{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/code/get-time","entry":"get_time","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":20,"n_papers_ran":14,"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":17,"n_samples_ran":9,"n_samples_fingerprinted":0,"n_places":22,"n_places_pointer_only":9,"by_status":{"ran_honours":1,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":7,"unverified":8},"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":"2607.07401","paper":"/paper/arxiv-2607-07401","title":"Heterogeneity-Adaptive Diffusion Schrödinger Bridge for PET-Guided Whole-Body MRI Translation","date":null,"month_inferred_from_arxiv_id":"2026-07","title_source":"syntology","repo":"xyw-medical-research/HADSB","path":"logger.py","file_url":"https://github.com/xyw-medical-research/HADSB/blob/HEAD/logger.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"cbb9d7563e2ebd55","mcp_get_code":{"code_sha256":"cbb9d7563e2ebd55"}},{"arxiv_id":"2606.18703","paper":"/paper/arxiv-2606-18703","title":"Contextualizing Biological Language Models across Modalities via Logit-Space Contrastive Alignment","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"bowen-gao/DrugCLIP","path":"HomoAug/utils/misc.py","file_url":"https://github.com/bowen-gao/DrugCLIP/blob/HEAD/HomoAug/utils/misc.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"885d339055ac8edc","mcp_get_code":{"code_sha256":"885d339055ac8edc"}},{"arxiv_id":"2601.21316","paper":"/paper/arxiv-2601-21316","title":"Heterogeneous Vertiport Selection Optimization for On-Demand Air Taxi Services: A Deep Reinforcement Learning Approach","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"Traffic-Alpha/UAGMC","path":"greedy.py","file_url":"https://github.com/Traffic-Alpha/UAGMC/blob/HEAD/greedy.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":"47cac6dda75c9ccc","mcp_get_code":{"code_sha256":"47cac6dda75c9ccc"}},{"arxiv_id":"2601.21316","paper":"/paper/arxiv-2601-21316","title":"Heterogeneous Vertiport Selection Optimization for On-Demand Air Taxi Services: A Deep Reinforcement Learning Approach","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"Traffic-Alpha/UAGMC","path":"ground_policy.py","file_url":"https://github.com/Traffic-Alpha/UAGMC/blob/HEAD/ground_policy.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":"c07a468bb4827102","mcp_get_code":{"code_sha256":"c07a468bb4827102"}},{"arxiv_id":"2601.21316","paper":"/paper/arxiv-2601-21316","title":"Heterogeneous Vertiport Selection Optimization for On-Demand Air Taxi Services: A Deep Reinforcement Learning Approach","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"Traffic-Alpha/UAGMC","path":"methods/minimum_distance.py","file_url":"https://github.com/Traffic-Alpha/UAGMC/blob/HEAD/methods/minimum_distance.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":"1caf58f728ced7fd","mcp_get_code":{"code_sha256":"1caf58f728ced7fd"}},{"arxiv_id":"2504.17568","paper":"/paper/beyond-cox-models-assessing-the-performance","title":"Beyond Cox Models: Assessing the Performance of Machine-Learning Methods in Non-Proportional Hazards and Non-Linear Survival Analysis","date":"2025-04-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"compbiomed-unito/survhive","path":"survhive/util.py","file_url":"https://github.com/compbiomed-unito/survhive/blob/HEAD/survhive/util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3002db785bb548c1","mcp_get_code":{"code_sha256":"3002db785bb548c1"}},{"arxiv_id":"2502.01989","paper":"/paper/t-scend-test-time-scalable-mcts-enhanced","title":"T-SCEND: Test-time Scalable MCTS-enhanced Diffusion Model","date":"2025-02-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ai4science-westlakeu/t_scend","path":"tscend_src/utils/utils.py","file_url":"https://github.com/ai4science-westlakeu/t_scend/blob/HEAD/tscend_src/utils/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"35d5144a8f9cae87","mcp_get_code":{"code_sha256":"35d5144a8f9cae87"}},{"arxiv_id":"2407.06494","paper":"/paper/a-generative-approach-to-control-complex","title":"DiffPhyCon: A Generative Approach to Control Complex Physical Systems","date":"2024-07-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AI4Science-WestlakeU/diffphycon","path":"utils.py","file_url":"https://github.com/AI4Science-WestlakeU/diffphycon/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"35d5144a8f9cae87","mcp_get_code":{"code_sha256":"35d5144a8f9cae87"}},{"arxiv_id":"2406.13945","paper":"/paper/citybench-evaluating-the-capabilities-of","title":"CityBench: Evaluating the Capabilities of Large Language Models for Urban Tasks","date":"2024-06-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tsinghua-fib-lab/citybench","path":"citybench/mobility_prediction/data_gen.py","file_url":"https://github.com/tsinghua-fib-lab/citybench/blob/HEAD/citybench/mobility_prediction/data_gen.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5642b97cf4bef5f0","mcp_get_code":{"code_sha256":"5642b97cf4bef5f0"}},{"arxiv_id":"2406.12837","paper":"/paper/layermerge-neural-network-depth-compression","title":"LayerMerge: Neural Network Depth Compression through Layer Pruning and Merging","date":"2024-06-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"snu-mllab/LayerMerge","path":"layer_merge/measure.py","file_url":"https://github.com/snu-mllab/LayerMerge/blob/HEAD/layer_merge/measure.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"53138d8dfe29a2a5","mcp_get_code":{"code_sha256":"53138d8dfe29a2a5"}},{"arxiv_id":"2405.15885","paper":"/paper/diffusion-bridge-implicit-models","title":"Diffusion Bridge Implicit Models","date":"2024-05-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thu-ml/DiffusionBridge","path":"logger.py","file_url":"https://github.com/thu-ml/DiffusionBridge/blob/HEAD/logger.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"cbb9d7563e2ebd55","mcp_get_code":{"code_sha256":"cbb9d7563e2ebd55"}},{"arxiv_id":"2405.09782","paper":"/paper/size-invariance-matters-rethinking-metrics","title":"Size-invariance Matters: Rethinking Metrics and Losses for Imbalanced Multi-object Salient Object Detection","date":"2024-05-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ferry-li/si-sod","path":"src/utils/logger.py","file_url":"https://github.com/ferry-li/si-sod/blob/HEAD/src/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":"cbb9d7563e2ebd55","mcp_get_code":{"code_sha256":"cbb9d7563e2ebd55"}},{"arxiv_id":"2403.06069","paper":"/paper/implicit-image-to-image-schrodinger-bridge","title":"Implicit Image-to-Image Schrodinger Bridge for Image Restoration","date":"2024-03-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wangya22/I3SB","path":"logger.py","file_url":"https://github.com/wangya22/I3SB/blob/HEAD/logger.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"cbb9d7563e2ebd55","mcp_get_code":{"code_sha256":"cbb9d7563e2ebd55"}},{"arxiv_id":"2402.08383","paper":"/paper/uncertainty-quantification-for-forward-and","title":"Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution","date":"2024-02-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AI4Science-WestlakeU/le-pde-uq","path":"MP_Neural_PDE_Solvers/common/utils.py","file_url":"https://github.com/AI4Science-WestlakeU/le-pde-uq/blob/HEAD/MP_Neural_PDE_Solvers/common/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0328298b19c25c7f","mcp_get_code":{"code_sha256":"0328298b19c25c7f"}},{"arxiv_id":"2401.13171","paper":"/paper/compositional-generative-inverse-design","title":"Compositional Generative Inverse Design","date":"2024-01-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AI4Science-WestlakeU/cindm","path":"GNS_model/utils.py","file_url":"https://github.com/AI4Science-WestlakeU/cindm/blob/HEAD/GNS_model/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"35d5144a8f9cae87","mcp_get_code":{"code_sha256":"35d5144a8f9cae87"}},{"arxiv_id":"2401.05907","paper":"/paper/efficient-image-deblurring-networks-based-on","title":"Efficient Image Deblurring Networks based on Diffusion Models","date":"2024-01-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bnm6900030/swintormer","path":"print_loss.py","file_url":"https://github.com/bnm6900030/swintormer/blob/HEAD/print_loss.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"dbdb5c1cd7f7e7b8","mcp_get_code":{"code_sha256":"dbdb5c1cd7f7e7b8"}},{"arxiv_id":"2312.11190","paper":"/paper/navigating-interfaces-with-ai-for-enhanced","title":"VisionTasker: Mobile Task Automation Using Vision Based UI Understanding and LLM Task Planning","date":null,"month_inferred_from_arxiv_id":"2023-12","title_source":"archive","repo":"akimotoayako/visiontasker","path":"main_zh_bystep.py","file_url":"https://github.com/akimotoayako/visiontasker/blob/HEAD/main_zh_bystep.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"46db112e176bc538","mcp_get_code":{"code_sha256":"46db112e176bc538"}},{"arxiv_id":"2310.00093","paper":"/paper/datadam-efficient-dataset-distillation-with-1","title":"DataDAM: Efficient Dataset Distillation with Attention Matching","date":"2023-09-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"datadistillation/datadam","path":"main_DataDAM.py","file_url":"https://github.com/datadistillation/datadam/blob/HEAD/main_DataDAM.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e98b4502952690a9","mcp_get_code":{"code_sha256":"e98b4502952690a9"}},{"arxiv_id":"2302.00796","paper":"/paper/unsupervised-entity-alignment-for-temporal","title":"Unsupervised Entity Alignment for Temporal Knowledge Graphs","date":"2023-02-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zju-daily/dualmatch","path":"utils2.py","file_url":"https://github.com/zju-daily/dualmatch/blob/HEAD/utils2.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":"44ea70c31791249e","mcp_get_code":{"code_sha256":"44ea70c31791249e"}},{"arxiv_id":"2109.10686","paper":"/paper/scale-efficiently-insights-from-pre-training","title":"Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers","date":"2021-09-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gsarti/it5","path":"finetuning/vars.py","file_url":"https://github.com/gsarti/it5/blob/HEAD/finetuning/vars.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":"07d60309bc634d37","mcp_get_code":{"code_sha256":"07d60309bc634d37"}},{"arxiv_id":"2007.06364","paper":"/paper/on-uncertainty-estimation-in-active-learning","title":"On uncertainty estimation in active learning for image segmentation","date":"2020-07-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lyn1874/region_based_active_learning","path":"eval_calibration/calc_calibration_score.py","file_url":"https://github.com/lyn1874/region_based_active_learning/blob/HEAD/eval_calibration/calc_calibration_score.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1ea8d82f1e501df8","mcp_get_code":{"code_sha256":"1ea8d82f1e501df8"}},{"arxiv_id":"2002.03794","paper":"/paper/the-deep-learning-compiler-a-comprehensive","title":"The Deep Learning Compiler: A Comprehensive Survey","date":"2020-02-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"buaa-hipo/dlcompiler-comparison","path":"utils/gather_data.py","file_url":"https://github.com/buaa-hipo/dlcompiler-comparison/blob/HEAD/utils/gather_data.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"903f8c14889802d9","mcp_get_code":{"code_sha256":"903f8c14889802d9"}}]}