{"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/most-common","entry":"most_common","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":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":6,"n_samples_ran":3,"n_samples_fingerprinted":3,"n_places":12,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":2,"ran_fixture":0,"ran":1,"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":"2511.20934","paper":"/paper/arxiv-2511-20934","title":"Guaranteed Optimal Compositional Explanations for Neurons","date":null,"month_inferred_from_arxiv_id":"2025-11","title_source":"syntology","repo":"CSAILVision/NetDissect","path":"src/bargraph.py","file_url":"https://github.com/CSAILVision/NetDissect/blob/HEAD/src/bargraph.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bff5a031c71168c8","mcp_get_code":{"code_sha256":"bff5a031c71168c8"}},{"arxiv_id":"2410.06195","paper":"/paper/entering-real-social-world-benchmarking-the","title":"Entering Real Social World! Benchmarking the Social Intelligence of Large Language Models from a First-person Perspective","date":"2024-10-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gyhou123/egosocialarena","path":"code/evaluate_coun.py","file_url":"https://github.com/gyhou123/egosocialarena/blob/HEAD/code/evaluate_coun.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d5d43d3fe4e42b0e","mcp_get_code":{"code_sha256":"d5d43d3fe4e42b0e"}},{"arxiv_id":"2406.12208","paper":"/paper/knowledge-fusion-by-evolving-weights-of","title":"Knowledge Fusion By Evolving Weights of Language Models","date":"2024-06-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"duguodong7/model-evolution","path":"src/model_merge/ensembler.py","file_url":"https://github.com/duguodong7/model-evolution/blob/HEAD/src/model_merge/ensembler.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"2ed51eac60e5be2a","mcp_get_code":{"code_sha256":"2ed51eac60e5be2a"}},{"arxiv_id":"2402.14963","paper":"/paper/mirror-a-multiple-perspective-self-reflection","title":"Mirror: A Multiple-perspective Self-Reflection Method for Knowledge-rich Reasoning","date":"2024-02-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hanqi-qi/mirror","path":"evaluate.py","file_url":"https://github.com/hanqi-qi/mirror/blob/HEAD/evaluate.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8eedb4eee9ede87c","mcp_get_code":{"code_sha256":"8eedb4eee9ede87c"}},{"arxiv_id":"2311.10227","paper":"/paper/think-twice-perspective-taking-improves-large","title":"Think Twice: Perspective-Taking Improves Large Language Models' Theory-of-Mind Capabilities","date":"2023-11-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shawnsihyunlee/simulatedtom","path":"code/evaluate_tomi.py","file_url":"https://github.com/shawnsihyunlee/simulatedtom/blob/HEAD/code/evaluate_tomi.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d5d43d3fe4e42b0e","mcp_get_code":{"code_sha256":"d5d43d3fe4e42b0e"}},{"arxiv_id":"2310.04408","paper":"/paper/recomp-improving-retrieval-augmented-lms-with","title":"RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentation","date":"2023-10-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"carriex/recomp","path":"eval_utils.py","file_url":"https://github.com/carriex/recomp/blob/HEAD/eval_utils.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2ed51eac60e5be2a","mcp_get_code":{"code_sha256":"2ed51eac60e5be2a"}},{"arxiv_id":"2305.18395","paper":"/paper/knowledge-augmented-reasoning-distillation-1","title":"Knowledge-Augmented Reasoning Distillation for Small Language Models in Knowledge-Intensive Tasks","date":"2023-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Nardien/KARD","path":"generate_predict.py","file_url":"https://github.com/Nardien/KARD/blob/HEAD/generate_predict.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2ed51eac60e5be2a","mcp_get_code":{"code_sha256":"2ed51eac60e5be2a"}},{"arxiv_id":"2305.09955","paper":"/paper/cook-empowering-general-purpose-language","title":"Knowledge Card: Filling LLMs' Knowledge Gaps with Plug-in Specialized Language Models","date":"2023-05-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bunsenfeng/knowledge_card","path":"eval_datasets/MidtermQA/odqa_utils.py","file_url":"https://github.com/bunsenfeng/knowledge_card/blob/HEAD/eval_datasets/MidtermQA/odqa_utils.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2ed51eac60e5be2a","mcp_get_code":{"code_sha256":"2ed51eac60e5be2a"}},{"arxiv_id":"2209.02071","paper":"/paper/concrete-improving-cross-lingual-fact","title":"CONCRETE: Improving Cross-lingual Fact-checking with Cross-lingual Retrieval","date":"2022-09-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"khuangaf/concrete","path":"src/xfact_eval.py","file_url":"https://github.com/khuangaf/concrete/blob/HEAD/src/xfact_eval.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":"2f5b553b69c63c9c","mcp_get_code":{"code_sha256":"2f5b553b69c63c9c"}},{"arxiv_id":"2106.09248","paper":"/paper/x-fact-a-new-benchmark-dataset-for","title":"X-FACT: A New Benchmark Dataset for Multilingual Fact Checking","date":"2021-06-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"utahnlp/x-fact","path":"transformers/calculate_fscore.py","file_url":"https://github.com/utahnlp/x-fact/blob/HEAD/transformers/calculate_fscore.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2f5b553b69c63c9c","mcp_get_code":{"code_sha256":"2f5b553b69c63c9c"}},{"arxiv_id":"2007.04911","paper":"/paper/gama-a-general-automated-machine-learning","title":"GAMA: a General Automated Machine learning Assistant","date":"2020-07-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jim-schwoebel/allie","path":"models/load.py","file_url":"https://github.com/jim-schwoebel/allie/blob/HEAD/models/load.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":"1deecc25893dafad","mcp_get_code":{"code_sha256":"1deecc25893dafad"}},{"arxiv_id":"1803.08823","paper":"/paper/a-high-bias-low-variance-introduction-to","title":"A high-bias, low-variance introduction to Machine Learning for physicists","date":"2018-03-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alexandreday/fast_density_clustering","path":"fdc/classify.py","file_url":"https://github.com/alexandreday/fast_density_clustering/blob/HEAD/fdc/classify.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"2f5b553b69c63c9c","mcp_get_code":{"code_sha256":"2f5b553b69c63c9c"}}]}