{"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-precision","entry":"compute_precision","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":4,"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":7,"n_samples_ran":4,"n_samples_fingerprinted":0,"n_places":8,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":3,"unverified":3},"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":"2605.31500","paper":"/paper/arxiv-2605-31500","title":"On Efficient Scaling of GNNs via IO-Aware Layers Implementations","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"yandex-research/On-Efficient-Scaling-Of-GNNs","path":"src/training/metrics.py","file_url":"https://github.com/yandex-research/On-Efficient-Scaling-Of-GNNs/blob/HEAD/src/training/metrics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"cd6f55a7a1817bde","mcp_get_code":{"code_sha256":"cd6f55a7a1817bde"}},{"arxiv_id":"2605.12370","paper":"/paper/arxiv-2605-12370","title":"Context Convergence Improves Answering Inferential Questions","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"DataScienceUIBK/Context-Convergence-Inferential-QA","path":"experiments/convergence_vs_cosine.py","file_url":"https://github.com/DataScienceUIBK/Context-Convergence-Inferential-QA/blob/HEAD/experiments/convergence_vs_cosine.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3f7e8fc166782345","mcp_get_code":{"code_sha256":"3f7e8fc166782345"}},{"arxiv_id":"2603.07567","paper":"/paper/arxiv-2603-07567","title":"Revisiting the LiRA Membership Inference Attack Under Realistic Assumptions","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"najeebjebreel/lira_analysis","path":"comprehensive_analysis/metrics.py","file_url":"https://github.com/najeebjebreel/lira_analysis/blob/HEAD/comprehensive_analysis/metrics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"54526585121a623a","mcp_get_code":{"code_sha256":"54526585121a623a"}},{"arxiv_id":"2401.14493","paper":"/paper/k-qa-a-real-world-medical-q-a-benchmark","title":"K-QA: A Real-World Medical Q&A Benchmark","date":"2024-01-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"itaymanes/k-qa","path":"evaluation/metrics.py","file_url":"https://github.com/itaymanes/k-qa/blob/HEAD/evaluation/metrics.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fa788cda714fd442","mcp_get_code":{"code_sha256":"fa788cda714fd442"}},{"arxiv_id":"2310.12979","paper":"/paper/predicting-a-protein-s-stability-under-a","title":"Predicting a Protein's Stability under a Million Mutations","date":"2023-10-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jozhang97/MutateEverything","path":"metrics.py","file_url":"https://github.com/jozhang97/MutateEverything/blob/HEAD/metrics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ef845b16cb85c032","mcp_get_code":{"code_sha256":"ef845b16cb85c032"}},{"arxiv_id":"1511.08308","paper":"/paper/named-entity-recognition-with-bidirectional","title":"Named Entity Recognition with Bidirectional LSTM-CNNs","date":"2015-11-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mxhofer/Named-Entity-Recognition-BidirectionalLSTM-CNN-CoNLL","path":"validation.py","file_url":"https://github.com/mxhofer/Named-Entity-Recognition-BidirectionalLSTM-CNN-CoNLL/blob/HEAD/validation.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f5cc0f3470ea30e7","mcp_get_code":{"code_sha256":"f5cc0f3470ea30e7"}},{"arxiv_id":"1510.01784","paper":"/paper/vbpr-visual-bayesian-personalized-ranking","title":"VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback","date":"2015-10-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jchanxtarov/vbpr","path":"src/utils/metrics.py","file_url":"https://github.com/jchanxtarov/vbpr/blob/HEAD/src/utils/metrics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ddc18fe321d7784d","mcp_get_code":{"code_sha256":"ddc18fe321d7784d"}},{"arxiv_id":"1205.2618","paper":"/paper/bpr-bayesian-personalized-ranking-from","title":"BPR: Bayesian Personalized Ranking from Implicit Feedback","date":"2012-05-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jchanxtarov/BPRMF","path":"src/utils/metrics.py","file_url":"https://github.com/jchanxtarov/BPRMF/blob/HEAD/src/utils/metrics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ddc18fe321d7784d","mcp_get_code":{"code_sha256":"ddc18fe321d7784d"}}]}