{"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/booleans-processing","entry":"booleans_processing","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":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":3,"n_samples_ran":1,"n_samples_fingerprinted":0,"n_places":9,"n_places_pointer_only":4,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"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":"2505.19235","paper":"/paper/corematching-a-co-adaptive-sparse-inference","title":"CoreMatching: A Co-adaptive Sparse Inference Framework with Token and Neuron Pruning for Comprehensive Acceleration of Vision-Language Models","date":"2025-05-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wangqinsi1/2025-ICML-CoreMatching","path":"transformers/modeling_tf_utils.py","file_url":"https://github.com/wangqinsi1/2025-ICML-CoreMatching/blob/HEAD/transformers/modeling_tf_utils.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":"743ceb9025a7955d","mcp_get_code":{"code_sha256":"743ceb9025a7955d"}},{"arxiv_id":"2404.19245","paper":"/paper/hydralora-an-asymmetric-lora-architecture-for","title":"HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning","date":"2024-04-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"clin0212/hydralora","path":"HydraLoRA/transformers/modeling_tf_utils.py","file_url":"https://github.com/clin0212/hydralora/blob/HEAD/HydraLoRA/transformers/modeling_tf_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"743ceb9025a7955d","mcp_get_code":{"code_sha256":"743ceb9025a7955d"}},{"arxiv_id":"2403.13263","paper":"/paper/sc-tune-unleashing-self-consistent","title":"SC-Tune: Unleashing Self-Consistent Referential Comprehension in Large Vision Language Models","date":"2024-03-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ivattyue/SC-Tune","path":"transformers/modeling_tf_utils.py","file_url":"https://github.com/ivattyue/SC-Tune/blob/HEAD/transformers/modeling_tf_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"743ceb9025a7955d","mcp_get_code":{"code_sha256":"743ceb9025a7955d"}},{"arxiv_id":"2402.12656","paper":"/paper/hypermoe-towards-better-mixture-of-experts","title":"HyperMoE: Towards Better Mixture of Experts via Transferring Among Experts","date":"2024-02-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bumble666/hypermoe_early_version","path":"gpt-2-moe/transformers/modeling_tf_utils.py","file_url":"https://github.com/bumble666/hypermoe_early_version/blob/HEAD/gpt-2-moe/transformers/modeling_tf_utils.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":"743ceb9025a7955d","mcp_get_code":{"code_sha256":"743ceb9025a7955d"}},{"arxiv_id":"2310.18738","paper":"/paper/tlm-token-level-masking-for-transformers","title":"TLM: Token-Level Masking for Transformers","date":"2023-10-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Young1993/tlm","path":"transformers/modeling_tf_utils.py","file_url":"https://github.com/Young1993/tlm/blob/HEAD/transformers/modeling_tf_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"743ceb9025a7955d","mcp_get_code":{"code_sha256":"743ceb9025a7955d"}},{"arxiv_id":"2310.09832","paper":"/paper/merging-experts-into-one-improving","title":"Merging Experts into One: Improving Computational Efficiency of Mixture of Experts","date":"2023-10-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shwai-he/meo","path":"transformers/modeling_tf_utils.py","file_url":"https://github.com/shwai-he/meo/blob/HEAD/transformers/modeling_tf_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"743ceb9025a7955d","mcp_get_code":{"code_sha256":"743ceb9025a7955d"}},{"arxiv_id":"2310.05107","paper":"/paper/ov-parts-towards-open-vocabulary-part","title":"OV-PARTS: Towards Open-Vocabulary Part Segmentation","date":"2023-10-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"openrobotlab/ov_parts","path":"transformers/modeling_tf_utils.py","file_url":"https://github.com/openrobotlab/ov_parts/blob/HEAD/transformers/modeling_tf_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"743ceb9025a7955d","mcp_get_code":{"code_sha256":"743ceb9025a7955d"}},{"arxiv_id":"2303.14582","paper":"/paper/identification-of-negative-transfers-in","title":"Identification of Negative Transfers in Multitask Learning Using Surrogate Models","date":"2023-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"neu-statsml-research/task-modeling","path":"transformers/modeling_tf_utils.py","file_url":"https://github.com/neu-statsml-research/task-modeling/blob/HEAD/transformers/modeling_tf_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a37e8f716afbe0c3","mcp_get_code":{"code_sha256":"a37e8f716afbe0c3"}},{"arxiv_id":"2109.03808","paper":"/paper/smelting-gold-and-silver-for-improved","title":"Smelting Gold and Silver for Improved Multilingual AMR-to-Text Generation","date":"2021-09-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"UKPLab/m-AMR2Text","path":"transformers/modeling_tf_utils.py","file_url":"https://github.com/UKPLab/m-AMR2Text/blob/HEAD/transformers/modeling_tf_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":"3c4b7e11935f3cf8","mcp_get_code":{"code_sha256":"3c4b7e11935f3cf8"}}]}