{"id":84557,"date":"2026-07-12T10:50:00","date_gmt":"2026-07-12T07:50:00","guid":{"rendered":"https:\/\/twodots.gr\/?p=84557"},"modified":"2026-07-12T11:09:29","modified_gmt":"2026-07-12T08:09:29","slug":"diffusion-montela-syntaxi-iatrikon-anaforon-ai-workflows","status":"publish","type":"post","link":"https:\/\/twodots.gr\/en\/diffusion-montela-syntaxi-iatrikon-anaforon-ai-workflows\/","title":{"rendered":"Diffusion \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03c3\u03c4\u03b7 \u03c3\u03cd\u03bd\u03c4\u03b1\u03be\u03b7 \u03b9\u03b1\u03c4\u03c1\u03b9\u03ba\u03ce\u03bd \u03b1\u03bd\u03b1\u03c6\u03bf\u03c1\u03ce\u03bd: \u03c4\u03b9 \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03c4\u03bf \u03bd\u03ad\u03bf paper \u03b3\u03b9\u03b1 AI workflows"},"content":{"rendered":"<div class=\"td-article-lede\">\n<p>\u03a4\u03b1 diffusion \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c0\u03bb\u03ce\u03c2 \u03ad\u03bd\u03b1\u03c2 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03cc\u03c2 \u03c4\u03c1\u03cc\u03c0\u03bf\u03c2 \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae\u03c2 \u03ba\u03b5\u03b9\u03bc\u03ad\u03bd\u03bf\u03c5. \u0397 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 <em>Discrete Diffusion Language Models for Interactive Radiology Report Drafting<\/em> \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03ae\u03c3\u03bf\u03c5\u03bd \u03c9\u03c2 \u03c3\u03c5\u03bd\u03b5\u03c1\u03b3\u03b1\u03c4\u03b9\u03ba\u03bf\u03af editors: \u03ba\u03c1\u03b1\u03c4\u03bf\u03cd\u03bd \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03ac \u03c4\u03b1 \u03b5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b1 \u03c4\u03bc\u03ae\u03bc\u03b1\u03c4\u03b1 \u03bc\u03b9\u03b1\u03c2 \u03b1\u03bd\u03b1\u03c6\u03bf\u03c1\u03ac\u03c2 \u03ba\u03b1\u03b9 \u03c3\u03c5\u03bc\u03c0\u03bb\u03b7\u03c1\u03ce\u03bd\u03bf\u03c5\u03bd \u03ad\u03bd\u03b1 \u03ba\u03b5\u03bd\u03cc \u03b1\u03be\u03b9\u03bf\u03c0\u03bf\u03b9\u03ce\u03bd\u03c4\u03b1\u03c2 \u03cc\u03c3\u03b1 \u03b2\u03c1\u03af\u03c3\u03ba\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c0\u03c1\u03b9\u03bd \u03ba\u03b1\u03b9 \u03bc\u03b5\u03c4\u03ac \u03b1\u03c0\u03cc \u03b1\u03c5\u03c4\u03cc. \u0393\u03b9\u03b1 \u03c4\u03b9\u03c2 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03ae\u03c3\u03b5\u03b9\u03c2, \u03c4\u03bf \u03bf\u03c5\u03c3\u03b9\u03b1\u03c3\u03c4\u03b9\u03ba\u03cc \u03bc\u03ac\u03b8\u03b7\u03bc\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03bf \u03c3\u03c7\u03b5\u03b4\u03b9\u03b1\u03c3\u03bc\u03cc\u03c2 AI workflows \u03bc\u03b5 locked facts, \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03bf \u03ad\u03bb\u03b5\u03b3\u03c7\u03bf \u03ba\u03b1\u03b9 \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b9\u03bc\u03b7 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u2014 \u03cc\u03c7\u03b9 \u03b7 \u03b1\u03c5\u03c4\u03cc\u03bd\u03bf\u03bc\u03b7 \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf\u03c5 \u03ba\u03b5\u03b9\u03bc\u03ad\u03bd\u03bf\u03c5.<\/p>\n<\/div>\n<div class=\"td-article-toc\">\n<div class=\"td-toc-title\">Contents<\/div>\n<ul>\n<li><a href=\"#ti-edeixe-synoptika-i-meleti\">\u03a4\u03b9 \u03ad\u03b4\u03b5\u03b9\u03be\u03b5 \u03c3\u03c5\u03bd\u03bf\u03c0\u03c4\u03b9\u03ba\u03ac \u03b7 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7<\/a><\/li>\n<li><a href=\"#pos-diaferei-ena-diffusion-language-model\">\u03a0\u03ce\u03c2 \u03b4\u03b9\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9 \u03ad\u03bd\u03b1 diffusion language model<\/a><\/li>\n<li><a href=\"#giati-i-sygkrisi-einai-chrisimi\">\u0393\u03b9\u03b1\u03c4\u03af \u03b7 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03b7<\/a><\/li>\n<li><a href=\"#ti-edeixan-ta-medical-vqa-tests\">\u03a4\u03b9 \u03ad\u03b4\u03b5\u03b9\u03be\u03b1\u03bd \u03c4\u03b1 medical VQA tests<\/a><\/li>\n<li><a href=\"#any-order-infill-to-workflow-pou-allazei-tin-epexergasia\">Any-order infill: \u03c4\u03bf workflow \u03c0\u03bf\u03c5 \u03b1\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9 \u03c4\u03b7\u03bd \u03b5\u03c0\u03b5\u03be\u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1<\/a><\/li>\n<li><a href=\"#tachytita-kai-throughput-stin-praxi\">\u03a4\u03b1\u03c7\u03cd\u03c4\u03b7\u03c4\u03b1 \u03ba\u03b1\u03b9 throughput \u03c3\u03c4\u03b7\u03bd \u03c0\u03c1\u03ac\u03be\u03b7<\/a><\/li>\n<li><a href=\"#ti-den-apodeiknyei-to-paper\">\u03a4\u03b9 \u03b4\u03b5\u03bd \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03b5\u03b9 \u03c4\u03bf paper<\/a><\/li>\n<li><a href=\"#ti-simainei-gia-epicheirimatika-ai-workflows\">\u03a4\u03b9 \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03b3\u03b9\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac AI workflows<\/a><\/li>\n<li><a href=\"#praktiko-plaisio-axiologisis-enos-ai-editor\">\u03a0\u03c1\u03b1\u03ba\u03c4\u03b9\u03ba\u03cc \u03c0\u03bb\u03b1\u03af\u03c3\u03b9\u03bf \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7\u03c2 \u03b5\u03bd\u03cc\u03c2 AI editor<\/a><\/li>\n<li><a href=\"#aftomatismoi-kai-ai-workflows-apo-tin-two-dots\">\u0391\u03c5\u03c4\u03bf\u03bc\u03b1\u03c4\u03b9\u03c3\u03bc\u03bf\u03af \u03ba\u03b1\u03b9 AI workflows \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd TWO DOTS<\/a><\/li>\n<\/ul>\n<\/div>\n<h2 id=\"ti-edeixe-synoptika-i-meleti\">\u03a4\u03b9 \u03ad\u03b4\u03b5\u03b9\u03be\u03b5 \u03c3\u03c5\u03bd\u03bf\u03c0\u03c4\u03b9\u03ba\u03ac \u03b7 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7<\/h2>\n<p>\u039f\u03b9 \u03b5\u03c1\u03b5\u03c5\u03bd\u03b7\u03c4\u03ad\u03c2 \u03c0\u03c1\u03bf\u03c3\u03ac\u03c1\u03bc\u03bf\u03c3\u03b1\u03bd \u03c4\u03bf DiffusionGemma-26B \u03c3\u03b5 medical visual question answering \u03ba\u03b1\u03b9 \u03c4\u03bf \u03c3\u03c5\u03bd\u03ad\u03ba\u03c1\u03b9\u03bd\u03b1\u03bd \u03bc\u03b5 \u03c4\u03bf autoregressive Gemma-4-26B. \u03a4\u03b1 \u03b4\u03cd\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03b1\u03bd\u03ae\u03ba\u03bf\u03c5\u03bd \u03c3\u03c4\u03b7\u03bd \u03af\u03b4\u03b9\u03b1 \u03bf\u03b9\u03ba\u03bf\u03b3\u03ad\u03bd\u03b5\u03b9\u03b1, \u03ad\u03c7\u03bf\u03c5\u03bd \u03af\u03b4\u03b9\u03bf \u03c3\u03c5\u03bd\u03bf\u03bb\u03b9\u03ba\u03cc \u03bc\u03ad\u03b3\u03b5\u03b8\u03bf\u03c2 \u03ba\u03b1\u03b9 \u03af\u03b4\u03b9\u03bf \u03b5\u03bd\u03b5\u03c1\u03b3\u03cc \u03c5\u03c0\u03bf\u03bb\u03bf\u03b3\u03b9\u03c3\u03c4\u03b9\u03ba\u03cc \u03b1\u03c0\u03bf\u03c4\u03cd\u03c0\u03c9\u03bc\u03b1 \u03c4\u03cd\u03c0\u03bf\u03c5 mixture-of-experts. \u0397 \u03b2\u03b1\u03c3\u03b9\u03ba\u03ae \u03bc\u03b5\u03c4\u03b1\u03b2\u03bb\u03b7\u03c4\u03ae \u03ae\u03c4\u03b1\u03bd \u03bf \u03c4\u03c1\u03cc\u03c0\u03bf\u03c2 \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae\u03c2: \u03b1\u03bc\u03c6\u03af\u03b4\u03c1\u03bf\u03bc\u03bf denoising \u03b3\u03b9\u03b1 \u03c4\u03bf diffusion \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf, \u03b4\u03b9\u03b1\u03b4\u03bf\u03c7\u03b9\u03ba\u03cc next-token generation \u03b3\u03b9\u03b1 \u03c4\u03bf autoregressive.<\/p>\n<p>\u03a3\u03c4\u03b1 \u03c4\u03c1\u03af\u03b1 medical VQA datasets \u03c4\u03b7\u03c2 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7\u03c2, \u03c4\u03bf fine-tuned diffusion \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03b9\u03c3\u03bf\u03c6\u03ac\u03c1\u03b9\u03c3\u03b5 \u03ae \u03be\u03b5\u03c0\u03ad\u03c1\u03b1\u03c3\u03b5 \u03c4\u03bf autoregressive sibling \u03c3\u03cd\u03bc\u03c6\u03c9\u03bd\u03b1 \u03bc\u03b5 \u03c4\u03bf\u03bd LLM judge. \u03a0\u03b1\u03c1\u03ac\u03bb\u03bb\u03b7\u03bb\u03b1, \u03c3\u03b5 \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 256 tokens \u03c0\u03ac\u03bd\u03c9 \u03c3\u03b5 \u03bc\u03af\u03b1 H100, \u03bf\u03bb\u03bf\u03ba\u03bb\u03ae\u03c1\u03c9\u03c3\u03b5 \u03c4\u03bf decoding 3,5 \u03ad\u03c9\u03c2 4,4 \u03c6\u03bf\u03c1\u03ad\u03c2 \u03c4\u03b1\u03c7\u03cd\u03c4\u03b5\u03c1\u03b1. \u03a4\u03bf \u03c3\u03b7\u03bc\u03b1\u03bd\u03c4\u03b9\u03ba\u03cc\u03c4\u03b5\u03c1\u03bf workflow \u03b5\u03cd\u03c1\u03b7\u03bc\u03b1 \u03ae\u03c4\u03b1\u03bd \u03cc\u03c4\u03b9 \u03b1\u03be\u03b9\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b5 \u03c0\u03bf\u03bb\u03cd \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b1 \u03c4\u03bf \u03ba\u03b5\u03af\u03bc\u03b5\u03bd\u03bf \u03ba\u03b1\u03b9 \u03c3\u03c4\u03b9\u03c2 \u03b4\u03cd\u03bf \u03c0\u03bb\u03b5\u03c5\u03c1\u03ad\u03c2 \u03b5\u03bd\u03cc\u03c2 \u03ba\u03b5\u03bd\u03bf\u03cd, \u03ce\u03c3\u03c4\u03b5 \u03bd\u03b1 \u03c3\u03c5\u03bc\u03c0\u03bb\u03b7\u03c1\u03ce\u03bd\u03b5\u03b9 \u03b5\u03bd\u03b4\u03b9\u03ac\u03bc\u03b5\u03c3\u03b5\u03c2 \u03c0\u03c1\u03bf\u03c4\u03ac\u03c3\u03b5\u03b9\u03c2 \u03c7\u03c9\u03c1\u03af\u03c2 \u03bd\u03b1 \u03be\u03b1\u03bd\u03b1\u03b3\u03c1\u03ac\u03c6\u03b5\u03b9 \u03bf\u03bb\u03cc\u03ba\u03bb\u03b7\u03c1\u03b7 \u03c4\u03b7\u03bd \u03b1\u03bd\u03b1\u03c6\u03bf\u03c1\u03ac.<\/p>\n<div class=\"td-article-note\"><strong>\u0397 \u03c3\u03c9\u03c3\u03c4\u03ae \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03b1\u03bd\u03ac\u03b3\u03bd\u03c9\u03c3\u03b7:<\/strong> \u03c4\u03bf paper \u03b4\u03b5\u03bd \u03b5\u03b3\u03ba\u03c1\u03af\u03bd\u03b5\u03b9 \u03b1\u03c5\u03c4\u03cc\u03bd\u03bf\u03bc\u03b7 \u03ba\u03bb\u03b9\u03bd\u03b9\u03ba\u03ae \u03c7\u03c1\u03ae\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03b4\u03b5\u03bd \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03ba\u03ac\u03b8\u03b5 diffusion \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03b5\u03af\u03bd\u03b1\u03b9 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf \u03b1\u03c0\u03cc \u03ba\u03ac\u03b8\u03b5 LLM. \u0394\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03b7 \u03b1\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03ae \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae\u03c2 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03ba\u03ac\u03bd\u03b5\u03b9 \u03ad\u03bd\u03b1 AI \u03b5\u03c1\u03b3\u03b1\u03bb\u03b5\u03af\u03bf \u03ba\u03b1\u03c4\u03b1\u03bb\u03bb\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03bf \u03b3\u03b9\u03b1 \u03b5\u03bb\u03b5\u03b3\u03c7\u03cc\u03bc\u03b5\u03bd\u03b7 \u03b5\u03c0\u03b5\u03be\u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03ba\u03b5\u03b9\u03bc\u03ad\u03bd\u03bf\u03c5.<\/div>\n<h2 id=\"pos-diaferei-ena-diffusion-language-model\">\u03a0\u03ce\u03c2 \u03b4\u03b9\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9 \u03ad\u03bd\u03b1 diffusion language model<\/h2>\n<p>\u0388\u03bd\u03b1 autoregressive language model \u03b3\u03c1\u03ac\u03c6\u03b5\u03b9 \u03b1\u03c0\u03cc \u03b1\u03c1\u03b9\u03c3\u03c4\u03b5\u03c1\u03ac \u03c0\u03c1\u03bf\u03c2 \u03c4\u03b1 \u03b4\u03b5\u03be\u03b9\u03ac: \u03ba\u03ac\u03b8\u03b5 \u03bd\u03ad\u03bf token \u03b5\u03be\u03b1\u03c1\u03c4\u03ac\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03cc\u03c3\u03b1 \u03ad\u03c7\u03bf\u03c5\u03bd \u03ae\u03b4\u03b7 \u03c0\u03b1\u03c1\u03b1\u03c7\u03b8\u03b5\u03af. \u0391\u03c5\u03c4\u03ae \u03b7 \u03bb\u03bf\u03b3\u03b9\u03ba\u03ae \u03b5\u03af\u03bd\u03b1\u03b9 \u03b9\u03c3\u03c7\u03c5\u03c1\u03ae \u03b3\u03b9\u03b1 \u03bf\u03bb\u03bf\u03ba\u03bb\u03ae\u03c1\u03c9\u03c3\u03b7 \u03ba\u03b5\u03b9\u03bc\u03ad\u03bd\u03bf\u03c5, \u03b1\u03bb\u03bb\u03ac \u03b4\u03c5\u03c3\u03ba\u03bf\u03bb\u03b5\u03cd\u03b5\u03c4\u03b1\u03b9 \u03cc\u03c4\u03b1\u03bd \u03bf \u03c7\u03c1\u03ae\u03c3\u03c4\u03b7\u03c2 \u03b8\u03ad\u03bb\u03b5\u03b9 \u03bd\u03b1 \u03ba\u03c1\u03b1\u03c4\u03ae\u03c3\u03b5\u03b9 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03cc \u03c4\u03bf \u03c4\u03ad\u03bb\u03bf\u03c2 \u03ba\u03b1\u03b9 \u03c4\u03b7\u03bd \u03b1\u03c1\u03c7\u03ae \u03bc\u03b9\u03b1\u03c2 \u03c0\u03b1\u03c1\u03b1\u03b3\u03c1\u03ac\u03c6\u03bf\u03c5 \u03ba\u03b1\u03b9 \u03bd\u03b1 \u03b1\u03bb\u03bb\u03ac\u03be\u03b5\u03b9 \u03bc\u03cc\u03bd\u03bf \u03c4\u03bf \u03b5\u03bd\u03b4\u03b9\u03ac\u03bc\u03b5\u03c3\u03bf \u03bc\u03ad\u03c1\u03bf\u03c2.<\/p>\n<p>\u0388\u03bd\u03b1 discrete diffusion language model \u03be\u03b5\u03ba\u03b9\u03bd\u03ac \u03b1\u03c0\u03cc \u03ad\u03bd\u03b1\u03bd token canvas \u03ba\u03b1\u03b9 \u03c4\u03bf\u03bd \u03b1\u03c0\u03bf\u03b8\u03bf\u03c1\u03c5\u03b2\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03b5\u03c0\u03b1\u03bd\u03b1\u03bb\u03b7\u03c0\u03c4\u03b9\u03ba\u03ac. \u039a\u03ac\u03b8\u03b5 \u03b8\u03ad\u03c3\u03b7 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u00ab\u03b2\u03bb\u03ad\u03c0\u03b5\u03b9\u00bb \u03bf\u03bb\u03cc\u03ba\u03bb\u03b7\u03c1\u03bf \u03c4\u03bf\u03bd canvas, \u03cc\u03c7\u03b9 \u03bc\u03cc\u03bd\u03bf \u03c4\u03b1 \u03c0\u03c1\u03bf\u03b7\u03b3\u03bf\u03cd\u03bc\u03b5\u03bd\u03b1 tokens. \u0391\u03c5\u03c4\u03cc \u03b5\u03c0\u03b9\u03c4\u03c1\u03ad\u03c0\u03b5\u03b9 \u03b1\u03bc\u03c6\u03af\u03b4\u03c1\u03bf\u03bc\u03b7 \u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03ba\u03ac\u03bd\u03b5\u03b9 \u03c6\u03c5\u03c3\u03b9\u03ba\u03cc \u03c4\u03bf any-order infill: \u03bf \u03c7\u03c1\u03ae\u03c3\u03c4\u03b7\u03c2 \u03ba\u03bb\u03b5\u03b9\u03b4\u03ce\u03bd\u03b5\u03b9 \u03cc\u03c3\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03ae\u03b4\u03b7 \u03c3\u03c9\u03c3\u03c4\u03ac \u03ba\u03b1\u03b9 \u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03c3\u03c5\u03bc\u03c0\u03bb\u03b7\u03c1\u03ce\u03bd\u03b5\u03b9 \u03bc\u03cc\u03bd\u03bf \u03c4\u03b1 \u03ba\u03b5\u03bd\u03ac.<\/p>\n<p>\u0397 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03ac \u03b8\u03c5\u03bc\u03af\u03b6\u03b5\u03b9 \u03b4\u03cd\u03bf \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03bf\u03cd\u03c2 \u03c4\u03c1\u03cc\u03c0\u03bf\u03c5\u03c2 \u03c3\u03c5\u03bd\u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1\u03c2. \u039f \u03c0\u03c1\u03ce\u03c4\u03bf\u03c2 \u03b6\u03b7\u03c4\u03ac \u03bd\u03ad\u03bf \u03ba\u03b5\u03af\u03bc\u03b5\u03bd\u03bf \u03bc\u03b5\u03c4\u03ac \u03b1\u03c0\u03cc \u03ad\u03bd\u03b1 \u03c3\u03b7\u03bc\u03b5\u03af\u03bf. \u039f \u03b4\u03b5\u03cd\u03c4\u03b5\u03c1\u03bf\u03c2 \u03b1\u03bd\u03c4\u03b9\u03bc\u03b5\u03c4\u03c9\u03c0\u03af\u03b6\u03b5\u03b9 \u03c4\u03bf \u03c5\u03c0\u03ac\u03c1\u03c7\u03bf\u03bd \u03ba\u03b5\u03af\u03bc\u03b5\u03bd\u03bf \u03c9\u03c2 \u03ba\u03b1\u03bc\u03b2\u03ac \u03bc\u03b5 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03ac \u03ba\u03b1\u03b9 \u03bc\u03b5\u03c4\u03b1\u03b2\u03bb\u03b7\u03c4\u03ac \u03c4\u03bc\u03ae\u03bc\u03b1\u03c4\u03b1. \u0393\u03b9\u03b1 \u03b5\u03c0\u03b1\u03b3\u03b3\u03b5\u03bb\u03bc\u03b1\u03c4\u03b9\u03ba\u03ad\u03c2 \u03c1\u03bf\u03ad\u03c2 \u03bc\u03b5 \u03b5\u03b3\u03ba\u03c1\u03af\u03c3\u03b5\u03b9\u03c2, disclaimers, \u03c4\u03b5\u03c7\u03bd\u03b9\u03ba\u03ac \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 \u03ae \u03bd\u03bf\u03bc\u03b9\u03ba\u03bf\u03cd\u03c2 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03bf\u03cd\u03c2, \u03b1\u03c5\u03c4\u03ae \u03b7 \u03b4\u03c5\u03bd\u03b1\u03c4\u03cc\u03c4\u03b7\u03c4\u03b1 \u03b5\u03c0\u03b7\u03c1\u03b5\u03ac\u03b6\u03b5\u03b9 \u03ac\u03bc\u03b5\u03c3\u03b1 \u03c4\u03bf\u03bd \u03ad\u03bb\u03b5\u03b3\u03c7\u03bf \u03ba\u03b1\u03b9 \u03c4\u03b7\u03bd \u03b9\u03c7\u03bd\u03b7\u03bb\u03b1\u03c3\u03b9\u03bc\u03cc\u03c4\u03b7\u03c4\u03b1.<\/p>\n<div class=\"td-comparison-cards\">\n<div class=\"td-comparison-card\">\n<h3>Autoregressive \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae<\/h3>\n<p>\u0395\u03af\u03bd\u03b1\u03b9 \u03c6\u03c5\u03c3\u03b9\u03ba\u03ac \u03c0\u03c1\u03bf\u03c3\u03b1\u03bd\u03b1\u03c4\u03bf\u03bb\u03b9\u03c3\u03bc\u03ad\u03bd\u03b7 \u03c3\u03c4\u03bf \u03b5\u03c0\u03cc\u03bc\u03b5\u03bd\u03bf token. \u0397 \u03b5\u03c0\u03b5\u03be\u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03c3\u03c4\u03b7 \u03bc\u03ad\u03c3\u03b7 \u03c3\u03c5\u03c7\u03bd\u03ac \u03b1\u03c0\u03b1\u03b9\u03c4\u03b5\u03af \u03b5\u03b9\u03b4\u03b9\u03ba\u03cc prompting \u03ae \u03b5\u03c0\u03b1\u03bd\u03b1\u03b4\u03b7\u03bc\u03b9\u03bf\u03c5\u03c1\u03b3\u03af\u03b1 \u03c4\u03bf\u03c5 \u03ba\u03b5\u03b9\u03bc\u03ad\u03bd\u03bf\u03c5 \u03c0\u03bf\u03c5 \u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03b5\u03af.<\/p>\n<\/div>\n<div class=\"td-comparison-card\">\n<h3>Diffusion \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae<\/h3>\n<p>\u0394\u03bf\u03c5\u03bb\u03b5\u03cd\u03b5\u03b9 \u03c0\u03ac\u03bd\u03c9 \u03c3\u03b5 \u03bf\u03bb\u03cc\u03ba\u03bb\u03b7\u03c1\u03bf token canvas \u03ba\u03b1\u03b9 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c3\u03c5\u03bc\u03c0\u03bb\u03b7\u03c1\u03ce\u03bd\u03b5\u03b9 \u03ba\u03b5\u03bd\u03ac \u03bc\u03b5 context \u03ba\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c4\u03b9\u03c2 \u03b4\u03cd\u03bf \u03c0\u03bb\u03b5\u03c5\u03c1\u03ad\u03c2, \u03b4\u03b9\u03b1\u03c4\u03b7\u03c1\u03ce\u03bd\u03c4\u03b1\u03c2 \u03c4\u03b1 \u03ba\u03bb\u03b5\u03b9\u03b4\u03c9\u03bc\u03ad\u03bd\u03b1 \u03c4\u03bc\u03ae\u03bc\u03b1\u03c4\u03b1.<\/p>\n<\/div>\n<\/div>\n<h2 id=\"giati-i-sygkrisi-einai-chrisimi\">\u0393\u03b9\u03b1\u03c4\u03af \u03b7 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03b7<\/h2>\n<p>\u03a0\u03bf\u03bb\u03bb\u03ac benchmark comparisons \u03bc\u03c0\u03b5\u03c1\u03b4\u03b5\u03cd\u03bf\u03c5\u03bd \u03c4\u03b7\u03bd \u03b1\u03c1\u03c7\u03b9\u03c4\u03b5\u03ba\u03c4\u03bf\u03bd\u03b9\u03ba\u03ae \u03bc\u03b5 \u03c4\u03bf \u03bc\u03ad\u03b3\u03b5\u03b8\u03bf\u03c2, \u03c4\u03b1 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 \u03ae \u03c4\u03bf training recipe. \u0395\u03b4\u03ce \u03bf\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03c0\u03c1\u03bf\u03c3\u03c0\u03ac\u03b8\u03b7\u03c3\u03b1\u03bd \u03bd\u03b1 \u03ba\u03c1\u03b1\u03c4\u03ae\u03c3\u03bf\u03c5\u03bd \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03ac \u03c4\u03b7\u03bd \u03bf\u03b9\u03ba\u03bf\u03b3\u03ad\u03bd\u03b5\u03b9\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5, \u03c4\u03bf vision tower, \u03c4\u03bf\u03c5\u03c2 LoRA targets \u03ba\u03b1\u03b9 \u03c4\u03b1 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1. \u039a\u03b1\u03b9 \u03c4\u03b1 \u03b4\u03cd\u03bf backbones \u03ad\u03c7\u03bf\u03c5\u03bd 25,2 \u03b4\u03b9\u03c3. \u03c3\u03c5\u03bd\u03bf\u03bb\u03b9\u03ba\u03ad\u03c2 \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03c4\u03c1\u03bf\u03c5\u03c2 \u03ba\u03b1\u03b9 3,8 \u03b4\u03b9\u03c3. \u03b5\u03bd\u03b5\u03c1\u03b3\u03ad\u03c2 \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03c4\u03c1\u03bf\u03c5\u03c2 \u03b1\u03bd\u03ac inference.<\/p>\n<p>\u0397 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c0\u03bf\u03bb\u03cd\u03c4\u03c9\u03c2 \u03b5\u03c1\u03b3\u03b1\u03c3\u03c4\u03b7\u03c1\u03b9\u03b1\u03ba\u03ac \u03c4\u03b1\u03c5\u03c4\u03cc\u03c3\u03b7\u03bc\u03b7 \u2014 \u03bf\u03b9 optimizers \u03ba\u03b1\u03b9 \u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03b5\u03c2 training \u03c1\u03c5\u03b8\u03bc\u03af\u03c3\u03b5\u03b9\u03c2 \u03b4\u03b9\u03b1\u03c6\u03ad\u03c1\u03bf\u03c5\u03bd \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03b5\u03be\u03c5\u03c0\u03b7\u03c1\u03b5\u03c4\u03bf\u03cd\u03bd \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac paradigms. \u0395\u03af\u03bd\u03b1\u03b9 \u03cc\u03bc\u03c9\u03c2 \u03b1\u03c1\u03ba\u03b5\u03c4\u03ac \u03b5\u03bb\u03b5\u03b3\u03c7\u03cc\u03bc\u03b5\u03bd\u03b7 \u03ce\u03c3\u03c4\u03b5 \u03bd\u03b1 \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03c4\u03bf diffusion approach \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c3\u03c4\u03b1\u03b8\u03b5\u03af \u03c3\u03b5 \u03b1\u03c0\u03b1\u03b9\u03c4\u03b7\u03c4\u03b9\u03ba\u03cc multimodal \u03c0\u03b5\u03c1\u03b9\u03b2\u03ac\u03bb\u03bb\u03bf\u03bd \u03c7\u03c9\u03c1\u03af\u03c2 \u03bd\u03b1 \u03b8\u03c5\u03c3\u03b9\u03ac\u03b6\u03b5\u03b9 \u03b1\u03bd\u03b1\u03b3\u03ba\u03b1\u03c3\u03c4\u03b9\u03ba\u03ac \u03c4\u03b7\u03bd \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03b3\u03b9\u03b1 \u03c7\u03ac\u03c1\u03b7 \u03c4\u03bf\u03c5 infill.<\/p>\n<p>\u0393\u03b9\u03b1 \u03bc\u03b9\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03af\u03c1\u03b7\u03c3\u03b7, \u03b1\u03c5\u03c4\u03cc \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 \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae\u03c2 AI. \u0394\u03b5\u03bd \u03b1\u03c1\u03ba\u03b5\u03af \u03b7 \u03b3\u03b5\u03bd\u03b9\u03ba\u03ae \u03ba\u03b1\u03c4\u03ac\u03c4\u03b1\u03be\u03b7 \u03b5\u03bd\u03cc\u03c2 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5. \u03a7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 matched test \u03c0\u03ac\u03bd\u03c9 \u03c3\u03c4\u03b7 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b7 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1: \u03af\u03b4\u03b9\u03b1 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1, \u03af\u03b4\u03b9\u03bf output format, \u03af\u03b4\u03b9\u03bf \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03bf review \u03ba\u03b1\u03b9 \u03af\u03b4\u03b9\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03b1\u03c0\u03bf\u03c4\u03c5\u03c7\u03af\u03b1\u03c2.<\/p>\n<h2 id=\"ti-edeixan-ta-medical-vqa-tests\">\u03a4\u03b9 \u03ad\u03b4\u03b5\u03b9\u03be\u03b1\u03bd \u03c4\u03b1 medical VQA tests<\/h2>\n<p>\u0397 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 \u03ba\u03ac\u03bb\u03c5\u03c8\u03b5 \u03c4\u03b1 VQA-RAD, SLAKE \u03ba\u03b1\u03b9 VQA-Med-2019, \u03bc\u03b5 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03cc \u03b4\u03b5\u03af\u03b3\u03bc\u03b1 350 \u03b5\u03c1\u03c9\u03c4\u03ae\u03c3\u03b5\u03c9\u03bd \u03b1\u03bd\u03ac dataset. \u03a4\u03bf fine-tuned diffusion \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03ad\u03c6\u03c4\u03b1\u03c3\u03b5 \u03c4\u03b7\u03bd \u03c5\u03c8\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03b7 judge accuracy \u03c3\u03c4\u03bf SLAKE \u03bc\u03b5 0,863, \u03b5\u03bd\u03ce \u03c4\u03b1 frontier \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03b4\u03b9\u03b1\u03c4\u03ae\u03c1\u03b7\u03c3\u03b1\u03bd \u03c0\u03c1\u03bf\u03b2\u03ac\u03b4\u03b9\u03c3\u03bc\u03b1 \u03c3\u03b5 \u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03b1 \u03ac\u03bb\u03bb\u03b1 datasets. \u03a3\u03c4\u03bf VQA-RAD, \u03b3\u03b9\u03b1 \u03c0\u03b1\u03c1\u03ac\u03b4\u03b5\u03b9\u03b3\u03bc\u03b1, \u03c4\u03bf Gemini-3.5-Flash \u03c3\u03b7\u03bc\u03b5\u03af\u03c9\u03c3\u03b5 0,777 \u03ba\u03b1\u03b9 \u03c4\u03bf fine-tuned DiffusionGemma 0,649.<\/p>\n<p>\u039f\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b1\u03bd LLM-as-a-judge \u03b5\u03c0\u03b5\u03b9\u03b4\u03ae \u03c4\u03bf exact match \u03c4\u03b9\u03bc\u03c9\u03c1\u03b5\u03af \u03c3\u03c9\u03c3\u03c4\u03ad\u03c2 \u03c0\u03b1\u03c1\u03b1\u03c6\u03c1\u03ac\u03c3\u03b5\u03b9\u03c2. \u0391\u03c5\u03c4\u03cc \u03b5\u03af\u03bd\u03b1\u03b9 \u03bb\u03bf\u03b3\u03b9\u03ba\u03cc \u03b3\u03b9\u03b1 \u03b1\u03bd\u03bf\u03b9\u03c7\u03c4\u03ad\u03c2 \u03b1\u03c0\u03b1\u03bd\u03c4\u03ae\u03c3\u03b5\u03b9\u03c2, \u03b1\u03bb\u03bb\u03ac \u03b4\u03b7\u03bc\u03b9\u03bf\u03c5\u03c1\u03b3\u03b5\u03af \u03bd\u03ad\u03bf \u03b5\u03c0\u03af\u03c0\u03b5\u03b4\u03bf \u03b1\u03b2\u03b5\u03b2\u03b1\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2: \u03bf judge \u03b5\u03af\u03bd\u03b1\u03b9 \u03b5\u03c0\u03af\u03c3\u03b7\u03c2 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03ba\u03b1\u03b9 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03ad\u03c7\u03b5\u03b9 \u03b4\u03b9\u03ba\u03ad\u03c2 \u03c4\u03bf\u03c5 \u03bc\u03b5\u03c1\u03bf\u03bb\u03b7\u03c8\u03af\u03b5\u03c2. \u0393\u03b9\u2019 \u03b1\u03c5\u03c4\u03cc \u03c4\u03b1 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b4\u03b9\u03b1\u03b2\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c9\u03c2 \u03ad\u03bd\u03b4\u03b5\u03b9\u03be\u03b7 \u03c3\u03c5\u03b3\u03ba\u03c1\u03b9\u03c4\u03b9\u03ba\u03ae\u03c2 \u03b1\u03c0\u03cc\u03b4\u03bf\u03c3\u03b7\u03c2 \u03c3\u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf setup, \u03cc\u03c7\u03b9 \u03c9\u03c2 \u03ba\u03bb\u03b9\u03bd\u03b9\u03ba\u03ae \u03c0\u03b9\u03c3\u03c4\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7.<\/p>\n<p>\u039c\u03ac\u03bb\u03b9\u03c3\u03c4\u03b1, \u03c3\u03c4\u03b1 \u03ad\u03be\u03b9 \u03b2\u03b1\u03c3\u03b9\u03ba\u03ac diffusion-versus-autoregressive comparisons \u03bc\u03cc\u03bd\u03bf \u03b4\u03cd\u03bf \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03ad\u03c2 \u03ae\u03c4\u03b1\u03bd \u03c3\u03c4\u03b1\u03c4\u03b9\u03c3\u03c4\u03b9\u03ba\u03ac \u03c3\u03b7\u03bc\u03b1\u03bd\u03c4\u03b9\u03ba\u03ad\u03c2: \u03c4\u03bf fine-tuned SLAKE \u03ba\u03b1\u03b9 \u03c4\u03bf base VQA-RAD. \u03a4\u03bf paper \u03b5\u03c0\u03bf\u03bc\u03ad\u03bd\u03c9\u03c2 \u03c3\u03c4\u03b7\u03c1\u03af\u03b6\u03b5\u03b9 \u03c0\u03c1\u03bf\u03c3\u03b5\u03ba\u03c4\u03b9\u03ba\u03ac \u03c4\u03b7 \u03b8\u03ad\u03c3\u03b7 \u00ab\u03b9\u03c3\u03bf\u03c6\u03b1\u03c1\u03af\u03b6\u03b5\u03b9 \u03ae \u03be\u03b5\u03c0\u03b5\u03c1\u03bd\u03ac\u00bb, \u03cc\u03c7\u03b9 \u03ad\u03bd\u03b1\u03bd \u03b3\u03b5\u03bd\u03b9\u03ba\u03cc \u03b9\u03c3\u03c7\u03c5\u03c1\u03b9\u03c3\u03bc\u03cc \u03cc\u03c4\u03b9 \u03b7 diffusion \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae \u03ba\u03b5\u03c1\u03b4\u03af\u03b6\u03b5\u03b9 \u03c0\u03b1\u03bd\u03c4\u03bf\u03cd.<\/p>\n<h2 id=\"any-order-infill-to-workflow-pou-allazei-tin-epexergasia\">Any-order infill: \u03c4\u03bf workflow \u03c0\u03bf\u03c5 \u03b1\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9 \u03c4\u03b7\u03bd \u03b5\u03c0\u03b5\u03be\u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1<\/h2>\n<p>\u03a4\u03bf \u03c0\u03b9\u03bf \u03b5\u03bd\u03b4\u03b9\u03b1\u03c6\u03ad\u03c1\u03bf\u03bd \u03c0\u03b5\u03af\u03c1\u03b1\u03bc\u03b1 \u03b4\u03b5\u03bd \u03ae\u03c4\u03b1\u03bd \u03ad\u03bd\u03b1 \u03b1\u03ba\u03cc\u03bc\u03b7 VQA score. \u039f\u03b9 \u03b5\u03c1\u03b5\u03c5\u03bd\u03b7\u03c4\u03ad\u03c2 \u03b1\u03c6\u03b1\u03af\u03c1\u03b5\u03c3\u03b1\u03bd \u03bc\u03af\u03b1 \u03bf\u03bb\u03cc\u03ba\u03bb\u03b7\u03c1\u03b7 \u03c0\u03c1\u03cc\u03c4\u03b1\u03c3\u03b7 \u03b1\u03c0\u03cc 249 \u03b1\u03bd\u03b1\u03c6\u03bf\u03c1\u03ad\u03c2 \u03c4\u03bf\u03c5 MIMIC-CXR \u03ba\u03b1\u03b9 \u03b6\u03ae\u03c4\u03b7\u03c3\u03b1\u03bd \u03b1\u03c0\u03cc \u03c4\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03bd\u03b1 \u03c3\u03c5\u03bc\u03c0\u03bb\u03b7\u03c1\u03ce\u03c3\u03bf\u03c5\u03bd \u03c4\u03bf \u03ba\u03b5\u03bd\u03cc. \u0388\u03c0\u03b5\u03b9\u03c4\u03b1 \u03c3\u03c5\u03bd\u03ad\u03ba\u03c1\u03b9\u03bd\u03b1\u03bd \u03b4\u03cd\u03bf \u03c3\u03c5\u03bd\u03b8\u03ae\u03ba\u03b5\u03c2: context \u03bc\u03cc\u03bd\u03bf \u03b1\u03c0\u03cc \u03c4\u03b1 \u03b1\u03c1\u03b9\u03c3\u03c4\u03b5\u03c1\u03ac \u03ba\u03b1\u03b9 context \u03ba\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c4\u03b9\u03c2 \u03b4\u03cd\u03bf \u03c0\u03bb\u03b5\u03c5\u03c1\u03ad\u03c2.<\/p>\n<p>\u039c\u03b5 \u03b1\u03bc\u03c6\u03af\u03b4\u03c1\u03bf\u03bc\u03bf context, \u03c4\u03bf diffusion \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03b2\u03b5\u03bb\u03c4\u03af\u03c9\u03c3\u03b5 \u03c4\u03bf token-F1 \u03ba\u03b1\u03c4\u03ac 0,109 \u03ba\u03b1\u03b9 \u03c4\u03b7\u03bd judge accuracy \u03ba\u03b1\u03c4\u03ac 0,129. \u03a4\u03bf autoregressive \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03b4\u03b5\u03bd \u03ad\u03b4\u03b5\u03b9\u03be\u03b5 \u03b1\u03bd\u03c4\u03af\u03c3\u03c4\u03bf\u03b9\u03c7\u03bf \u03c3\u03b7\u03bc\u03b1\u03bd\u03c4\u03b9\u03ba\u03cc \u03cc\u03c6\u03b5\u03bb\u03bf\u03c2, \u03b1\u03ba\u03cc\u03bc\u03b7 \u03ba\u03b1\u03b9 \u03cc\u03c4\u03b1\u03bd \u03c4\u03bf \u03b4\u03b5\u03be\u03af context \u03c0\u03c1\u03bf\u03c3\u03c4\u03ad\u03b8\u03b7\u03ba\u03b5 \u03c3\u03c4\u03bf prompt. \u0397 \u03b1\u03bb\u03bb\u03b7\u03bb\u03b5\u03c0\u03af\u03b4\u03c1\u03b1\u03c3\u03b7 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5 \u03ba\u03b1\u03b9 context \u03ae\u03c4\u03b1\u03bd \u03c3\u03b7\u03bc\u03b1\u03bd\u03c4\u03b9\u03ba\u03ae \u03ba\u03b1\u03b9 \u03c3\u03c4\u03b9\u03c2 \u03b4\u03cd\u03bf \u03bc\u03b5\u03c4\u03c1\u03b9\u03ba\u03ad\u03c2, \u03b4\u03b5\u03af\u03c7\u03bd\u03bf\u03bd\u03c4\u03b1\u03c2 \u03cc\u03c4\u03b9 \u03c4\u03bf diffusion \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03b1\u03be\u03b9\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b5 \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 3,5 \u03c6\u03bf\u03c1\u03ad\u03c2 \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf \u03c4\u03b7\u03bd \u03c0\u03bb\u03b7\u03c1\u03bf\u03c6\u03bf\u03c1\u03af\u03b1 \u03bc\u03b5\u03c4\u03ac \u03c4\u03bf \u03ba\u03b5\u03bd\u03cc.<\/p>\n<div class=\"td-decision-band\">\n<p class=\"td-decision-label\">\u03a4\u03bf \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc product requirement<\/p>\n<p><strong>\u039c\u03c0\u03bf\u03c1\u03b5\u03af \u03c4\u03bf AI \u03bd\u03b1 \u03b1\u03bb\u03bb\u03ac\u03be\u03b5\u03b9 \u03bc\u03cc\u03bd\u03bf \u03c4\u03bf \u03b5\u03c0\u03b9\u03bb\u03b5\u03b3\u03bc\u03ad\u03bd\u03bf \u03c4\u03bc\u03ae\u03bc\u03b1 \u03c7\u03c9\u03c1\u03af\u03c2 \u03bd\u03b1 \u03c0\u03b5\u03b9\u03c1\u03ac\u03be\u03b5\u03b9 \u03c4\u03b1 \u03b5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b1 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1;<\/strong><\/p>\n<p>\u0391\u03bd \u03b7 \u03b1\u03c0\u03ac\u03bd\u03c4\u03b7\u03c3\u03b7 \u03b4\u03b5\u03bd \u03bc\u03b5\u03c4\u03c1\u03b9\u03ad\u03c4\u03b1\u03b9, \u03c4\u03bf \u03b5\u03c1\u03b3\u03b1\u03bb\u03b5\u03af\u03bf \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c0\u03bb\u03ce\u03c2 \u03b3\u03c1\u03ae\u03b3\u03bf\u03c1\u03bf autocomplete. \u03a3\u03b5 \u03c3\u03bf\u03b2\u03b1\u03c1\u03cc workflow \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 locked fragments, diff \u03c0\u03c1\u03b9\u03bd \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03b1\u03c0\u03bf\u03b4\u03bf\u03c7\u03ae, version history \u03ba\u03b1\u03b9 \u03c3\u03b1\u03c6\u03ae\u03c2 \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03bf\u03c2 \u03b9\u03b4\u03b9\u03bf\u03ba\u03c4\u03ae\u03c4\u03b7\u03c2 \u03c4\u03b7\u03c2 \u03c4\u03b5\u03bb\u03b9\u03ba\u03ae\u03c2 \u03ad\u03ba\u03b4\u03bf\u03c3\u03b7\u03c2.<\/p>\n<\/div>\n<p>\u0397 \u03af\u03b4\u03b9\u03b1 \u03bb\u03bf\u03b3\u03b9\u03ba\u03ae \u03ad\u03c7\u03b5\u03b9 \u03b1\u03be\u03af\u03b1 \u03ad\u03be\u03c9 \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03b1\u03ba\u03c4\u03b9\u03bd\u03bf\u03bb\u03bf\u03b3\u03af\u03b1. \u03a3\u03b5 \u03bc\u03b9\u03b1 \u03c0\u03b5\u03c1\u03b9\u03b3\u03c1\u03b1\u03c6\u03ae \u03c0\u03c1\u03bf\u03ca\u03cc\u03bd\u03c4\u03bf\u03c2 \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03bf\u03c5\u03bd \u03ba\u03bb\u03b5\u03b9\u03b4\u03c9\u03bc\u03ad\u03bd\u03b5\u03c2 \u03bf\u03b9 \u03c4\u03b5\u03c7\u03bd\u03b9\u03ba\u03ad\u03c2 \u03c0\u03c1\u03bf\u03b4\u03b9\u03b1\u03b3\u03c1\u03b1\u03c6\u03ad\u03c2, \u03b5\u03bd\u03ce \u03b1\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9 \u03bc\u03cc\u03bd\u03bf \u03b7 \u03b5\u03c0\u03b5\u03be\u03ae\u03b3\u03b7\u03c3\u03b7. \u03a3\u03b5 \u03ad\u03bd\u03b1 knowledge base \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03b4\u03b9\u03b1\u03c4\u03b7\u03c1\u03bf\u03cd\u03bd\u03c4\u03b1\u03b9 \u03c4\u03b1 \u03b5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b1 \u03b2\u03ae\u03bc\u03b1\u03c4\u03b1 \u03ba\u03b1\u03b9 \u03bd\u03b1 \u03c0\u03c1\u03bf\u03c3\u03b1\u03c1\u03bc\u03cc\u03b6\u03b5\u03c4\u03b1\u03b9 \u03b7 \u03b5\u03b9\u03c3\u03b1\u03b3\u03c9\u03b3\u03ae. \u03a3\u03b5 \u03ad\u03bd\u03b1 email workflow \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03ba\u03bb\u03b5\u03b9\u03b4\u03ce\u03bd\u03bf\u03bd\u03c4\u03b1\u03b9 \u03bf\u03b9 \u03bd\u03bf\u03bc\u03b9\u03ba\u03bf\u03af \u03cc\u03c1\u03bf\u03b9 \u03ba\u03b1\u03b9 \u03bd\u03b1 \u03c0\u03c1\u03bf\u03c3\u03c9\u03c0\u03bf\u03c0\u03bf\u03b9\u03b5\u03af\u03c4\u03b1\u03b9 \u03c4\u03bf \u03c5\u03c0\u03cc\u03bb\u03bf\u03b9\u03c0\u03bf \u03bc\u03ae\u03bd\u03c5\u03bc\u03b1.<\/p>\n<h2 id=\"tachytita-kai-throughput-stin-praxi\">\u03a4\u03b1\u03c7\u03cd\u03c4\u03b7\u03c4\u03b1 \u03ba\u03b1\u03b9 throughput \u03c3\u03c4\u03b7\u03bd \u03c0\u03c1\u03ac\u03be\u03b7<\/h2>\n<p>\u03a3\u03c4\u03b7 \u03b4\u03bf\u03ba\u03b9\u03bc\u03ae \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 256-token generation \u03c0\u03ac\u03bd\u03c9 \u03c3\u03b5 \u03bc\u03af\u03b1 H100, \u03c4\u03bf Gemma-4 autoregressive \u03c7\u03c1\u03b5\u03b9\u03ac\u03c3\u03c4\u03b7\u03ba\u03b5 6,43 \u03b4\u03b5\u03c5\u03c4\u03b5\u03c1\u03cc\u03bb\u03b5\u03c0\u03c4\u03b1. \u03a4\u03bf DiffusionGemma \u03c7\u03c1\u03b5\u03b9\u03ac\u03c3\u03c4\u03b7\u03ba\u03b5 \u03b1\u03c0\u03cc 1,46 \u03ad\u03c9\u03c2 1,84 \u03b4\u03b5\u03c5\u03c4\u03b5\u03c1\u03cc\u03bb\u03b5\u03c0\u03c4\u03b1, \u03b1\u03bd\u03ac\u03bb\u03bf\u03b3\u03b1 \u03bc\u03b5 \u03c4\u03bf budget \u03c4\u03c9\u03bd 16, 32 \u03ae 48 denoising steps. \u0391\u03c5\u03c4\u03cc \u03b1\u03bd\u03c4\u03b9\u03c3\u03c4\u03bf\u03b9\u03c7\u03b5\u03af \u03c3\u03b5 3,5 \u03ad\u03c9\u03c2 4,4 \u03c6\u03bf\u03c1\u03ad\u03c2 \u03c4\u03b1\u03c7\u03cd\u03c4\u03b5\u03c1\u03bf latency \u03ba\u03b1\u03b9 5,7 \u03ad\u03c9\u03c2 7,1 \u03c6\u03bf\u03c1\u03ad\u03c2 \u03c5\u03c8\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03bf throughput.<\/p>\n<div class=\"td-chart td-chart--metrics\">\n<p class=\"td-chart-title\">\u03a4\u03b1 \u03c4\u03c1\u03af\u03b1 \u03bc\u03b5\u03b3\u03ad\u03b8\u03b7 \u03c0\u03bf\u03c5 \u03b1\u03be\u03af\u03b6\u03b5\u03b9 \u03bd\u03b1 \u03ba\u03c1\u03b1\u03c4\u03ae\u03c3\u03bf\u03c5\u03bc\u03b5<\/p>\n<p class=\"td-chart-subtitle\">\u0391\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1 \u03c4\u03bf\u03c5 paper \u03c3\u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf hardware \u03ba\u03b1\u03b9 experimental setup.<\/p>\n<div class=\"td-chart-body\">\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">3,5\u20134,4\u00d7<\/span><span class=\"td-metric-label\">\u03a4\u03b1\u03c7\u03cd\u03c4\u03b5\u03c1\u03bf decoding<\/span><\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">5,7\u20137,1\u00d7<\/span><span class=\"td-metric-label\">\u03a5\u03c8\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03bf throughput<\/span><\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">+0,109<\/span><span class=\"td-metric-label\">Token-F1 \u03b1\u03c0\u03cc \u03b1\u03bc\u03c6\u03af\u03b4\u03c1\u03bf\u03bc\u03bf context<\/span><\/div>\n<\/div>\n<\/div>\n<p>\u039f\u03b9 \u03b1\u03c1\u03b9\u03b8\u03bc\u03bf\u03af \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03ad\u03c4\u03bf\u03b9\u03bc\u03bf business case \u03b3\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 \u03c5\u03c0\u03bf\u03b4\u03bf\u03bc\u03ae. \u0391\u03c6\u03bf\u03c1\u03bf\u03cd\u03bd \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf, \u03bc\u03af\u03b1 H100, bf16 \u03ba\u03b1\u03b9 canvas \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 256 tokens. \u0394\u03b5\u03af\u03c7\u03bd\u03bf\u03c5\u03bd \u03cc\u03bc\u03c9\u03c2 \u03b3\u03b9\u03b1\u03c4\u03af \u03c4\u03bf latency \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b1\u03bd\u03c4\u03b9\u03bc\u03b5\u03c4\u03c9\u03c0\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c9\u03c2 \u03bc\u03ad\u03c1\u03bf\u03c2 \u03c4\u03b7\u03c2 \u03b5\u03bc\u03c0\u03b5\u03b9\u03c1\u03af\u03b1\u03c2 \u03c7\u03c1\u03ae\u03c3\u03c4\u03b7. \u0388\u03bd\u03b1\u03c2 editor \u03c0\u03bf\u03c5 \u03b1\u03c0\u03b1\u03bd\u03c4\u03ac \u03b3\u03c1\u03ae\u03b3\u03bf\u03c1\u03b1 \u03b5\u03c0\u03b9\u03c4\u03c1\u03ad\u03c0\u03b5\u03b9 \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03bf\u03c5\u03c2 \u03ba\u03cd\u03ba\u03bb\u03bf\u03c5\u03c2 \u03b4\u03b9\u03cc\u03c1\u03b8\u03c9\u03c3\u03b7\u03c2 \u03ba\u03b1\u03b9 \u03bc\u03b5\u03b9\u03ce\u03bd\u03b5\u03b9 \u03c4\u03b7\u03bd \u03c4\u03ac\u03c3\u03b7 \u03c4\u03bf\u03c5 \u03c7\u03c1\u03ae\u03c3\u03c4\u03b7 \u03bd\u03b1 \u03b1\u03c0\u03bf\u03b4\u03ad\u03c7\u03b5\u03c4\u03b1\u03b9 \u03b2\u03b9\u03b1\u03c3\u03c4\u03b9\u03ba\u03ac \u03bc\u03b9\u03b1 \u03b1\u03c4\u03b5\u03bb\u03ae \u03b5\u03ba\u03b4\u03bf\u03c7\u03ae.<\/p>\n<h2 id=\"ti-den-apodeiknyei-to-paper\">\u03a4\u03b9 \u03b4\u03b5\u03bd \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03b5\u03b9 \u03c4\u03bf paper<\/h2>\n<p>\u03a0\u03c1\u03ce\u03c4\u03bf\u03bd, \u03b4\u03b5\u03bd \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03b5\u03b9 \u03ba\u03bb\u03b9\u03bd\u03b9\u03ba\u03ae \u03b1\u03c3\u03c6\u03ac\u03bb\u03b5\u03b9\u03b1. \u03a4\u03b1 medical VQA benchmarks \u03ba\u03b1\u03b9 \u03c4\u03bf sentence infill \u03b4\u03b5\u03bd \u03b9\u03c3\u03bf\u03b4\u03c5\u03bd\u03b1\u03bc\u03bf\u03cd\u03bd \u03bc\u03b5 \u03c0\u03c1\u03bf\u03bf\u03c0\u03c4\u03b9\u03ba\u03ae \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 \u03bc\u03ad\u03c3\u03b1 \u03c3\u03b5 \u03bd\u03bf\u03c3\u03bf\u03ba\u03bf\u03bc\u03b5\u03b9\u03b1\u03ba\u03ae \u03c1\u03bf\u03ae, \u03bf\u03cd\u03c4\u03b5 \u03b1\u03bd\u03c4\u03b9\u03ba\u03b1\u03b8\u03b9\u03c3\u03c4\u03bf\u03cd\u03bd \u03c4\u03bf\u03bd \u03b1\u03ba\u03c4\u03b9\u03bd\u03bf\u03bb\u03cc\u03b3\u03bf. \u03a3\u03b5 high-stakes \u03b5\u03c6\u03b1\u03c1\u03bc\u03bf\u03b3\u03ad\u03c2 \u03b1\u03c0\u03b1\u03b9\u03c4\u03bf\u03cd\u03bd\u03c4\u03b1\u03b9 \u03ba\u03bb\u03b9\u03bd\u03b9\u03ba\u03ae \u03b5\u03c0\u03b9\u03ba\u03cd\u03c1\u03c9\u03c3\u03b7, \u03b4\u03b9\u03b1\u03ba\u03c5\u03b2\u03ad\u03c1\u03bd\u03b7\u03c3\u03b7 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03c9\u03bd, monitoring \u03ba\u03b1\u03b9 \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03b7 \u03b5\u03c5\u03b8\u03cd\u03bd\u03b7.<\/p>\n<p>\u0394\u03b5\u03cd\u03c4\u03b5\u03c1\u03bf\u03bd, \u03b7 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03bc\u03b5\u03c4\u03c1\u03ae\u03b8\u03b7\u03ba\u03b5 \u03c3\u03b5 \u03bc\u03b5\u03b3\u03ac\u03bb\u03bf \u03b2\u03b1\u03b8\u03bc\u03cc \u03b1\u03c0\u03cc LLM judge. \u0397 semantic equivalence \u03b5\u03af\u03bd\u03b1\u03b9 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03cc\u03c4\u03b5\u03c1\u03b7 \u03b1\u03c0\u03cc \u03c4\u03bf exact match, \u03b1\u03bb\u03bb\u03ac \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03b5\u03c0\u03b9\u03b2\u03b5\u03b2\u03b1\u03af\u03c9\u03c3\u03b7 \u03bc\u03b5 domain experts \u03cc\u03c4\u03b1\u03bd \u03ad\u03bd\u03b1 \u03bb\u03ac\u03b8\u03bf\u03c2 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03bb\u03bb\u03ac\u03be\u03b5\u03b9 \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7. \u03a4\u03c1\u03af\u03c4\u03bf\u03bd, \u03b7 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7 latency \u03b4\u03b5\u03bd \u03b5\u03b3\u03b3\u03c5\u03ac\u03c4\u03b1\u03b9 \u03af\u03b4\u03b9\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03c3\u03b5 \u03ac\u03bb\u03bb\u03bf hardware, \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b1 \u03ba\u03b5\u03af\u03bc\u03b5\u03bd\u03b1 \u03ae \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ad\u03c2 \u03c1\u03c5\u03b8\u03bc\u03af\u03c3\u03b5\u03b9\u03c2 denoising.<\/p>\n<p>\u03a4\u03ad\u03bb\u03bf\u03c2, \u03c4\u03bf paper \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03c1\u03cc\u03c3\u03c6\u03b1\u03c4\u03b7 \u03b5\u03c1\u03b5\u03c5\u03bd\u03b7\u03c4\u03b9\u03ba\u03ae \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03ba\u03b1\u03b8\u03b9\u03b5\u03c1\u03c9\u03bc\u03ad\u03bd\u03bf production standard. \u0397 \u03c3\u03c9\u03c3\u03c4\u03ae \u03c3\u03c4\u03ac\u03c3\u03b7 \u03b5\u03af\u03bd\u03b1\u03b9 \u03bd\u03b1 \u03b1\u03be\u03b9\u03bf\u03c0\u03bf\u03b9\u03b7\u03b8\u03b5\u03af \u03c9\u03c2 \u03c4\u03b5\u03c7\u03bd\u03b9\u03ba\u03cc \u03c3\u03ae\u03bc\u03b1 \u03b3\u03b9\u03b1 controlled editing \u03ba\u03b1\u03b9 \u03bd\u03b1 \u03b4\u03bf\u03ba\u03b9\u03bc\u03b1\u03c3\u03c4\u03b5\u03af \u03bc\u03b5 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03bf pilot, \u03cc\u03c7\u03b9 \u03bd\u03b1 \u03bc\u03b5\u03c4\u03b1\u03c6\u03b5\u03c1\u03b8\u03bf\u03cd\u03bd \u03b1\u03c5\u03c4\u03bf\u03cd\u03c3\u03b9\u03b5\u03c2 \u03bf\u03b9 \u03b9\u03b1\u03c4\u03c1\u03b9\u03ba\u03ad\u03c2 \u03b5\u03c0\u03b9\u03b4\u03cc\u03c3\u03b5\u03b9\u03c2 \u03c3\u03b5 marketing, e-commerce \u03ae customer support.<\/p>\n<h2 id=\"ti-simainei-gia-epicheirimatika-ai-workflows\">\u03a4\u03b9 \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03b3\u03b9\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac AI workflows<\/h2>\n<p>\u03a4\u03bf \u03c0\u03b9\u03bf \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03bf \u03bc\u03ac\u03b8\u03b7\u03bc\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03b7 \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae \u03ba\u03b5\u03b9\u03bc\u03ad\u03bd\u03bf\u03c5 \u03b4\u03b5\u03bd \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03bd\u03b1 \u03be\u03b5\u03ba\u03b9\u03bd\u03ac \u03c0\u03ac\u03bd\u03c4\u03b1 \u03b1\u03c0\u03cc \u03bb\u03b5\u03c5\u03ba\u03ae \u03c3\u03b5\u03bb\u03af\u03b4\u03b1. \u03a0\u03bf\u03bb\u03bb\u03ad\u03c2 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b5\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 constrained editing: \u03c5\u03c0\u03ac\u03c1\u03c7\u03bf\u03c5\u03bd \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 \u03c0\u03bf\u03c5 \u03b4\u03b5\u03bd \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b1\u03bb\u03bb\u03ac\u03be\u03bf\u03c5\u03bd, \u03c4\u03bc\u03ae\u03bc\u03b1\u03c4\u03b1 \u03c0\u03bf\u03c5 \u03ad\u03c7\u03bf\u03c5\u03bd \u03ae\u03b4\u03b7 \u03b5\u03b3\u03ba\u03c1\u03b9\u03b8\u03b5\u03af \u03ba\u03b1\u03b9 \u03ba\u03b5\u03bd\u03ac \u03c0\u03bf\u03c5 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c0\u03c1\u03bf\u03c3\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae \u03bc\u03b5 \u03b2\u03ac\u03c3\u03b7 \u03c4\u03bf \u03c3\u03c5\u03bd\u03bf\u03bb\u03b9\u03ba\u03cc context.<\/p>\n<p>\u03a3\u03b5 e-commerce \u03b1\u03c5\u03c4\u03cc \u03b1\u03c6\u03bf\u03c1\u03ac product descriptions \u03bc\u03b5 locked SKUs, \u03b4\u03b9\u03b1\u03c3\u03c4\u03ac\u03c3\u03b5\u03b9\u03c2, \u03c3\u03c5\u03bc\u03b2\u03b1\u03c4\u03cc\u03c4\u03b7\u03c4\u03b5\u03c2 \u03ba\u03b1\u03b9 \u03cc\u03c1\u03bf\u03c5\u03c2 \u03b5\u03b3\u03b3\u03cd\u03b7\u03c3\u03b7\u03c2. \u03a3\u03c4\u03bf customer support \u03b1\u03c6\u03bf\u03c1\u03ac macros \u03bc\u03b5 \u03c5\u03c0\u03bf\u03c7\u03c1\u03b5\u03c9\u03c4\u03b9\u03ba\u03ac \u03b2\u03ae\u03bc\u03b1\u03c4\u03b1 \u03ba\u03b1\u03b9 \u03bc\u03b5\u03c4\u03b1\u03b2\u03bb\u03b7\u03c4\u03ae \u03b5\u03be\u03b1\u03c4\u03bf\u03bc\u03af\u03ba\u03b5\u03c5\u03c3\u03b7. \u03a3\u03c4\u03bf content marketing \u03b1\u03c6\u03bf\u03c1\u03ac localized \u03c3\u03b5\u03bb\u03af\u03b4\u03b5\u03c2 \u03cc\u03c0\u03bf\u03c5 \u03c4\u03b1 claims, \u03bf\u03b9 \u03c0\u03b7\u03b3\u03ad\u03c2 \u03ba\u03b1\u03b9 \u03c4\u03b1 SEO entities \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03bf\u03c5\u03bd \u03c3\u03c5\u03bd\u03b5\u03c0\u03ae. \u03a3\u03b5 B2B \u03b4\u03b9\u03b1\u03b4\u03b9\u03ba\u03b1\u03c3\u03af\u03b5\u03c2 \u03b1\u03c6\u03bf\u03c1\u03ac \u03c0\u03c1\u03bf\u03c4\u03ac\u03c3\u03b5\u03b9\u03c2, \u03c4\u03b5\u03c7\u03bd\u03b9\u03ba\u03ad\u03c2 \u03b1\u03bd\u03b1\u03c6\u03bf\u03c1\u03ad\u03c2 \u03ba\u03b1\u03b9 SOPs \u03c0\u03bf\u03c5 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b5\u03bb\u03b5\u03b3\u03c7\u03cc\u03bc\u03b5\u03bd\u03b5\u03c2 \u03b1\u03bb\u03bb\u03b1\u03b3\u03ad\u03c2.<\/p>\n<p>\u0397 TWO DOTS \u03b1\u03bd\u03c4\u03b9\u03bc\u03b5\u03c4\u03c9\u03c0\u03af\u03b6\u03b5\u03b9 \u03c4\u03bf\u03c5\u03c2 <a href=\"https:\/\/twodots.gr\/aftomatismoi-epicheiriseon-ai\/\">\u03b1\u03c5\u03c4\u03bf\u03bc\u03b1\u03c4\u03b9\u03c3\u03bc\u03bf\u03cd\u03c2 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03ae\u03c3\u03b5\u03c9\u03bd \u03ba\u03b1\u03b9 \u03c4\u03b9\u03c2 AI \u03b5\u03c6\u03b1\u03c1\u03bc\u03bf\u03b3\u03ad\u03c2<\/a> \u03c9\u03c2 \u03c3\u03c7\u03b5\u03b4\u03b9\u03b1\u03c3\u03bc\u03cc \u03c1\u03bf\u03ae\u03c2, \u03cc\u03c7\u03b9 \u03c9\u03c2 \u03b1\u03c0\u03bf\u03bc\u03bf\u03bd\u03c9\u03bc\u03ad\u03bd\u03bf prompt. \u03a4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c3\u03c5\u03bd\u03b4\u03ad\u03b5\u03c4\u03b1\u03b9 \u03bc\u03b5 \u03ba\u03b1\u03b8\u03b1\u03c1\u03ad\u03c2 \u03c0\u03b7\u03b3\u03ad\u03c2 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03c9\u03bd, \u03b4\u03b9\u03ba\u03b1\u03b9\u03ce\u03bc\u03b1\u03c4\u03b1, approvals, logs \u03ba\u03b1\u03b9 fallback \u03b4\u03b9\u03b1\u03b4\u03b9\u03ba\u03b1\u03c3\u03af\u03b5\u03c2. \u03a4\u03bf \u03af\u03b4\u03b9\u03bf \u03c3\u03ba\u03b5\u03c0\u03c4\u03b9\u03ba\u03cc \u03b9\u03c3\u03c7\u03cd\u03b5\u03b9 \u03ba\u03b1\u03b9 \u03b3\u03b9\u03b1 <a href=\"https:\/\/twodots.gr\/i-alitheia-paragogikotita-ti-prosferoun-pragmatika-voithoi-technitis-noimosynis-michanikous-firmware\/\">AI coding assistants \u03c3\u03b5 \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03b1 \u03c4\u03b5\u03c7\u03bd\u03b9\u03ba\u03ac \u03c3\u03c5\u03c3\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1<\/a>: \u03b7 \u03c4\u03b1\u03c7\u03cd\u03c4\u03b7\u03c4\u03b1 \u03ad\u03c7\u03b5\u03b9 \u03b1\u03be\u03af\u03b1 \u03bc\u03cc\u03bd\u03bf \u03cc\u03c4\u03b1\u03bd \u03c3\u03c5\u03bd\u03bf\u03b4\u03b5\u03cd\u03b5\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03b5\u03bb\u03b5\u03b3\u03c7\u03cc\u03bc\u03b5\u03bd\u03b5\u03c2 \u03b1\u03bb\u03bb\u03b1\u03b3\u03ad\u03c2 \u03ba\u03b1\u03b9 \u03b4\u03c5\u03bd\u03b1\u03c4\u03cc\u03c4\u03b7\u03c4\u03b1 \u03b5\u03c0\u03b1\u03bb\u03ae\u03b8\u03b5\u03c5\u03c3\u03b7\u03c2.<\/p>\n<h2 id=\"praktiko-plaisio-axiologisis-enos-ai-editor\">\u03a0\u03c1\u03b1\u03ba\u03c4\u03b9\u03ba\u03cc \u03c0\u03bb\u03b1\u03af\u03c3\u03b9\u03bf \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7\u03c2 \u03b5\u03bd\u03cc\u03c2 AI editor<\/h2>\n<p>\u039c\u03b9\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03af\u03c1\u03b7\u03c3\u03b7 \u03c0\u03bf\u03c5 \u03b5\u03be\u03b5\u03c4\u03ac\u03b6\u03b5\u03b9 AI editing \u03b4\u03b5\u03bd \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03bd\u03b1 \u03be\u03b5\u03ba\u03b9\u03bd\u03ae\u03c3\u03b5\u03b9 \u03b1\u03c0\u03cc \u03c4\u03bf \u03c0\u03b9\u03bf \u03b5\u03bd\u03c4\u03c5\u03c0\u03c9\u03c3\u03b9\u03b1\u03ba\u03cc \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf. \u03a7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c0\u03c1\u03ce\u03c4\u03b1 \u03bd\u03b1 \u03c0\u03b5\u03c1\u03b9\u03b3\u03c1\u03ac\u03c8\u03b5\u03b9 \u03bc\u03b5 \u03b1\u03ba\u03c1\u03af\u03b2\u03b5\u03b9\u03b1 \u03c0\u03bf\u03b9\u03b1 \u03c3\u03c4\u03bf\u03b9\u03c7\u03b5\u03af\u03b1 \u03b5\u03c0\u03b9\u03c4\u03c1\u03ad\u03c0\u03b5\u03c4\u03b1\u03b9 \u03bd\u03b1 \u03b1\u03bb\u03bb\u03ac\u03b6\u03bf\u03c5\u03bd, \u03c0\u03bf\u03b9\u03b1 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03bf\u03c5\u03bd \u03b1\u03bc\u03b5\u03c4\u03ac\u03b2\u03bb\u03b7\u03c4\u03b1 \u03ba\u03b1\u03b9 \u03c0\u03bf\u03b9\u03bf\u03c2 \u03b5\u03b3\u03ba\u03c1\u03af\u03bd\u03b5\u03b9 \u03c4\u03bf \u03c4\u03b5\u03bb\u03b9\u03ba\u03cc \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1.<\/p>\n<div class=\"td-step-list\">\n<p class=\"td-step-list-title\">\u03a0\u03c1\u03b1\u03ba\u03c4\u03b9\u03ba\u03ac \u03b2\u03ae\u03bc\u03b1\u03c4\u03b1 \u03b3\u03b9\u03b1 \u03b1\u03c3\u03c6\u03b1\u03bb\u03ad\u03c2 AI workflow \u03c3\u03b5 \u03b9\u03b1\u03c4\u03c1\u03b9\u03ba\u03ad\u03c2 \u03b1\u03bd\u03b1\u03c6\u03bf\u03c1\u03ad\u03c2<\/p>\n<ol>\n<li><span class=\"td-step-kicker\">Step 1<\/span><strong>\u03a7\u03b1\u03c1\u03c4\u03bf\u03b3\u03c1\u03b1\u03c6\u03ae\u03c3\u03c4\u03b5 \u03c4\u03b1 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03ac \u03ba\u03b1\u03b9 \u03c4\u03b1 \u03bc\u03b5\u03c4\u03b1\u03b2\u03bb\u03b7\u03c4\u03ac \u03c4\u03bc\u03ae\u03bc\u03b1\u03c4\u03b1.<\/strong>\n<p>\u03a3\u03b7\u03bc\u03b5\u03b9\u03ce\u03c3\u03c4\u03b5 \u03c4\u03b5\u03c7\u03bd\u03b9\u03ba\u03ac \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1, \u03bd\u03bf\u03bc\u03b9\u03ba\u03bf\u03cd\u03c2 \u03cc\u03c1\u03bf\u03c5\u03c2, approved claims \u03ba\u03b1\u03b9 source references \u03c0\u03bf\u03c5 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03ba\u03bb\u03b5\u03b9\u03b4\u03ce\u03bd\u03bf\u03c5\u03bd \u03c3\u03b5 \u03ba\u03ac\u03b8\u03b5 \u03ad\u03ba\u03b4\u03bf\u03c3\u03b7.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Step 2<\/span><strong>\u0394\u03b7\u03bc\u03b9\u03bf\u03c5\u03c1\u03b3\u03ae\u03c3\u03c4\u03b5 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac test cases.<\/strong>\n<p>\u03a7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ae\u03c3\u03c4\u03b5 \u03b1\u03bd\u03ce\u03bd\u03c5\u03bc\u03b1 \u03b4\u03b5\u03af\u03b3\u03bc\u03b1\u03c4\u03b1 \u03b1\u03c0\u03cc \u03c4\u03b7 \u03b4\u03b9\u03ba\u03ae \u03c3\u03b1\u03c2 \u03c1\u03bf\u03ae: \u03b4\u03b9\u03bf\u03c1\u03b8\u03ce\u03c3\u03b5\u03b9\u03c2 \u03c3\u03c4\u03b7 \u03bc\u03ad\u03c3\u03b7, \u03b1\u03bd\u03c4\u03b9\u03ba\u03c1\u03bf\u03c5\u03cc\u03bc\u03b5\u03bd\u03b1 \u03c3\u03c4\u03bf\u03b9\u03c7\u03b5\u03af\u03b1, \u03b5\u03bb\u03bb\u03b9\u03c0\u03ad\u03c2 context \u03ba\u03b1\u03b9 \u03c0\u03b5\u03c1\u03b9\u03c0\u03c4\u03ce\u03c3\u03b5\u03b9\u03c2 \u03cc\u03c0\u03bf\u03c5 \u03b7 \u03c3\u03c9\u03c3\u03c4\u03ae \u03b1\u03c0\u03ac\u03bd\u03c4\u03b7\u03c3\u03b7 \u03b5\u03af\u03bd\u03b1\u03b9 \u03bd\u03b1 \u03bc\u03b7 \u03b3\u03af\u03bd\u03b5\u03b9 \u03b1\u03bb\u03bb\u03b1\u03b3\u03ae.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Step 3<\/span><strong>\u039c\u03b5\u03c4\u03c1\u03ae\u03c3\u03c4\u03b5 fidelity \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03bc\u03cc\u03bd\u03bf fluency.<\/strong>\n<p>\u0395\u03bb\u03ad\u03b3\u03be\u03c4\u03b5 \u03b1\u03bd \u03b4\u03b9\u03b1\u03c4\u03b7\u03c1\u03bf\u03cd\u03bd\u03c4\u03b1\u03b9 \u03bf\u03b9 locked facts, \u03b1\u03bd \u03bc\u03b5\u03b9\u03ce\u03bd\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c4\u03b1 \u03bb\u03ac\u03b8\u03b7 \u03ba\u03b1\u03b9 \u03c0\u03cc\u03c3\u03b5\u03c2 \u03b1\u03bb\u03bb\u03b1\u03b3\u03ad\u03c2 \u03b1\u03c0\u03bf\u03b4\u03ad\u03c7\u03b5\u03c4\u03b1\u03b9 \u03bf reviewer \u03c7\u03c9\u03c1\u03af\u03c2 \u03b5\u03c0\u03b1\u03bd\u03b1\u03c6\u03bf\u03c1\u03ac.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Step 4<\/span><strong>\u039c\u03b5\u03c4\u03c1\u03ae\u03c3\u03c4\u03b5 latency, \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03ba\u03b1\u03b9 review time \u03bc\u03b1\u03b6\u03af.<\/strong>\n<p>\u0388\u03bd\u03b1 \u03b3\u03c1\u03ae\u03b3\u03bf\u03c1\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03bf\u03b9\u03ba\u03bf\u03bd\u03bf\u03bc\u03b9\u03ba\u03cc \u03b1\u03bd \u03b1\u03c5\u03be\u03ac\u03bd\u03b5\u03b9 \u03c4\u03b9\u03c2 \u03b4\u03b9\u03bf\u03c1\u03b8\u03ce\u03c3\u03b5\u03b9\u03c2. \u03a4\u03bf \u03c3\u03c9\u03c3\u03c4\u03cc KPI \u03c3\u03c5\u03bd\u03b4\u03c5\u03ac\u03b6\u03b5\u03b9 \u03c7\u03c1\u03cc\u03bd\u03bf \u03b1\u03c0\u03cc\u03ba\u03c1\u03b9\u03c3\u03b7\u03c2, \u03c5\u03c0\u03bf\u03bb\u03bf\u03b3\u03b9\u03c3\u03c4\u03b9\u03ba\u03cc \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03ba\u03b1\u03b9 \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03bf \u03c7\u03c1\u03cc\u03bd\u03bf \u03b5\u03bb\u03ad\u03b3\u03c7\u03bf\u03c5.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Step 5<\/span><strong>\u03a0\u03c1\u03bf\u03c3\u03b8\u03ad\u03c3\u03c4\u03b5 versioning \u03ba\u03b1\u03b9 \u03b1\u03c3\u03c6\u03b1\u03bb\u03ad\u03c2 fallback.<\/strong>\n<p>\u039a\u03ac\u03b8\u03b5 \u03b1\u03bb\u03bb\u03b1\u03b3\u03ae \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03bf\u03c1\u03b1\u03c4\u03ae \u03c0\u03c1\u03b9\u03bd \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03b1\u03c0\u03bf\u03b4\u03bf\u03c7\u03ae, \u03bd\u03b1 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03bd\u03b1\u03b9\u03c1\u03b5\u03b8\u03b5\u03af \u03ba\u03b1\u03b9 \u03bd\u03b1 \u03b4\u03c1\u03bf\u03bc\u03bf\u03bb\u03bf\u03b3\u03b5\u03af\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03ac\u03bd\u03b8\u03c1\u03c9\u03c0\u03bf \u03cc\u03c4\u03b1\u03bd \u03b7 \u03b2\u03b5\u03b2\u03b1\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03ae \u03b7 \u03c0\u03b7\u03b3\u03ae \u03b4\u03b5\u03bd \u03b5\u03c0\u03b1\u03c1\u03ba\u03b5\u03af.<\/p>\n<\/li>\n<\/ol>\n<\/div>\n<p>\u03a4\u03bf pilot \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03ad\u03c7\u03b5\u03b9 \u03c0\u03c1\u03bf\u03ba\u03b1\u03b8\u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03bf baseline \u03ba\u03b1\u03b9 exit criteria. \u0391\u03bd \u03c4\u03bf AI \u03bc\u03b5\u03b9\u03ce\u03bd\u03b5\u03b9 \u03c4\u03bf\u03bd \u03c7\u03c1\u03cc\u03bd\u03bf \u03c3\u03cd\u03bd\u03c4\u03b1\u03be\u03b7\u03c2 \u03b1\u03bb\u03bb\u03ac \u03b1\u03c5\u03be\u03ac\u03bd\u03b5\u03b9 \u03c4\u03b1 factual errors, \u03b4\u03b5\u03bd \u03ad\u03c7\u03b5\u03b9 \u03c0\u03b5\u03c4\u03cd\u03c7\u03b5\u03b9. \u0391\u03bd \u03b2\u03b5\u03bb\u03c4\u03b9\u03ce\u03bd\u03b5\u03b9 \u03c4\u03b7\u03bd \u03c4\u03b1\u03c7\u03cd\u03c4\u03b7\u03c4\u03b1, \u03b4\u03b9\u03b1\u03c4\u03b7\u03c1\u03b5\u03af \u03c4\u03b1 \u03ba\u03bb\u03b5\u03b9\u03b4\u03c9\u03bc\u03ad\u03bd\u03b1 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 \u03ba\u03b1\u03b9 \u03bc\u03b5\u03b9\u03ce\u03bd\u03b5\u03b9 \u03c4\u03bf\u03bd \u03c7\u03c1\u03cc\u03bd\u03bf review, \u03c4\u03cc\u03c4\u03b5 \u03c5\u03c0\u03ac\u03c1\u03c7\u03b5\u03b9 \u03c4\u03b5\u03ba\u03bc\u03b7\u03c1\u03b9\u03c9\u03bc\u03ad\u03bd\u03b7 \u03b2\u03ac\u03c3\u03b7 \u03b3\u03b9\u03b1 \u03ba\u03bb\u03b9\u03bc\u03ac\u03ba\u03c9\u03c3\u03b7.<\/p>\n<section class=\"td-service-cta\">\n<div class=\"td-service-cta-content\">\n<p class=\"td-service-cta-eyebrow\">\u0391\u03c0\u03cc \u03c4\u03bf \u03c0\u03b5\u03af\u03c1\u03b1\u03bc\u03b1 \u03c3\u03b5 \u03b5\u03bb\u03b5\u03b3\u03c7\u03cc\u03bc\u03b5\u03bd\u03b7 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03c3\u03b9\u03b1\u03ba\u03ae \u03c1\u03bf\u03ae<\/p>\n<h2 id=\"aftomatismoi-kai-ai-workflows-apo-tin-two-dots\">\u0391\u03c5\u03c4\u03bf\u03bc\u03b1\u03c4\u03b9\u03c3\u03bc\u03bf\u03af \u03ba\u03b1\u03b9 AI workflows \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd TWO DOTS<\/h2>\n<p>\u0397 TWO DOTS \u03c7\u03b1\u03c1\u03c4\u03bf\u03b3\u03c1\u03b1\u03c6\u03b5\u03af \u03c4\u03b9\u03c2 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b4\u03b9\u03b1\u03b4\u03b9\u03ba\u03b1\u03c3\u03af\u03b5\u03c2 \u03c4\u03b7\u03c2 \u03b5\u03c0\u03b9\u03c7\u03b5\u03af\u03c1\u03b7\u03c3\u03b7\u03c2, \u03c3\u03c5\u03bd\u03b4\u03ad\u03b5\u03b9 \u03b5\u03c1\u03b3\u03b1\u03bb\u03b5\u03af\u03b1 \u03ba\u03b1\u03b9 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 \u03ba\u03b1\u03b9 \u03c3\u03c7\u03b5\u03b4\u03b9\u03ac\u03b6\u03b5\u03b9 AI \u03c1\u03bf\u03ad\u03c2 \u03bc\u03b5 approvals, \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b9\u03bc\u03b1 KPIs \u03ba\u03b1\u03b9 \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03bf \u03ad\u03bb\u03b5\u03b3\u03c7\u03bf. \u03a3\u03c4\u03cc\u03c7\u03bf\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03bb\u03b9\u03b3\u03cc\u03c4\u03b5\u03c1\u03b1 \u03c7\u03b5\u03b9\u03c1\u03bf\u03ba\u03af\u03bd\u03b7\u03c4\u03b1 \u03b2\u03ae\u03bc\u03b1\u03c4\u03b1 \u03ba\u03b1\u03b9 \u03bb\u03ac\u03b8\u03b7, \u03c7\u03c9\u03c1\u03af\u03c2 \u03bd\u03b1 \u03c7\u03ac\u03bd\u03b5\u03c4\u03b1\u03b9 \u03b7 \u03b5\u03c5\u03b8\u03cd\u03bd\u03b7 \u03b3\u03b9\u03b1 \u03c4\u03bf \u03c4\u03b5\u03bb\u03b9\u03ba\u03cc \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1.<\/p>\n<div class=\"td-service-cta-actions\"><a class=\"td-service-cta-button\" href=\"https:\/\/twodots.gr\/aftomatismoi-epicheiriseon-ai\/\">\u0394\u03b5\u03af\u03c4\u03b5 \u03c4\u03bf\u03c5\u03c2 \u0391\u03c5\u03c4\u03bf\u03bc\u03b1\u03c4\u03b9\u03c3\u03bc\u03bf\u03cd\u03c2 &amp; 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 diffusion language model;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u0395\u03af\u03bd\u03b1\u03b9 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03c0\u03bf\u03c5 \u03c0\u03b1\u03c1\u03ac\u03b3\u03b5\u03b9 \u03ba\u03b5\u03af\u03bc\u03b5\u03bd\u03bf \u03b1\u03c0\u03bf\u03b8\u03bf\u03c1\u03c5\u03b2\u03bf\u03c0\u03bf\u03b9\u03ce\u03bd\u03c4\u03b1\u03c2 \u03b5\u03c0\u03b1\u03bd\u03b1\u03bb\u03b7\u03c0\u03c4\u03b9\u03ba\u03ac \u03ad\u03bd\u03b1\u03bd token canvas. \u03a3\u03b5 \u03b1\u03bd\u03c4\u03af\u03b8\u03b5\u03c3\u03b7 \u03bc\u03b5 \u03c4\u03b7\u03bd \u03ba\u03bb\u03b1\u03c3\u03b9\u03ba\u03ae \u03b1\u03c1\u03b9\u03c3\u03c4\u03b5\u03c1\u03ac-\u03c0\u03c1\u03bf\u03c2-\u03b4\u03b5\u03be\u03b9\u03ac \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae, \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af context \u03b1\u03c0\u03cc \u03bf\u03bb\u03cc\u03ba\u03bb\u03b7\u03c1\u03b7 \u03c4\u03b7 \u03b4\u03b9\u03b1\u03b8\u03ad\u03c3\u03b9\u03bc\u03b7 \u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03af\u03b1.<\/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 any-order infill;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03bf \u03c7\u03c1\u03ae\u03c3\u03c4\u03b7\u03c2 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03ba\u03bb\u03b5\u03b9\u03b4\u03ce\u03c3\u03b5\u03b9 \u03c3\u03c9\u03c3\u03c4\u03ac \u03c4\u03bc\u03ae\u03bc\u03b1\u03c4\u03b1 \u03c0\u03c1\u03b9\u03bd \u03ba\u03b1\u03b9 \u03bc\u03b5\u03c4\u03ac \u03b1\u03c0\u03cc \u03ad\u03bd\u03b1 \u03ba\u03b5\u03bd\u03cc \u03ba\u03b1\u03b9 \u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03bd\u03b1 \u03c3\u03c5\u03bc\u03c0\u03bb\u03b7\u03c1\u03ce\u03c3\u03b5\u03b9 \u03bc\u03cc\u03bd\u03bf \u03c4\u03bf \u03b5\u03bd\u03b4\u03b9\u03ac\u03bc\u03b5\u03c3\u03bf, \u03c7\u03c9\u03c1\u03af\u03c2 \u03bd\u03b1 \u03be\u03b1\u03bd\u03b1\u03b3\u03c1\u03ac\u03c8\u03b5\u03b9 \u03cc\u03bb\u03bf \u03c4\u03bf \u03ba\u03b5\u03af\u03bc\u03b5\u03bd\u03bf.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u0389\u03c4\u03b1\u03bd \u03c4\u03bf DiffusionGemma \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf \u03c3\u03b5 \u03cc\u03bb\u03b1 \u03c4\u03b1 tests;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u038c\u03c7\u03b9. \u0399\u03c3\u03bf\u03c6\u03ac\u03c1\u03b9\u03c3\u03b5 \u03ae \u03be\u03b5\u03c0\u03ad\u03c1\u03b1\u03c3\u03b5 \u03c4\u03bf autoregressive sibling \u03c3\u03c4\u03b1 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b1 datasets \u03ba\u03b1\u03b9 metrics, \u03b1\u03bb\u03bb\u03ac \u03bc\u03cc\u03bd\u03bf \u03b4\u03cd\u03bf \u03b1\u03c0\u03cc \u03c4\u03b9\u03c2 \u03ad\u03be\u03b9 \u03b2\u03b1\u03c3\u03b9\u03ba\u03ad\u03c2 \u03c3\u03c5\u03b3\u03ba\u03c1\u03af\u03c3\u03b5\u03b9\u03c2 \u03b5\u03af\u03c7\u03b1\u03bd \u03c3\u03c4\u03b1\u03c4\u03b9\u03c3\u03c4\u03b9\u03ba\u03ac \u03c3\u03b7\u03bc\u03b1\u03bd\u03c4\u03b9\u03ba\u03ae \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03ac.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a0\u03cc\u03c3\u03bf \u03c4\u03b1\u03c7\u03cd\u03c4\u03b5\u03c1\u03bf \u03ae\u03c4\u03b1\u03bd \u03c4\u03bf diffusion \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a3\u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf benchmark \u03c0\u03b5\u03c1\u03af\u03c0\u03bf\u03c5 256 tokens \u03c0\u03ac\u03bd\u03c9 \u03c3\u03b5 \u03bc\u03af\u03b1 H100, \u03c4\u03bf decoding \u03ae\u03c4\u03b1\u03bd 3,5 \u03ad\u03c9\u03c2 4,4 \u03c6\u03bf\u03c1\u03ad\u03c2 \u03c4\u03b1\u03c7\u03cd\u03c4\u03b5\u03c1\u03bf \u03ba\u03b1\u03b9 \u03c4\u03bf throughput 5,7 \u03ad\u03c9\u03c2 7,1 \u03c6\u03bf\u03c1\u03ad\u03c2 \u03c5\u03c8\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03bf.<\/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 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03bd\u03b1 \u03b3\u03c1\u03ac\u03c6\u03b5\u03b9 \u03b1\u03c5\u03c4\u03cc\u03bd\u03bf\u03bc\u03b1 \u03b9\u03b1\u03c4\u03c1\u03b9\u03ba\u03ad\u03c2 \u03b1\u03bd\u03b1\u03c6\u03bf\u03c1\u03ad\u03c2;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a4\u03bf paper \u03b4\u03b5\u03bd \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03b5\u03af \u03ba\u03bb\u03b9\u03bd\u03b9\u03ba\u03ae \u03c0\u03b9\u03c3\u03c4\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7. \u0397 \u03b1\u03c3\u03c6\u03b1\u03bb\u03ae\u03c2 \u03b9\u03b1\u03c4\u03c1\u03b9\u03ba\u03ae \u03c7\u03c1\u03ae\u03c3\u03b7 \u03b1\u03c0\u03b1\u03b9\u03c4\u03b5\u03af \u03b1\u03bd\u03b5\u03be\u03ac\u03c1\u03c4\u03b7\u03c4\u03b7 \u03ba\u03bb\u03b9\u03bd\u03b9\u03ba\u03ae \u03b5\u03c0\u03b9\u03ba\u03cd\u03c1\u03c9\u03c3\u03b7, \u03b4\u03b9\u03b1\u03ba\u03c5\u03b2\u03ad\u03c1\u03bd\u03b7\u03c3\u03b7 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03c9\u03bd, \u03c3\u03c5\u03bd\u03b5\u03c7\u03ae monitoring \u03ba\u03b1\u03b9 \u03c4\u03b5\u03bb\u03b9\u03ba\u03ae \u03b5\u03c5\u03b8\u03cd\u03bd\u03b7 \u03b1\u03c0\u03cc \u03b5\u03b9\u03b4\u03b9\u03ba\u03cc.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a0\u03bf\u03cd \u03ad\u03c7\u03b5\u03b9 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03b1\u03be\u03af\u03b1 \u03c4\u03bf controlled infill;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a3\u03b5 product descriptions, knowledge bases, support macros, \u03c0\u03c1\u03bf\u03c4\u03ac\u03c3\u03b5\u03b9\u03c2 \u03ba\u03b1\u03b9 SOPs \u03cc\u03c0\u03bf\u03c5 \u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03b1 facts \u03ae \u03cc\u03c1\u03bf\u03b9 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03bf\u03c5\u03bd \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03ac \u03b5\u03bd\u03ce \u03b1\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9 \u03bc\u03cc\u03bd\u03bf \u03ad\u03bd\u03b1 \u03b5\u03bb\u03b5\u03b3\u03c7\u03cc\u03bc\u03b5\u03bd\u03bf \u03c4\u03bc\u03ae\u03bc\u03b1.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a0\u03bf\u03b9\u03bf \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03bf \u03c0\u03c1\u03ce\u03c4\u03bf \u03b2\u03ae\u03bc\u03b1 \u03b3\u03b9\u03b1 \u03ad\u03bd\u03b1 AI editing pilot;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u039d\u03b1 \u03bf\u03c1\u03b9\u03c3\u03c4\u03bf\u03cd\u03bd \u03c4\u03b1 locked facts, \u03c4\u03b1 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac test cases, \u03bf\u03b9 \u03bc\u03b5\u03c4\u03c1\u03b9\u03ba\u03ad\u03c2 fidelity \u03ba\u03b1\u03b9 \u03bf \u03ac\u03bd\u03b8\u03c1\u03c9\u03c0\u03bf\u03c2 \u03c0\u03bf\u03c5 \u03b5\u03b3\u03ba\u03c1\u03af\u03bd\u03b5\u03b9 \u03ae \u03b1\u03c0\u03bf\u03c1\u03c1\u03af\u03c0\u03c4\u03b5\u03b9 \u03ba\u03ac\u03b8\u03b5 \u03b1\u03bb\u03bb\u03b1\u03b3\u03ae \u03c0\u03c1\u03b9\u03bd \u03b1\u03c5\u03c4\u03ae \u03c0\u03b5\u03c1\u03ac\u03c3\u03b5\u03b9 \u03c3\u03c4\u03b7\u03bd \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae.<\/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\/2607.01436\" target=\"_blank\" rel=\"noopener\">Discrete Diffusion Language Models for Interactive Radiology Report Drafting \u2014 arXiv<\/a><\/li>\n<li><a href=\"https:\/\/physionet.org\/content\/mimic-cxr\/\" target=\"_blank\" rel=\"noopener\">MIMIC-CXR Database \u2014 PhysioNet<\/a><\/li>\n<li><a href=\"https:\/\/arxiv.org\/abs\/2404.07424\" target=\"_blank\" rel=\"noopener\">CopilotCAD: report completion models for radiologists \u2014 arXiv<\/a><\/li>\n<\/ul>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>\u03a4\u03b9 \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03bd\u03ad\u03bf paper \u03b3\u03b9\u03b1 diffusion \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1, any-order infill, \u03c4\u03b1\u03c7\u03cd\u03c4\u03b7\u03c4\u03b1 \u03ba\u03b1\u03b9 \u03b5\u03bb\u03b5\u03b3\u03c7\u03cc\u03bc\u03b5\u03bd\u03b1 AI workflows \u03bc\u03b5 locked facts \u03ba\u03b1\u03b9 \u03b1\u03bd\u03b8\u03c1\u03ce\u03c0\u03b9\u03bd\u03bf review.<\/p>","protected":false},"author":1,"featured_media":85347,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_gspb_post_css":"","footnotes":""},"categories":[17366],"tags":[18032,17841,18092,18094,18093],"class_list":["post-84557","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-digital-marketing","tag-ai-stin-ygeia","tag-business-ai","tag-diffusion-models","tag-llm-workflows","tag-iatriki-techniti-noimosyni"],"blocksy_meta":[],"_links":{"self":[{"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/posts\/84557","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=84557"}],"version-history":[{"count":0,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/posts\/84557\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/media\/85347"}],"wp:attachment":[{"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/media?parent=84557"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/categories?post=84557"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/twodots.gr\/en\/wp-json\/wp\/v2\/tags?post=84557"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}