{"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/make","entry":"make","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":38,"n_papers_ran":1,"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":32,"n_samples_ran":1,"n_samples_fingerprinted":0,"n_places":43,"n_places_pointer_only":13,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":0,"unverified":31},"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":"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":"d4d746521afc0313","mcp_get_code":{"code_sha256":"d4d746521afc0313"}},{"arxiv_id":"2510.20235","paper":"/paper/arxiv-2510-20235","title":"Multi-Objective Reinforcement Learning with Max-Min Criterion: A Game-Theoretic Approach","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"whbyeon/ERAM-ARAM","path":"mo_gymnasium/utils.py","file_url":"https://github.com/whbyeon/ERAM-ARAM/blob/HEAD/mo_gymnasium/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8b6938dfd4c09200","mcp_get_code":{"code_sha256":"8b6938dfd4c09200"}},{"arxiv_id":"2507.23521","paper":null,"title":"arXiv:2507.23521","date":null,"month_inferred_from_arxiv_id":"2025-07","title_source":null,"repo":"WooKyoungHan/JPNeO","path":"models/JPNeO.py","file_url":"https://github.com/WooKyoungHan/JPNeO/blob/HEAD/models/JPNeO.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":"e55e3bfb410702d2","mcp_get_code":{"code_sha256":"e55e3bfb410702d2"}},{"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":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"58cdb07707464c7f","mcp_get_code":{"code_sha256":"58cdb07707464c7f"}},{"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":"97511d8162c40d92","mcp_get_code":{"code_sha256":"97511d8162c40d92"}},{"arxiv_id":"2504.11442","paper":"/paper/textarena","title":"TextArena","date":"2025-04-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"leonguertler/textarena","path":"textarena/envs/registration.py","file_url":"https://github.com/leonguertler/textarena/blob/HEAD/textarena/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":"2a595c27e38cf10d","mcp_get_code":{"code_sha256":"2a595c27e38cf10d"}},{"arxiv_id":"2501.06058","paper":"/paper/learning-flexible-heterogeneous-coordination","title":"Capability-Aware Shared Hypernetworks for Flexible Heterogeneous Multi-Robot Coordination","date":"2025-01-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kfu02/jaxmarl","path":"jaxmarl/registration.py","file_url":"https://github.com/kfu02/jaxmarl/blob/HEAD/jaxmarl/registration.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":"e94d26d1c6b08022","mcp_get_code":{"code_sha256":"e94d26d1c6b08022"}},{"arxiv_id":"2412.04233","paper":"/paper/hypermarl-adaptive-hypernetworks-for-multi","title":"HyperMARL: Adaptive Hypernetworks for Multi-Agent RL","date":"2024-12-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"KaleabTessera/HyperMARL","path":"jaxmarl/registration.py","file_url":"https://github.com/KaleabTessera/HyperMARL/blob/HEAD/jaxmarl/registration.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":"e322b3637217c7eb","mcp_get_code":{"code_sha256":"e322b3637217c7eb"}},{"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":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8003b350e3563bc9","mcp_get_code":{"code_sha256":"8003b350e3563bc9"}},{"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":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0c6441af780ce58d","mcp_get_code":{"code_sha256":"0c6441af780ce58d"}},{"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":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0c6441af780ce58d","mcp_get_code":{"code_sha256":"0c6441af780ce58d"}},{"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":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9e49a3839a22867f","mcp_get_code":{"code_sha256":"9e49a3839a22867f"}},{"arxiv_id":"2406.18043","paper":"/paper/multimodal-foundation-world-models-for","title":"GenRL: Multimodal-foundation world models for generalization in embodied agents","date":"2024-06-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mazpie/genrl","path":"envs/custom_dmc_tasks/cheetah.py","file_url":"https://github.com/mazpie/genrl/blob/HEAD/envs/custom_dmc_tasks/cheetah.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bf18772e2ed3e413","mcp_get_code":{"code_sha256":"bf18772e2ed3e413"}},{"arxiv_id":"2404.16451","paper":"/paper/latent-modulated-function-for-computational","title":"Latent Modulated Function for Computational Optimal Continuous Image Representation","date":"2024-04-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HeZongyao/LMF","path":"models/lmliif.py","file_url":"https://github.com/HeZongyao/LMF/blob/HEAD/models/lmliif.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":"b414010f60ca559c","mcp_get_code":{"code_sha256":"b414010f60ca559c"}},{"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":"finetune.py","file_url":"https://github.com/oliverxuzy/foudation-model_multitask/blob/HEAD/finetune.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":"5c0a845766c67c36","mcp_get_code":{"code_sha256":"5c0a845766c67c36"}},{"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":"db58e5a4a5aafdab","mcp_get_code":{"code_sha256":"db58e5a4a5aafdab"}},{"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":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"232045c11773f4a3","mcp_get_code":{"code_sha256":"232045c11773f4a3"}},{"arxiv_id":"2401.17205","paper":"/paper/adaptive-experiment-design-with-synthetic","title":"Adaptive Experiment Design with Synthetic Controls","date":"2024-01-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vanderschaarlab/syntax","path":"src/envs.py","file_url":"https://github.com/vanderschaarlab/syntax/blob/HEAD/src/envs.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9aeeb56796c21ffe","mcp_get_code":{"code_sha256":"9aeeb56796c21ffe"}},{"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":"7fdb65394809a6a8","mcp_get_code":{"code_sha256":"7fdb65394809a6a8"}},{"arxiv_id":"2311.01734","paper":"/paper/mixcon3d-synergizing-multi-view-and-cross","title":"Sculpting Holistic 3D Representation in Contrastive Language-Image-3D Pre-training","date":"2023-11-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ucsc-vlaa/mixcon3d","path":"src/models/pointmlp.py","file_url":"https://github.com/ucsc-vlaa/mixcon3d/blob/HEAD/src/models/pointmlp.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6b8de911693ef6b0","mcp_get_code":{"code_sha256":"6b8de911693ef6b0"}},{"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":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"223edb92edf72b15","mcp_get_code":{"code_sha256":"223edb92edf72b15"}},{"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":"03122b1841d3f72e","mcp_get_code":{"code_sha256":"03122b1841d3f72e"}},{"arxiv_id":"2305.10764","paper":"/paper/openshape-scaling-up-3d-shape-representation-1","title":"OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding","date":"2023-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Colin97/OpenShape_code","path":"src/models/pointmlp.py","file_url":"https://github.com/Colin97/OpenShape_code/blob/HEAD/src/models/pointmlp.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":"6b8de911693ef6b0","mcp_get_code":{"code_sha256":"6b8de911693ef6b0"}},{"arxiv_id":"2303.17503","paper":"/paper/pgx-hardware-accelerated-parallel-game-1","title":"Pgx: Hardware-Accelerated Parallel Game Simulators for Reinforcement Learning","date":"2023-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sotetsuk/pgx","path":"pgx/core.py","file_url":"https://github.com/sotetsuk/pgx/blob/HEAD/pgx/core.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":"f9f3e8bdaf3a2bdd","mcp_get_code":{"code_sha256":"f9f3e8bdaf3a2bdd"}},{"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":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0c6441af780ce58d","mcp_get_code":{"code_sha256":"0c6441af780ce58d"}},{"arxiv_id":"2202.12180","paper":"/paper/quantum-deep-reinforcement-learning-for-robot","title":"Quantum Deep Reinforcement Learning for Robot Navigation Tasks","date":"2022-02-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dfki-ric-quantum/qdrl-turtlebot-env","path":"qturtle/core.py","file_url":"https://github.com/dfki-ric-quantum/qdrl-turtlebot-env/blob/HEAD/qturtle/core.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":"c54986e5c79ff74d","mcp_get_code":{"code_sha256":"c54986e5c79ff74d"}},{"arxiv_id":"2112.06174","paper":"/paper/implicit-transformer-network-for-screen-1","title":"Implicit Transformer Network for Screen Content Image Continuous Super-Resolution","date":"2021-12-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"codyshen0000/itsrn","path":"code/models/ITNSR.py","file_url":"https://github.com/codyshen0000/itsrn/blob/HEAD/code/models/ITNSR.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"cd644f887666134f","mcp_get_code":{"code_sha256":"cd644f887666134f"}},{"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":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"0c6441af780ce58d","mcp_get_code":{"code_sha256":"0c6441af780ce58d"}},{"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":"7fdb65394809a6a8","mcp_get_code":{"code_sha256":"7fdb65394809a6a8"}},{"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":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"0c6441af780ce58d","mcp_get_code":{"code_sha256":"0c6441af780ce58d"}},{"arxiv_id":"2108.05877","paper":"/paper/dexmv-imitation-learning-for-dexterous","title":"DexMV: Imitation Learning for Dexterous Manipulation from Human Videos","date":"2021-08-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yzqin/dexmv-sim","path":"hand_imitation/env/environments/base.py","file_url":"https://github.com/yzqin/dexmv-sim/blob/HEAD/hand_imitation/env/environments/base.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":"117267bf6a8ae1dd","mcp_get_code":{"code_sha256":"117267bf6a8ae1dd"}},{"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":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"220503b76dce8b41","mcp_get_code":{"code_sha256":"220503b76dce8b41"}},{"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":"mlpc-ucsd/ConstellationNet","path":"models/models.py","file_url":"https://github.com/mlpc-ucsd/ConstellationNet/blob/HEAD/models/models.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":"232045c11773f4a3","mcp_get_code":{"code_sha256":"232045c11773f4a3"}},{"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":"98c1773aa7ded37f","mcp_get_code":{"code_sha256":"98c1773aa7ded37f"}},{"arxiv_id":"1908.08342","paper":"/paper/a-generalized-algorithm-for-multi-objective","title":"A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation","date":"2019-08-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lucasalegre/mo-gym","path":"mo_gymnasium/utils.py","file_url":"https://github.com/lucasalegre/mo-gym/blob/HEAD/mo_gymnasium/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8b6938dfd4c09200","mcp_get_code":{"code_sha256":"8b6938dfd4c09200"}},{"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":"8a1139c533d8f08b","mcp_get_code":{"code_sha256":"8a1139c533d8f08b"}},{"arxiv_id":"1602.01783","paper":"/paper/asynchronous-methods-for-deep-reinforcement","title":"Asynchronous Methods for Deep Reinforcement Learning","date":"2016-02-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"arnomoonens/yarll","path":"yarll/agents/tf1/actorcritic/a3c_worker.py","file_url":"https://github.com/arnomoonens/yarll/blob/HEAD/yarll/agents/tf1/actorcritic/a3c_worker.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"02e7f96aac12676a","mcp_get_code":{"code_sha256":"02e7f96aac12676a"}},{"arxiv_id":"aaai_17143","paper":null,"title":"arXiv:aaai_17143","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"madoibito80/advantage-nas","path":"cifar10/nb201_plot.py","file_url":"https://github.com/madoibito80/advantage-nas/blob/HEAD/cifar10/nb201_plot.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":"9385111087b7daee","mcp_get_code":{"code_sha256":"9385111087b7daee"}},{"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":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"0c6441af780ce58d","mcp_get_code":{"code_sha256":"0c6441af780ce58d"}},{"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":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1276c80088716fc3","mcp_get_code":{"code_sha256":"1276c80088716fc3"}},{"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":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cd644f887666134f","mcp_get_code":{"code_sha256":"cd644f887666134f"}},{"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":"7fdb65394809a6a8","mcp_get_code":{"code_sha256":"7fdb65394809a6a8"}},{"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":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"4557dbe8e8c23e04","mcp_get_code":{"code_sha256":"4557dbe8e8c23e04"}}]}