{"id":12834,"date":"2026-03-18T07:19:15","date_gmt":"2026-03-18T07:19:15","guid":{"rendered":"https:\/\/techtrendfeed.com\/?p=12834"},"modified":"2026-03-18T07:19:16","modified_gmt":"2026-03-18T07:19:16","slug":"ames-approximate-multi-modal-enterprise-search-through-late-interplay-retrieval","status":"publish","type":"post","link":"https:\/\/techtrendfeed.com\/?p=12834","title":{"rendered":"AMES: Approximate Multi-modal Enterprise Search through Late Interplay Retrieval"},"content":{"rendered":"<p> <br \/>\n<\/p>\n<div>\n<p>We current AMES (Approximate Multimodal Enterprise Search), a unified multimodal late interplay retrieval structure which is backend agnostic. AMES demonstrates that fine-grained multimodal late interplay retrieval will be deployed inside a manufacturing grade enterprise search engine with out architectural redesign. Textual content tokens, picture patches, and video frames are embedded right into a shared illustration area utilizing multi-vector encoders, enabling cross-modal retrieval with out modality particular retrieval logic. AMES employs a two-stage pipeline: parallel token degree ANN search with per doc High-M MaxSim approximation, adopted by accelerator optimized Precise MaxSim re-ranking. Experiments on the ViDoRe V3 benchmark present that AMES achieves aggressive rating efficiency inside a scalable, manufacturing prepared Solr based mostly system.<\/p>\n<\/div>\n\n","protected":false},"excerpt":{"rendered":"<p>We current AMES (Approximate Multimodal Enterprise Search), a unified multimodal late interplay retrieval structure which is backend agnostic. AMES demonstrates that fine-grained multimodal late interplay retrieval will be deployed inside a manufacturing grade enterprise search engine with out architectural redesign. Textual content tokens, picture patches, and video frames are embedded right into a shared illustration [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":12836,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[55],"tags":[8272,8273,3128,1731,5468,306,6042,1100],"class_list":["post-12834","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-machine-learning","tag-ames","tag-approximate","tag-enterprise","tag-interaction","tag-late","tag-multimodal","tag-retrieval","tag-search"],"_links":{"self":[{"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/posts\/12834","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=12834"}],"version-history":[{"count":1,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/posts\/12834\/revisions"}],"predecessor-version":[{"id":12835,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/posts\/12834\/revisions\/12835"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=\/wp\/v2\/media\/12836"}],"wp:attachment":[{"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=12834"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=12834"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/techtrendfeed.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=12834"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}<!-- This website is optimized by Airlift. Learn more: https://airlift.net. Template:. Learn more: https://airlift.net. Template: 69d9690a190636c2e0989534. Config Timestamp: 2026-04-10 21:18:02 UTC, Cached Timestamp: 2026-05-04 14:43:50 UTC -->