{"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/f-beta","entry":"f_beta","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":5,"n_papers_ran":5,"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":4,"n_samples_ran":4,"n_samples_fingerprinted":4,"n_places":5,"n_places_pointer_only":4,"by_status":{"ran_honours":2,"ran_violates":1,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"unverified":0},"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":"2505.23349","paper":"/paper/towards-reward-fairness-in-rlhf-from-a","title":"Towards Reward Fairness in RLHF: From a Resource Allocation Perspective","date":"2025-05-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shoyua/towards-reward-fairness","path":"Fair-RM/llama3_8B_rm.py","file_url":"https://github.com/shoyua/towards-reward-fairness/blob/HEAD/Fair-RM/llama3_8B_rm.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"58d25c4fc94b72c0","mcp_get_code":{"code_sha256":"58d25c4fc94b72c0"}},{"arxiv_id":"2403.00143","paper":"/paper/ensemble-based-unsupervised-discontinuous","title":"Tree-Averaging Algorithms for Ensemble-Based Unsupervised Discontinuous Constituency Parsing","date":"2024-02-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"manga-uofa/taa4eudcp","path":"library/utils.py","file_url":"https://github.com/manga-uofa/taa4eudcp/blob/HEAD/library/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"772cc2a861384aa4","mcp_get_code":{"code_sha256":"772cc2a861384aa4"}},{"arxiv_id":"2305.13981","paper":"/paper/preserving-knowledge-invariance-rethinking","title":"Preserving Knowledge Invariance: Rethinking Robustness Evaluation of Open Information Extraction","date":"2023-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qijimrc/ROBUST","path":"src/utils/CaRB/carb.py","file_url":"https://github.com/qijimrc/ROBUST/blob/HEAD/src/utils/CaRB/carb.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a9ace46c90ab02c8","mcp_get_code":{"code_sha256":"a9ace46c90ab02c8"}},{"arxiv_id":"2010.02347","paper":"/paper/learning-with-instance-dependent-label-noise-1","title":"Learning with Instance-Dependent Label Noise: A Sample Sieve Approach","date":"2020-10-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"UCSC-REAL/cores","path":"loss.py","file_url":"https://github.com/UCSC-REAL/cores/blob/HEAD/loss.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"66e2df8b49609716","mcp_get_code":{"code_sha256":"66e2df8b49609716"}},{"arxiv_id":"1901.10879","paper":"/paper/span-based-open-information-extraction","title":"Span Model for Open Information Extraction on Accurate Corpus","date":"2019-01-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhanjunlang/Span_OIE","path":"evaluate/evaluate.py","file_url":"https://github.com/zhanjunlang/Span_OIE/blob/HEAD/evaluate/evaluate.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a9ace46c90ab02c8","mcp_get_code":{"code_sha256":"a9ace46c90ab02c8"}}]}