{"id":87630,"date":"2026-07-31T10:58:08","date_gmt":"2026-07-31T07:58:08","guid":{"rendered":"https:\/\/twodots.gr\/?p=87630"},"modified":"2026-07-31T10:58:11","modified_gmt":"2026-07-31T07:58:11","slug":"care-diffusion-llms-benchmark-lathos-proodo","status":"publish","type":"post","link":"https:\/\/twodots.gr\/en\/care-diffusion-llms-benchmark-lathos-proodo\/","title":{"rendered":"CaRE \u03b3\u03b9\u03b1 diffusion LLMs: \u03cc\u03c4\u03b1\u03bd \u03c4\u03bf benchmark \u03bc\u03b5\u03c4\u03c1\u03ac \u03bb\u03ac\u03b8\u03bf\u03c2 \u03c0\u03c1\u03cc\u03bf\u03b4\u03bf"},"content":{"rendered":"<div class=\"td-article-lede\">\n<p><strong>\u03a4\u03bf CaRE \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03ad\u03bd\u03b1 diffusion LLM benchmark \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03bb\u03bb\u03ac\u03be\u03b5\u03b9 \u03bd\u03b9\u03ba\u03b7\u03c4\u03ae \u03cc\u03c4\u03b1\u03bd \u03b5\u03be\u03b9\u03c3\u03c9\u03b8\u03bf\u03cd\u03bd \u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc compute, \u03c4\u03b1 metrics \u03ba\u03b1\u03b9 \u03bf\u03b9 \u03c1\u03c5\u03b8\u03bc\u03af\u03c3\u03b5\u03b9\u03c2 stochasticity.<\/strong> \u0386\u03c1\u03b1 \u03b7 \u03c3\u03c9\u03c3\u03c4\u03ae \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03b5\u03c1\u03ce\u03c4\u03b7\u03c3\u03b7 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03bf\u03b9\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03ad\u03c7\u03b5\u03b9 \u03c4\u03bf \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf headline score, \u03b1\u03bb\u03bb\u03ac \u03c0\u03bf\u03b9\u03b1 \u03b4\u03b9\u03b1\u03bc\u03cc\u03c1\u03c6\u03c9\u03c3\u03b7 \u03c0\u03b5\u03c4\u03c5\u03c7\u03b1\u03af\u03bd\u03b5\u03b9 \u03c4\u03bf \u03b1\u03c0\u03b1\u03b9\u03c4\u03bf\u03cd\u03bc\u03b5\u03bd\u03bf \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03bc\u03b5 \u03ba\u03bf\u03b9\u03bd\u03cc budget, \u03b5\u03c0\u03b1\u03bd\u03b1\u03bb\u03ae\u03c8\u03b9\u03bc\u03b5\u03c2 \u03c3\u03c5\u03bd\u03b8\u03ae\u03ba\u03b5\u03c2 \u03ba\u03b1\u03b9 \u03b1\u03c0\u03bf\u03b4\u03b5\u03ba\u03c4\u03cc \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2.<\/p>\n<p>\u0397 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03b4\u03b5\u03bd \u03ba\u03b1\u03c4\u03b1\u03b4\u03b9\u03ba\u03ac\u03b6\u03b5\u03b9 \u03c4\u03bf remasking. \u03a0\u03c1\u03bf\u03c4\u03b5\u03af\u03bd\u03b5\u03b9 \u03ad\u03bd\u03b1\u03bd \u03b1\u03c5\u03c3\u03c4\u03b7\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf \u03c4\u03c1\u03cc\u03c0\u03bf \u03bd\u03b1 \u03be\u03b5\u03c7\u03c9\u03c1\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03b7 \u03b1\u03bb\u03b3\u03bf\u03c1\u03b9\u03b8\u03bc\u03b9\u03ba\u03ae \u03c0\u03c1\u03cc\u03bf\u03b4\u03bf\u03c2 \u03b1\u03c0\u03cc \u03c4\u03bf \u03b5\u03c0\u03b9\u03c0\u03bb\u03ad\u03bf\u03bd NFE, \u03c4\u03b7 \u03b8\u03b5\u03c1\u03bc\u03bf\u03ba\u03c1\u03b1\u03c3\u03af\u03b1 sampling \u03ba\u03b1\u03b9 \u03c4\u03b7\u03bd \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae metric.<\/p>\n<\/div>\n<div class=\"td-article-note\">\n<p><strong>\u038c\u03c1\u03b9\u03bf \u03b5\u03c1\u03bc\u03b7\u03bd\u03b5\u03af\u03b1\u03c2:<\/strong> \u03c4\u03b1 \u03b5\u03c5\u03c1\u03ae\u03bc\u03b1\u03c4\u03b1 \u03b1\u03c6\u03bf\u03c1\u03bf\u03cd\u03bd masked discrete-token diffusion language models, \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b1 datasets, budgets \u03ba\u03b1\u03b9 sampling settings. \u0394\u03b5\u03bd \u03bc\u03b5\u03c4\u03b1\u03c6\u03ad\u03c1\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b1\u03c5\u03c4\u03bf\u03cd\u03c3\u03b9\u03b1 \u03c3\u03b5 autoregressive, continuous-state \u03ae flow-based \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03ba\u03b1\u03b9 \u03b4\u03b5\u03bd \u03b1\u03bd\u03c4\u03b9\u03ba\u03b1\u03b8\u03b9\u03c3\u03c4\u03bf\u03cd\u03bd production \u03b4\u03bf\u03ba\u03b9\u03bc\u03ad\u03c2 \u03c0\u03ac\u03bd\u03c9 \u03c3\u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc workload \u03bc\u03b9\u03b1\u03c2 \u03b5\u03c0\u03b9\u03c7\u03b5\u03af\u03c1\u03b7\u03c3\u03b7\u03c2.<\/p>\n<\/div>\n<div class=\"td-article-toc\">\n<div class=\"td-toc-title\">Contents<\/div>\n<ul>\n<li><a href=\"#giati-mdlms-chreiazontai-diaforetiko-elegcho\">\u0393\u03b9\u03b1\u03c4\u03af \u03c4\u03b1 masked diffusion language models \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03cc \u03ad\u03bb\u03b5\u03b3\u03c7\u03bf<\/a><\/li>\n<li><a href=\"#treis-sygchytikoi-paragontes\">\u039f\u03b9 \u03c4\u03c1\u03b5\u03b9\u03c2 \u03c3\u03c5\u03b3\u03c7\u03c5\u03c4\u03b9\u03ba\u03bf\u03af \u03c0\u03b1\u03c1\u03ac\u03b3\u03bf\u03bd\u03c4\u03b5\u03c2 \u03c0\u03bf\u03c5 \u03b1\u03bb\u03bb\u03ac\u03b6\u03bf\u03c5\u03bd \u03c4\u03bf \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1<\/a><\/li>\n<li><a href=\"#thermokrasia-metavlitotita-mauve\">\u0397 \u03b8\u03b5\u03c1\u03bc\u03bf\u03ba\u03c1\u03b1\u03c3\u03af\u03b1 \u03b5\u03be\u03b7\u03b3\u03b5\u03af \u03c4\u03bf \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf \u03bc\u03ad\u03c1\u03bf\u03c2 \u03c4\u03b7\u03c2 \u03bc\u03b5\u03c4\u03b1\u03b2\u03bb\u03b7\u03c4\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2<\/a><\/li>\n<li><a href=\"#paradoxo-remasking-token-churn\">\u03a4\u03bf \u03c0\u03b1\u03c1\u03ac\u03b4\u03bf\u03be\u03bf \u03c4\u03bf\u03c5 \u00ab\u03ad\u03be\u03c5\u03c0\u03bd\u03bf\u03c5\u00bb remasking \u03ba\u03b1\u03b9 \u03c4\u03bf token churn<\/a><\/li>\n<li><a href=\"#benchmark-diaforetikoi-nikites\">\u038c\u03c4\u03b1\u03bd \u03c4\u03bf \u03af\u03b4\u03b9\u03bf benchmark \u03b1\u03bd\u03b1\u03ba\u03b7\u03c1\u03cd\u03c3\u03c3\u03b5\u03b9 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03bf\u03cd\u03c2 \u03bd\u03b9\u03ba\u03b7\u03c4\u03ad\u03c2<\/a><\/li>\n<li><a href=\"#epta-simeia-protokollo-care\">\u03a4\u03bf \u03b5\u03c0\u03c4\u03ac-\u03c3\u03b7\u03bc\u03b5\u03af\u03c9\u03bd \u03c0\u03c1\u03c9\u03c4\u03cc\u03ba\u03bf\u03bb\u03bb\u03bf CaRE<\/a><\/li>\n<li><a href=\"#epivevaiosi-montela-datasets\">\u03a4\u03b9 \u03b5\u03c0\u03b9\u03b2\u03b5\u03b2\u03b1\u03b9\u03ce\u03b8\u03b7\u03ba\u03b5 \u03c0\u03ad\u03c1\u03b1 \u03b1\u03c0\u03cc \u03ad\u03bd\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03ba\u03b1\u03b9 \u03ad\u03bd\u03b1 dataset<\/a><\/li>\n<li><a href=\"#epicheirisi-state-of-the-art\">\u03a0\u03ce\u03c2 \u03bd\u03b1 \u03b4\u03b9\u03b1\u03b2\u03ac\u03b6\u03b5\u03b9 \u03bc\u03b9\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03af\u03c1\u03b7\u03c3\u03b7 \u03c4\u03bf\u03c5\u03c2 \u03b9\u03c3\u03c7\u03c5\u03c1\u03b9\u03c3\u03bc\u03bf\u03cd\u03c2 \u00abstate of the art\u00bb<\/a><\/li>\n<li><a href=\"#oria-care-symperasmata\">\u03a4\u03b1 \u03cc\u03c1\u03b9\u03b1 \u03c4\u03bf\u03c5 CaRE \u03ba\u03b1\u03b9 \u03c4\u03b9 \u03b4\u03b5\u03bd \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c3\u03c5\u03bc\u03c0\u03b5\u03c1\u03ac\u03bd\u03bf\u03c5\u03bc\u03b5<\/a><\/li>\n<li><a href=\"#benchmark-governance-ai-governance\">\u0391\u03c0\u03cc \u03c4\u03bf benchmark governance \u03c3\u03c4\u03bf AI governance<\/a><\/li>\n<\/ul>\n<\/div>\n<p>\u0388\u03bd\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c6\u03b1\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf \u03c3\u03b5 \u03ad\u03bd\u03b1 AI benchmark \u03c7\u03c9\u03c1\u03af\u03c2 \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf. \u039c\u03c0\u03bf\u03c1\u03b5\u03af \u03b1\u03c0\u03bb\u03ce\u03c2 \u03bd\u03b1 \u03ad\u03c7\u03b5\u03b9 \u03ba\u03b1\u03c4\u03b1\u03bd\u03b1\u03bb\u03ce\u03c3\u03b5\u03b9 \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03b1 forward passes, \u03bd\u03b1 \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03ae\u03b8\u03b7\u03ba\u03b5 \u03bc\u03b5 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ae \u03b8\u03b5\u03c1\u03bc\u03bf\u03ba\u03c1\u03b1\u03c3\u03af\u03b1 \u03ae \u03bd\u03b1 \u03b5\u03c5\u03bd\u03bf\u03ae\u03b8\u03b7\u03ba\u03b5 \u03b1\u03c0\u03cc \u03ad\u03bd\u03b1 metric \u03c0\u03bf\u03c5 \u03b4\u03b5\u03bd \u03b1\u03c0\u03bf\u03c4\u03c5\u03c0\u03ce\u03bd\u03b5\u03b9 \u03c4\u03b7\u03bd \u03af\u03b4\u03b9\u03b1 \u03b4\u03b9\u03ac\u03c3\u03c4\u03b1\u03c3\u03b7 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2. \u0391\u03c5\u03c4\u03cc \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03bf \u03b2\u03b1\u03c3\u03b9\u03ba\u03cc \u03c0\u03c1\u03cc\u03b2\u03bb\u03b7\u03bc\u03b1 \u03c0\u03bf\u03c5 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b5\u03af \u03bd\u03b1 \u03bb\u03cd\u03c3\u03b5\u03b9 \u03c4\u03bf <strong>CaRE \u2014 Compute-aware Remasking Evaluation<\/strong>, \u03ad\u03bd\u03b1 \u03bd\u03ad\u03bf \u03c0\u03c1\u03c9\u03c4\u03cc\u03ba\u03bf\u03bb\u03bb\u03bf \u03b3\u03b9\u03b1 \u03c4\u03b7\u03bd \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 masked diffusion language models.<\/p>\n<p>\u03a4\u03bf paper \u03b5\u03be\u03b5\u03c4\u03ac\u03b6\u03b5\u03b9 \u03b5\u03c0\u03c4\u03ac \u03c3\u03c4\u03c1\u03b1\u03c4\u03b7\u03b3\u03b9\u03ba\u03ad\u03c2 remasking \u03c3\u03b5 \u03b4\u03cd\u03bf \u03b2\u03b1\u03c3\u03b9\u03ba\u03ad\u03c2 \u03bf\u03b9\u03ba\u03bf\u03b3\u03ad\u03bd\u03b5\u03b9\u03b5\u03c2 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03c9\u03bd, LLaDA-8B-Base \u03ba\u03b1\u03b9 Dream-7B-Base, \u03c3\u03b5 \u03c4\u03ad\u03c3\u03c3\u03b5\u03c1\u03b1 \u03b5\u03c0\u03af\u03c0\u03b5\u03b4\u03b1 stochasticity \u03ba\u03b1\u03b9 \u03c4\u03c1\u03af\u03b1 step budgets, \u03bc\u03b5 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 \u03b1\u03c0\u03cc OpenWebText \u03ba\u03b1\u03b9 LM1B. \u03a4\u03bf \u03ba\u03b5\u03bd\u03c4\u03c1\u03b9\u03ba\u03cc \u03c4\u03bf\u03c5 \u03b5\u03cd\u03c1\u03b7\u03bc\u03b1 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03bc\u03af\u03b1 \u03c4\u03b5\u03c7\u03bd\u03b9\u03ba\u03ae \u03ba\u03b5\u03c1\u03b4\u03af\u03b6\u03b5\u03b9 \u03c0\u03b1\u03bd\u03c4\u03bf\u03cd. \u0395\u03af\u03bd\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03b7 \u03af\u03b4\u03b9\u03b1 \u03b7 \u03ba\u03b1\u03c4\u03ac\u03c4\u03b1\u03be\u03b7 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03bd\u03b1\u03c3\u03c4\u03c1\u03b1\u03c6\u03b5\u03af \u03cc\u03c4\u03b1\u03bd \u03bf\u03b9 \u03c3\u03c5\u03b3\u03ba\u03c1\u03af\u03c3\u03b5\u03b9\u03c2 \u03b3\u03af\u03bd\u03bf\u03c5\u03bd \u03bc\u03b5 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac \u03b9\u03c3\u03cc\u03c4\u03b9\u03bc\u03bf compute \u03ba\u03b1\u03b9 \u03ba\u03bf\u03b9\u03bd\u03ad\u03c2 \u03c1\u03c5\u03b8\u03bc\u03af\u03c3\u03b5\u03b9\u03c2.<\/p>\n<h2 id=\"giati-mdlms-chreiazontai-diaforetiko-elegcho\">\u0393\u03b9\u03b1\u03c4\u03af \u03c4\u03b1 masked diffusion language models \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03cc \u03ad\u03bb\u03b5\u03b3\u03c7\u03bf<\/h2>\n<p>\u03a4\u03b1 autoregressive \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03c0\u03b1\u03c1\u03ac\u03b3\u03bf\u03c5\u03bd \u03ba\u03b5\u03af\u03bc\u03b5\u03bd\u03bf token \u03c0\u03c1\u03bf\u03c2 token. \u03a4\u03b1 masked diffusion language models \u03be\u03b5\u03ba\u03b9\u03bd\u03bf\u03cd\u03bd \u03bc\u03b5 masked \u03b8\u03ad\u03c3\u03b5\u03b9\u03c2 \u03ba\u03b1\u03b9 \u03c4\u03b9\u03c2 \u03b1\u03c0\u03bf\u03ba\u03b1\u03bb\u03cd\u03c0\u03c4\u03bf\u03c5\u03bd \u03c3\u03c4\u03b1\u03b4\u03b9\u03b1\u03ba\u03ac \u03bc\u03ad\u03c3\u03b1 \u03b1\u03c0\u03cc \u03b5\u03c0\u03b1\u03bd\u03b1\u03bb\u03b1\u03bc\u03b2\u03b1\u03bd\u03cc\u03bc\u03b5\u03bd\u03b1 \u03b2\u03ae\u03bc\u03b1\u03c4\u03b1 denoising. \u03a3\u03b5 \u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03b5\u03c2 \u03c3\u03c4\u03c1\u03b1\u03c4\u03b7\u03b3\u03b9\u03ba\u03ad\u03c2, \u03ad\u03bd\u03b1 token \u03c0\u03bf\u03c5 \u03b5\u03af\u03c7\u03b5 \u03ae\u03b4\u03b7 \u03b1\u03c0\u03bf\u03c6\u03b1\u03c3\u03b9\u03c3\u03c4\u03b5\u03af \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03bc\u03b1\u03c3\u03ba\u03b1\u03c1\u03b9\u03c3\u03c4\u03b5\u03af \u03be\u03b1\u03bd\u03ac \u03ce\u03c3\u03c4\u03b5 \u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03bd\u03b1 \u03c4\u03bf \u03b5\u03c0\u03b1\u03bd\u03b5\u03be\u03b5\u03c4\u03ac\u03c3\u03b5\u03b9. \u0391\u03c5\u03c4\u03ae \u03b7 \u03b4\u03b9\u03b1\u03b4\u03b9\u03ba\u03b1\u03c3\u03af\u03b1 \u03bf\u03bd\u03bf\u03bc\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 remasking.<\/p>\n<p>\u03a4\u03bf remasking \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03ba\u03bf\u03cd\u03b3\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b1\u03bd \u03bb\u03b5\u03c0\u03c4\u03bf\u03bc\u03ad\u03c1\u03b5\u03b9\u03b1 \u03c5\u03bb\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7\u03c2, \u03b1\u03bb\u03bb\u03ac \u03b5\u03c0\u03b7\u03c1\u03b5\u03ac\u03b6\u03b5\u03b9 \u03ac\u03bc\u03b5\u03c3\u03b1 \u03c4\u03cc\u03c3\u03bf \u03c4\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03cc\u03c3\u03bf \u03ba\u03b1\u03b9 \u03c4\u03b7 \u03c3\u03c5\u03bc\u03c0\u03b5\u03c1\u03b9\u03c6\u03bf\u03c1\u03ac \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae\u03c2. \u0394\u03cd\u03bf \u03bc\u03ad\u03b8\u03bf\u03b4\u03bf\u03b9 \u03bc\u03b5 \u03c4\u03b7\u03bd \u03af\u03b4\u03b9\u03b1 \u03ad\u03bd\u03b4\u03b5\u03b9\u03be\u03b7 \u00ab256 steps\u00bb \u03b4\u03b5\u03bd \u03b5\u03ba\u03c4\u03b5\u03bb\u03bf\u03cd\u03bd \u03ba\u03b1\u03c4\u2019 \u03b1\u03bd\u03ac\u03b3\u03ba\u03b7 \u03c4\u03bf \u03af\u03b4\u03b9\u03bf \u03ad\u03c1\u03b3\u03bf. \u03a3\u03c4\u03b1 \u03c0\u03b5\u03b9\u03c1\u03ac\u03bc\u03b1\u03c4\u03b1 \u03c4\u03bf\u03c5 CaRE, \u03b7 \u03bf\u03bd\u03bf\u03bc\u03b1\u03c3\u03c4\u03b9\u03ba\u03ae \u03b1\u03c5\u03c4\u03ae \u03c1\u03cd\u03b8\u03bc\u03b9\u03c3\u03b7 \u03b1\u03bd\u03c4\u03b9\u03c3\u03c4\u03bf\u03b9\u03c7\u03bf\u03cd\u03c3\u03b5 \u03c3\u03b5 \u03b5\u03cd\u03c1\u03bf\u03c2 128 \u03ad\u03c9\u03c2 513 forward passes, \u03b4\u03b7\u03bb\u03b1\u03b4\u03ae \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 \u03c4\u03b5\u03c4\u03c1\u03b1\u03c0\u03bb\u03ac\u03c3\u03b9\u03b1 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03ac. \u0386\u03c1\u03b1, \u03bc\u03b9\u03b1 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7 \u03c0\u03bf\u03c5 \u03ba\u03bf\u03b9\u03c4\u03ac \u03bc\u03cc\u03bd\u03bf \u03c4\u03bf nominal step budget \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03c0\u03bf\u03b4\u03ce\u03c3\u03b5\u03b9 \u03c3\u03b5 \u03b1\u03bb\u03b3\u03bf\u03c1\u03b9\u03b8\u03bc\u03b9\u03ba\u03ae \u03c5\u03c0\u03b5\u03c1\u03bf\u03c7\u03ae \u03ba\u03ac\u03c4\u03b9 \u03c0\u03bf\u03c5 \u03c3\u03c4\u03b7\u03bd \u03c0\u03c1\u03ac\u03be\u03b7 \u03b1\u03b3\u03bf\u03c1\u03ac\u03c3\u03c4\u03b7\u03ba\u03b5 \u03bc\u03b5 \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf compute.<\/p>\n<p>\u0393\u03b9\u03b1 \u03bc\u03b9\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03af\u03c1\u03b7\u03c3\u03b7 \u03c0\u03bf\u03c5 \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03b5\u03af AI \u03c5\u03c0\u03bf\u03b4\u03bf\u03bc\u03ad\u03c2, \u03b1\u03c5\u03c4\u03ae \u03b7 \u03b4\u03b9\u03ac\u03ba\u03c1\u03b9\u03c3\u03b7 \u03b5\u03af\u03bd\u03b1\u03b9 \u03bf\u03c5\u03c3\u03b9\u03b1\u03c3\u03c4\u03b9\u03ba\u03ae. \u0397 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03b4\u03b5\u03bd \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03c0\u03bf\u03c3\u03c5\u03bd\u03b4\u03b5\u03b8\u03b5\u03af \u03b1\u03c0\u03cc latency, throughput \u03ba\u03b1\u03b9 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 inference \u2014 \u03c4\u03bf \u03af\u03b4\u03b9\u03bf \u03c4\u03c1\u03af\u03c0\u03c4\u03c5\u03c7\u03bf \u03c0\u03bf\u03c5 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03ad\u03bb\u03b5\u03b3\u03c7\u03bf \u03ba\u03b1\u03b9 \u03c3\u03c4\u03bf <a href=\"https:\/\/twodots.gr\/llm-routing-latency-accuracy-cost\/\">LLM routing \u03bc\u03b5 \u03b5\u03c0\u03af\u03b3\u03bd\u03c9\u03c3\u03b7 latency \u03ba\u03b1\u03b9 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c5\u03c2<\/a>. \u0388\u03bd\u03b1 benchmark \u03c0\u03bf\u03c5 \u03b1\u03b3\u03bd\u03bf\u03b5\u03af \u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc NFE, \u03b4\u03b7\u03bb\u03b1\u03b4\u03ae \u03c4\u03bf\u03bd \u03b1\u03c1\u03b9\u03b8\u03bc\u03cc \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03ae\u03c3\u03b5\u03c9\u03bd \u03c4\u03bf\u03c5 \u03bd\u03b5\u03c5\u03c1\u03c9\u03bd\u03b9\u03ba\u03bf\u03cd \u03b4\u03b9\u03ba\u03c4\u03cd\u03bf\u03c5, \u03b4\u03b5\u03bd \u03c0\u03c1\u03bf\u03c3\u03c6\u03ad\u03c1\u03b5\u03b9 \u03b5\u03c0\u03b1\u03c1\u03ba\u03ae \u03b2\u03ac\u03c3\u03b7 \u03b3\u03b9\u03b1 \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5 \u03ae \u03b1\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03ae\u03c2.<\/p>\n<h2 id=\"treis-sygchytikoi-paragontes\">\u039f\u03b9 \u03c4\u03c1\u03b5\u03b9\u03c2 \u03c3\u03c5\u03b3\u03c7\u03c5\u03c4\u03b9\u03ba\u03bf\u03af \u03c0\u03b1\u03c1\u03ac\u03b3\u03bf\u03bd\u03c4\u03b5\u03c2 \u03c0\u03bf\u03c5 \u03b1\u03bb\u03bb\u03ac\u03b6\u03bf\u03c5\u03bd \u03c4\u03bf \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1<\/h2>\n<p>\u03a4\u03bf CaRE \u03bf\u03c1\u03b3\u03b1\u03bd\u03ce\u03bd\u03b5\u03b9 \u03c4\u03bf \u03c0\u03c1\u03cc\u03b2\u03bb\u03b7\u03bc\u03b1 \u03b3\u03cd\u03c1\u03c9 \u03b1\u03c0\u03cc \u03c4\u03c1\u03b5\u03b9\u03c2 confounders: compute, metric \u03ba\u03b1\u03b9 stochasticity. \u039f \u03c0\u03c1\u03ce\u03c4\u03bf\u03c2 \u03b1\u03c6\u03bf\u03c1\u03ac \u03c4\u03b7\u03bd \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03c5\u03c0\u03bf\u03bb\u03bf\u03b3\u03b9\u03c3\u03c4\u03b9\u03ba\u03ae \u03c0\u03c1\u03bf\u03c3\u03c0\u03ac\u03b8\u03b5\u03b9\u03b1. \u039f \u03b4\u03b5\u03cd\u03c4\u03b5\u03c1\u03bf\u03c2 \u03b1\u03c6\u03bf\u03c1\u03ac \u03c4\u03bf \u03c4\u03b9 \u03b1\u03ba\u03c1\u03b9\u03b2\u03ce\u03c2 \u03bf\u03bd\u03bf\u03bc\u03ac\u03b6\u03bf\u03c5\u03bc\u03b5 \u00ab\u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf \u03ba\u03b5\u03af\u03bc\u03b5\u03bd\u03bf\u00bb. \u039f \u03c4\u03c1\u03af\u03c4\u03bf\u03c2 \u03b1\u03c6\u03bf\u03c1\u03ac \u03c0\u03cc\u03c3\u03bf \u03c4\u03c5\u03c7\u03b1\u03af\u03b1 \u03ae \u03bd\u03c4\u03b5\u03c4\u03b5\u03c1\u03bc\u03b9\u03bd\u03b9\u03c3\u03c4\u03b9\u03ba\u03ac \u03b3\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03b7 \u03b1\u03c0\u03bf\u03ba\u03ac\u03bb\u03c5\u03c8\u03b7 \u03c4\u03c9\u03bd tokens.<\/p>\n<p>\u03a4\u03bf compute \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03bc\u03b5\u03c4\u03c1\u03b9\u03ad\u03c4\u03b1\u03b9 \u03bc\u03b5 actual NFE \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03bc\u03cc\u03bd\u03bf \u03bc\u03b5 \u03c4\u03bf \u03c0\u03bb\u03ae\u03b8\u03bf\u03c2 \u03c4\u03c9\u03bd \u03b4\u03b7\u03bb\u03c9\u03bc\u03ad\u03bd\u03c9\u03bd \u03b2\u03b7\u03bc\u03ac\u03c4\u03c9\u03bd. \u03a4\u03b1 metrics \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b5\u03bc\u03c6\u03b1\u03bd\u03af\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03bc\u03b1\u03b6\u03af, \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03c4\u03bf perplexity \u03ba\u03b1\u03b9 \u03c4\u03bf MAUVE \u03b4\u03b5\u03bd \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03bf\u03cd\u03bd \u03c4\u03b7\u03bd \u03af\u03b4\u03b9\u03b1 \u03b9\u03b4\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1. \u03a4\u03bf perplexity \u03c3\u03c5\u03bd\u03b4\u03ad\u03b5\u03c4\u03b1\u03b9 \u03bc\u03b5 \u03c4\u03b7 \u03b3\u03bb\u03c9\u03c3\u03c3\u03b9\u03ba\u03ae \u03c0\u03b9\u03b8\u03b1\u03bd\u03cc\u03c4\u03b7\u03c4\u03b1 \u03ae fluency \u03c5\u03c0\u03cc \u03ad\u03bd\u03b1\u03bd \u03b5\u03be\u03c9\u03c4\u03b5\u03c1\u03b9\u03ba\u03cc evaluator, \u03b5\u03bd\u03ce \u03c4\u03bf MAUVE \u03c3\u03c5\u03b3\u03ba\u03c1\u03af\u03bd\u03b5\u03b9 \u03c4\u03b7 \u03b4\u03b9\u03b1\u03bd\u03bf\u03bc\u03ae \u03c4\u03c9\u03bd \u03c0\u03b1\u03c1\u03b1\u03b3\u03cc\u03bc\u03b5\u03bd\u03c9\u03bd \u03ba\u03b5\u03b9\u03bc\u03ad\u03bd\u03c9\u03bd \u03bc\u03b5 \u03c4\u03b7 \u03b4\u03b9\u03b1\u03bd\u03bf\u03bc\u03ae \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03bf\u03c5 \u03ba\u03b5\u03b9\u03bc\u03ad\u03bd\u03bf\u03c5. \u039c\u03af\u03b1 \u03c3\u03c4\u03c1\u03b1\u03c4\u03b7\u03b3\u03b9\u03ba\u03ae \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03ad\u03c7\u03b5\u03b9 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf PPL \u03b1\u03bb\u03bb\u03ac \u03cc\u03c7\u03b9 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf MAUVE.<\/p>\n<p>\u0397 stochasticity \u03b5\u03bb\u03ad\u03b3\u03c7\u03b5\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c1\u03c5\u03b8\u03bc\u03af\u03c3\u03b5\u03b9\u03c2 \u03cc\u03c0\u03c9\u03c2 \u03c4\u03bf unmask temperature. \u03a4\u03bf paper \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03bc\u03b9\u03b1 \u03c6\u03b1\u03b9\u03bd\u03bf\u03bc\u03b5\u03bd\u03b9\u03ba\u03ac \u03bc\u03b9\u03ba\u03c1\u03ae \u03b1\u03bb\u03bb\u03b1\u03b3\u03ae \u03c3\u03c4\u03b7 \u03b8\u03b5\u03c1\u03bc\u03bf\u03ba\u03c1\u03b1\u03c3\u03af\u03b1 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03c0\u03b7\u03c1\u03b5\u03ac\u03c3\u03b5\u03b9 \u03c4\u03bf MAUVE \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03b1\u03bb\u03bb\u03b1\u03b3\u03ae \u03c3\u03c4\u03c1\u03b1\u03c4\u03b7\u03b3\u03b9\u03ba\u03ae\u03c2 remasking. \u0395\u03c0\u03bf\u03bc\u03ad\u03bd\u03c9\u03c2, \u03b4\u03cd\u03bf \u03b4\u03b7\u03bc\u03bf\u03c3\u03b9\u03b5\u03c5\u03bc\u03ad\u03bd\u03b1 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1 \u03c0\u03bf\u03c5 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03bf\u03cd\u03bd \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ae \u03b8\u03b5\u03c1\u03bc\u03bf\u03ba\u03c1\u03b1\u03c3\u03af\u03b1 \u03b4\u03b5\u03bd \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b1\u03bd\u03c4\u03b9\u03bc\u03b5\u03c4\u03c9\u03c0\u03af\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c9\u03c2 \u03ac\u03bc\u03b5\u03c3\u03b7 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7 \u03b1\u03bb\u03b3\u03bf\u03c1\u03af\u03b8\u03bc\u03c9\u03bd.<\/p>\n<h2 id=\"thermokrasia-metavlitotita-mauve\">\u0397 \u03b8\u03b5\u03c1\u03bc\u03bf\u03ba\u03c1\u03b1\u03c3\u03af\u03b1 \u03b5\u03be\u03b7\u03b3\u03b5\u03af \u03c4\u03bf \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf \u03bc\u03ad\u03c1\u03bf\u03c2 \u03c4\u03b7\u03c2 \u03bc\u03b5\u03c4\u03b1\u03b2\u03bb\u03b7\u03c4\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2<\/h2>\n<p>\u03a3\u03c4\u03b7\u03bd \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7 \u03c4\u03bf\u03c5 CaRE, \u03b7 \u03b8\u03b5\u03c1\u03bc\u03bf\u03ba\u03c1\u03b1\u03c3\u03af\u03b1 \u03b5\u03be\u03b7\u03b3\u03b5\u03af \u03c4\u03b7\u03bd \u03c0\u03bb\u03b5\u03b9\u03bf\u03bd\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c4\u03b7\u03c2 \u03b4\u03b9\u03b1\u03ba\u03cd\u03bc\u03b1\u03bd\u03c3\u03b7\u03c2 \u03c4\u03bf\u03c5 MAUVE, \u03bc\u03b5 \u03b1\u03bd\u03b1\u03c6\u03b5\u03c1\u03cc\u03bc\u03b5\u03bd\u03bf \u03b7\u00b2=0,91. \u03a4\u03bf \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03b4\u03b5\u03bd \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03b7 \u03c3\u03c4\u03c1\u03b1\u03c4\u03b7\u03b3\u03b9\u03ba\u03ae remasking \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03b4\u03b9\u03ac\u03c6\u03bf\u03c1\u03b7. \u03a3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9, \u03c7\u03c9\u03c1\u03af\u03c2 \u03ba\u03bf\u03b9\u03bd\u03ae \u03b8\u03b5\u03c1\u03bc\u03bf\u03ba\u03c1\u03b1\u03c3\u03af\u03b1, \u03b7 \u03b5\u03c0\u03af\u03b4\u03c1\u03b1\u03c3\u03b7 \u03c4\u03b7\u03c2 \u03c3\u03c4\u03c1\u03b1\u03c4\u03b7\u03b3\u03b9\u03ba\u03ae\u03c2 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03ba\u03b1\u03bb\u03c5\u03c6\u03b8\u03b5\u03af \u03ae \u03bd\u03b1 \u03c0\u03b1\u03c1\u03bf\u03c5\u03c3\u03b9\u03b1\u03c3\u03c4\u03b5\u03af \u03c0\u03b1\u03c1\u03b1\u03c0\u03bb\u03b1\u03bd\u03b7\u03c4\u03b9\u03ba\u03ac.<\/p>\n<p>\u03a3\u03b5 LLaDA-8B-Base, \u03b7 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7 \u03c3\u03b5 \u03c4\u03c1\u03b5\u03b9\u03c2 seeds \u03ba\u03b1\u03b9 unmask temperature 0,25 \u03ad\u03b4\u03c9\u03c3\u03b5 MAUVE 0,948\u00b10,021 \u03b3\u03b9\u03b1 \u03c4\u03b7 baseline \u03c7\u03c9\u03c1\u03af\u03c2 remasking \u03ba\u03b1\u03b9 0,652\u00b10,025 \u03b3\u03b9\u03b1 high-entropy remasking. \u03a4\u03bf paper \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9 \u03b5\u03c0\u03af\u03c3\u03b7\u03c2 \u03cc\u03c4\u03b9, \u03c3\u03c4\u03b1 256 steps, \u03bf \u03c3\u03c5\u03bd\u03b4\u03c5\u03b1\u03c3\u03bc\u03cc\u03c2 high-entropy remasking \u03ba\u03b1\u03b9 stochastic unmasking \u03bc\u03b5\u03af\u03c9\u03c3\u03b5 \u03c4\u03bf MAUVE \u03ba\u03b1\u03c4\u03ac 0,296, \u03bc\u03b5 p=0,020. \u0391\u03c5\u03c4\u03ac \u03c4\u03b1 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1 \u03b1\u03bd\u03ae\u03ba\u03bf\u03c5\u03bd \u03c3\u03c4\u03b9\u03c2 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b5\u03c2 \u03c0\u03b5\u03b9\u03c1\u03b1\u03bc\u03b1\u03c4\u03b9\u03ba\u03ad\u03c2 \u03c3\u03c5\u03bd\u03b8\u03ae\u03ba\u03b5\u03c2\u00b7 \u03b4\u03b5\u03bd \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03bf\u03cd\u03bd \u03ba\u03b1\u03b8\u03bf\u03bb\u03b9\u03ba\u03ae \u03b1\u03c0\u03cc\u03b4\u03b5\u03b9\u03be\u03b7 \u03cc\u03c4\u03b9 \u03ba\u03ac\u03b8\u03b5 \u03bc\u03bf\u03c1\u03c6\u03ae remasking \u03b5\u03af\u03bd\u03b1\u03b9 \u03c7\u03b5\u03b9\u03c1\u03cc\u03c4\u03b5\u03c1\u03b7.<\/p>\n<p>\u0397 \u03c3\u03c9\u03c3\u03c4\u03ae \u03b1\u03bd\u03ac\u03b3\u03bd\u03c9\u03c3\u03b7 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03b9\u03bf \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03b7: \u03c5\u03c0\u03ac\u03c1\u03c7\u03b5\u03b9 \u03b1\u03bb\u03bb\u03b7\u03bb\u03b5\u03c0\u03af\u03b4\u03c1\u03b1\u03c3\u03b7 \u03b1\u03bd\u03ac\u03bc\u03b5\u03c3\u03b1 \u03c3\u03c4\u03b7 \u03c3\u03c4\u03c1\u03b1\u03c4\u03b7\u03b3\u03b9\u03ba\u03ae \u03ba\u03b1\u03b9 \u03c3\u03c4\u03b7 stochasticity. \u039c\u03b9\u03b1 \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae \u03c0\u03bf\u03c5 \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03b5\u03af \u03c3\u03b5 \u03bd\u03c4\u03b5\u03c4\u03b5\u03c1\u03bc\u03b9\u03bd\u03b9\u03c3\u03c4\u03b9\u03ba\u03cc sampling \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c3\u03c5\u03bc\u03c0\u03b5\u03c1\u03b9\u03c6\u03b5\u03c1\u03b8\u03b5\u03af \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac \u03cc\u03c4\u03b1\u03bd \u03b1\u03c5\u03be\u03b7\u03b8\u03b5\u03af \u03b7 \u03c4\u03c5\u03c7\u03b1\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1. \u0393\u03b9\u03b1 production \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7, \u03bf\u03b9 \u03c0\u03b1\u03c1\u03ac\u03bc\u03b5\u03c4\u03c1\u03bf\u03b9 sampling \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b8\u03b5\u03c9\u03c1\u03bf\u03cd\u03bd\u03c4\u03b1\u03b9 \u03bc\u03ad\u03c1\u03bf\u03c2 \u03c4\u03bf\u03c5 \u03c3\u03c5\u03c3\u03c4\u03ae\u03bc\u03b1\u03c4\u03bf\u03c2 \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03bc\u03b5\u03c4\u03b1\u03b3\u03b5\u03bd\u03ad\u03c3\u03c4\u03b5\u03c1\u03b7 \u03bb\u03b5\u03c0\u03c4\u03bf\u03bc\u03ad\u03c1\u03b5\u03b9\u03b1.<\/p>\n<div class=\"td-chart td-chart--metrics\">\n<p class=\"td-chart-title\">\u03a4\u03ad\u03c3\u03c3\u03b5\u03c1\u03b1 \u03c4\u03b5\u03ba\u03bc\u03b7\u03c1\u03b9\u03c9\u03bc\u03ad\u03bd\u03b1 \u03c3\u03ae\u03bc\u03b1\u03c4\u03b1 \u03b1\u03c0\u03cc \u03c4\u03bf CaRE<\/p>\n<p class=\"td-chart-intro\">\u039f\u03b9 \u03c4\u03b9\u03bc\u03ad\u03c2 \u03b1\u03c6\u03bf\u03c1\u03bf\u03cd\u03bd \u03c4\u03b9\u03c2 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b5\u03c2 \u03b4\u03bf\u03ba\u03b9\u03bc\u03ad\u03c2 \u03c4\u03b7\u03c2 \u03c0\u03c1\u03c9\u03c4\u03bf\u03b3\u03b5\u03bd\u03bf\u03cd\u03c2 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1\u03c2 \u03c3\u03b5 masked diffusion language models. \u0394\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b3\u03b5\u03bd\u03b9\u03ba\u03bf\u03af \u03b4\u03b5\u03af\u03ba\u03c4\u03b5\u03c2 \u03b1\u03c0\u03cc\u03b4\u03bf\u03c3\u03b7\u03c2 \u03b3\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 LLM \u03ae production workload.<\/p>\n<div class=\"td-metric-grid\">\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">128\u2013513<\/span><strong>actual NFE<\/strong><\/p>\n<p>\u03a4\u03bf \u03b5\u03cd\u03c1\u03bf\u03c2 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ce\u03bd forward passes \u03c0\u03bf\u03c5 \u03ba\u03c1\u03c5\u03b2\u03cc\u03c4\u03b1\u03bd \u03c0\u03af\u03c3\u03c9 \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03af\u03b4\u03b9\u03b1 \u03ad\u03bd\u03b4\u03b5\u03b9\u03be\u03b7 256 nominal steps.<\/p>\n<\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">\u03b7\u00b2=0,91<\/span><strong>\u03b5\u03c0\u03af\u03b4\u03c1\u03b1\u03c3\u03b7 \u03b8\u03b5\u03c1\u03bc\u03bf\u03ba\u03c1\u03b1\u03c3\u03af\u03b1\u03c2<\/strong><\/p>\n<p>\u0397 \u03b1\u03bd\u03b1\u03c6\u03b5\u03c1\u03cc\u03bc\u03b5\u03bd\u03b7 \u03bc\u03b5\u03c1\u03b9\u03ba\u03ae \u03b5\u03c0\u03af\u03b4\u03c1\u03b1\u03c3\u03b7 \u03c4\u03b7\u03c2 temperature \u03c3\u03c4\u03b7 \u03b4\u03b9\u03b1\u03ba\u03cd\u03bc\u03b1\u03bd\u03c3\u03b7 \u03c4\u03bf\u03c5 MAUVE \u03c3\u03c4\u03b7\u03bd \u03ba\u03cd\u03c1\u03b9\u03b1 \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7.<\/p>\n<\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">0,296<\/span><strong>\u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03ac MAUVE<\/strong><\/p>\n<p>\u03a4\u03bf \u03c7\u03ac\u03c3\u03bc\u03b1 none \u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 high-entropy \u03c3\u03c4\u03b1 256 steps \u03ba\u03b1\u03b9 unmask temperature 0,25, \u03bc\u03b5 \u03c4\u03c1\u03af\u03b1 seeds.<\/p>\n<\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">4,3%<\/span><strong>\u03b1\u03bd\u03c4\u03b9\u03ba\u03b1\u03c4\u03ac\u03c3\u03c4\u03b1\u03c3\u03b7 tokens<\/strong><\/p>\n<p>\u03a4\u03bf \u03c0\u03bf\u03c3\u03bf\u03c3\u03c4\u03cc tokens \u03b1\u03bd\u03ac \u03b2\u03ae\u03bc\u03b1 \u03c0\u03bf\u03c5 \u03b1\u03bd\u03c4\u03b9\u03ba\u03b1\u03b8\u03b9\u03c3\u03c4\u03bf\u03cd\u03c3\u03b5 \u03c4\u03bf high-entropy remasking \u03c3\u03c4\u03b7 \u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03c3\u03c4\u03b9\u03ba\u03ae \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<h2 id=\"paradoxo-remasking-token-churn\">\u03a4\u03bf \u03c0\u03b1\u03c1\u03ac\u03b4\u03bf\u03be\u03bf \u03c4\u03bf\u03c5 \u00ab\u03ad\u03be\u03c5\u03c0\u03bd\u03bf\u03c5\u00bb remasking \u03ba\u03b1\u03b9 \u03c4\u03bf token churn<\/h2>\n<p>\u0397 high-entropy \u03c3\u03c4\u03c1\u03b1\u03c4\u03b7\u03b3\u03b9\u03ba\u03ae \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03c0\u03b1\u03bd\u03b5\u03be\u03b5\u03c4\u03ac\u03b6\u03b5\u03b9 tokens \u03b3\u03b9\u03b1 \u03c4\u03b1 \u03bf\u03c0\u03bf\u03af\u03b1 \u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03b5\u03bc\u03c6\u03b1\u03bd\u03af\u03b6\u03b5\u03b9 \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b7 \u03b1\u03b2\u03b5\u03b2\u03b1\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1. \u0394\u03b9\u03b1\u03b9\u03c3\u03b8\u03b7\u03c4\u03b9\u03ba\u03ac, \u03b1\u03c5\u03c4\u03cc \u03bc\u03bf\u03b9\u03ac\u03b6\u03b5\u03b9 \u03bb\u03bf\u03b3\u03b9\u03ba\u03cc: \u03b1\u03bd \u03ad\u03bd\u03b1 token \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c3\u03c4\u03b1\u03b8\u03ad\u03c2, \u03b4\u03ce\u03c3\u03b5 \u03c3\u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03ac\u03bb\u03bb\u03b7 \u03bc\u03af\u03b1 \u03b5\u03c5\u03ba\u03b1\u03b9\u03c1\u03af\u03b1. \u038c\u03bc\u03c9\u03c2 \u03c4\u03bf CaRE \u03b5\u03bd\u03c4\u03bf\u03c0\u03af\u03b6\u03b5\u03b9 \u03ad\u03bd\u03c4\u03b1\u03c3\u03b7 \u03b1\u03bd\u03ac\u03bc\u03b5\u03c3\u03b1 \u03c3\u03b5 \u03b1\u03c5\u03c4\u03ae \u03c4\u03b7\u03bd \u03b5\u03bd\u03b7\u03bc\u03b5\u03c1\u03c9\u03bc\u03ad\u03bd\u03b7 \u03b5\u03c0\u03b1\u03bd\u03b5\u03be\u03ad\u03c4\u03b1\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03c3\u03c4\u03bf stochastic unmasking.<\/p>\n<p>\u03a3\u03c4\u03b7 \u03bc\u03b7\u03c7\u03b1\u03bd\u03b9\u03c3\u03c4\u03b9\u03ba\u03ae \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7, \u03c4\u03bf high-entropy remasking \u03b1\u03bd\u03c4\u03b9\u03ba\u03b1\u03b8\u03b9\u03c3\u03c4\u03bf\u03cd\u03c3\u03b5 4,3% \u03c4\u03c9\u03bd tokens \u03b1\u03bd\u03ac \u03b2\u03ae\u03bc\u03b1, \u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 0% \u03c3\u03c4\u03b7 \u03c3\u03c4\u03c1\u03b1\u03c4\u03b7\u03b3\u03b9\u03ba\u03ae none. \u03a0\u03b1\u03c1\u03ae\u03b3\u03b1\u03b3\u03b5 2.522 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03ac token-step cells \u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 8.129 \u03b3\u03b9\u03b1 \u03c4\u03bf none, \u03b4\u03b7\u03bb\u03b1\u03b4\u03ae 3,2 \u03c6\u03bf\u03c1\u03ad\u03c2 \u03bb\u03b9\u03b3\u03cc\u03c4\u03b5\u03c1\u03b7 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03cc\u03c4\u03b7\u03c4\u03b1. \u039f\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03c0\u03b5\u03c1\u03b9\u03b3\u03c1\u03ac\u03c6\u03bf\u03c5\u03bd \u03b1\u03c5\u03c4\u03ae \u03c4\u03b7 \u03b4\u03b9\u03b1\u03c1\u03ba\u03ae \u03b1\u03bb\u03bb\u03b1\u03b3\u03ae \u03c9\u03c2 token churn: \u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03c3\u03c5\u03bd\u03b5\u03c7\u03af\u03b6\u03b5\u03b9 \u03bd\u03b1 \u03be\u03b1\u03bd\u03b1\u03bd\u03bf\u03af\u03b3\u03b5\u03b9 \u03b1\u03c0\u03bf\u03c6\u03ac\u03c3\u03b5\u03b9\u03c2, \u03ba\u03b1\u03c4\u03b1\u03bd\u03b1\u03bb\u03ce\u03bd\u03b5\u03b9 \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf compute \u03ba\u03b1\u03b9 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03af\u03b6\u03b5\u03b9 \u03c4\u03b7 \u03b4\u03b9\u03b1\u03bd\u03b5\u03bc\u03b7\u03c4\u03b9\u03ba\u03ae \u03ba\u03ac\u03bb\u03c5\u03c8\u03b7, \u03b1\u03ba\u03cc\u03bc\u03b7 \u03ba\u03b9 \u03b1\u03bd \u03c4\u03bf \u03ba\u03b5\u03af\u03bc\u03b5\u03bd\u03bf \u03b4\u03b9\u03b1\u03c4\u03b7\u03c1\u03b5\u03af \u03b5\u03c0\u03b9\u03c6\u03b1\u03bd\u03b5\u03b9\u03b1\u03ba\u03ae \u03c0\u03bf\u03b9\u03ba\u03b9\u03bb\u03af\u03b1.<\/p>\n<p>\u03a4\u03bf \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03b1\u03bd\u03ac\u03bb\u03bf\u03b3\u03bf \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03cc\u03c4\u03b9 \u00ab\u03b7 \u03b5\u03c0\u03b1\u03bd\u03ac\u03bb\u03b7\u03c8\u03b7 \u03b5\u03af\u03bd\u03b1\u03b9 \u03ba\u03b1\u03ba\u03ae\u00bb. \u0395\u03af\u03bd\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03ba\u03ac\u03b8\u03b5 loop \u03b2\u03b5\u03bb\u03c4\u03af\u03c9\u03c3\u03b7\u03c2 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b9\u03bc\u03bf stopping condition. \u03a3\u03b5 agentic workflows, content generation \u03ae automated customer support, \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03b1 refinement passes \u03b4\u03b5\u03bd \u03b5\u03b3\u03b3\u03c5\u03ce\u03bd\u03c4\u03b1\u03b9 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1. \u03a7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03bd\u03b1 \u03b3\u03bd\u03c9\u03c1\u03af\u03b6\u03bf\u03c5\u03bc\u03b5 \u03c0\u03bf\u03b9\u03bf metric \u03b2\u03b5\u03bb\u03c4\u03b9\u03ce\u03bd\u03b5\u03c4\u03b1\u03b9, \u03c0\u03cc\u03c3\u03bf \u03ba\u03bf\u03c3\u03c4\u03af\u03b6\u03b5\u03b9 \u03bf \u03b5\u03c0\u03b9\u03c0\u03bb\u03ad\u03bf\u03bd \u03ba\u03cd\u03ba\u03bb\u03bf\u03c2 \u03ba\u03b1\u03b9 \u03b1\u03bd \u03c4\u03bf \u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03bf\u03c0\u03bf\u03b9\u03b5\u03af\u03c4\u03b1\u03b9 \u03ae \u03b1\u03c0\u03bb\u03ce\u03c2 \u03b1\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9 \u03b4\u03b9\u03b1\u03c4\u03cd\u03c0\u03c9\u03c3\u03b7.<\/p>\n<h2 id=\"benchmark-diaforetikoi-nikites\">\u038c\u03c4\u03b1\u03bd \u03c4\u03bf \u03af\u03b4\u03b9\u03bf benchmark \u03b1\u03bd\u03b1\u03ba\u03b7\u03c1\u03cd\u03c3\u03c3\u03b5\u03b9 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03bf\u03cd\u03c2 \u03bd\u03b9\u03ba\u03b7\u03c4\u03ad\u03c2<\/h2>\n<p>\u03a4\u03bf paper \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9 \u03cc\u03c4\u03b9 compute-matched comparisons \u03b1\u03bd\u03b1\u03c4\u03c1\u03ad\u03c0\u03bf\u03c5\u03bd \u03b1\u03c1\u03ba\u03b5\u03c4\u03ad\u03c2 \u03b4\u03b7\u03bc\u03bf\u03c3\u03b9\u03b5\u03c5\u03bc\u03ad\u03bd\u03b5\u03c2 \u03ba\u03b1\u03c4\u03b1\u03c4\u03ac\u03be\u03b5\u03b9\u03c2 \u03c3\u03c4\u03c1\u03b1\u03c4\u03b7\u03b3\u03b9\u03ba\u03ce\u03bd. \u0391\u03ba\u03cc\u03bc\u03b7 \u03ba\u03b1\u03b9 \u03bc\u03b5\u03c4\u03ac \u03c4\u03b7\u03bd \u03b5\u03be\u03af\u03c3\u03c9\u03c3\u03b7 \u03c4\u03bf\u03c5 compute, \u03cc\u03bc\u03c9\u03c2, \u03ad\u03bd\u03b1 \u03bc\u03cc\u03bd\u03bf metric \u03b4\u03b5\u03bd \u03b1\u03c1\u03ba\u03b5\u03af. \u0397 baseline none \u03c0\u03ad\u03c4\u03c5\u03c7\u03b5 \u03c4\u03bf \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf perplexity \u03c3\u03b5 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b5\u03c2 \u03c1\u03c5\u03b8\u03bc\u03af\u03c3\u03b5\u03b9\u03c2, \u03b1\u03bb\u03bb\u03ac \u03cc\u03c7\u03b9 \u03c4\u03bf \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf MAUVE. \u0391\u03c5\u03c4\u03cc \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03bf \u03cc\u03c1\u03bf\u03c2 \u00ab\u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1\u00bb \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b1\u03c0\u03bf\u03c3\u03c5\u03bd\u03c4\u03af\u03b8\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03c3\u03b1\u03c6\u03b5\u03af\u03c2 \u03b4\u03b9\u03b1\u03c3\u03c4\u03ac\u03c3\u03b5\u03b9\u03c2.<\/p>\n<p>\u0393\u03b9\u03b1 \u03ad\u03bd\u03b1 e-commerce chatbot, \u03b3\u03b9\u03b1 \u03c0\u03b1\u03c1\u03ac\u03b4\u03b5\u03b9\u03b3\u03bc\u03b1, \u03b7 \u03b3\u03bb\u03c9\u03c3\u03c3\u03b9\u03ba\u03ae \u03bf\u03bc\u03b1\u03bb\u03cc\u03c4\u03b7\u03c4\u03b1 \u03b4\u03b5\u03bd \u03b1\u03c1\u03ba\u03b5\u03af \u03b1\u03bd \u03bf\u03b9 \u03b1\u03c0\u03b1\u03bd\u03c4\u03ae\u03c3\u03b5\u03b9\u03c2 \u03b4\u03b5\u03bd \u03ba\u03b1\u03bb\u03cd\u03c0\u03c4\u03bf\u03c5\u03bd \u03c4\u03b9\u03c2 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ad\u03c2 \u03c0\u03c1\u03bf\u03b8\u03ad\u03c3\u03b5\u03b9\u03c2 \u03c4\u03c9\u03bd \u03c0\u03b5\u03bb\u03b1\u03c4\u03ce\u03bd. \u0393\u03b9\u03b1 \u03ad\u03bd\u03b1 \u03b5\u03c1\u03b3\u03b1\u03bb\u03b5\u03af\u03bf marketing content, \u03b7 \u03c0\u03bf\u03b9\u03ba\u03b9\u03bb\u03af\u03b1 \u03b4\u03b5\u03bd \u03b1\u03c1\u03ba\u03b5\u03af \u03b1\u03bd \u03b7 \u03b1\u03ba\u03c1\u03af\u03b2\u03b5\u03b9\u03b1 \u03c4\u03c9\u03bd claims \u03ae \u03b7 \u03c3\u03c5\u03bd\u03ad\u03c0\u03b5\u03b9\u03b1 \u03c4\u03bf\u03c5 brand voice \u03c5\u03c0\u03bf\u03c7\u03c9\u03c1\u03b5\u03af. \u03a4\u03bf CaRE \u03b4\u03b5\u03bd \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03b5\u03af \u03b1\u03c5\u03c4\u03ac \u03c4\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac use cases \u03ac\u03bc\u03b5\u03c3\u03b1, \u03b1\u03bb\u03bb\u03ac \u03b7 \u03bc\u03b5\u03b8\u03bf\u03b4\u03bf\u03bb\u03bf\u03b3\u03b9\u03ba\u03ae \u03c4\u03bf\u03c5 \u03b1\u03c1\u03c7\u03ae \u03bc\u03b5\u03c4\u03b1\u03c6\u03ad\u03c1\u03b5\u03c4\u03b1\u03b9 \u03ba\u03b1\u03b8\u03b1\u03c1\u03ac: \u03ba\u03ac\u03b8\u03b5 metric \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b1\u03bd\u03c4\u03b9\u03c3\u03c4\u03bf\u03b9\u03c7\u03b5\u03af \u03c3\u03b5 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf failure mode.<\/p>\n<p>\u0393\u03b9\u2019 \u03b1\u03c5\u03c4\u03cc \u03ad\u03bd\u03b1 \u03c3\u03bf\u03b2\u03b1\u03c1\u03cc scorecard \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c4\u03bf\u03c5\u03bb\u03ac\u03c7\u03b9\u03c3\u03c4\u03bf\u03bd \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1, \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2, latency, \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03cc\u03c4\u03b7\u03c4\u03b1 \u03ba\u03b1\u03b9 task-specific correctness. \u0391\u03bd \u03ad\u03bd\u03b1 dashboard \u03c3\u03c5\u03bc\u03c0\u03c4\u03cd\u03be\u03b5\u03b9 \u03cc\u03bb\u03b5\u03c2 \u03b1\u03c5\u03c4\u03ad\u03c2 \u03c4\u03b9\u03c2 \u03b4\u03b9\u03b1\u03c3\u03c4\u03ac\u03c3\u03b5\u03b9\u03c2 \u03c3\u03b5 \u03ad\u03bd\u03b1\u03bd \u03b1\u03c1\u03b9\u03b8\u03bc\u03cc, \u03b7 \u03c4\u03b5\u03bb\u03b9\u03ba\u03ae \u03ba\u03b1\u03c4\u03ac\u03c4\u03b1\u03be\u03b7 \u03b8\u03b1 \u03b5\u03be\u03b1\u03c1\u03c4\u03ac\u03c4\u03b1\u03b9 \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf \u03b1\u03c0\u03cc \u03c4\u03b9\u03c2 \u03ba\u03c1\u03c5\u03c6\u03ad\u03c2 \u03c3\u03c4\u03b1\u03b8\u03bc\u03af\u03c3\u03b5\u03b9\u03c2 \u03c0\u03b1\u03c1\u03ac \u03b1\u03c0\u03cc \u03bc\u03b9\u03b1 \u03b1\u03bd\u03c4\u03b9\u03ba\u03b5\u03b9\u03bc\u03b5\u03bd\u03b9\u03ba\u03ae \u03ad\u03bd\u03bd\u03bf\u03b9\u03b1 \u00ab\u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf\u03c5 AI\u00bb.<\/p>\n<div class=\"td-comparison\">\n<p class=\"td-comparison-title\">Headline benchmark \u03ae \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7 \u03bc\u03b5 \u03b5\u03bb\u03b5\u03b3\u03c7\u03cc\u03bc\u03b5\u03bd\u03b5\u03c2 \u03bc\u03b5\u03c4\u03b1\u03b2\u03bb\u03b7\u03c4\u03ad\u03c2;<\/p>\n<div class=\"td-comparison-cards td-comparison-cards--horizontal\">\n<div class=\"td-comparison-grid td-comparison-grid--two\">\n<div class=\"td-platform-card\">\n<h3>Nominal steps<\/h3>\n<p>\u0394\u03b5\u03af\u03c7\u03bd\u03bf\u03c5\u03bd \u03c4\u03b7 \u03b4\u03b7\u03bb\u03c9\u03bc\u03ad\u03bd\u03b7 \u03c1\u03cd\u03b8\u03bc\u03b9\u03c3\u03b7, \u03b1\u03bb\u03bb\u03ac \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03ba\u03c1\u03cd\u03b2\u03bf\u03c5\u03bd \u03c0\u03bf\u03bb\u03cd \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03cc \u03b1\u03c1\u03b9\u03b8\u03bc\u03cc forward passes.<\/p>\n<div class=\"td-badge-row\"><span class=\"td-badge\">\u039a\u03c1\u03c5\u03c6\u03cc compute<\/span><span class=\"td-badge\">\u0386\u03b4\u03b9\u03ba\u03b7 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7<\/span><\/div>\n<\/div>\n<div class=\"td-platform-card td-platform-card--navy\">\n<h3>Actual NFE<\/h3>\n<p>\u0395\u03be\u03b9\u03c3\u03ce\u03bd\u03b5\u03b9 \u03c4\u03b7\u03bd \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03c5\u03c0\u03bf\u03bb\u03bf\u03b3\u03b9\u03c3\u03c4\u03b9\u03ba\u03ae \u03c0\u03c1\u03bf\u03c3\u03c0\u03ac\u03b8\u03b5\u03b9\u03b1 \u03c0\u03c1\u03b9\u03bd \u03b1\u03c0\u03bf\u03b4\u03bf\u03b8\u03b5\u03af \u03b7 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03ac \u03c3\u03c4\u03b7 \u03c3\u03c4\u03c1\u03b1\u03c4\u03b7\u03b3\u03b9\u03ba\u03ae remasking.<\/p>\n<div class=\"td-badge-row\"><span class=\"td-badge\">\u039a\u03bf\u03b9\u03bd\u03cc budget<\/span><span class=\"td-badge\">\u0395\u03bb\u03ad\u03b3\u03be\u03b9\u03bc\u03b7 \u03b2\u03ac\u03c3\u03b7<\/span><\/div>\n<\/div>\n<div class=\"td-platform-card\">\n<h3>\u0388\u03bd\u03b1 metric \u03ba\u03b1\u03b9 \u03ad\u03bd\u03b1 seed<\/h3>\n<p>\u039c\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03bd\u03b1\u03ba\u03b7\u03c1\u03cd\u03be\u03b5\u03b9 \u03bd\u03b9\u03ba\u03b7\u03c4\u03ae \u03c7\u03c9\u03c1\u03af\u03c2 \u03bd\u03b1 \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03ac\u03bb\u03bb\u03b7 \u03b4\u03b9\u03ac\u03c3\u03c4\u03b1\u03c3\u03b7 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2 \u03ae \u03c4\u03b7 \u03c3\u03c4\u03b1\u03c4\u03b9\u03c3\u03c4\u03b9\u03ba\u03ae \u03b1\u03b2\u03b5\u03b2\u03b1\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1.<\/p>\n<div class=\"td-badge-row\"><span class=\"td-badge\">\u03a8\u03b5\u03c5\u03b4\u03ae\u03c2 \u03b1\u03ba\u03c1\u03af\u03b2\u03b5\u03b9\u03b1<\/span><span class=\"td-badge\">\u0391\u03c4\u03b5\u03bb\u03ae\u03c2 \u03b5\u03b9\u03ba\u03cc\u03bd\u03b1<\/span><\/div>\n<\/div>\n<div class=\"td-platform-card td-platform-card--navy\">\n<h3>\u03a0\u03bf\u03bb\u03bb\u03b1\u03c0\u03bb\u03ac metrics \u03ba\u03b1\u03b9 uncertainty<\/h3>\n<p>\u03a3\u03c5\u03bd\u03b4\u03ad\u03b5\u03b9 \u03ba\u03ac\u03b8\u03b5 \u03bc\u03ad\u03c4\u03c1\u03b7\u03c3\u03b7 \u03bc\u03b5 failure mode \u03ba\u03b1\u03b9 \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03b1\u03bd \u03b7 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03ac \u03b5\u03c0\u03b9\u03b2\u03b9\u03ce\u03bd\u03b5\u03b9 \u03c3\u03b5 \u03b5\u03c0\u03b1\u03bd\u03b1\u03bb\u03ae\u03c8\u03b5\u03b9\u03c2 \u03ba\u03b1\u03b9 \u03ba\u03bf\u03b9\u03bd\u03cc sampling.<\/p>\n<div class=\"td-badge-row\"><span class=\"td-badge\">Task fit<\/span><span class=\"td-badge\">\u0391\u03bd\u03b1\u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03b9\u03bc\u03cc\u03c4\u03b7\u03c4\u03b1<\/span><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<h2 id=\"epta-simeia-protokollo-care\">\u03a4\u03bf \u03b5\u03c0\u03c4\u03ac-\u03c3\u03b7\u03bc\u03b5\u03af\u03c9\u03bd \u03c0\u03c1\u03c9\u03c4\u03cc\u03ba\u03bf\u03bb\u03bb\u03bf CaRE<\/h2>\n<div class=\"td-step-list\">\n<p class=\"td-step-list-title\">\u03a4\u03b1 \u03b5\u03c0\u03c4\u03ac \u03c3\u03b7\u03bc\u03b5\u03af\u03b1 \u03c4\u03bf\u03c5 \u03c0\u03c1\u03c9\u03c4\u03bf\u03ba\u03cc\u03bb\u03bb\u03bf\u03c5 CaRE<\/p>\n<ol>\n<li><span class=\"td-step-kicker\">\u03a3\u03b7\u03bc\u03b5\u03af\u03bf 1<\/span><strong>\u0391\u03bd\u03b1\u03c6\u03ad\u03c1\u03b5\u03c4\u03b5 \u03c4\u03bf actual NFE<\/strong>\n<p>\u039c\u03b5\u03c4\u03c1\u03ae\u03c3\u03c4\u03b5 \u03c4\u03b9\u03c2 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03ae\u03c3\u03b5\u03b9\u03c2 \u03c4\u03bf\u03c5 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5 \u03ce\u03c3\u03c4\u03b5 \u03c4\u03bf \u03ba\u03bf\u03b9\u03bd\u03cc nominal step budget \u03bd\u03b1 \u03bc\u03b7 \u03c3\u03c5\u03b3\u03ba\u03b1\u03bb\u03cd\u03c0\u03c4\u03b5\u03b9 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03cc compute.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u03a3\u03b7\u03bc\u03b5\u03af\u03bf 2<\/span><strong>\u03a7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ae\u03c3\u03c4\u03b5 \u03c0\u03bf\u03bb\u03bb\u03b1\u03c0\u03bb\u03ac metrics<\/strong>\n<p>\u0394\u03b9\u03b1\u03b2\u03ac\u03c3\u03c4\u03b5 \u03bc\u03b1\u03b6\u03af perplexity, MAUVE \u03ba\u03b1\u03b9 \u03c4\u03b9\u03c2 \u03c5\u03c0\u03cc\u03bb\u03bf\u03b9\u03c0\u03b5\u03c2 \u03c3\u03c7\u03b5\u03c4\u03b9\u03ba\u03ad\u03c2 \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b5\u03b9\u03c2, \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03b4\u03b5\u03bd \u03b5\u03c0\u03b9\u03b2\u03c1\u03b1\u03b2\u03b5\u03cd\u03bf\u03c5\u03bd \u03c0\u03ac\u03bd\u03c4\u03b1 \u03c4\u03b7\u03bd \u03af\u03b4\u03b9\u03b1 \u03c3\u03c5\u03bc\u03c0\u03b5\u03c1\u03b9\u03c6\u03bf\u03c1\u03ac.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u03a3\u03b7\u03bc\u03b5\u03af\u03bf 3<\/span><strong>\u039a\u03c1\u03b1\u03c4\u03ae\u03c3\u03c4\u03b5 \u03ba\u03bf\u03b9\u03bd\u03cc \u03b5\u03be\u03c9\u03c4\u03b5\u03c1\u03b9\u03ba\u03cc evaluator<\/strong>\n<p>\u03a5\u03c0\u03bf\u03bb\u03bf\u03b3\u03af\u03c3\u03c4\u03b5 \u03c4\u03bf perplexity \u03bc\u03b5 \u03c4\u03b7\u03bd \u03af\u03b4\u03b9\u03b1 \u03b1\u03bd\u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c4\u03b7 \u03b2\u03ac\u03c3\u03b7, \u03ce\u03c3\u03c4\u03b5 \u03ba\u03bf\u03b9\u03bd\u03cc \u03bb\u03b5\u03be\u03b9\u03bb\u03cc\u03b3\u03b9\u03bf \u03ae evaluator \u03bd\u03b1 \u03bc\u03b7\u03bd \u03b5\u03c5\u03bd\u03bf\u03b5\u03af \u03bc\u03af\u03b1 \u03bc\u03ad\u03b8\u03bf\u03b4\u03bf.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u03a3\u03b7\u03bc\u03b5\u03af\u03bf 4<\/span><strong>\u0395\u03bb\u03ad\u03b3\u03be\u03c4\u03b5 \u03c1\u03b7\u03c4\u03ac \u03c4\u03b7 stochasticity<\/strong>\n<p>\u0394\u03b7\u03bb\u03ce\u03c3\u03c4\u03b5 \u03ba\u03b1\u03b9 \u03c3\u03b1\u03c1\u03ce\u03c3\u03c4\u03b5 \u03c4\u03b7\u03bd unmask temperature \u03b1\u03bd\u03c4\u03af \u03bd\u03b1 \u03c4\u03b7 \u03bc\u03b5\u03c4\u03b1\u03c7\u03b5\u03b9\u03c1\u03af\u03b6\u03b5\u03c3\u03c4\u03b5 \u03c9\u03c2 \u03b1\u03cc\u03c1\u03b1\u03c4\u03b7 \u03bb\u03b5\u03c0\u03c4\u03bf\u03bc\u03ad\u03c1\u03b5\u03b9\u03b1 \u03c4\u03bf\u03c5 sampling.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u03a3\u03b7\u03bc\u03b5\u03af\u03bf 5<\/span><strong>\u03a6\u03b9\u03bb\u03c4\u03c1\u03ac\u03c1\u03b5\u03c4\u03b5 \u03c4\u03b7\u03bd off-target \u03b3\u03bb\u03ce\u03c3\u03c3\u03b1<\/strong>\n<p>\u0395\u03bb\u03ad\u03b3\u03be\u03c4\u03b5 \u03c4\u03b7 \u03b3\u03bb\u03c9\u03c3\u03c3\u03b9\u03ba\u03ae \u03ba\u03b1\u03c4\u03b1\u03bb\u03bb\u03b7\u03bb\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c4\u03c9\u03bd samples, \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03b7 \u03c0\u03bf\u03bb\u03c5\u03b3\u03bb\u03c9\u03c3\u03c3\u03b9\u03ba\u03ae \u03b4\u03b9\u03b1\u03c1\u03c1\u03bf\u03ae \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03bb\u03bb\u03bf\u03b9\u03ce\u03c3\u03b5\u03b9 \u03c4\u03bf MAUVE.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u03a3\u03b7\u03bc\u03b5\u03af\u03bf 6<\/span><strong>\u03a3\u03c5\u03bc\u03c0\u03b5\u03c1\u03b9\u03bb\u03ac\u03b2\u03b5\u03c4\u03b5 baseline \u03c7\u03c9\u03c1\u03af\u03c2 remasking<\/strong>\n<p>\u03a3\u03c5\u03b3\u03ba\u03c1\u03af\u03bd\u03b5\u03c4\u03b5 \u03ba\u03ac\u03b8\u03b5 \u03c3\u03c4\u03c1\u03b1\u03c4\u03b7\u03b3\u03b9\u03ba\u03ae \u03bc\u03b5 \u03c4\u03bf none baseline \u03b3\u03b9\u03b1 \u03bd\u03b1 \u03c6\u03b1\u03bd\u03b5\u03af \u03b1\u03bd \u03c4\u03bf \u03c0\u03c1\u03cc\u03c3\u03b8\u03b5\u03c4\u03bf compute \u03c0\u03c1\u03bf\u03c3\u03c6\u03ad\u03c1\u03b5\u03b9 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03cc\u03c6\u03b5\u03bb\u03bf\u03c2.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">\u03a3\u03b7\u03bc\u03b5\u03af\u03bf 7<\/span><strong>\u0391\u03bd\u03b1\u03c6\u03ad\u03c1\u03b5\u03c4\u03b5 \u03b1\u03b2\u03b5\u03b2\u03b1\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1<\/strong>\n<p>\u03a7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ae\u03c3\u03c4\u03b5 \u03b5\u03c0\u03b1\u03bd\u03b1\u03bb\u03ae\u03c8\u03b5\u03b9\u03c2, seeds \u03ba\u03b1\u03b9 \u03b4\u03b9\u03b1\u03c3\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1 \u03b1\u03b2\u03b5\u03b2\u03b1\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2 \u03b1\u03bd\u03c4\u03af \u03bd\u03b1 \u03c0\u03b1\u03c1\u03bf\u03c5\u03c3\u03b9\u03ac\u03b6\u03b5\u03c4\u03b5 \u03ad\u03bd\u03b1 single-seed \u03b4\u03b5\u03ba\u03b1\u03b4\u03b9\u03ba\u03cc \u03c9\u03c2 \u03b2\u03ad\u03b2\u03b1\u03b9\u03bf \u03bd\u03b9\u03ba\u03b7\u03c4\u03ae.<\/p>\n<\/li>\n<\/ol>\n<\/div>\n<p>\u03a4\u03bf \u03c4\u03b5\u03bb\u03b5\u03c5\u03c4\u03b1\u03af\u03bf \u03c3\u03b7\u03bc\u03b5\u03af\u03bf \u03ad\u03c7\u03b5\u03b9 \u03b9\u03b4\u03b9\u03b1\u03af\u03c4\u03b5\u03c1\u03b7 \u03c3\u03b7\u03bc\u03b1\u03c3\u03af\u03b1. \u03a4\u03bf paper \u03c3\u03b7\u03bc\u03b5\u03b9\u03ce\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03ad\u03c2 MAUVE \u03ba\u03ac\u03c4\u03c9 \u03b1\u03c0\u03cc 0,1 \u03c3\u03b5 single-seed runs \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03bc\u03b7\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03be\u03b9\u03cc\u03c0\u03b9\u03c3\u03c4\u03b5\u03c2, \u03bc\u03b5 standard error \u03c0\u03bf\u03c5 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c6\u03c4\u03ac\u03c3\u03b5\u03b9 \u03c4\u03bf 0,05. \u0397 \u03b1\u03bd\u03b1\u03c6\u03bf\u03c1\u03ac \u03b5\u03bd\u03cc\u03c2 \u03b4\u03b5\u03ba\u03b1\u03b4\u03b9\u03ba\u03bf\u03cd \u00ab\u03bd\u03b9\u03ba\u03b7\u03c4\u03ae\u00bb \u03c7\u03c9\u03c1\u03af\u03c2 confidence interval \u03ae \u03b5\u03c0\u03b1\u03bd\u03b1\u03bb\u03ae\u03c8\u03b5\u03b9\u03c2 \u03b4\u03b7\u03bc\u03b9\u03bf\u03c5\u03c1\u03b3\u03b5\u03af \u03c8\u03b5\u03c5\u03b4\u03ae \u03b1\u03ba\u03c1\u03af\u03b2\u03b5\u03b9\u03b1.<\/p>\n<p>\u0393\u03b9\u03b1 \u03bf\u03bc\u03ac\u03b4\u03b5\u03c2 procurement \u03ae AI governance, \u03b1\u03c5\u03c4\u03ac \u03c4\u03b1 \u03b5\u03c0\u03c4\u03ac \u03c3\u03b7\u03bc\u03b5\u03af\u03b1 \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03bc\u03b5\u03c4\u03b1\u03c4\u03c1\u03b1\u03c0\u03bf\u03cd\u03bd \u03c3\u03b5 checklist \u03c0\u03c1\u03bf\u03bc\u03b7\u03b8\u03b5\u03c5\u03c4\u03ae: \u03c0\u03bf\u03b9\u03bf \u03ae\u03c4\u03b1\u03bd \u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc compute, \u03c0\u03bf\u03b9\u03b5\u03c2 \u03c1\u03c5\u03b8\u03bc\u03af\u03c3\u03b5\u03b9\u03c2 sampling \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ae\u03b8\u03b7\u03ba\u03b1\u03bd, \u03c0\u03bf\u03b9\u03bf baseline \u03bc\u03c0\u03ae\u03ba\u03b5 \u03c3\u03c4\u03b7 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7, \u03c0\u03cc\u03c3\u03b5\u03c2 \u03b5\u03c0\u03b1\u03bd\u03b1\u03bb\u03ae\u03c8\u03b5\u03b9\u03c2 \u03ad\u03b3\u03b9\u03bd\u03b1\u03bd \u03ba\u03b1\u03b9 \u03c0\u03bf\u03b9\u03b1 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 \u03b1\u03c0\u03bf\u03ba\u03bb\u03b5\u03af\u03c3\u03c4\u03b7\u03ba\u03b1\u03bd. \u0391\u03bd \u03bf \u03c0\u03c1\u03bf\u03bc\u03b7\u03b8\u03b5\u03c5\u03c4\u03ae\u03c2 \u03b4\u03b5\u03bd \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03c0\u03b1\u03bd\u03c4\u03ae\u03c3\u03b5\u03b9, \u03c4\u03bf headline score \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c1\u03ba\u03b5\u03c4\u03cc \u03b3\u03b9\u03b1 \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7.<\/p>\n<h2 id=\"epivevaiosi-montela-datasets\">\u03a4\u03b9 \u03b5\u03c0\u03b9\u03b2\u03b5\u03b2\u03b1\u03b9\u03ce\u03b8\u03b7\u03ba\u03b5 \u03c0\u03ad\u03c1\u03b1 \u03b1\u03c0\u03cc \u03ad\u03bd\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03ba\u03b1\u03b9 \u03ad\u03bd\u03b1 dataset<\/h2>\n<p>\u0397 \u03b2\u03b1\u03c3\u03b9\u03ba\u03ae \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 \u03ba\u03b1\u03bb\u03cd\u03c0\u03c4\u03b5\u03b9 LLaDA-8B-Base \u03ba\u03b1\u03b9 Dream-7B-Base, \u03b5\u03bd\u03ce \u03c4\u03bf leaderboard \u03b5\u03c0\u03b5\u03ba\u03c4\u03b5\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 12 open-weight MDLMs \u03b1\u03c0\u03cc 150 \u03b5\u03ba\u03b1\u03c4\u03bf\u03bc\u03bc\u03cd\u03c1\u03b9\u03b1 \u03ad\u03c9\u03c2 8 \u03b4\u03b9\u03c3\u03b5\u03ba\u03b1\u03c4\u03bf\u03bc\u03bc\u03cd\u03c1\u03b9\u03b1 \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03c4\u03c1\u03bf\u03c5\u03c2. \u0397 \u03ba\u03b1\u03c4\u03b5\u03cd\u03b8\u03c5\u03bd\u03c3\u03b7 \u03c4\u03b7\u03c2 \u03b1\u03bb\u03bb\u03b7\u03bb\u03b5\u03c0\u03af\u03b4\u03c1\u03b1\u03c3\u03b7\u03c2 \u03b5\u03bc\u03c6\u03b1\u03bd\u03af\u03c3\u03c4\u03b7\u03ba\u03b5 \u03c3\u03b5 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b1\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03ad\u03c2 \u03ba\u03b1\u03b9 \u03ba\u03bb\u03af\u03bc\u03b1\u03ba\u03b5\u03c2. \u039f\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03b5\u03be\u03ad\u03c4\u03b1\u03c3\u03b1\u03bd \u03b5\u03c0\u03af\u03c3\u03b7\u03c2 OpenWebText \u03ba\u03b1\u03b9 LM1B.<\/p>\n<p>\u03a3\u03c4\u03bf HumanEval, \u03c4\u03bf NFE confound \u03b5\u03bc\u03c6\u03b1\u03bd\u03af\u03c3\u03c4\u03b7\u03ba\u03b5 \u03ba\u03b1\u03b9 \u03c3\u03c4\u03b7\u03bd \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae \u03ba\u03ce\u03b4\u03b9\u03ba\u03b1. \u03a3\u03b5 compute-matched NFE 437, \u03b7 none baseline \u03c4\u03b1\u03af\u03c1\u03b9\u03b1\u03be\u03b5 \u03ae \u03be\u03b5\u03c0\u03ad\u03c1\u03b1\u03c3\u03b5 \u03c4\u03b9\u03c2 \u03c3\u03c4\u03c1\u03b1\u03c4\u03b7\u03b3\u03b9\u03ba\u03ad\u03c2 remasking \u03c3\u03c4\u03b9\u03c2 \u03b1\u03bd\u03b1\u03c6\u03b5\u03c1\u03cc\u03bc\u03b5\u03bd\u03b5\u03c2 \u03c3\u03c5\u03bd\u03b8\u03ae\u03ba\u03b5\u03c2. \u03a4\u03bf paper \u03b5\u03c0\u03b9\u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03c0\u03c1\u03bf\u03c3\u03b5\u03ba\u03c4\u03b9\u03ba\u03ac \u03cc\u03c4\u03b9 \u03c4\u03bf seed variance \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 \u00b10,3 \u03c0\u03bf\u03c3\u03bf\u03c3\u03c4\u03b9\u03b1\u03af\u03b5\u03c2 \u03bc\u03bf\u03bd\u03ac\u03b4\u03b5\u03c2 \u03b4\u03b5\u03bd \u03c0\u03b5\u03c1\u03b9\u03bb\u03b1\u03bc\u03b2\u03ac\u03bd\u03b5\u03b9 \u03cc\u03bb\u03b7 \u03c4\u03b7\u03bd benchmark uncertainty: \u03bc\u03b5 n=100, \u03b7 \u03b4\u03b9\u03c9\u03bd\u03c5\u03bc\u03b9\u03ba\u03ae \u03b1\u03b2\u03b5\u03b2\u03b1\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03b5\u03ba\u03c4\u03b9\u03bc\u03ac\u03c4\u03b1\u03b9 \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 \u00b14,7 \u03bc\u03bf\u03bd\u03ac\u03b4\u03b5\u03c2.<\/p>\n<p>\u0386\u03bb\u03bb\u03b1 benchmarks, \u03cc\u03c0\u03c9\u03c2 HellaSwag \u03ba\u03b1\u03b9 BBH, \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ae\u03b8\u03b7\u03ba\u03b1\u03bd \u03c9\u03c2 sanity checks \u03ba\u03b1\u03b9 \u03c0\u03b1\u03c1\u03ad\u03bc\u03b5\u03b9\u03bd\u03b1\u03bd \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03ac \u03b1\u03c0\u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 \u03c3\u03c4\u03b9\u03c2 \u03b1\u03bb\u03bb\u03b1\u03b3\u03ad\u03c2 inference \u03c0\u03bf\u03c5 \u03bc\u03b5\u03bb\u03b5\u03c4\u03ae\u03b8\u03b7\u03ba\u03b1\u03bd. \u0391\u03c5\u03c4\u03cc \u03b2\u03bf\u03b7\u03b8\u03ac \u03bd\u03b1 \u03b1\u03c0\u03bf\u03bc\u03bf\u03bd\u03c9\u03b8\u03b5\u03af \u03cc\u03c4\u03b9 \u03bf\u03b9 \u03c0\u03b1\u03c1\u03b5\u03bc\u03b2\u03ac\u03c3\u03b5\u03b9\u03c2 \u03b5\u03c0\u03b7\u03c1\u03b5\u03ac\u03b6\u03bf\u03c5\u03bd \u03c4\u03b7 \u03b4\u03b9\u03b1\u03b4\u03b9\u03ba\u03b1\u03c3\u03af\u03b1 generation \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03b1\u03bd\u03b1\u03b3\u03ba\u03b1\u03c3\u03c4\u03b9\u03ba\u03ac \u03ba\u03ac\u03b8\u03b5 \u03b9\u03ba\u03b1\u03bd\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c4\u03bf\u03c5 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5.<\/p>\n<h2 id=\"epicheirisi-state-of-the-art\">\u03a0\u03ce\u03c2 \u03bd\u03b1 \u03b4\u03b9\u03b1\u03b2\u03ac\u03b6\u03b5\u03b9 \u03bc\u03b9\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03af\u03c1\u03b7\u03c3\u03b7 \u03c4\u03bf\u03c5\u03c2 \u03b9\u03c3\u03c7\u03c5\u03c1\u03b9\u03c3\u03bc\u03bf\u03cd\u03c2 \u00abstate of the art\u00bb<\/h2>\n<p>\u03a4\u03bf \u03c0\u03c1\u03ce\u03c4\u03bf \u03b5\u03c1\u03ce\u03c4\u03b7\u03bc\u03b1 \u03b4\u03b5\u03bd \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u00ab\u03c0\u03bf\u03b9\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03c1\u03ce\u03c4\u03bf;\u00bb, \u03b1\u03bb\u03bb\u03ac \u00ab\u03c5\u03c0\u03cc \u03c0\u03bf\u03b9\u03b5\u03c2 \u03b9\u03c3\u03cc\u03c4\u03b9\u03bc\u03b5\u03c2 \u03c3\u03c5\u03bd\u03b8\u03ae\u03ba\u03b5\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03c1\u03ce\u03c4\u03bf;\u00bb. \u0396\u03b7\u03c4\u03ae\u03c3\u03c4\u03b5 \u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc compute \u03b1\u03bd\u03ac \u03c0\u03b1\u03c1\u03b1\u03b3\u03cc\u03bc\u03b5\u03bd\u03bf output \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03bc\u03cc\u03bd\u03bf \u03c4\u03bf\u03bd \u03b8\u03b5\u03c9\u03c1\u03b7\u03c4\u03b9\u03ba\u03cc \u03b1\u03c1\u03b9\u03b8\u03bc\u03cc steps. \u0397 \u03af\u03b4\u03b9\u03b1 \u03b1\u03c1\u03c7\u03ae \u03b9\u03c3\u03c7\u03cd\u03b5\u03b9 \u03cc\u03c4\u03b1\u03bd \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03bf\u03cd\u03bd\u03c4\u03b1\u03b9 \u03b2\u03b5\u03bb\u03c4\u03b9\u03c3\u03c4\u03bf\u03c0\u03bf\u03b9\u03ae\u03c3\u03b5\u03b9\u03c2 \u03c5\u03c0\u03bf\u03b4\u03bf\u03bc\u03ae\u03c2, \u03cc\u03c0\u03c9\u03c2 \u03c4\u03bf <a href=\"https:\/\/twodots.gr\/glide-yvridiki-prosochi-meionei-kostos-llm-megalou-context\/\">GLIDE \u03b3\u03b9\u03b1 long-context LLMs<\/a>: \u03b7 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03bc\u03ad\u03c4\u03c1\u03b7\u03c3\u03b7 \u03c3\u03c4\u03bf workload \u03c0\u03c1\u03bf\u03b7\u03b3\u03b5\u03af\u03c4\u03b1\u03b9 \u03c4\u03bf\u03c5 headline. \u0395\u03bb\u03ad\u03b3\u03be\u03c4\u03b5 \u03b1\u03bd \u03cc\u03bb\u03b5\u03c2 \u03bf\u03b9 \u03bc\u03ad\u03b8\u03bf\u03b4\u03bf\u03b9 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b1\u03bd \u03af\u03b4\u03b9\u03bf sampling, \u03af\u03b4\u03b9\u03bf evaluator \u03ba\u03b1\u03b9 \u03af\u03b4\u03b9\u03b1 preprocessing \u03c6\u03af\u03bb\u03c4\u03c1\u03b1.<\/p>\n<p>\u0394\u03b5\u03cd\u03c4\u03b5\u03c1\u03bf\u03bd, \u03be\u03b5\u03c7\u03c9\u03c1\u03af\u03c3\u03c4\u03b5 \u03c4\u03b7\u03bd \u03b5\u03c1\u03b5\u03c5\u03bd\u03b7\u03c4\u03b9\u03ba\u03ae \u03bc\u03ad\u03c4\u03c1\u03b7\u03c3\u03b7 \u03b1\u03c0\u03cc \u03c4\u03bf production KPI. \u03a4\u03bf MAUVE \u03ba\u03b1\u03b9 \u03c4\u03bf perplexity \u03b5\u03af\u03bd\u03b1\u03b9 \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03b1 \u03b3\u03b9\u03b1 \u03c4\u03b7 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 \u03b4\u03b9\u03b1\u03bd\u03bf\u03bc\u03ae\u03c2 \u03ba\u03b1\u03b9 fluency, \u03b1\u03bb\u03bb\u03ac \u03b4\u03b5\u03bd \u03b1\u03bd\u03c4\u03b9\u03ba\u03b1\u03b8\u03b9\u03c3\u03c4\u03bf\u03cd\u03bd conversion quality, factuality, compliance, response time \u03ae \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03b7 \u03c0\u03c1\u03bf\u03c4\u03af\u03bc\u03b7\u03c3\u03b7 \u03c3\u03c4\u03bf \u03b4\u03b9\u03ba\u03cc \u03c3\u03b1\u03c2 \u03c0\u03b5\u03c1\u03b9\u03b2\u03ac\u03bb\u03bb\u03bf\u03bd. \u0388\u03bd\u03b1 benchmark \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03bc\u03b5\u03b8\u03bf\u03b4\u03bf\u03bb\u03bf\u03b3\u03b9\u03ba\u03ac \u03c3\u03c9\u03c3\u03c4\u03cc \u03ba\u03b1\u03b9 \u03c0\u03b1\u03c1\u2019 \u03cc\u03bb\u03b1 \u03b1\u03c5\u03c4\u03ac \u03bd\u03b1 \u03bc\u03b7\u03bd \u03b1\u03c0\u03b1\u03bd\u03c4\u03ac \u03c3\u03c4\u03b7 \u03b4\u03b9\u03ba\u03ae \u03c3\u03b1\u03c2 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03b5\u03c1\u03ce\u03c4\u03b7\u03c3\u03b7.<\/p>\n<p>\u03a4\u03c1\u03af\u03c4\u03bf\u03bd, \u03b6\u03b7\u03c4\u03ae\u03c3\u03c4\u03b5 Pareto front \u03b1\u03bd\u03c4\u03af \u03b3\u03b9\u03b1 \u03bc\u03bf\u03bd\u03bf\u03b4\u03b9\u03ac\u03c3\u03c4\u03b1\u03c4\u03b7 \u03ba\u03b1\u03c4\u03ac\u03c4\u03b1\u03be\u03b7: \u03c0\u03bf\u03b9\u03b5\u03c2 \u03b4\u03b9\u03b1\u03bc\u03bf\u03c1\u03c6\u03ce\u03c3\u03b5\u03b9\u03c2 \u03b4\u03af\u03bd\u03bf\u03c5\u03bd \u03c4\u03bf \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf trade-off \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2 \u03ba\u03b1\u03b9 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c5\u03c2; \u03a4\u03bf \u03af\u03b4\u03b9\u03bf \u03c4\u03bf CaRE \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03b1\u03ba\u03cc\u03bc\u03b7 \u03ba\u03b1\u03b9 \u03bc\u03b9\u03b1 PPL\u2013entropy \u03ae PPL\u2013MAUVE \u03b1\u03c0\u03b5\u03b9\u03ba\u03cc\u03bd\u03b9\u03c3\u03b7 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03ba\u03c1\u03cd\u03c8\u03b5\u03b9 \u03c4\u03b7\u03bd \u03b1\u03bb\u03bb\u03b7\u03bb\u03b5\u03c0\u03af\u03b4\u03c1\u03b1\u03c3\u03b7 strategy \u00d7 temperature. \u0386\u03c1\u03b1 \u03c4\u03bf dashboard \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b5\u03c0\u03b9\u03c4\u03c1\u03ad\u03c0\u03b5\u03b9 drill-down \u03c3\u03c4\u03b9\u03c2 \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03c4\u03c1\u03bf\u03c5\u03c2, \u03cc\u03c7\u03b9 \u03bc\u03cc\u03bd\u03bf \u03bd\u03b1 \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03bc\u03b9\u03b1 \u03c3\u03c5\u03bd\u03bf\u03bb\u03b9\u03ba\u03ae \u03ba\u03b1\u03bc\u03c0\u03cd\u03bb\u03b7.<\/p>\n<h2 id=\"oria-care-symperasmata\">\u03a4\u03b1 \u03cc\u03c1\u03b9\u03b1 \u03c4\u03bf\u03c5 CaRE \u03ba\u03b1\u03b9 \u03c4\u03b9 \u03b4\u03b5\u03bd \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c3\u03c5\u03bc\u03c0\u03b5\u03c1\u03ac\u03bd\u03bf\u03c5\u03bc\u03b5<\/h2>\n<p>\u03a4\u03bf CaRE \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03bb\u03b1\u03af\u03c3\u03b9\u03bf \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7\u03c2, \u03cc\u03c7\u03b9 \u03bd\u03ad\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03c4\u03b5\u03c7\u03bd\u03b9\u03ba\u03ae \u03c0\u03bf\u03c5 \u03b1\u03c5\u03be\u03ac\u03bd\u03b5\u03b9 \u03ac\u03bc\u03b5\u03c3\u03b1 \u03b4\u03c5\u03bd\u03b1\u03c4\u03cc\u03c4\u03b7\u03c4\u03b5\u03c2. \u0395\u03c3\u03c4\u03b9\u03ac\u03b6\u03b5\u03b9 \u03c3\u03c4\u03b1 masked discrete-token diffusion language models. \u039f\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03b4\u03b7\u03bb\u03ce\u03bd\u03bf\u03c5\u03bd \u03c1\u03b7\u03c4\u03ac \u03cc\u03c4\u03b9 \u03c4\u03bf \u03c0\u03c1\u03c9\u03c4\u03cc\u03ba\u03bf\u03bb\u03bb\u03bf \u03b4\u03b5\u03bd \u03b5\u03c6\u03b1\u03c1\u03bc\u03cc\u03b6\u03b5\u03c4\u03b1\u03b9 \u03b1\u03c5\u03c4\u03bf\u03cd\u03c3\u03b9\u03bf \u03c3\u03b5 continuous-state diffusion LMs \u03ae flow-based language models, \u03c4\u03b1 \u03bf\u03c0\u03bf\u03af\u03b1 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ae \u03bb\u03bf\u03b3\u03b9\u03c3\u03c4\u03b9\u03ba\u03ae compute \u03ba\u03b1\u03b9 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac metrics.<\/p>\n<p>\u0395\u03c0\u03af\u03c3\u03b7\u03c2, \u03c4\u03bf paper \u03b4\u03b5\u03bd \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03c4\u03bf remasking \u03b4\u03b5\u03bd \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c0\u03c1\u03bf\u03c3\u03c6\u03ad\u03c1\u03b5\u03b9 \u03b1\u03be\u03af\u03b1. \u0394\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03b7 \u03b1\u03be\u03af\u03b1 \u03c4\u03bf\u03c5 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03bc\u03b5\u03c4\u03c1\u03b9\u03ad\u03c4\u03b1\u03b9 \u03ba\u03ac\u03c4\u03c9 \u03b1\u03c0\u03cc \u03ba\u03bf\u03b9\u03bd\u03ad\u03c2 \u03c3\u03c5\u03bd\u03b8\u03ae\u03ba\u03b5\u03c2, \u03bc\u03b5 \u03ba\u03b1\u03c4\u03ac\u03bb\u03bb\u03b7\u03bb\u03bf baseline \u03ba\u03b1\u03b9 uncertainty. \u03a4\u03b1 \u03b5\u03c5\u03c1\u03ae\u03bc\u03b1\u03c4\u03b1 \u03b1\u03c6\u03bf\u03c1\u03bf\u03cd\u03bd \u03c4\u03b9\u03c2 \u03bc\u03b5\u03bb\u03b5\u03c4\u03b7\u03bc\u03ad\u03bd\u03b5\u03c2 \u03b1\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03ad\u03c2, datasets, budgets \u03ba\u03b1\u03b9 temperatures. \u0397 \u03b3\u03b5\u03bd\u03af\u03ba\u03b5\u03c5\u03c3\u03b7 \u03c3\u03b5 \u03ba\u03ac\u03b8\u03b5 production workload \u03b8\u03b1 \u03ae\u03c4\u03b1\u03bd unsupported conclusion.<\/p>\n<p>\u03a4\u03ad\u03bb\u03bf\u03c2, \u03b7 \u03c4\u03c5\u03c0\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03ad\u03c7\u03b5\u03b9 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2. \u039f\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03b1\u03bd\u03b1\u03b3\u03bd\u03c9\u03c1\u03af\u03b6\u03bf\u03c5\u03bd \u03cc\u03c4\u03b9 \u03ad\u03bd\u03b1 \u03b1\u03c5\u03c3\u03c4\u03b7\u03c1\u03cc protocol \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b3\u03af\u03bd\u03b5\u03b9 \u03b5\u03bc\u03c0\u03cc\u03b4\u03b9\u03bf \u03b3\u03b9\u03b1 \u03bc\u03b9\u03ba\u03c1\u03cc\u03c4\u03b5\u03c1\u03b5\u03c2 \u03b5\u03c1\u03b5\u03c5\u03bd\u03b7\u03c4\u03b9\u03ba\u03ad\u03c2 \u03bf\u03bc\u03ac\u03b4\u03b5\u03c2. \u0393\u03b9\u2019 \u03b1\u03c5\u03c4\u03cc \u03c0\u03b1\u03c1\u03bf\u03c5\u03c3\u03b9\u03ac\u03b6\u03bf\u03c5\u03bd \u03c4\u03b1 \u03b5\u03c0\u03c4\u03ac practices \u03c9\u03c2 \u03b1\u03bd\u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c4\u03b1 \u03b1\u03bd\u03b1\u03c6\u03b5\u03c1\u03cc\u03bc\u03b5\u03bd\u03b1 \u03c3\u03c4\u03bf\u03b9\u03c7\u03b5\u03af\u03b1 \u03ba\u03b1\u03b9 \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03bf\u03c5\u03bd \u03cc\u03c4\u03b9 \u03b7 \u03c0\u03c1\u03bf\u03c3\u03b8\u03ae\u03ba\u03b7 \u03bd\u03ad\u03bf\u03c5 MDLM \u03c3\u03c4\u03bf framework \u03b1\u03c0\u03b1\u03b9\u03c4\u03b5\u03af \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 50 \u03b3\u03c1\u03b1\u03bc\u03bc\u03ad\u03c2 \u03ba\u03ce\u03b4\u03b9\u03ba\u03b1.<\/p>\n<h2 id=\"benchmark-governance-ai-governance\">\u0391\u03c0\u03cc \u03c4\u03bf benchmark governance \u03c3\u03c4\u03bf AI governance<\/h2>\n<p>\u0397 \u03b2\u03b1\u03b8\u03cd\u03c4\u03b5\u03c1\u03b7 \u03b1\u03be\u03af\u03b1 \u03c4\u03bf\u03c5 CaRE \u03b2\u03c1\u03af\u03c3\u03ba\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c4\u03b7 \u03b4\u03b9\u03b1\u03c6\u03ac\u03bd\u03b5\u03b9\u03b1 \u03c4\u03c9\u03bd \u03c3\u03c5\u03b3\u03ba\u03c1\u03af\u03c3\u03b5\u03c9\u03bd. \u0391\u03bd \u03ad\u03bd\u03b1 \u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1 \u03ba\u03b5\u03c1\u03b4\u03af\u03b6\u03b5\u03b9 \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf compute, \u03b1\u03c5\u03c4\u03cc \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c0\u03bf\u03b4\u03b5\u03ba\u03c4\u03cc \u2014 \u03b1\u03c1\u03ba\u03b5\u03af \u03bd\u03b1 \u03b4\u03b7\u03bb\u03ce\u03bd\u03b5\u03c4\u03b1\u03b9. \u0391\u03bd \u03ba\u03b5\u03c1\u03b4\u03af\u03b6\u03b5\u03b9 \u03bc\u03cc\u03bd\u03bf \u03c3\u03b5 \u03ad\u03bd\u03b1 metric, \u03b5\u03c0\u03af\u03c3\u03b7\u03c2 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03bf \u2014 \u03b1\u03c1\u03ba\u03b5\u03af \u03c4\u03bf metric \u03bd\u03b1 \u03c3\u03c5\u03bd\u03b4\u03ad\u03b5\u03c4\u03b1\u03b9 \u03bc\u03b5 \u03c4\u03bf\u03bd \u03c3\u03c4\u03cc\u03c7\u03bf. \u03a4\u03bf \u03c0\u03c1\u03cc\u03b2\u03bb\u03b7\u03bc\u03b1 \u03b1\u03c1\u03c7\u03af\u03b6\u03b5\u03b9 \u03cc\u03c4\u03b1\u03bd \u03b1\u03c5\u03c4\u03ad\u03c2 \u03bf\u03b9 \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ad\u03c2 \u03ba\u03c1\u03cd\u03b2\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c0\u03af\u03c3\u03c9 \u03b1\u03c0\u03cc \u03ad\u03bd\u03b1\u03bd \u03b5\u03bd\u03b9\u03b1\u03af\u03bf \u03b9\u03c3\u03c7\u03c5\u03c1\u03b9\u03c3\u03bc\u03cc \u03c0\u03c1\u03bf\u03cc\u03b4\u03bf\u03c5.<\/p>\n<p>\u0393\u03b9\u03b1 marketers, e-commerce owners \u03ba\u03b1\u03b9 \u03c3\u03c4\u03b5\u03bb\u03ad\u03c7\u03b7, \u03c4\u03bf \u03bc\u03ac\u03b8\u03b7\u03bc\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03c1\u03b1\u03ba\u03c4\u03b9\u03ba\u03cc: \u03bc\u03b7\u03bd \u03b1\u03b3\u03bf\u03c1\u03ac\u03b6\u03b5\u03c4\u03b5 \u00abAI performance\u00bb \u03c9\u03c2 \u03b1\u03c6\u03b7\u03c1\u03b7\u03bc\u03ad\u03bd\u03b7 \u03c5\u03c0\u03cc\u03c3\u03c7\u03b5\u03c3\u03b7. \u0391\u03b3\u03bf\u03c1\u03ac\u03b6\u03b5\u03c4\u03b5 \u03bc\u03b9\u03b1 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b7 \u03b4\u03b9\u03b1\u03bc\u03cc\u03c1\u03c6\u03c9\u03c3\u03b7 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5, sampling, \u03c5\u03c0\u03bf\u03b4\u03bf\u03bc\u03ae\u03c2 \u03ba\u03b1\u03b9 \u03b5\u03bb\u03ad\u03b3\u03c7\u03c9\u03bd. \u0397 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b1\u03bd\u03b1\u03c0\u03b1\u03c1\u03ac\u03b3\u03b5\u03b9 \u03b1\u03c5\u03c4\u03ae \u03c4\u03b7 \u03b4\u03b9\u03b1\u03bc\u03cc\u03c1\u03c6\u03c9\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03bd\u03b1 \u03b1\u03c0\u03bf\u03c4\u03c5\u03c0\u03ce\u03bd\u03b5\u03b9 \u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03c4\u03b7\u03c2.<\/p>\n<p>\u03a4\u03bf CaRE \u03c0\u03c1\u03bf\u03c4\u03b5\u03af\u03bd\u03b5\u03b9 \u03ad\u03bd\u03b1\u03bd \u03c0\u03b9\u03bf \u03ce\u03c1\u03b9\u03bc\u03bf \u03c4\u03c1\u03cc\u03c0\u03bf \u03c3\u03c5\u03b6\u03ae\u03c4\u03b7\u03c3\u03b7\u03c2: \u03b1\u03c0\u03cc \u03c4\u03bf \u00ab\u03c0\u03bf\u03b9\u03bf\u03c2 \u03ba\u03ad\u03c1\u03b4\u03b9\u03c3\u03b5;\u00bb \u03c3\u03c4\u03bf \u00ab\u03c0\u03bf\u03b9\u03b1 \u03c5\u03c0\u03cc\u03b8\u03b5\u03c3\u03b7 \u03b5\u03bb\u03ad\u03b3\u03be\u03b1\u03bc\u03b5, \u03bc\u03b5 \u03c0\u03bf\u03b9\u03bf budget, \u03c0\u03bf\u03b9\u03b1 \u03b1\u03b2\u03b5\u03b2\u03b1\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03ba\u03b1\u03b9 \u03c0\u03bf\u03b9\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03c3\u03c5\u03bd\u03ad\u03c0\u03b5\u03b9\u03b1;\u00bb. \u0391\u03c5\u03c4\u03ae \u03b7 \u03b1\u03bb\u03bb\u03b1\u03b3\u03ae \u03b4\u03b5\u03bd \u03b1\u03c6\u03bf\u03c1\u03ac \u03bc\u03cc\u03bd\u03bf \u03c4\u03b1 diffusion LLMs. \u0395\u03af\u03bd\u03b1\u03b9 \u03b8\u03b5\u03bc\u03ad\u03bb\u03b9\u03bf \u03b3\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 \u03c5\u03c0\u03b5\u03cd\u03b8\u03c5\u03bd\u03b7 \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7 \u03b3\u03cd\u03c1\u03c9 \u03b1\u03c0\u03cc generative AI.<\/p>\n<section class=\"td-service-cta\">\n<div class=\"td-service-cta-content\">\n<p class=\"td-service-cta-eyebrow\">Business automation and AI from TWO DOTS<\/p>\n<p class=\"td-service-cta-title\">\u039c\u03b5\u03c4\u03b1\u03c4\u03c1\u03ad\u03c8\u03c4\u03b5 \u03ad\u03bd\u03b1 AI benchmark \u03c3\u03b5 \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b9\u03bc\u03b7 \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7 \u03b3\u03b9\u03b1 \u03c4\u03bf \u03b4\u03b9\u03ba\u03cc \u03c3\u03b1\u03c2 workflow.<\/p>\n<p>\u0397 TWO DOTS \u03c7\u03b1\u03c1\u03c4\u03bf\u03b3\u03c1\u03b1\u03c6\u03b5\u03af use case, \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1, quality gates, latency, \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03ba\u03b1\u03b9 fallback \u03bb\u03bf\u03b3\u03b9\u03ba\u03ae \u03c0\u03c1\u03b9\u03bd \u03bc\u03b9\u03b1 AI \u03c1\u03bf\u03ae \u03c0\u03b5\u03c1\u03ac\u03c3\u03b5\u03b9 \u03c3\u03b5 \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae.<\/p>\n<div class=\"td-service-cta-actions\"><a class=\"td-service-cta-button\" href=\"https:\/\/twodots.gr\/aftomatismoi-epicheiriseon-ai\/\">See business automation with AI<\/a><\/div>\n<\/div>\n<\/section>\n<section id=\"sychnes-erotiseis\" class=\"td-faq-section\">\n<div class=\"td-faq\">\n<p class=\"td-faq-heading\">Frequently Asked Questions (FAQs)<\/p>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a4\u03b9 \u03b5\u03af\u03bd\u03b1\u03b9 \u03ad\u03bd\u03b1 masked diffusion language model;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u0395\u03af\u03bd\u03b1\u03b9 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03b3\u03bb\u03ce\u03c3\u03c3\u03b1\u03c2 \u03c0\u03bf\u03c5 \u03c0\u03b1\u03c1\u03ac\u03b3\u03b5\u03b9 \u03ba\u03b5\u03af\u03bc\u03b5\u03bd\u03bf \u03b1\u03c0\u03bf\u03ba\u03b1\u03bb\u03cd\u03c0\u03c4\u03bf\u03bd\u03c4\u03b1\u03c2 \u03c3\u03c4\u03b1\u03b4\u03b9\u03b1\u03ba\u03ac masked \u03b8\u03ad\u03c3\u03b5\u03b9\u03c2 \u03bc\u03ad\u03c3\u03c9 denoising, \u03b1\u03bd\u03c4\u03af \u03bd\u03b1 \u03b3\u03c1\u03ac\u03c6\u03b5\u03b9 \u03b1\u03c0\u03bf\u03ba\u03bb\u03b5\u03b9\u03c3\u03c4\u03b9\u03ba\u03ac token \u03c0\u03c1\u03bf\u03c2 token \u03cc\u03c0\u03c9\u03c2 \u03ad\u03bd\u03b1 autoregressive \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a4\u03b9 \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 remasking;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u0395\u03af\u03bd\u03b1\u03b9 \u03b7 \u03b5\u03c0\u03b1\u03bd\u03b1\u03c6\u03bf\u03c1\u03ac \u03ae\u03b4\u03b7 \u03b1\u03c0\u03bf\u03ba\u03b1\u03bb\u03c5\u03bc\u03bc\u03ad\u03bd\u03c9\u03bd tokens \u03c3\u03b5 masked \u03ba\u03b1\u03c4\u03ac\u03c3\u03c4\u03b1\u03c3\u03b7 \u03ce\u03c3\u03c4\u03b5 \u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03bd\u03b1 \u03c4\u03b1 \u03b5\u03c0\u03b1\u03bd\u03b5\u03be\u03b5\u03c4\u03ac\u03c3\u03b5\u03b9 \u03c3\u03b5 \u03b5\u03c0\u03cc\u03bc\u03b5\u03bd\u03bf \u03b2\u03ae\u03bc\u03b1.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u0393\u03b9\u03b1\u03c4\u03af \u03c4\u03bf nominal step count \u03b4\u03b5\u03bd \u03b1\u03c1\u03ba\u03b5\u03af;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u0395\u03c0\u03b5\u03b9\u03b4\u03ae \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ad\u03c2 \u03c3\u03c4\u03c1\u03b1\u03c4\u03b7\u03b3\u03b9\u03ba\u03ad\u03c2 \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03b5\u03ba\u03c4\u03b5\u03bb\u03bf\u03cd\u03bd \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03cc \u03b1\u03c1\u03b9\u03b8\u03bc\u03cc \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ce\u03bd forward passes. \u03a3\u03c4\u03bf CaRE, \u03c4\u03b1 256 nominal steps \u03ad\u03ba\u03c1\u03c5\u03b2\u03b1\u03bd \u03b5\u03cd\u03c1\u03bf\u03c2 128 \u03ad\u03c9\u03c2 513 NFE.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a0\u03bf\u03b9\u03b1 metrics \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03c4\u03bf CaRE;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a4\u03bf framework \u03b4\u03af\u03bd\u03b5\u03b9 \u03ad\u03bc\u03c6\u03b1\u03c3\u03b7 \u03c3\u03b5 \u03c0\u03bf\u03bb\u03bb\u03b1\u03c0\u03bb\u03ad\u03c2 \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b5\u03b9\u03c2, \u03bc\u03b5\u03c4\u03b1\u03be\u03cd \u03ac\u03bb\u03bb\u03c9\u03bd perplexity \u03ba\u03b1\u03b9 MAUVE, \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03bf\u03b9 \u03ba\u03b1\u03c4\u03b1\u03c4\u03ac\u03be\u03b5\u03b9\u03c2 \u03c4\u03bf\u03c5\u03c2 \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03b4\u03b9\u03b1\u03c6\u03ad\u03c1\u03bf\u03c5\u03bd.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a4\u03b9 \u03ad\u03b4\u03b5\u03b9\u03be\u03b5 \u03c4\u03bf paper \u03b3\u03b9\u03b1 \u03c4\u03b7 \u03b8\u03b5\u03c1\u03bc\u03bf\u03ba\u03c1\u03b1\u03c3\u03af\u03b1;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a3\u03c4\u03b9\u03c2 \u03ba\u03cd\u03c1\u03b9\u03b5\u03c2 \u03b1\u03bd\u03b1\u03bb\u03cd\u03c3\u03b5\u03b9\u03c2, \u03b7 temperature \u03b5\u03be\u03b7\u03b3\u03bf\u03cd\u03c3\u03b5 \u03c4\u03bf \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf \u03bc\u03ad\u03c1\u03bf\u03c2 \u03c4\u03b7\u03c2 \u03b4\u03b9\u03b1\u03ba\u03cd\u03bc\u03b1\u03bd\u03c3\u03b7\u03c2 \u03c4\u03bf\u03c5 MAUVE, \u03bc\u03b5 \u03b7\u00b2=0,91. \u0391\u03c5\u03c4\u03cc \u03c5\u03c0\u03bf\u03b3\u03c1\u03b1\u03bc\u03bc\u03af\u03b6\u03b5\u03b9 \u03c4\u03b7\u03bd \u03b1\u03bd\u03ac\u03b3\u03ba\u03b7 \u03ba\u03bf\u03b9\u03bd\u03ce\u03bd sampling settings.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u0391\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03b5\u03b9 \u03c4\u03bf CaRE \u03cc\u03c4\u03b9 \u03c4\u03bf remasking \u03b5\u03af\u03bd\u03b1\u03b9 \u03ba\u03b1\u03ba\u03cc;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u038c\u03c7\u03b9. \u0394\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03c4\u03b1 \u03bf\u03c6\u03ad\u03bb\u03b7 \u03c4\u03bf\u03c5 \u03b5\u03be\u03b1\u03c1\u03c4\u03ce\u03bd\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc compute, metric \u03ba\u03b1\u03b9 stochasticity \u03ba\u03b1\u03b9 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03bf\u03cd\u03bd\u03c4\u03b1\u03b9 \u03bc\u03b5 \u03b9\u03c3\u03cc\u03c4\u03b9\u03bc\u03b5\u03c2, \u03b1\u03bd\u03b1\u03c0\u03b1\u03c1\u03b1\u03b3\u03ce\u03b3\u03b9\u03bc\u03b5\u03c2 \u03c3\u03c5\u03bd\u03b8\u03ae\u03ba\u03b5\u03c2.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u039c\u03c0\u03bf\u03c1\u03b5\u03af \u03c4\u03bf \u03c0\u03c1\u03c9\u03c4\u03cc\u03ba\u03bf\u03bb\u03bb\u03bf \u03bd\u03b1 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b7\u03b8\u03b5\u03af \u03c3\u03b5 \u03cc\u03bb\u03b1 \u03c4\u03b1 diffusion models;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u038c\u03c7\u03b9 \u03b1\u03c5\u03c4\u03bf\u03cd\u03c3\u03b9\u03bf. \u03a4\u03bf paper \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03af\u03b6\u03b5\u03b9 \u03c4\u03bf scope \u03c4\u03bf\u03c5 \u03c3\u03c4\u03b1 masked discrete-token diffusion language models \u03ba\u03b1\u03b9 \u03b5\u03be\u03b1\u03b9\u03c1\u03b5\u03af continuous-state \u03ba\u03b1\u03b9 flow-based \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1.<\/p>\n<\/div>\n<\/details>\n<\/div>\n<\/section>\n<div class=\"td-source-list\">\n<p id=\"piges\" class=\"td-source-list-title\">Sources<\/p>\n<ul>\n<li><a href=\"https:\/\/arxiv.org\/abs\/2607.24763\" target=\"_blank\" rel=\"noopener\">Shah, Chakraborty &amp; Gupta: CaRE \u2014 Compute-aware Remasking Evaluation Protocol for Masked Diffusion Language Models<\/a><\/li>\n<li><a href=\"https:\/\/arxiv.org\/abs\/2502.09992\" target=\"_blank\" rel=\"noopener\">Nie et al.: Large Language Diffusion Models \u2014 LLaDA<\/a><\/li>\n<li><a href=\"https:\/\/arxiv.org\/abs\/2508.15487\" target=\"_blank\" rel=\"noopener\">Ye et al.: Dream 7B \u2014 Diffusion Large Language Models<\/a><\/li>\n<li><a href=\"https:\/\/arxiv.org\/abs\/2102.01454\" target=\"_blank\" rel=\"noopener\">Pillutla et al.: MAUVE \u2014 Measuring the Gap Between Neural Text and Human Text<\/a><\/li>\n<\/ul>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>\u03a4\u03bf CaRE \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03c0\u03ce\u03c2 \u03c4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc compute, \u03c4\u03b1 metrics \u03ba\u03b1\u03b9 \u03b7 stochasticity \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03b1\u03bb\u03bb\u03ac\u03be\u03bf\u03c5\u03bd \u03c4\u03b7\u03bd \u03ba\u03b1\u03c4\u03ac\u03c4\u03b1\u03be\u03b7 \u03b5\u03bd\u03cc\u03c2 diffusion LLM benchmark. \u03a0\u03c1\u03b1\u03ba\u03c4\u03b9\u03ba\u03cc\u03c2 \u03bf\u03b4\u03b7\u03b3\u03cc\u03c2 \u03b3\u03b9\u03b1 \u03b4\u03af\u03ba\u03b1\u03b9\u03b5\u03c2 \u03c3\u03c5\u03b3\u03ba\u03c1\u03af\u03c3\u03b5\u03b9\u03c2 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03c9\u03bd \u03ba\u03b1\u03b9 \u03b1\u03c3\u03c6\u03b1\u03bb\u03ad\u03c3\u03c4\u03b5\u03c1\u03b5\u03c2 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b1\u03c0\u03bf\u03c6\u03ac\u03c3\u03b5\u03b9\u03c2.<\/p>","protected":false},"author":1,"featured_media":87754,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_gspb_post_css":"","content-type":"","footnotes":""},"categories":[17366],"tags":[17818,9261,18984,7204,7477],"class_list":["post-87630","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-digital-marketing","tag-ai-benchmarks","tag-ai-strategy","tag-diffusion-llms","tag-generative-ai","tag-machine-learning"],"blocksy_meta":[],"_links":{"self":[{"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/posts\/87630","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/comments?post=87630"}],"version-history":[{"count":0,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/posts\/87630\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/media\/87754"}],"wp:attachment":[{"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/media?parent=87630"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/categories?post=87630"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/tags?post=87630"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}