{"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/split-cluster-acc-v2","entry":"split_cluster_acc_v2","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":9,"n_papers_ran":6,"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":8,"n_samples_ran":5,"n_samples_fingerprinted":0,"n_places":9,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":4,"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":"2512.16202","paper":"/paper/arxiv-2512-16202","title":"Open Ad-hoc Categorization with Contextualized Feature Learning","date":null,"month_inferred_from_arxiv_id":"2025-12","title_source":"syntology","repo":"Wayne2Wang/OAK","path":"src/engine/cluster_eval.py","file_url":"https://github.com/Wayne2Wang/OAK/blob/HEAD/src/engine/cluster_eval.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e211b877949b1b84","mcp_get_code":{"code_sha256":"e211b877949b1b84"}},{"arxiv_id":"2504.03755","paper":"/paper/protogcd-unified-and-unbiased-prototype","title":"ProtoGCD: Unified and Unbiased Prototype Learning for Generalized Category Discovery","date":"2025-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mashijie1028/protogcd","path":"my_utils/cluster_and_log_utils.py","file_url":"https://github.com/mashijie1028/protogcd/blob/HEAD/my_utils/cluster_and_log_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"965fcd5f9ab03841","mcp_get_code":{"code_sha256":"965fcd5f9ab03841"}},{"arxiv_id":"2410.19213","paper":"/paper/prototypical-hash-encoding-for-on-the-fly","title":"Prototypical Hash Encoding for On-the-Fly Fine-Grained Category Discovery","date":"2024-10-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HaiyangZheng/PHE","path":"utils/evaluate_utils.py","file_url":"https://github.com/HaiyangZheng/PHE/blob/HEAD/utils/evaluate_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e0eef651c9789663","mcp_get_code":{"code_sha256":"e0eef651c9789663"}},{"arxiv_id":"2410.06535","paper":"/paper/happy-a-debiased-learning-framework-for","title":"Happy: A Debiased Learning Framework for Continual Generalized Category Discovery","date":"2024-10-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mashijie1028/happy-cgcd","path":"train_happy.py","file_url":"https://github.com/mashijie1028/happy-cgcd/blob/HEAD/train_happy.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"285f08752cf15565","mcp_get_code":{"code_sha256":"285f08752cf15565"}},{"arxiv_id":"2403.09974","paper":"/paper/get-unlocking-the-multi-modal-potential-of","title":"Unlocking the Multi-modal Potential of CLIP for Generalized Category Discovery","date":"2024-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"enguangw/get","path":"util/cluster_and_log_utils.py","file_url":"https://github.com/enguangw/get/blob/HEAD/util/cluster_and_log_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2513029b5bd17df9","mcp_get_code":{"code_sha256":"2513029b5bd17df9"}},{"arxiv_id":"2403.04272","paper":"/paper/active-generalized-category-discovery","title":"Active Generalized Category Discovery","date":"2024-03-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mashijie1028/activegcd","path":"utils_al/train_recipes_ema.py","file_url":"https://github.com/mashijie1028/activegcd/blob/HEAD/utils_al/train_recipes_ema.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"21b51b923477b0ce","mcp_get_code":{"code_sha256":"21b51b923477b0ce"}},{"arxiv_id":"2310.01376","paper":"/paper/towards-distribution-agnostic-generalized-1","title":"Towards Distribution-Agnostic Generalized Category Discovery","date":"2023-10-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jianhongbai/bacon","path":"model/bacon.py","file_url":"https://github.com/jianhongbai/bacon/blob/HEAD/model/bacon.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e2b8c1d1e98d7709","mcp_get_code":{"code_sha256":"e2b8c1d1e98d7709"}},{"arxiv_id":"2304.06928","paper":"/paper/cipr-an-efficient-framework-with-cross","title":"CiPR: An Efficient Framework with Cross-instance Positive Relations for Generalized Category Discovery","date":"2023-04-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"haoosz/cipr","path":"eval_snc.py","file_url":"https://github.com/haoosz/cipr/blob/HEAD/eval_snc.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"12e4656cde039f68","mcp_get_code":{"code_sha256":"12e4656cde039f68"}},{"arxiv_id":"Wang_GET_Unlocking_the_Multi-modal_Potential_of_CLIP_for_Generalized_Category_CVPR_2025_paper","paper":null,"title":"arXiv:Wang_GET_Unlocking_the_Multi-modal_Potential_of_CLIP_for_Generalized_Category_CVPR_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"enguangW/GET","path":"util/cluster_and_log_utils.py","file_url":"https://github.com/enguangW/GET/blob/HEAD/util/cluster_and_log_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2513029b5bd17df9","mcp_get_code":{"code_sha256":"2513029b5bd17df9"}}]}