{"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/logger","entry":"Logger","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":25,"n_papers_ran":20,"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":25,"n_samples_ran":20,"n_samples_fingerprinted":0,"n_places":25,"n_places_pointer_only":14,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":20,"unverified":5},"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":"2606.26327","paper":"/paper/arxiv-2606-26327","title":"EVOM: Agentic Meta-Evolution of Actor-Critic Architectures for Reinforcement Learning","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"xiaofangxd/EVOM","path":"evom.py","file_url":"https://github.com/xiaofangxd/EVOM/blob/HEAD/evom.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6c9eab99b324f828","mcp_get_code":{"code_sha256":"6c9eab99b324f828"}},{"arxiv_id":"2604.23528","paper":"/paper/arxiv-2604-23528","title":"When PINNs Go Wrong: Pseudo-Time Stepping Against Spurious Solutions","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"sifanexisted/jaxpi2","path":"jaxpi/training.py","file_url":"https://github.com/sifanexisted/jaxpi2/blob/HEAD/jaxpi/training.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":"4c0c5f40e0b4b0c0","mcp_get_code":{"code_sha256":"4c0c5f40e0b4b0c0"}},{"arxiv_id":"2505.23864","paper":"/paper/personalized-subgraph-federated-learning-with","title":"Personalized Subgraph Federated Learning with Differentiable Auxiliary Projections","date":"2025-05-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JhuoW/FedAux","path":"models/fedaux/client.py","file_url":"https://github.com/JhuoW/FedAux/blob/HEAD/models/fedaux/client.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3267a11712b57f43","mcp_get_code":{"code_sha256":"3267a11712b57f43"}},{"arxiv_id":"2504.11944","paper":"/paper/vipo-value-function-inconsistency-penalized","title":"VIPO: Value Function Inconsistency Penalized Offline Reinforcement Learning","date":"2025-04-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"NUS-CORE/vipo","path":"vipo/OfflineRL-Kit/offlinerlkit/dynamics/vipo_ensemble_dynamics.py","file_url":"https://github.com/NUS-CORE/vipo/blob/HEAD/vipo/OfflineRL-Kit/offlinerlkit/dynamics/vipo_ensemble_dynamics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2657d4a21b19c7b2","mcp_get_code":{"code_sha256":"2657d4a21b19c7b2"}},{"arxiv_id":"2503.11781","paper":null,"title":"arXiv:2503.11781","date":null,"month_inferred_from_arxiv_id":"2025-03","title_source":null,"repo":"gosha20777/cmKAN","path":"cm_kan/ml/models/cm_kan.py","file_url":"https://github.com/gosha20777/cmKAN/blob/HEAD/cm_kan/ml/models/cm_kan.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"3de1eee462616fe5","mcp_get_code":{"code_sha256":"3de1eee462616fe5"}},{"arxiv_id":"2502.10937","paper":"/paper/scale-towards-collaborative-content-analysis","title":"SCALE: Towards Collaborative Content Analysis in Social Science with Large Language Model Agents and Human Intervention","date":"2025-02-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ChengshuaiZhao0/SCALE","path":"simulation/content_analysis_simulation.py","file_url":"https://github.com/ChengshuaiZhao0/SCALE/blob/HEAD/simulation/content_analysis_simulation.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"288cc22811b2a887","mcp_get_code":{"code_sha256":"288cc22811b2a887"}},{"arxiv_id":"2501.14997","paper":"/paper/causal-discovery-via-bayesian-optimization","title":"Causal Discovery via Bayesian Optimization","date":"2025-01-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"baosws/drbo","path":"drbo/drbo.py","file_url":"https://github.com/baosws/drbo/blob/HEAD/drbo/drbo.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":"6c20b8e69aa7ebba","mcp_get_code":{"code_sha256":"6c20b8e69aa7ebba"}},{"arxiv_id":"2410.14214","paper":"/paper/mambasci-efficient-mamba-unet-for-quad-bayer","title":"MambaSCI: Efficient Mamba-UNet for Quad-Bayer Patterned Video Snapshot Compressive Imaging","date":"2024-10-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PAN083/MambaSCI","path":"cacti/utils/logger.py","file_url":"https://github.com/PAN083/MambaSCI/blob/HEAD/cacti/utils/logger.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a14798763595a94c","mcp_get_code":{"code_sha256":"a14798763595a94c"}},{"arxiv_id":"2408.01972","paper":"/paper/rvi-sac-average-reward-off-policy-deep","title":"RVI-SAC: Average Reward Off-Policy Deep Reinforcement Learning","date":"2024-08-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yhisaki/average-reward-drl","path":"average_reward_drl/algorithms/rvi_sac.py","file_url":"https://github.com/yhisaki/average-reward-drl/blob/HEAD/average_reward_drl/algorithms/rvi_sac.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f4d7745cbf416977","mcp_get_code":{"code_sha256":"f4d7745cbf416977"}},{"arxiv_id":"2311.10572","paper":"/paper/ssb-simple-but-strong-baseline-for-boosting-1","title":"SSB: Simple but Strong Baseline for Boosting Performance of Open-Set Semi-Supervised Learning","date":"2023-11-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YUE-FAN/SSB","path":"trainer_sim_ssb.py","file_url":"https://github.com/YUE-FAN/SSB/blob/HEAD/trainer_sim_ssb.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4024208f887303d6","mcp_get_code":{"code_sha256":"4024208f887303d6"}},{"arxiv_id":"2307.15043","paper":"/paper/universal-and-transferable-adversarial","title":"Universal and Transferable Adversarial Attacks on Aligned Language Models","date":"2023-07-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fra31/rlhf-trojan-competition-submission","path":"method/attacks.py","file_url":"https://github.com/fra31/rlhf-trojan-competition-submission/blob/HEAD/method/attacks.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":"a445b00c2e83ec24","mcp_get_code":{"code_sha256":"a445b00c2e83ec24"}},{"arxiv_id":"2305.12239","paper":"/paper/off-policy-average-reward-actor-critic-with","title":"Off-Policy Average Reward Actor-Critic with Deterministic Policy Search","date":"2023-05-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"namansaxena9/ARO-DDPG","path":"cheetah_run/ddpg_model.py","file_url":"https://github.com/namansaxena9/ARO-DDPG/blob/HEAD/cheetah_run/ddpg_model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"54a47607104f9c47","mcp_get_code":{"code_sha256":"54a47607104f9c47"}},{"arxiv_id":"2301.12187","paper":"/paper/efficient-latency-aware-cnn-depth-compression","title":"Efficient Latency-Aware CNN Depth Compression via Two-Stage Dynamic Programming","date":"2023-01-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"snu-mllab/efficient-cnn-depth-compression","path":"utils/dp.py","file_url":"https://github.com/snu-mllab/efficient-cnn-depth-compression/blob/HEAD/utils/dp.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"de28451553f99b21","mcp_get_code":{"code_sha256":"de28451553f99b21"}},{"arxiv_id":"2211.08008","paper":"/paper/mora-improving-ensemble-robustness-evaluation","title":"MORA: Improving Ensemble Robustness Evaluation with Model-Reweighing Attack","date":"2022-11-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lafeat/mora","path":"mora.py","file_url":"https://github.com/lafeat/mora/blob/HEAD/mora.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b468449dd9f01141","mcp_get_code":{"code_sha256":"b468449dd9f01141"}},{"arxiv_id":"2210.17323","paper":"/paper/gptq-accurate-post-training-quantization-for","title":"GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers","date":"2022-10-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cornell-zhang/llm-datatypes","path":"neural_compressor/torch/algorithms/weight_only/gptq.py","file_url":"https://github.com/cornell-zhang/llm-datatypes/blob/HEAD/neural_compressor/torch/algorithms/weight_only/gptq.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"c1a5aa86dbaf659b","mcp_get_code":{"code_sha256":"c1a5aa86dbaf659b"}},{"arxiv_id":"2204.11181","paper":"/paper/realistic-evaluation-of-transductive-few-shot-1","title":"Realistic Evaluation of Transductive Few-Shot Learning","date":"2022-04-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"oveilleux/realistic_transductive_few_shot","path":"src/methods/tim.py","file_url":"https://github.com/oveilleux/realistic_transductive_few_shot/blob/HEAD/src/methods/tim.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c2bb0431f66349c4","mcp_get_code":{"code_sha256":"c2bb0431f66349c4"}},{"arxiv_id":"2105.06138","paper":"/paper/unsupervised-hashing-with-contrastive","title":"Unsupervised Hashing with Contrastive Information Bottleneck","date":"2021-05-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qiuzx2/CIBHash","path":"model/CIBHash.py","file_url":"https://github.com/qiuzx2/CIBHash/blob/HEAD/model/CIBHash.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a5f55809f0ced9ca","mcp_get_code":{"code_sha256":"a5f55809f0ced9ca"}},{"arxiv_id":"2101.08152","paper":"/paper/rank-the-episodes-a-simple-approach-for-1","title":"Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environments","date":"2021-01-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aklein1995/exploration_sil_im","path":"rapid/rapid_agent.py","file_url":"https://github.com/aklein1995/exploration_sil_im/blob/HEAD/rapid/rapid_agent.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ecaf9ce3efa32820","mcp_get_code":{"code_sha256":"ecaf9ce3efa32820"}},{"arxiv_id":"2004.03991","paper":"/paper/learning-discrete-structured-representations","title":"Learning Discrete Structured Representations by Adversarially Maximizing Mutual Information","date":"2020-04-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"karlstratos/ammi","path":"ammi.py","file_url":"https://github.com/karlstratos/ammi/blob/HEAD/ammi.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"62aafeb07c870d8c","mcp_get_code":{"code_sha256":"62aafeb07c870d8c"}},{"arxiv_id":"2003.03196","paper":"/paper/federated-continual-learning-with-adaptive","title":"Federated Continual Learning with Weighted Inter-client Transfer","date":"2020-03-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wyjeong/FedWeIT","path":"models/fedweit/client.py","file_url":"https://github.com/wyjeong/FedWeIT/blob/HEAD/models/fedweit/client.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8509dc40a8932c5b","mcp_get_code":{"code_sha256":"8509dc40a8932c5b"}},{"arxiv_id":"1801.01290","paper":"/paper/soft-actor-critic-off-policy-maximum-entropy","title":"Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor","date":"2018-01-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yhisaki/average-reward-drl","path":"average_reward_drl/algorithms/sac.py","file_url":"https://github.com/yhisaki/average-reward-drl/blob/HEAD/average_reward_drl/algorithms/sac.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4aec8353051fa16d","mcp_get_code":{"code_sha256":"4aec8353051fa16d"}},{"arxiv_id":"1611.02779","paper":"/paper/rl2-fast-reinforcement-learning-via-slow","title":"RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning","date":"2016-11-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aliengirlliv/teachable","path":"algos/ppo.py","file_url":"https://github.com/aliengirlliv/teachable/blob/HEAD/algos/ppo.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"35fc183b2b26ff4a","mcp_get_code":{"code_sha256":"35fc183b2b26ff4a"}},{"arxiv_id":"1511.06434","paper":"/paper/unsupervised-representation-learning-with-1","title":"Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks","date":"2015-11-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zeleni9/pytorch-wgan","path":"models/dcgan.py","file_url":"https://github.com/zeleni9/pytorch-wgan/blob/HEAD/models/dcgan.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"064cfba85812c032","mcp_get_code":{"code_sha256":"064cfba85812c032"}},{"arxiv_id":"1509.06461","paper":"/paper/deep-reinforcement-learning-with-double-q","title":"Deep Reinforcement Learning with Double Q-learning","date":"2015-09-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"daviddcho/supermario","path":"agent.py","file_url":"https://github.com/daviddcho/supermario/blob/HEAD/agent.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"908cf1edb8faa4b5","mcp_get_code":{"code_sha256":"908cf1edb8faa4b5"}},{"arxiv_id":"1509.02971","paper":"/paper/continuous-control-with-deep-reinforcement","title":"Continuous control with deep reinforcement learning","date":"2015-09-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Souphis/mobile_robot_rl","path":"mobile_robot_rl/agents/sac.py","file_url":"https://github.com/Souphis/mobile_robot_rl/blob/HEAD/mobile_robot_rl/agents/sac.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"416bd507555ea4fa","mcp_get_code":{"code_sha256":"416bd507555ea4fa"}}]}