{"id":3739,"date":"2025-09-24T22:40:16","date_gmt":"2025-09-24T22:40:16","guid":{"rendered":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=3739"},"modified":"2025-09-25T01:48:59","modified_gmt":"2025-09-25T01:48:59","slug":"latent-aggregation-for-real-time-compression-of-multi-modal-metrics","status":"publish","type":"post","link":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=3739","title":{"rendered":"Latent Aggregation for Real-Time Compression of Multi-Modal Metrics"},"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=\"GLir9xURLA\"><a href=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?page_id=3736\">Latent Aggregation for Real-Time Compression of Multi-Modal Metrics<\/a><\/blockquote><iframe class=\"wp-embedded-content\" sandbox=\"allow-scripts\" security=\"restricted\" style=\"position: absolute; visibility: hidden;\" title=\"&#8220;Latent Aggregation for Real-Time Compression of Multi-Modal Metrics&#8221; &#8212; Spectrcyde\" src=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?page_id=3736&#038;embed=true#?secret=NJKiA59K3m#?secret=GLir9xURLA\" data-secret=\"GLir9xURLA\" width=\"600\" height=\"338\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\"><\/iframe>\n<\/div><\/figure>\n\n\n\n<p>We implement a Multi-Head Latent Attentioninspired aggregator that compresses multi-modal telemetry into<br>per-topic latent summaries (count\/avg\/min\/max, trend direction,<br>anomaly counts) and evaluate (i) anomaly detection quality, (ii)<br>trend direction accuracy, and (iii) lossy compression efficiency.<br>We show high F1 for anomalies, accurate trend sign, and 10\u2013<br>40\u00d7 compression at useful fidelity. The design follows your<br>LatentAggregator (trend and anomaly routines) and its<br>downstream latent-summary analysis.<\/p>\n\n\n\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:\/\/172-234-197-23.ip.linodeusercontent.com\/wp-content\/uploads\/2025\/09\/Latent-Aggregation-for-Real-Time-Compression.pdf\" type=\"application\/pdf\" style=\"width:100%;height:600px\" aria-label=\"Embed of Latent Aggregation for Real-Time Compression.\"><\/object><a id=\"wp-block-file--media-52c0cf23-9aa0-4f74-97ea-d59996815f63\" href=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/wp-content\/uploads\/2025\/09\/Latent-Aggregation-for-Real-Time-Compression.pdf\">Latent Aggregation for Real-Time Compression<\/a><a href=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/wp-content\/uploads\/2025\/09\/Latent-Aggregation-for-Real-Time-Compression.pdf\" class=\"wp-block-file__button wp-element-button\" download aria-describedby=\"wp-block-file--media-52c0cf23-9aa0-4f74-97ea-d59996815f63\">Download<\/a><\/div>\n\n\n\n<p>#WuqingXinhaoLiandao \/ bgilbert1984<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We implement a Multi-Head Latent Attentioninspired aggregator that compresses multi-modal telemetry intoper-topic latent summaries (count\/avg\/min\/max, trend direction,anomaly counts) and evaluate (i) anomaly detection quality, (ii)trend direction accuracy, and (iii) lossy compression efficiency.We show high F1 for anomalies, accurate trend sign, and 10\u201340\u00d7 compression at useful fidelity. The design follows yourLatentAggregator (trend and anomaly routines) and&hellip;&nbsp;<a href=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=3739\" rel=\"bookmark\"><span class=\"screen-reader-text\">Latent Aggregation for Real-Time Compression of Multi-Modal Metrics<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":2156,"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":[10],"tags":[],"class_list":["post-3739","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-signal_scythe"],"_links":{"self":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/3739","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=3739"}],"version-history":[{"count":3,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/3739\/revisions"}],"predecessor-version":[{"id":3744,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/3739\/revisions\/3744"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/media\/2156"}],"wp:attachment":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3739"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3739"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3739"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}