{"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/levenshtein","entry":"levenshtein","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":8,"n_papers_ran":2,"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":8,"n_samples_ran":2,"n_samples_fingerprinted":1,"n_places":8,"n_places_pointer_only":0,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":2,"unverified":6},"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":"2608.08239","paper":"/paper/arxiv-2608-08239","title":"The Replay Gap: Static Evaluation of Model Switching in LLM Agents Scores the Wrong World","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"AshrithaG/replay-gap","path":"src/replay_gap/metrics.py","file_url":"https://github.com/AshrithaG/replay-gap/blob/HEAD/src/replay_gap/metrics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0ec3f8fbc6028884","mcp_get_code":{"code_sha256":"0ec3f8fbc6028884"}},{"arxiv_id":"2605.07024","paper":"/paper/arxiv-2605-07024","title":"DELULU: A Verified Multi-Lingual Benchmark for Code Hallucination Detection in Fill-in-the-Middle Tasks","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"microsoft/delulu","path":"evaluations/run_completion_metrics.py","file_url":"https://github.com/microsoft/delulu/blob/HEAD/evaluations/run_completion_metrics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"886224736275f977","mcp_get_code":{"code_sha256":"886224736275f977"}},{"arxiv_id":"2601.11283","paper":"/paper/arxiv-2601-11283","title":"Metabolomic Biomarker Discovery for ADHD Diagnosis Using Interpretable Machine Learning","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"nbelacel/Closest-Resemblance","path":"MGLVQ_Classifier.py","file_url":"https://github.com/nbelacel/Closest-Resemblance/blob/HEAD/MGLVQ_Classifier.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"107aba82259389d4","mcp_get_code":{"code_sha256":"107aba82259389d4"}},{"arxiv_id":"2110.15797","paper":"/paper/discovering-non-monotonic-autoregressive","title":"Discovering Non-monotonic Autoregressive Orderings with Variational Inference","date":"2021-10-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xuanlinli17/autoregressive_inference","path":"voi/algorithms/levenshtein.py","file_url":"https://github.com/xuanlinli17/autoregressive_inference/blob/HEAD/voi/algorithms/levenshtein.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f1dd530f5c706f2f","mcp_get_code":{"code_sha256":"f1dd530f5c706f2f"}},{"arxiv_id":"2007.06866","paper":"/paper/alleviating-over-segmentation-errors-by","title":"Alleviating Over-segmentation Errors by Detecting Action Boundaries","date":"2020-07-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yiskw713/asrf","path":"libs/metric.py","file_url":"https://github.com/yiskw713/asrf/blob/HEAD/libs/metric.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3b9ef968d52cf9c1","mcp_get_code":{"code_sha256":"3b9ef968d52cf9c1"}},{"arxiv_id":"1905.11286","paper":"/paper/stochastic-gradient-methods-with-layer-wise","title":"Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep Networks","date":"2019-05-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"NVIDIA/OpenSeq2Seq","path":"external_lm_rescore/process_beam_dump.py","file_url":"https://github.com/NVIDIA/OpenSeq2Seq/blob/HEAD/external_lm_rescore/process_beam_dump.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":"3ca5033f79170e1d","mcp_get_code":{"code_sha256":"3ca5033f79170e1d"}},{"arxiv_id":"1612.01744","paper":"/paper/listen-and-translate-a-proof-of-concept-for","title":"Listen and Translate: A Proof of Concept for End-to-End Speech-to-Text Translation","date":"2016-12-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eske/seq2seq","path":"translate/evaluation.py","file_url":"https://github.com/eske/seq2seq/blob/HEAD/translate/evaluation.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":"d8042102ebe27b4c","mcp_get_code":{"code_sha256":"d8042102ebe27b4c"}},{"arxiv_id":"1507.05717","paper":"/paper/an-end-to-end-trainable-neural-network-for","title":"An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition","date":"2015-07-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nithyadurai87/pottan-ocr-tamil","path":"ocropy/ocrolib/edist.py","file_url":"https://github.com/nithyadurai87/pottan-ocr-tamil/blob/HEAD/ocropy/ocrolib/edist.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f50d8818f0a6b156","mcp_get_code":{"code_sha256":"f50d8818f0a6b156"}}]}