{"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/remove-outliers","entry":"remove_outliers","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":6,"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":6,"n_samples_ran":4,"n_samples_fingerprinted":1,"n_places":6,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":1,"ran_draft_wrong":0,"ran_fixture":0,"ran":3,"unverified":2},"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":"2506.00388","paper":"/paper/clarify-contrastive-preference-reinforcement","title":"CLARIFY: Contrastive Preference Reinforcement Learning for Untangling Ambiguous Queries","date":"2025-05-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"moonoutcloudback/clarify_pbrl","path":"reward_model.py","file_url":"https://github.com/moonoutcloudback/clarify_pbrl/blob/HEAD/reward_model.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b2f4aec40f7b3084","mcp_get_code":{"code_sha256":"b2f4aec40f7b3084"}},{"arxiv_id":"2410.09290","paper":"/paper/ranking-over-regression-for-bayesian","title":"Ranking over Regression for Bayesian Optimization and Molecule Selection","date":"2024-10-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gkwt/rbo","path":"rbbo/utils.py","file_url":"https://github.com/gkwt/rbo/blob/HEAD/rbbo/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"21a75c973c77e021","mcp_get_code":{"code_sha256":"21a75c973c77e021"}},{"arxiv_id":"2402.13125","paper":"/paper/treeeval-benchmark-free-evaluation-of-large","title":"TreeEval: Benchmark-Free Evaluation of Large Language Models through Tree Planning","date":"2024-02-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ashura5/treeeval","path":"score.py","file_url":"https://github.com/ashura5/treeeval/blob/HEAD/score.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"87f22e05dbadc88a","mcp_get_code":{"code_sha256":"87f22e05dbadc88a"}},{"arxiv_id":"2307.09288","paper":"/paper/llama-2-open-foundation-and-fine-tuned-chat","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","date":"2023-07-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"squeezeailab/squeezellm","path":"squeezellm/outliers.py","file_url":"https://github.com/squeezeailab/squeezellm/blob/HEAD/squeezellm/outliers.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"303e2b878d149f4a","mcp_get_code":{"code_sha256":"303e2b878d149f4a"}},{"arxiv_id":"1910.00130","paper":"/paper/track-to-reconstruct-and-reconstruct-to-track","title":"Track to Reconstruct and Reconstruct to Track","date":"2019-09-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tobiasfshr/MOTSFusion","path":"tracker/create_dynamic_transforms.py","file_url":"https://github.com/tobiasfshr/MOTSFusion/blob/HEAD/tracker/create_dynamic_transforms.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"88ad30c2db8f1ac8","mcp_get_code":{"code_sha256":"88ad30c2db8f1ac8"}},{"arxiv_id":"1903.11269","paper":"/paper/css10-a-collection-of-single-speaker-speech","title":"CSS10: A Collection of Single Speaker Speech Datasets for 10 Languages","date":"2019-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Kyubyong/css10","path":"MOS/utils.py","file_url":"https://github.com/Kyubyong/css10/blob/HEAD/MOS/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":"718b8b4bba64f8d9","mcp_get_code":{"code_sha256":"718b8b4bba64f8d9"}}]}