{"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/format-results","entry":"format_results","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":3,"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":0,"n_places":5,"n_places_pointer_only":0,"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":"paper_utils/script_bpe/monolingual_compression.py","file_url":"https://github.com/sanderland/script_tok/blob/HEAD/paper_utils/script_bpe/monolingual_compression.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":"7a5f392192c6a1f1","mcp_get_code":{"code_sha256":"7a5f392192c6a1f1"}},{"arxiv_id":"2605.30675","paper":"/paper/arxiv-2605-30675","title":"Human-Alignment, Calibration, and Activation Patterns in Large Language Model Uncertainty","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"KyleAMoore/LLM-UQ-Align-and-Calibrate","path":"LinearProbing/10FoldLinearReg.py","file_url":"https://github.com/KyleAMoore/LLM-UQ-Align-and-Calibrate/blob/HEAD/LinearProbing/10FoldLinearReg.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7f42302ce226f319","mcp_get_code":{"code_sha256":"7f42302ce226f319"}},{"arxiv_id":"2602.19020","paper":"/paper/arxiv-2602-19020","title":"Learning to Detect Language Model Training Data via Active Reconstruction","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"oseyosey/MIA-RL","path":"adra/utils/format_mia_aurocs.py","file_url":"https://github.com/oseyosey/MIA-RL/blob/HEAD/adra/utils/format_mia_aurocs.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":"493d2de212395f45","mcp_get_code":{"code_sha256":"493d2de212395f45"}},{"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":"paper_utils/script_bpe/monolingual_compression.py","file_url":"https://github.com/sanderland/script_bpe/blob/HEAD/paper_utils/script_bpe/monolingual_compression.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":"7a5f392192c6a1f1","mcp_get_code":{"code_sha256":"7a5f392192c6a1f1"}},{"arxiv_id":"2503.06580","paper":"/paper/agent-models-internalizing-chain-of-action","title":"Agent models: Internalizing Chain-of-Action Generation into Reasoning models","date":"2025-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"adam-bjtu/autocoa","path":"models/wiki_engine.py","file_url":"https://github.com/adam-bjtu/autocoa/blob/HEAD/models/wiki_engine.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fe8edf17e376de99","mcp_get_code":{"code_sha256":"fe8edf17e376de99"}}]}