{"id":12778,"date":"2026-07-30T17:44:20","date_gmt":"2026-07-30T08:44:20","guid":{"rendered":"https:\/\/www.ibs.re.kr\/bimag\/?post_type=tribe_events&#038;p=12778"},"modified":"2026-07-30T17:44:20","modified_gmt":"2026-07-30T08:44:20","slug":"automl-agent-a-multi-agent-llm-framework-for-full-pipeline-automl-jin-woo-hyun","status":"publish","type":"tribe_events","link":"https:\/\/www.ibs.re.kr\/bimag\/event\/automl-agent-a-multi-agent-llm-framework-for-full-pipeline-automl-jin-woo-hyun\/","title":{"rendered":"AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML &#8211; Jin Woo Hyun"},"content":{"rendered":"<p>In this talk, we discuss the paper &#8220;AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML&#8221; by Patara Trirat et al, <em>ICML<\/em>, 2025.<\/p>\n<p>Abstract:<\/p>\n<p>Automated machine learning (AutoML) accelerates AI development by automating tasks in the development pipeline, such as optimal model search and hyperparameter tuning. Existing AutoML systems often require technical expertise to set up complex tools, which is in general time-consuming and requires a large amount of human effort. Therefore, recent works have started exploiting large language models (LLM) to lessen such burden and increase the usability of AutoML frameworks via a natural language interface, allowing non-expert users to build their data-driven solutions. These methods, however, are usually designed only for a particular process in the AI development pipeline and do not efficiently use the inherent capacity of the LLMs. This paper proposes\u00a0<em>AutoML-Agent<\/em>, a novel multi-agent framework tailored for full-pipeline AutoML, i.e., from data retrieval to model deployment.\u00a0<em>AutoML-Agent<\/em>\u00a0takes user&#8217;s task descriptions, facilitates collaboration between specialized LLM agents, and delivers deployment-ready models. Unlike existing work, instead of devising a single plan, we introduce a retrieval-augmented planning strategy to enhance exploration to search for more optimal plans. We also decompose each plan into sub-tasks (e.g., data preprocessing and neural network design) each of which is solved by a specialized agent we build via prompting executing in parallel, making the search process more efficient. Moreover, we propose a multi-stage verification to verify executed results and guide the code generation LLM in implementing successful solutions. Extensive experiments on seven downstream tasks using fourteen datasets show that\u00a0<em>AutoML-Agent<\/em>\u00a0achieves a higher success rate in automating the full AutoML process, yielding systems with good performance throughout the diverse domains.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this talk, we discuss the paper &#8220;AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML&#8221; by Patara Trirat et al, ICML, 2025. Abstract: Automated machine learning (AutoML) accelerates AI development &hellip; <\/p>\n<p class=\"link-more\"><a href=\"https:\/\/www.ibs.re.kr\/bimag\/event\/automl-agent-a-multi-agent-llm-framework-for-full-pipeline-automl-jin-woo-hyun\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML &#8211; Jin Woo Hyun&#8221;<\/span><\/a><\/p>\n","protected":false},"author":13,"featured_media":0,"template":"","meta":{"_editorskit_title_hidden":false,"_editorskit_reading_time":0,"_editorskit_is_block_options_detached":false,"_editorskit_block_options_position":"{}","_uag_custom_page_level_css":"","_tribe_events_status":"","_tribe_events_status_reason":"","footnotes":""},"tags":[],"tribe_events_cat":[219],"class_list":["post-12778","tribe_events","type-tribe_events","status-publish","hentry","tribe_events_cat-journal-club","cat_journal-club"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML - Jin Woo Hyun - Biomedical Mathematics Group<\/title>\n<meta name=\"description\" content=\"In this talk, we discuss the paper &quot;AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML&quot; by Patara Trirat et al, ICML, 2025.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.ibs.re.kr\/bimag\/event\/automl-agent-a-multi-agent-llm-framework-for-full-pipeline-automl-jin-woo-hyun\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML - Jin Woo Hyun - Biomedical Mathematics Group\" \/>\n<meta property=\"og:description\" content=\"In this talk, we discuss the paper &quot;AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML&quot; by Patara Trirat et al, ICML, 2025.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.ibs.re.kr\/bimag\/event\/automl-agent-a-multi-agent-llm-framework-for-full-pipeline-automl-jin-woo-hyun\/\" \/>\n<meta property=\"og:site_name\" content=\"Biomedical Mathematics Group\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/event\\\/automl-agent-a-multi-agent-llm-framework-for-full-pipeline-automl-jin-woo-hyun\\\/\",\"url\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/event\\\/automl-agent-a-multi-agent-llm-framework-for-full-pipeline-automl-jin-woo-hyun\\\/\",\"name\":\"AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML - Jin Woo Hyun - Biomedical Mathematics Group\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/#website\"},\"datePublished\":\"2026-07-30T08:44:20+00:00\",\"description\":\"In this talk, we discuss the paper \\\"AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML\\\" by Patara Trirat et al, ICML, 2025.\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/event\\\/automl-agent-a-multi-agent-llm-framework-for-full-pipeline-automl-jin-woo-hyun\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/event\\\/automl-agent-a-multi-agent-llm-framework-for-full-pipeline-automl-jin-woo-hyun\\\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/event\\\/automl-agent-a-multi-agent-llm-framework-for-full-pipeline-automl-jin-woo-hyun\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Events\",\"item\":\"https:\\\/\\\/www.ibs.re.kr\\\/bimag\\\/events\\\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML &#8211; 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Jin Woo Hyun"}]},{"@type":"WebSite","@id":"https:\/\/www.ibs.re.kr\/bimag\/#website","url":"https:\/\/www.ibs.re.kr\/bimag\/","name":"Biomedical Mathematics Group","description":"\uae30\ucd08\uacfc\ud559\uc5f0\uad6c\uc6d0 \uc758\uc0dd\uba85\uc218\ud559\uadf8\ub8f9","publisher":{"@id":"https:\/\/www.ibs.re.kr\/bimag\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.ibs.re.kr\/bimag\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/www.ibs.re.kr\/bimag\/#organization","name":"IBS Biomedical Mathematics Group","url":"https:\/\/www.ibs.re.kr\/bimag\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.ibs.re.kr\/bimag\/#\/schema\/logo\/image\/","url":"https:\/\/www.ibs.re.kr\/bimag\/cms\/wp-content\/uploads\/2021\/02\/ibs-circle-1.png","contentUrl":"https:\/\/www.ibs.re.kr\/bimag\/cms\/wp-content\/uploads\/2021\/02\/ibs-circle-1.png","width":250,"height":250,"caption":"IBS Biomedical Mathematics Group"},"image":{"@id":"https:\/\/www.ibs.re.kr\/bimag\/#\/schema\/logo\/image\/"}}]}},"uagb_featured_image_src":{"full":false,"thumbnail":false,"medium":false,"medium_large":false,"large":false,"1536x1536":false,"2048x2048":false,"dimag-thumbnail":false,"twentyseventeen-featured-image":false,"twentyseventeen-thumbnail-avatar":false},"uagb_author_info":{"display_name":"Hyeong Jun Jang","author_link":"https:\/\/www.ibs.re.kr\/bimag\/author\/hyeong-jun-jang\/"},"uagb_comment_info":0,"uagb_excerpt":"In this talk, we discuss the paper &#8220;AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML&#8221; 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