{"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/instance","entry":"Instance","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":5,"n_samples_ran":3,"n_samples_fingerprinted":0,"n_places":5,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":3,"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":"2608.10288","paper":"/paper/arxiv-2608-10288","title":"Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"burcgokden/lm-evaluation-harness-with-PLDR-LLM","path":"lm_eval/models/pldrllm.py","file_url":"https://github.com/burcgokden/lm-evaluation-harness-with-PLDR-LLM/blob/HEAD/lm_eval/models/pldrllm.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"adb6b7194eb0be02","mcp_get_code":{"code_sha256":"adb6b7194eb0be02"}},{"arxiv_id":"2608.05670","paper":"/paper/arxiv-2608-05670","title":"When Does Consensus Mean Correctness? Measuring the Agreement-Accuracy Coupling with Semantics-Preserving Re-Rendering","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"KurbanIntelligenceLab/rendeq","path":"rendeq_generator.py","file_url":"https://github.com/KurbanIntelligenceLab/rendeq/blob/HEAD/rendeq_generator.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9121a499dd24e987","mcp_get_code":{"code_sha256":"9121a499dd24e987"}},{"arxiv_id":"2501.12619","paper":"/paper/distillation-quantification-for-large","title":"Distillation Quantification for Large Language Models","date":"2025-01-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Aegis1863/LLMs-Distillation-Quantification","path":"easyjailbreak/metrics/Evaluator/Evaluator_ClassificationGetScore.py","file_url":"https://github.com/Aegis1863/LLMs-Distillation-Quantification/blob/HEAD/easyjailbreak/metrics/Evaluator/Evaluator_ClassificationGetScore.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"03e2455f600b42bd","mcp_get_code":{"code_sha256":"03e2455f600b42bd"}},{"arxiv_id":"2408.03246","paper":"/paper/2408-03246","title":"Making Long-Context Language Models Better Multi-Hop Reasoners","date":"2024-08-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lavi-lab/longcontextreasoner","path":"common.py","file_url":"https://github.com/lavi-lab/longcontextreasoner/blob/HEAD/common.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f4be9d5f5352ce94","mcp_get_code":{"code_sha256":"f4be9d5f5352ce94"}},{"arxiv_id":"2010.05700","paper":"/paper/reformulating-unsupervised-style-transfer-as","title":"Reformulating Unsupervised Style Transfer as Paraphrase Generation","date":"2020-10-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"martiansideofthemoon/style-transfer-paraphrase","path":"style_paraphrase/run_lm_finetuning.py","file_url":"https://github.com/martiansideofthemoon/style-transfer-paraphrase/blob/HEAD/style_paraphrase/run_lm_finetuning.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6a7861df4980b949","mcp_get_code":{"code_sha256":"6a7861df4980b949"}}]}