{"id":3672,"date":"2025-09-22T20:29:08","date_gmt":"2025-09-22T20:29:08","guid":{"rendered":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?page_id=3672"},"modified":"2025-09-22T20:29:08","modified_gmt":"2025-09-22T20:29:08","slug":"active-learning-for-synthetic-rf-benches-from-random-grids-to-agentic-sweeps","status":"publish","type":"page","link":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/?page_id=3672","title":{"rendered":"Active Learning for Synthetic RF Benches: From Random Grids to Agentic Sweeps"},"content":{"rendered":"\n<div data-wp-interactive=\"core\/file\" class=\"wp-block-file\"><object data-wp-bind--hidden=\"!state.hasPdfPreview\" hidden class=\"wp-block-file__embed\" data=\"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/09\/Active_Learning_for_Synthetic_RF_Benches.pdf\" type=\"application\/pdf\" style=\"width:100%;height:600px\" aria-label=\"Embed of Active_Learning_for_Synthetic_RF_Benches.\"><\/object><a id=\"wp-block-file--media-fc87b073-f49b-4599-9e0c-870c01def099\" href=\"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/09\/Active_Learning_for_Synthetic_RF_Benches.pdf\">Active_Learning_for_Synthetic_RF_Benches<\/a><a href=\"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/09\/Active_Learning_for_Synthetic_RF_Benches.pdf\" class=\"wp-block-file__button wp-element-button\" download aria-describedby=\"wp-block-file--media-fc87b073-f49b-4599-9e0c-870c01def099\">Download<\/a><\/div>\n\n\n\n<p class=\"wp-block-paragraph\">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;<\/p>\n","protected":false},"author":2,"featured_media":2899,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","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":""},"class_list":["post-3672","page","type-page","status-publish","has-post-thumbnail","hentry"],"_links":{"self":[{"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/pages\/3672","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=3672"}],"version-history":[{"count":0,"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/pages\/3672\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/media\/2899"}],"wp:attachment":[{"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3672"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}