{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/code/compute-distance","entry":"compute_distance","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":21,"n_papers_ran":6,"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":20,"n_samples_ran":6,"n_samples_fingerprinted":3,"n_places":24,"n_places_pointer_only":10,"by_status":{"ran_honours":0,"ran_violates":1,"ran_draft_wrong":0,"ran_fixture":2,"ran":3,"unverified":14},"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":"2609.00591","paper":"/paper/arxiv-2609-00591","title":"A Glance Is All You Need: Single-Pass Fine-Grained Image Captioning with SimLoss","date":null,"month_inferred_from_arxiv_id":"2026-09","title_source":"syntology","repo":"srynsh/SimLoss-Image-Captioning","path":"simloss/encoder/eval_caption_similarity.py","file_url":"https://github.com/srynsh/SimLoss-Image-Captioning/blob/HEAD/simloss/encoder/eval_caption_similarity.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c4f6db555f997579","mcp_get_code":{"code_sha256":"c4f6db555f997579"}},{"arxiv_id":"2606.18209","paper":"/paper/arxiv-2606-18209","title":"Rethinking Dataset Distillation for Classification: Do Distilled Sets Outperform Coresets?","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"AyushRoy2001/ManifoldGD","path":"sample_mode_guidance.py","file_url":"https://github.com/AyushRoy2001/ManifoldGD/blob/HEAD/sample_mode_guidance.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"dfaf3be9474cef03","mcp_get_code":{"code_sha256":"dfaf3be9474cef03"}},{"arxiv_id":"2602.21846","paper":"/paper/arxiv-2602-21846","title":"Scalable Kernel-Based Distances for Statistical Inference and Integration","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"MashaNaslidnyk/kqe","path":"kqe/kqd.py","file_url":"https://github.com/MashaNaslidnyk/kqe/blob/HEAD/kqe/kqd.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"29781541bb784925","mcp_get_code":{"code_sha256":"29781541bb784925"}},{"arxiv_id":"2503.21771","paper":"/paper/a-unified-image-dense-annotation-generation","title":"A Unified Image-Dense Annotation Generation Model for Underwater Scenes","date":"2025-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hongklin/tide","path":"tide/utils/mask_process.py","file_url":"https://github.com/hongklin/tide/blob/HEAD/tide/utils/mask_process.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":"8578046d76d864c0","mcp_get_code":{"code_sha256":"8578046d76d864c0"}},{"arxiv_id":"2410.18926","paper":"/paper/lorann-low-rank-matrix-factorization-for","title":"LoRANN: Low-Rank Matrix Factorization for Approximate Nearest Neighbor Search","date":"2024-10-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ejaasaari/lorann-experiments","path":"ann_benchmarks/distance.py","file_url":"https://github.com/ejaasaari/lorann-experiments/blob/HEAD/ann_benchmarks/distance.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"33781da3207cc49d","mcp_get_code":{"code_sha256":"33781da3207cc49d"}},{"arxiv_id":"2410.08589","paper":"/paper/retraining-free-merging-of-sparse-mixture-of","title":"Retraining-Free Merging of Sparse MoE via Hierarchical Clustering","date":"2024-10-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wazenmai/hc-smoe","path":"hcsmoe/merging/clustering.py","file_url":"https://github.com/wazenmai/hc-smoe/blob/HEAD/hcsmoe/merging/clustering.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"89eaaed986467ca4","mcp_get_code":{"code_sha256":"89eaaed986467ca4"}},{"arxiv_id":"2410.05873","paper":"/paper/mexa-multilingual-evaluation-of-english","title":"MEXA: Multilingual Evaluation of English-Centric LLMs via Cross-Lingual Alignment","date":"2024-10-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cisnlp/mexa","path":"compute_mexa.py","file_url":"https://github.com/cisnlp/mexa/blob/HEAD/compute_mexa.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":"ba6588b1c55fb556","mcp_get_code":{"code_sha256":"ba6588b1c55fb556"}},{"arxiv_id":"2405.15613","paper":"/paper/automatic-data-curation-for-self-supervised","title":"Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach","date":"2024-05-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/ssl-data-curation","path":"src/kmeans_gpu.py","file_url":"https://github.com/facebookresearch/ssl-data-curation/blob/HEAD/src/kmeans_gpu.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"f0ba8fa85b2c379a","mcp_get_code":{"code_sha256":"f0ba8fa85b2c379a"}},{"arxiv_id":"2401.02094","paper":"/paper/federated-class-incremental-learning-with-1","title":"PILoRA: Prototype Guided Incremental LoRA for Federated Class-Incremental Learning","date":"2024-01-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ghy0501/pilora","path":"PILoRA-cifar/CPN.py","file_url":"https://github.com/ghy0501/pilora/blob/HEAD/PILoRA-cifar/CPN.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ca4998fc119fd95d","mcp_get_code":{"code_sha256":"ca4998fc119fd95d"}},{"arxiv_id":"2312.01473","paper":"/paper/regularity-as-intrinsic-reward-for-free-play-1","title":"Regularity as Intrinsic Reward for Free Play","date":"2023-12-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"orybkin/lexa-benchmark","path":"d4rl/carla/carla_env.py","file_url":"https://github.com/orybkin/lexa-benchmark/blob/HEAD/d4rl/carla/carla_env.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b7eec81dfe2b1b23","mcp_get_code":{"code_sha256":"b7eec81dfe2b1b23"}},{"arxiv_id":"2312.01473","paper":"/paper/regularity-as-intrinsic-reward-for-free-play-1","title":"Regularity as Intrinsic Reward for Free Play","date":"2023-12-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"orybkin/lexa-benchmark","path":"d4rl/carla/data_collection_agent_lane.py","file_url":"https://github.com/orybkin/lexa-benchmark/blob/HEAD/d4rl/carla/data_collection_agent_lane.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b5b4d8429fac0ada","mcp_get_code":{"code_sha256":"b5b4d8429fac0ada"}},{"arxiv_id":"2211.09817","paper":"/paper/on-the-effect-of-pre-training-for-transformer","title":"On the Effect of Pre-training for Transformer in Different Modality on Offline Reinforcement Learning","date":"2022-11-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rail-berkeley/d4rl","path":"d4rl/carla/carla_env.py","file_url":"https://github.com/rail-berkeley/d4rl/blob/HEAD/d4rl/carla/carla_env.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":"b7eec81dfe2b1b23","mcp_get_code":{"code_sha256":"b7eec81dfe2b1b23"}},{"arxiv_id":"2106.02193","paper":"/paper/cross-trajectory-representation-learning-for","title":"Cross-Trajectory Representation Learning for Zero-Shot Generalization in RL","date":"2021-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bmazoure/ctrl_public","path":"algo.py","file_url":"https://github.com/bmazoure/ctrl_public/blob/HEAD/algo.py","status":"unverified","verification_level":0,"contract_check":"VIOLATES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f8b1ad84240423bd","mcp_get_code":{"code_sha256":"f8b1ad84240423bd"}},{"arxiv_id":"2011.11284","paper":"/paper/peeking-inside-the-black-box-interpreting","title":"Peeking inside the Black Box: Interpreting Deep Learning Models for Exoplanet Atmospheric Retrievals","date":"2020-11-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ucl-exoplanets/Spectra_Sensitivity_analysis","path":"sensitivity_test/sensitivity.py","file_url":"https://github.com/ucl-exoplanets/Spectra_Sensitivity_analysis/blob/HEAD/sensitivity_test/sensitivity.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e272cd364bffdec5","mcp_get_code":{"code_sha256":"e272cd364bffdec5"}},{"arxiv_id":"2011.00359","paper":"/paper/tartanvo-a-generalizable-learning-based-vo","title":"TartanVO: A Generalizable Learning-based VO","date":"2020-10-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"castacks/tartanair_tools","path":"evaluation/evaluate_rpe.py","file_url":"https://github.com/castacks/tartanair_tools/blob/HEAD/evaluation/evaluate_rpe.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":"1f2de08785e4af82","mcp_get_code":{"code_sha256":"1f2de08785e4af82"}},{"arxiv_id":"2004.07219","paper":"/paper/datasets-for-data-driven-reinforcement","title":"D4RL: Datasets for Deep Data-Driven Reinforcement Learning","date":"2020-04-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"anuragajay/d4rl","path":"d4rl/carla/carla_env.py","file_url":"https://github.com/anuragajay/d4rl/blob/HEAD/d4rl/carla/carla_env.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":"b7eec81dfe2b1b23","mcp_get_code":{"code_sha256":"b7eec81dfe2b1b23"}},{"arxiv_id":"2004.07219","paper":"/paper/datasets-for-data-driven-reinforcement","title":"D4RL: Datasets for Deep Data-Driven Reinforcement Learning","date":"2020-04-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"anuragajay/d4rl","path":"d4rl/carla/data_collection_agent_lane.py","file_url":"https://github.com/anuragajay/d4rl/blob/HEAD/d4rl/carla/data_collection_agent_lane.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":"b5b4d8429fac0ada","mcp_get_code":{"code_sha256":"b5b4d8429fac0ada"}},{"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_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"87baeee1051adfd9","mcp_get_code":{"code_sha256":"87baeee1051adfd9"}},{"arxiv_id":"1911.08947","paper":"/paper/real-time-scene-text-detection-with","title":"Real-time Scene Text Detection with Differentiable Binarization","date":"2019-11-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"huyhoang17/DB_text_minimal","path":"src/db_transforms.py","file_url":"https://github.com/huyhoang17/DB_text_minimal/blob/HEAD/src/db_transforms.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0362f3cc6b5edc59","mcp_get_code":{"code_sha256":"0362f3cc6b5edc59"}},{"arxiv_id":"1908.09885","paper":"/paper/direct-shape-optimization-through-deep","title":"Direct shape optimization through deep reinforcement learning","date":null,"month_inferred_from_arxiv_id":"2019-08","title_source":"archive","repo":"jviquerat/shape_optimization_DRL","path":"src/shapes_utils.py","file_url":"https://github.com/jviquerat/shape_optimization_DRL/blob/HEAD/src/shapes_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7a635e3bd78a9dc2","mcp_get_code":{"code_sha256":"7a635e3bd78a9dc2"}},{"arxiv_id":"1908.09885","paper":"/paper/direct-shape-optimization-through-deep","title":"Direct shape optimization through deep reinforcement learning","date":null,"month_inferred_from_arxiv_id":"2019-08","title_source":"archive","repo":"jviquerat/bezier_shapes","path":"shapes.py","file_url":"https://github.com/jviquerat/bezier_shapes/blob/HEAD/shapes.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"19c6651e6bea51df","mcp_get_code":{"code_sha256":"19c6651e6bea51df"}},{"arxiv_id":"1610.00768","paper":"/paper/technical-report-on-the-cleverhans-v210","title":"Technical Report on the CleverHans v2.1.0 Adversarial Examples Library","date":"2016-10-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"elites2k19/prism-attack","path":"cleverhans/attacks/hop_skip_jump_attack.py","file_url":"https://github.com/elites2k19/prism-attack/blob/HEAD/cleverhans/attacks/hop_skip_jump_attack.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"33098e6217d6f519","mcp_get_code":{"code_sha256":"33098e6217d6f519"}},{"arxiv_id":"aaai_28602","paper":null,"title":"arXiv:aaai_28602","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Sainzerjj/SFERD","path":"metrics/precision_recall.py","file_url":"https://github.com/Sainzerjj/SFERD/blob/HEAD/metrics/precision_recall.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"07ff17e38337d973","mcp_get_code":{"code_sha256":"07ff17e38337d973"}},{"arxiv_id":"2025.findings-acl.1385","paper":null,"title":"arXiv:2025.findings-acl.1385","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"cisnlp/MEXA","path":"compute_mexa.py","file_url":"https://github.com/cisnlp/MEXA/blob/HEAD/compute_mexa.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":"ba6588b1c55fb556","mcp_get_code":{"code_sha256":"ba6588b1c55fb556"}}]}