{"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-error","entry":"get_error","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":13,"n_papers_ran":8,"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":11,"n_samples_ran":6,"n_samples_fingerprinted":1,"n_places":13,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":3,"ran_fixture":0,"ran":3,"unverified":5},"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":"2409.09778","paper":"/paper/rewind-to-delete-certified-machine-unlearning","title":"Rewind-to-Delete: Certified Machine Unlearning for Nonconvex Functions","date":"2024-09-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"siqiaomu/r2d","path":"r2d.py","file_url":"https://github.com/siqiaomu/r2d/blob/HEAD/r2d.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"de35b5dbce9d27e9","mcp_get_code":{"code_sha256":"de35b5dbce9d27e9"}},{"arxiv_id":"2407.11867","paper":"/paper/single-layer-single-gradient-unlearning","title":"Unlearning Targeted Information via Single Layer Unlearning Gradient","date":"2024-07-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"CSIPlab/SLUG","path":"src/utils.py","file_url":"https://github.com/CSIPlab/SLUG/blob/HEAD/src/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1c6bcc03390a9997","mcp_get_code":{"code_sha256":"1c6bcc03390a9997"}},{"arxiv_id":"2402.18747","paper":"/paper/fine-tuned-machine-translation-metrics","title":"Fine-Tuned Machine Translation Metrics Struggle in Unseen Domains","date":"2024-02-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"amazon-science/bio-mqm-dataset","path":"metrics_domain_adaptation/scripts/02-data/04a-get_mqm_bio.py","file_url":"https://github.com/amazon-science/bio-mqm-dataset/blob/HEAD/metrics_domain_adaptation/scripts/02-data/04a-get_mqm_bio.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"79fd3d4cc3316e33","mcp_get_code":{"code_sha256":"79fd3d4cc3316e33"}},{"arxiv_id":"2401.00211","paper":"/paper/open-ti-open-traffic-intelligence-with","title":"Open-TI: Open Traffic Intelligence with Augmented Language Model","date":"2023-12-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"darl-libsignal/openti","path":"llm_od/pipeline/utils.py","file_url":"https://github.com/darl-libsignal/openti/blob/HEAD/llm_od/pipeline/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"aa35ccf5b9973b00","mcp_get_code":{"code_sha256":"aa35ccf5b9973b00"}},{"arxiv_id":"2308.05061","paper":"/paper/prompting-in-context-operator-learning-with","title":"Fine-Tune Language Models as Multi-Modal Differential Equation Solvers","date":"2023-08-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liuyangmage/in-context-operator-networks","path":"icon-lm/operator_weno/analysis_plot.py","file_url":"https://github.com/liuyangmage/in-context-operator-networks/blob/HEAD/icon-lm/operator_weno/analysis_plot.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ccc3d008753c19f3","mcp_get_code":{"code_sha256":"ccc3d008753c19f3"}},{"arxiv_id":"2304.07993","paper":"/paper/in-context-operator-learning-for-differential","title":"In-Context Operator Learning with Data Prompts for Differential Equation Problems","date":"2023-04-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LiuYangMage/in-context-operator-networks","path":"icon-lm/operator_weno/analysis_plot.py","file_url":"https://github.com/LiuYangMage/in-context-operator-networks/blob/HEAD/icon-lm/operator_weno/analysis_plot.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ccc3d008753c19f3","mcp_get_code":{"code_sha256":"ccc3d008753c19f3"}},{"arxiv_id":"2303.04679","paper":"/paper/flow-reconstruction-by-multiresolution","title":"Flow reconstruction by multiresolution optimization of a discrete loss with automatic differentiation","date":null,"month_inferred_from_arxiv_id":"2023-03","title_source":"archive","repo":"cselab/odil","path":"src/odil/util.py","file_url":"https://github.com/cselab/odil/blob/HEAD/src/odil/util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9c50d0e45ae4e4e4","mcp_get_code":{"code_sha256":"9c50d0e45ae4e4e4"}},{"arxiv_id":"2204.12386","paper":"/paper/learning-meta-word-embeddings-by-unsupervised-1","title":"Learning Meta Word Embeddings by Unsupervised Weighted Concatenation of Source Embeddings","date":"2022-04-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LivNLP/meta-concat","path":"GlobalME.py","file_url":"https://github.com/LivNLP/meta-concat/blob/HEAD/GlobalME.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"55b475a76b5ea24d","mcp_get_code":{"code_sha256":"55b475a76b5ea24d"}},{"arxiv_id":"2006.12557","paper":"/paper/just-how-toxic-is-data-poisoning-a-unified","title":"Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks","date":"2020-06-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aks2203/poisoning-benchmark","path":"benchmark_results_table.py","file_url":"https://github.com/aks2203/poisoning-benchmark/blob/HEAD/benchmark_results_table.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1eefa08642082292","mcp_get_code":{"code_sha256":"1eefa08642082292"}},{"arxiv_id":"1911.08085","paper":"/paper/outlier-robust-high-dimensional-sparse-1","title":"Outlier-Robust High-Dimensional Sparse Estimation via Iterative Filtering","date":"2019-11-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sushrutk/robust_sparse_mean_estimation","path":"robustlib.py","file_url":"https://github.com/sushrutk/robust_sparse_mean_estimation/blob/HEAD/robustlib.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"88e19c4ec091c85b","mcp_get_code":{"code_sha256":"88e19c4ec091c85b"}},{"arxiv_id":"1902.03545","paper":"/paper/task2vec-task-embedding-for-meta-learning","title":"Task2Vec: Task Embedding for Meta-Learning","date":"2019-02-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"awslabs/aws-cv-task2vec","path":"utils.py","file_url":"https://github.com/awslabs/aws-cv-task2vec/blob/HEAD/utils.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":"525c6605bb5ab89a","mcp_get_code":{"code_sha256":"525c6605bb5ab89a"}},{"arxiv_id":"1810.12348","paper":"/paper/gather-excite-exploiting-feature-context-in","title":"Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks","date":"2018-10-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"BayesWatch/pytorch-GENet","path":"utils.py","file_url":"https://github.com/BayesWatch/pytorch-GENet/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"789378d75fb1946e","mcp_get_code":{"code_sha256":"789378d75fb1946e"}},{"arxiv_id":"ijcai2025_0850","paper":null,"title":"arXiv:ijcai2025_0850","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Hongyi-Lyu-MQ/SULI","path":"utils/utils.py","file_url":"https://github.com/Hongyi-Lyu-MQ/SULI/blob/HEAD/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":"1c6bcc03390a9997","mcp_get_code":{"code_sha256":"1c6bcc03390a9997"}}]}