{"id":2612,"date":"2026-07-20T04:00:00","date_gmt":"2026-07-20T04:00:00","guid":{"rendered":"https:\/\/futurenews24.com\/index.php\/2026\/07\/20\/2603-29139\/"},"modified":"2026-07-20T17:59:05","modified_gmt":"2026-07-20T17:59:05","slug":"2603-29139","status":"publish","type":"post","link":"https:\/\/futurenews24.com\/index.php\/2026\/07\/20\/2603-29139\/","title":{"rendered":"[2603.29139] SciVisAgentBench: A Benchmark for Evaluating Scientific Knowledge Evaluation and Visualization Brokers"},"content":{"rendered":"<p><br \/>\n<\/p>\n<div id=\"content-inner\">\n<div id=\"abs\">\n<div class=\"dateline\">\n  [Submitted on 31 Mar 2026 (v1), last revised 16 Jul 2026 (this version, v3)]<\/div>\n<div class=\"authors\"><span class=\"descriptor\">Authors:<\/span>Kuangshi Ai, Haichao Miao, Kaiyuan Tang, Nathaniel Gorski, Jianxin Solar, Guoxi Liu, Helgi I. Ingolfsson, David Lenz, Hanqi Guo, Hongfeng Yu, Teja Leburu, Michael Molash, Bei Wang, Tom Peterka, Chaoli Wang, Shusen Liu<\/div>\n<p>View a PDF of the paper titled SciVisAgentBench: A Benchmark for Evaluating Scientific Knowledge Evaluation and Visualization Brokers, by Kuangshi Ai and 15 different authors<\/p>\n<p>    View PDF<br \/>\n    HTML (experimental)<\/p>\n<blockquote class=\"abstract mathjax\"><p>\n            <span class=\"descriptor\">Summary:<\/span>Current advances in massive language fashions (LLMs) have enabled agentic methods to translate natural-language intent into executable scientific visualization (SciVis) duties. Regardless of fast progress, the group lacks a principled and reproducible benchmark for evaluating these rising SciVis brokers in life like, multi-step evaluation settings. We current SciVisAgentBench, a complete and extensible benchmark for evaluating scientific knowledge evaluation and visualization brokers. Our benchmark is grounded in a structured taxonomy spanning 4 dimensions: software area, knowledge kind, complexity degree, and visualization operation. It at the moment includes 108 expert-crafted instances protecting various SciVis situations. To allow dependable evaluation, we introduce a multimodal outcome-centric analysis pipeline that mixes LLM-based judging with deterministic evaluators, together with image-based metrics, code checkers, rule-based verifiers, and case-specific evaluators. We additionally conduct a validity research with 12 SciVis specialists to look at the settlement between human and LLM judges. Utilizing this framework, we consider consultant SciVis brokers and general-purpose coding brokers to determine preliminary baselines and reveal functionality gaps. SciVisAgentBench is designed as a residing benchmark to help systematic comparability, diagnose failure modes, and drive progress in agentic SciVis. The benchmark is offered at this https URL.\n    <\/p><\/blockquote><\/div>\n<\/div>\n<div>\n<h2>Submission historical past<\/h2>\n<p> From: Kuangshi Ai [view email]                  [v1]<br \/>\n        Tue, 31 Mar 2026 01:41:28 UTC (38,545 KB)<br \/>\n            [v2]<br \/>\n        Fri, 26 Jun 2026 20:48:37 UTC (40,668 KB)<br \/>\n    [v3]<br \/>\n        Thu, 16 Jul 2026 18:43:17 UTC (40,668 KB)\n<\/p><\/div>\n<p><br \/>\n<br \/><a href=\"https:\/\/arxiv.org\/abs\/2603.29139\">Source link <\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>[Submitted on 31 Mar 2026 (v1), last revised 16 Jul 2026 (this version, v3)] Authors:Kuangshi Ai, Haichao Miao, Kaiyuan Tang, Nathaniel Gorski, Jianxin Solar, Guoxi Liu, Helgi I. Ingolfsson, David Lenz, Hanqi Guo, Hongfeng Yu, Teja Leburu, Michael Molash, Bei Wang, Tom Peterka, Chaoli Wang, Shusen Liu View a PDF of the paper titled SciVisAgentBench: [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":2614,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"fifu_image_url":"http:\/\/arxiv.org\/static\/browse\/0.3.4\/images\/arxiv-logo-fb.png","fifu_image_alt":"","jnews-multi-image_gallery":[],"jnews_single_post":[],"jnews_primary_category":[],"jnews_override_bookmark_settings":[],"jnews_social_meta":[],"jnews_override_counter":[],"footnotes":""},"categories":[2],"tags":[210,218,1315,160,3091,788,3090,3092],"class_list":["post-2612","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-research-breakthroughs","tag-agents","tag-analysis","tag-benchmark","tag-data","tag-evaluating","tag-scientific","tag-scivisagentbench","tag-visualization"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.7 - 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