{"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/normalize-whitespace","entry":"normalize_whitespace","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":14,"n_papers_ran":12,"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":2,"n_samples_fingerprinted":2,"n_places":14,"n_places_pointer_only":3,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":2,"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":"2608.08847","paper":"/paper/arxiv-2608-08847","title":"↑¦explicit¦ ↑¦boundary¦ ↑¦markers¦ ¦for¦ ↑¦subword¦ ↑¦vocabularies¦","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"sanderland/script_tok","path":"script_bpe/corpus/registry.py","file_url":"https://github.com/sanderland/script_tok/blob/HEAD/script_bpe/corpus/registry.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"0d64647d6f44ca2c","mcp_get_code":{"code_sha256":"0d64647d6f44ca2c"}},{"arxiv_id":"2606.09124","paper":"/paper/arxiv-2606-09124","title":"A Regret Minimization Framework on Preference Learning in Large Language Models","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"YSLIU627/Regularized-Preference-Optimization","path":"alignment-handbook/src/alignment/decontaminate.py","file_url":"https://github.com/YSLIU627/Regularized-Preference-Optimization/blob/HEAD/alignment-handbook/src/alignment/decontaminate.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"f43e59d239019e86","mcp_get_code":{"code_sha256":"f43e59d239019e86"}},{"arxiv_id":"2602.14594","paper":"/paper/arxiv-2602-14594","title":"The Wikidata Query Logs Dataset","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"ad-freiburg/wikidata-query-logs","path":"check_overlap.py","file_url":"https://github.com/ad-freiburg/wikidata-query-logs/blob/HEAD/check_overlap.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":"5e98056300a67fa9","mcp_get_code":{"code_sha256":"5e98056300a67fa9"}},{"arxiv_id":"2510.16713","paper":"/paper/arxiv-2510-16713","title":"so much depends / upon / a whitespace: Why Whitespace Matters for Poets and LLMs","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"miso-belica/jusText","path":"justext/utils.py","file_url":"https://github.com/miso-belica/jusText/blob/HEAD/justext/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"852f7967e5e02478","mcp_get_code":{"code_sha256":"852f7967e5e02478"}},{"arxiv_id":"2505.24689","paper":"/paper/bpe-stays-on-script-structured-encoding-for","title":"BPE Stays on SCRIPT: Structured Encoding for Robust Multilingual Pretokenization","date":"2025-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sanderland/script_bpe","path":"script_bpe/corpus/registry.py","file_url":"https://github.com/sanderland/script_bpe/blob/HEAD/script_bpe/corpus/registry.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"0d64647d6f44ca2c","mcp_get_code":{"code_sha256":"0d64647d6f44ca2c"}},{"arxiv_id":"2503.12524","paper":"/paper/exaone-deep-reasoning-enhanced-language","title":"EXAONE Deep: Reasoning Enhanced Language Models","date":"2025-03-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tengxiao1/simper","path":"alignment/decontaminate.py","file_url":"https://github.com/tengxiao1/simper/blob/HEAD/alignment/decontaminate.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f43e59d239019e86","mcp_get_code":{"code_sha256":"f43e59d239019e86"}},{"arxiv_id":"2502.18274","paper":"/paper/citrus-leveraging-expert-cognitive-pathways","title":"Citrus: Leveraging Expert Cognitive Pathways in a Medical Language Model for Advanced Medical Decision Support","date":"2025-02-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jdh-algo/Citrus","path":"model_train/alignment/decontaminate.py","file_url":"https://github.com/jdh-algo/Citrus/blob/HEAD/model_train/alignment/decontaminate.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f43e59d239019e86","mcp_get_code":{"code_sha256":"f43e59d239019e86"}},{"arxiv_id":"2410.10148","paper":"/paper/a-dpo-adaptive-reward-margin-is-what-direct","title":"$α$-DPO: Adaptive Reward Margin is What Direct Preference Optimization Needs","date":"2024-10-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"junkangwu/alpha-dpo","path":"alignment/decontaminate.py","file_url":"https://github.com/junkangwu/alpha-dpo/blob/HEAD/alignment/decontaminate.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f43e59d239019e86","mcp_get_code":{"code_sha256":"f43e59d239019e86"}},{"arxiv_id":"2410.03145","paper":"/paper/margin-matching-preference-optimization","title":"Margin Matching Preference Optimization: Enhanced Model Alignment with Granular Feedback","date":"2024-10-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kykim0/margin-matching-pref-opt","path":"src/alignment/decontaminate.py","file_url":"https://github.com/kykim0/margin-matching-pref-opt/blob/HEAD/src/alignment/decontaminate.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"f43e59d239019e86","mcp_get_code":{"code_sha256":"f43e59d239019e86"}},{"arxiv_id":"2409.03444","paper":"/paper/fine-tuning-large-language-models-for-domain-1","title":"Fine-tuning large language models for domain adaptation: Exploration of training strategies, scaling, model merging and synergistic capabilities","date":"2024-09-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lamm-mit/llm-finetuning","path":"alignment-handbook/src/alignment/decontaminate.py","file_url":"https://github.com/lamm-mit/llm-finetuning/blob/HEAD/alignment-handbook/src/alignment/decontaminate.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f43e59d239019e86","mcp_get_code":{"code_sha256":"f43e59d239019e86"}},{"arxiv_id":"2407.09014","paper":"/paper/compact-compressing-retrieved-documents","title":"CompAct: Compressing Retrieved Documents Actively for Question Answering","date":"2024-07-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dmis-lab/CompAct","path":"alignment-handbook/src/alignment/decontaminate.py","file_url":"https://github.com/dmis-lab/CompAct/blob/HEAD/alignment-handbook/src/alignment/decontaminate.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f43e59d239019e86","mcp_get_code":{"code_sha256":"f43e59d239019e86"}},{"arxiv_id":"2406.19371","paper":"/paper/suri-multi-constraint-instruction-following","title":"Suri: Multi-constraint Instruction Following for Long-form Text Generation","date":"2024-06-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chtmp223/suri","path":"ft/lib/alignment_mod/decontaminate.py","file_url":"https://github.com/chtmp223/suri/blob/HEAD/ft/lib/alignment_mod/decontaminate.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f43e59d239019e86","mcp_get_code":{"code_sha256":"f43e59d239019e86"}},{"arxiv_id":"2406.08414","paper":"/paper/discovering-preference-optimization","title":"Discovering Preference Optimization Algorithms with and for Large Language Models","date":"2024-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"luchris429/DiscoPOP","path":"src/alignment/decontaminate.py","file_url":"https://github.com/luchris429/DiscoPOP/blob/HEAD/src/alignment/decontaminate.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f43e59d239019e86","mcp_get_code":{"code_sha256":"f43e59d239019e86"}},{"arxiv_id":"2405.14734","paper":"/paper/simpo-simple-preference-optimization-with-a","title":"SimPO: Simple Preference Optimization with a Reference-Free Reward","date":"2024-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"princeton-nlp/SimPO","path":"alignment/decontaminate.py","file_url":"https://github.com/princeton-nlp/SimPO/blob/HEAD/alignment/decontaminate.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f43e59d239019e86","mcp_get_code":{"code_sha256":"f43e59d239019e86"}}]}