{"id":3677,"date":"2025-09-22T20:50:30","date_gmt":"2025-09-22T20:50:30","guid":{"rendered":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=3677"},"modified":"2025-09-22T20:50:30","modified_gmt":"2025-09-22T20:50:30","slug":"active-learning-for-synthetic-rf-benches-from-random-grids-to-agentic-sweeps-2","status":"publish","type":"post","link":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=3677","title":{"rendered":"Active Learning for Synthetic RF Benches: From Random Grids to Agentic Sweeps"},"content":{"rendered":"\n<figure class=\"wp-block-embed is-type-wp-embed is-provider-spectrcyde wp-block-embed-spectrcyde\"><div class=\"wp-block-embed__wrapper\">\n<blockquote class=\"wp-embedded-content\" data-secret=\"C5YPYYJOlM\"><a href=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=3675\">Active Learning for Synthetic RF Benches: From Random Grids to Agentic Sweeps<\/a><\/blockquote><iframe class=\"wp-embedded-content\" sandbox=\"allow-scripts\" security=\"restricted\" style=\"position: absolute; visibility: hidden;\" title=\"&#8220;Active Learning for Synthetic RF Benches: From Random Grids to Agentic Sweeps&#8221; &#8212; Spectrcyde\" src=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=3675&#038;embed=true#?secret=1AOr7QMJFb#?secret=C5YPYYJOlM\" data-secret=\"C5YPYYJOlM\" width=\"600\" height=\"338\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\"><\/iframe>\n<\/div><\/figure>\n\n\n\n<p>Exhaustive parameter sweeps are the de facto method for benchmarking RF pipelines, but<br>they scale poorly with dimensionality[2]. For example, a 10-parameter grid with 10 points each<br>requires 1010 evaluations. Active learning promises to achieve comparable confidence using far<br>fewer samples by targeting the most informative points[1]. This paper constructs a synthetic<br>ground-truth generator for an RF performance field and compares random grid sampling to<br>a Gaussian process (GP) guided \u201cagentic sweep.\u201d We measure coverage of the true decision<br>boundary as a function of sample budget and explore how exploration versus exploitation balances affect performance. Results show that uncertainty-guided sampling achieves the same<br>classification coverage with approximately 3\u00d7 fewer samples than random grids at 90% coverage thresholds, and that a modest amount of random exploration is beneficial. These insights<br>support using agentic sweeps for efficient characterization of RF demodulation pipelines.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Exhaustive parameter sweeps are the de facto method for benchmarking RF pipelines, butthey scale poorly with dimensionality[2]. For example, a 10-parameter grid with 10 points eachrequires 1010 evaluations. Active learning promises to achieve comparable confidence using farfewer samples by targeting the most informative points[1]. This paper constructs a syntheticground-truth generator for an RF performance field&hellip;&nbsp;<a href=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=3677\" rel=\"bookmark\"><span class=\"screen-reader-text\">Active Learning for Synthetic RF Benches: From Random Grids to Agentic Sweeps<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":1494,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"neve_meta_sidebar":"","neve_meta_container":"","neve_meta_enable_content_width":"","neve_meta_content_width":0,"neve_meta_title_alignment":"","neve_meta_author_avatar":"","neve_post_elements_order":"","neve_meta_disable_header":"","neve_meta_disable_footer":"","neve_meta_disable_title":"","footnotes":""},"categories":[1],"tags":[],"class_list":["post-3677","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/3677","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=3677"}],"version-history":[{"count":1,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/3677\/revisions"}],"predecessor-version":[{"id":3678,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/3677\/revisions\/3678"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/media\/1494"}],"wp:attachment":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3677"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3677"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3677"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}