{"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-ids","entry":"get_ids","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":12,"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":11,"n_samples_ran":2,"n_samples_fingerprinted":0,"n_places":12,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":1,"unverified":9},"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":"2509.09396","paper":"/paper/arxiv-2509-09396","title":"LLMs Don't Know Their Own Decision Boundaries: The Unreliability of Self-Generated Counterfactual Explanations","date":null,"month_inferred_from_arxiv_id":"2025-09","title_source":"syntology","repo":"HarryMayne/SCEs","path":"analysis/operationalising_distance/experiment.py","file_url":"https://github.com/HarryMayne/SCEs/blob/HEAD/analysis/operationalising_distance/experiment.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"03e1dd2fa835bd92","mcp_get_code":{"code_sha256":"03e1dd2fa835bd92"}},{"arxiv_id":"2404.18812","paper":"/paper/efficient-inverted-indexes-for-approximate","title":"Efficient Inverted Indexes for Approximate Retrieval over Learned Sparse Representations","date":"2024-04-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"castorini/anserini","path":"src/main/python/openresearch/convert_pubmed_dblp_to_anserini_format.py","file_url":"https://github.com/castorini/anserini/blob/HEAD/src/main/python/openresearch/convert_pubmed_dblp_to_anserini_format.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"9cd632164c3a2c37","mcp_get_code":{"code_sha256":"9cd632164c3a2c37"}},{"arxiv_id":"2305.17303","paper":"/paper/distilling-blackbox-to-interpretable-models","title":"Distilling BlackBox to Interpretable models for Efficient Transfer Learning","date":"2023-05-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"batmanlab/miccai-2023-route-interpret-repeat-cxrs","path":"src/codebase/data_preprocessing/mimic-cxr/miccai-main/radgraph_itemized.py","file_url":"https://github.com/batmanlab/miccai-2023-route-interpret-repeat-cxrs/blob/HEAD/src/codebase/data_preprocessing/mimic-cxr/miccai-main/radgraph_itemized.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9bf4a7ca4e072b70","mcp_get_code":{"code_sha256":"9bf4a7ca4e072b70"}},{"arxiv_id":"2210.15031","paper":"/paper/characterizing-datapoints-via-second-split","title":"Characterizing Datapoints via Second-Split Forgetting","date":"2022-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pratyushmaini/ssft","path":"remove_examples.py","file_url":"https://github.com/pratyushmaini/ssft/blob/HEAD/remove_examples.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"309ab557549eed53","mcp_get_code":{"code_sha256":"309ab557549eed53"}},{"arxiv_id":"2209.00891","paper":"/paper/multi-modal-contrastive-representation","title":"Multi-modal Contrastive Representation Learning for Entity Alignment","date":"2022-09-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lzxlin/mclea","path":"src/Load.py","file_url":"https://github.com/lzxlin/mclea/blob/HEAD/src/Load.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7c1bdadfedbe321c","mcp_get_code":{"code_sha256":"7c1bdadfedbe321c"}},{"arxiv_id":"2009.13603","paper":"/paper/visual-pivoting-for-unsupervised-entity","title":"Visual Pivoting for (Unsupervised) Entity Alignment","date":"2020-09-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cambridgeltl/eva","path":"src/Load.py","file_url":"https://github.com/cambridgeltl/eva/blob/HEAD/src/Load.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7c1bdadfedbe321c","mcp_get_code":{"code_sha256":"7c1bdadfedbe321c"}},{"arxiv_id":"1904.07850","paper":"/paper/objects-as-points","title":"Objects as Points","date":"2019-04-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"goodxue/CenterNet","path":"carla_ros/multiview_fusion_34.py","file_url":"https://github.com/goodxue/CenterNet/blob/HEAD/carla_ros/multiview_fusion_34.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b2749fa442e73f84","mcp_get_code":{"code_sha256":"b2749fa442e73f84"}},{"arxiv_id":"1812.03282","paper":"/paper/spatial-temporal-person-re-identification","title":"Spatial-Temporal Person Re-identification","date":"2018-12-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SurajDonthi/Multi-Camera-Person-Re-Identification","path":"mtmct_reid/utils.py","file_url":"https://github.com/SurajDonthi/Multi-Camera-Person-Re-Identification/blob/HEAD/mtmct_reid/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b710451e7a124eee","mcp_get_code":{"code_sha256":"b710451e7a124eee"}},{"arxiv_id":"1804.09541","paper":"/paper/qanet-combining-local-convolution-with-global","title":"QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension","date":"2018-04-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Tao-Mind/QDD_Net","path":"pre_process.py","file_url":"https://github.com/Tao-Mind/QDD_Net/blob/HEAD/pre_process.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fc42ce0856c29480","mcp_get_code":{"code_sha256":"fc42ce0856c29480"}},{"arxiv_id":"1804.07754","paper":"/paper/learning-semantic-textual-similarity-from","title":"Learning Semantic Textual Similarity from Conversations","date":"2018-04-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nickyeolk/info_retrieve","path":"src/bert_tokenization.py","file_url":"https://github.com/nickyeolk/info_retrieve/blob/HEAD/src/bert_tokenization.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":"6b4945d2e09fe154","mcp_get_code":{"code_sha256":"6b4945d2e09fe154"}},{"arxiv_id":"1703.02161","paper":"/paper/distance-metric-learning-using-graph","title":"Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks","date":"2017-03-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sk1712/gcn_metric_learning","path":"lib/abide_utils.py","file_url":"https://github.com/sk1712/gcn_metric_learning/blob/HEAD/lib/abide_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"63ac2cdc4528de0c","mcp_get_code":{"code_sha256":"63ac2cdc4528de0c"}},{"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":"domainxz/top-k-rec","path":"evaluate.py","file_url":"https://github.com/domainxz/top-k-rec/blob/HEAD/evaluate.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"90e0988708b2beae","mcp_get_code":{"code_sha256":"90e0988708b2beae"}}]}