{"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/format-duration","entry":"format_duration","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":11,"n_papers_ran":7,"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":10,"n_samples_ran":6,"n_samples_fingerprinted":6,"n_places":11,"n_places_pointer_only":3,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":6,"unverified":4},"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":"2608.14705","paper":"/paper/arxiv-2608-14705","title":"On Cross-Validation for Hyperparameter Optimization of Deep Learning Image Classifiers","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"ljbuturovic/cvic","path":"cvic/common_cvic.py","file_url":"https://github.com/ljbuturovic/cvic/blob/HEAD/cvic/common_cvic.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"241e27c132282bb0","mcp_get_code":{"code_sha256":"241e27c132282bb0"}},{"arxiv_id":"2608.07439","paper":"/paper/arxiv-2608-07439","title":"An Exploratory Evaluation of LLM-Assisted Rewriting of Moderate-Complexity Financial Sentences for DisCoCat-Based Sentiment Analysis","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"bllin001/qnlp-discocat-llms-finance-rewriting","path":"code/summarize_discocat_paper_runs.py","file_url":"https://github.com/bllin001/qnlp-discocat-llms-finance-rewriting/blob/HEAD/code/summarize_discocat_paper_runs.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1c8d4b29390d47af","mcp_get_code":{"code_sha256":"1c8d4b29390d47af"}},{"arxiv_id":"2607.11338","paper":"/paper/arxiv-2607-11338","title":"AutoVSR: Automatic Visual-to-Symbolic Reasoning for Symbolic Expression Generation from Circuit Schematic","date":null,"month_inferred_from_arxiv_id":"2026-07","title_source":"syntology","repo":"LongfeiLi1/AutoVSR","path":"src/utils/metrics.py","file_url":"https://github.com/LongfeiLi1/AutoVSR/blob/HEAD/src/utils/metrics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5cba50003f3b5a37","mcp_get_code":{"code_sha256":"5cba50003f3b5a37"}},{"arxiv_id":"2604.04469","paper":"/paper/arxiv-2604-04469","title":"Same Geometry, Opposite Noise: Transformer Magnitude Representations Lack Scalar Variability","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"synthiumjp/weber","path":"m3_pilot/m3_batch_run.py","file_url":"https://github.com/synthiumjp/weber/blob/HEAD/m3_pilot/m3_batch_run.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"47e2bf1a6a4e395a","mcp_get_code":{"code_sha256":"47e2bf1a6a4e395a"}},{"arxiv_id":"2509.23413","paper":"/paper/arxiv-2509-23413","title":"URS: A Unified Neural Routing Solver for Cross-Problem Zero-Shot Generalization","date":null,"month_inferred_from_arxiv_id":"2025-09","title_source":"syntology","repo":"CIAM-Group/URS","path":"utils/utils.py","file_url":"https://github.com/CIAM-Group/URS/blob/HEAD/utils/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"AGPL-3.0","inline_ok":false,"code_sha256_prefix":"0b8f10aaac740c27","mcp_get_code":{"code_sha256":"0b8f10aaac740c27"}},{"arxiv_id":"2506.09659","paper":"/paper/intent-factored-generation-unleashing-the","title":"Intent Factored Generation: Unleashing the Diversity in Your Language Model","date":"2025-06-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"flairox/ifg","path":"hendrycks_math/experiment_pipelines/hparam_sweeper.py","file_url":"https://github.com/flairox/ifg/blob/HEAD/hendrycks_math/experiment_pipelines/hparam_sweeper.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":"606bde9343246a50","mcp_get_code":{"code_sha256":"606bde9343246a50"}},{"arxiv_id":"2505.22389","paper":"/paper/train-with-perturbation-infer-after-merging-a","title":"Train with Perturbation, Infer after Merging: A Two-Stage Framework for Continual Learning","date":"2025-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qhmiao/P-M-for-Continual-Learning","path":"utils/misc.py","file_url":"https://github.com/qhmiao/P-M-for-Continual-Learning/blob/HEAD/utils/misc.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"d06fe57a613a4c54","mcp_get_code":{"code_sha256":"d06fe57a613a4c54"}},{"arxiv_id":"2406.05658","paper":"/paper/visual-prompt-tuning-in-null-space-for","title":"Visual Prompt Tuning in Null Space for Continual Learning","date":"2024-06-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zugexiaodui/VPTinNSforCL","path":"utils/misc.py","file_url":"https://github.com/zugexiaodui/VPTinNSforCL/blob/HEAD/utils/misc.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d06fe57a613a4c54","mcp_get_code":{"code_sha256":"d06fe57a613a4c54"}},{"arxiv_id":"2310.01225","paper":"/paper/a-path-norm-toolkit-for-modern-networks","title":"A path-norm toolkit for modern networks: consequences, promises and challenges","date":"2023-10-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"agonon/pathnorm_toolkit","path":"repro/iclr24/utils/train_imagenet.py","file_url":"https://github.com/agonon/pathnorm_toolkit/blob/HEAD/repro/iclr24/utils/train_imagenet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"108ebe73d0b32ec2","mcp_get_code":{"code_sha256":"108ebe73d0b32ec2"}},{"arxiv_id":"2309.14356","paper":"/paper/coco-counterfactuals-automatically","title":"COCO-Counterfactuals: Automatically Constructed Counterfactual Examples for Image-Text Pairs","date":"2023-09-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"UKPLab/sentence-transformers","path":"sentence_transformers/base/model_card.py","file_url":"https://github.com/UKPLab/sentence-transformers/blob/HEAD/sentence_transformers/base/model_card.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"e12c9d90a7538793","mcp_get_code":{"code_sha256":"e12c9d90a7538793"}},{"arxiv_id":"2007.03051","paper":"/paper/carbontracker-tracking-and-predicting-the","title":"Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models","date":"2020-07-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lfwa/carbontracker","path":"carbontracker/report.py","file_url":"https://github.com/lfwa/carbontracker/blob/HEAD/carbontracker/report.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"db565056124af7a3","mcp_get_code":{"code_sha256":"db565056124af7a3"}}]}