https://github.com/uber/pyro/blob/HEAD/pyro/settings.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":"4ccde65baa528cbc","mcp_get_code":{"code_sha256":"4ccde65baa528cbc"}},{"arxiv_id":"1808.00671","paper":"/paper/pcn-point-completion-network","title":"PCN: Point Completion Network","date":"2018-08-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wentaoyuan/pcn","path":"kitti_registration.py","file_url":"https://github.com/wentaoyuan/pcn/blob/HEAD/kitti_registration.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"079447ff7ae596e5","mcp_get_code":{"code_sha256":"079447ff7ae596e5"}},{"arxiv_id":"1803.07055","paper":"/paper/simple-random-search-provides-a-competitive","title":"Simple random search provides a competitive approach to reinforcement learning","date":"2018-03-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eric-erki/robotics-rl-srl","path":"environments/registry.py","file_url":"https://github.com/eric-erki/robotics-rl-srl/blob/HEAD/environments/registry.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"171decf255010ed6","mcp_get_code":{"code_sha256":"171decf255010ed6"}},{"arxiv_id":"1708.05144","paper":"/paper/scalable-trust-region-method-for-deep","title":"Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation","date":"2017-08-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"openai/baselines","path":"baselines/common/models.py","file_url":"https://github.com/openai/baselines/blob/HEAD/baselines/common/models.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":"a44574392d168384","mcp_get_code":{"code_sha256":"a44574392d168384"}},{"arxiv_id":"1704.00028","paper":"/paper/improved-training-of-wasserstein-gans","title":"Improved Training of Wasserstein GANs","date":"2017-03-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"unicredit/ganzo","path":"src/loss.py","file_url":"https://github.com/unicredit/ganzo/blob/HEAD/src/loss.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"28c0e133abb7a340","mcp_get_code":{"code_sha256":"28c0e133abb7a340"}},{"arxiv_id":"1611.05763","paper":"/paper/learning-to-reinforcement-learn","title":"Learning to reinforcement learn","date":"2016-11-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gomerudo/openai-baselines","path":"baselines/common/models.py","file_url":"https://github.com/gomerudo/openai-baselines/blob/HEAD/baselines/common/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"a44574392d168384","mcp_get_code":{"code_sha256":"a44574392d168384"}},{"arxiv_id":"1007.1727","paper":"/paper/asymptotic-formulae-for-likelihood-based","title":"Asymptotic formulae for likelihood-based tests of new physics","date":null,"month_inferred_from_arxiv_id":"2010-07","title_source":"archive","repo":"scikit-hep/pyhf","path":"src/pyhf/events.py","file_url":"https://github.com/scikit-hep/pyhf/blob/HEAD/src/pyhf/events.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":"ca0999c2f2915b3a","mcp_get_code":{"code_sha256":"ca0999c2f2915b3a"}},{"arxiv_id":"aaai_20297","paper":null,"title":"arXiv:aaai_20297","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"linkedin/DuaLip","path":"src/dualip/projections/base.py","file_url":"https://github.com/linkedin/DuaLip/blob/HEAD/src/dualip/projections/base.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"ae4b5099637ff070","mcp_get_code":{"code_sha256":"ae4b5099637ff070"}},{"arxiv_id":"Xiao_Towards_Progressive_Multi-Frequency_Representation_for_Image_Warping_CVPR_2024_paper","paper":null,"title":"arXiv:Xiao_Towards_Progressive_Multi-Frequency_Representation_for_Image_Warping_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"junxiao01/MFR","path":"models/models.py","file_url":"https://github.com/junxiao01/MFR/blob/HEAD/models/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"44c3aa4c610ea3f7","mcp_get_code":{"code_sha256":"44c3aa4c610ea3f7"}},{"arxiv_id":"Wang_Object_Detection_using_Event_Camera_A_MoE_Heat_Conduction_based_CVPR_2025_paper","paper":null,"title":"arXiv:Wang_Object_Detection_using_Event_Camera_A_MoE_Heat_Conduction_based_CVPR_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Event-AHU/OpenEvDET","path":"CvHeat-DET/src/core/yaml_utils.py","file_url":"https://github.com/Event-AHU/OpenEvDET/blob/HEAD/CvHeat-DET/src/core/yaml_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ac0b264881e880f3","mcp_get_code":{"code_sha256":"ac0b264881e880f3"}},{"arxiv_id":"Wang_ISP2HRNet_Learning_to_Reconstruct_High_Resolution_Image_from_Irregularly_Sampled_ICCV_2025_paper","paper":null,"title":"arXiv:Wang_ISP2HRNet_Learning_to_Reconstruct_High_Resolution_Image_from_Irregularly_Sampled_ICCV_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"yuanlinwang/ISP2HRNet","path":"models/models.py","file_url":"https://github.com/yuanlinwang/ISP2HRNet/blob/HEAD/models/models.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":"44c3aa4c610ea3f7","mcp_get_code":{"code_sha256":"44c3aa4c610ea3f7"}},{"arxiv_id":"Pak_B-Spline_Texture_Coefficients_Estimator_for_Screen_Content_Image_Super-Resolution_CVPR_2023_paper","paper":null,"title":"arXiv:Pak_B-Spline_Texture_Coefficients_Estimator_for_Screen_Content_Image_Super-Resolution_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"ByeongHyunPak/btc","path":"models/models.py","file_url":"https://github.com/ByeongHyunPak/btc/blob/HEAD/models/models.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":"44c3aa4c610ea3f7","mcp_get_code":{"code_sha256":"44c3aa4c610ea3f7"}},{"arxiv_id":"Pak_B-Spline_Texture_Coefficients_Estimator_for_Screen_Content_Image_Super-Resolution_CVPR_2023_paper","paper":null,"title":"arXiv:Pak_B-Spline_Texture_Coefficients_Estimator_for_Screen_Content_Image_Super-Resolution_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"ByeongHyunPak/btc","path":"datasets/datasets.py","file_url":"https://github.com/ByeongHyunPak/btc/blob/HEAD/datasets/datasets.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9687bf7a922f098a","mcp_get_code":{"code_sha256":"9687bf7a922f098a"}},{"arxiv_id":"Han_ABCD_Arbitrary_Bitwise_Coefficient_for_De-Quantization_CVPR_2023_paper","paper":null,"title":"arXiv:Han_ABCD_Arbitrary_Bitwise_Coefficient_for_De-Quantization_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"WooKyoungHan/ABCD","path":"models/models.py","file_url":"https://github.com/WooKyoungHan/ABCD/blob/HEAD/models/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"44c3aa4c610ea3f7","mcp_get_code":{"code_sha256":"44c3aa4c610ea3f7"}}]}