{"id":40375,"date":"2025-07-29T12:42:19","date_gmt":"2025-07-29T04:42:19","guid":{"rendered":"https:\/\/www.1ai.net\/?p=40375"},"modified":"2025-07-29T12:42:19","modified_gmt":"2025-07-29T04:42:19","slug":"%e8%9a%82%e8%9a%81%e6%95%b0%e7%a7%91%e5%8f%91%e5%b8%83%e9%87%91%e8%9e%8d%e6%8e%a8%e7%90%86%e5%a4%a7%e6%a8%a1%e5%9e%8b","status":"publish","type":"post","link":"https:\/\/www.1ai.net\/en\/40375.html","title":{"rendered":"Ant Mathematics Releases Big Model of Financial Reasoning"},"content":{"rendered":"<p>July 28, at<a href=\"https:\/\/www.1ai.net\/en\/tag\/%e4%b8%96%e7%95%8c%e4%ba%ba%e5%b7%a5%e6%99%ba%e8%83%bd%e5%a4%a7%e4%bc%9a\" title=\"[Sees articles with tags of the World Congress of Artificial Intelligence]\" target=\"_blank\" >World Artificial Intelligence Conference<\/a>On, Ant Group's technology subsidiary<a href=\"https:\/\/www.1ai.net\/en\/tag\/%e8%9a%82%e8%9a%81%e6%95%b0%e7%a7%91\" title=\"[Sees articles with labels]\" target=\"_blank\" >Ant Count Family<\/a>Official Release<a href=\"https:\/\/www.1ai.net\/en\/tag\/%e9%87%91%e8%9e%8d%e6%8e%a8%e7%90%86%e5%a4%a7%e6%a8%a1%e5%9e%8b\" title=\"[Sees articles that contain labels of financial reasoning]\" target=\"_blank\" >The Great Model of Financial Reasoning<\/a> Agentar-Fin-R1.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-40376\" title=\"6e632721j00t0591f00iqd000u000k0m\" src=\"https:\/\/www.1ai.net\/wp-content\/uploads\/2025\/07\/6e632721j00t0591f00iqd000u000k0m.jpg\" alt=\"6e632721j00t0591f00iqd000u000k0m\" width=\"1080\" height=\"720\" \/><\/p>\n<p>It is reported that Agentar-Fin-R1 is not only a new model, but also a \"professional hub\" tailor-made for financial scenarios such as banking, securities, insurance, etc., focusing on \"reliable, controllable and optimizable\" .<\/p>\n<p>Specifically, Agentar-Fin-R1 is based on Qwen3 and outperforms Deepseek-R1 on benchmarks such as FinEval1.0, FinanceIQ, and other authoritative financial big models, as well as financial big models of the same size as open source generic big models.<\/p>\n<p>performance performance.<strong>Agentar-Fin-R1's general-purpose ability, such as the 32B version, scored 93.80 on MATH and 68.18 on GPQA; its financial ability is even more outstanding, with the 32B model comprehensively outperforming high-parameter models such as DeepSeek-R1 and GPT-o1 on the two major financial benchmarks, FinEval1.0 and FinanceIQ. Generalized models such as DeepSeek-R1, GPT-o1 and other high-parameter models.<\/strong><\/p>\n<p>The R&amp;D team has also constructed an extremely comprehensive and professional financial data corpus in the industry. A financial task system covering all scenarios of banks, securities, insurance, funds, trusts, etc., including 6 major categories and 66 sub-scenarios, constitutes the most systematic and real financial data set in the industry. What's more special is that \"principle-based synthetic data\" is also introduced in the training, so that the model naturally complies with the red line of financial regulation.<\/p>\n<p>Currently, Agentar-Fin-R1 is available in 32B and 8B parameterized versions, in addition to the MOE architectural model based on the Beringia model, and 14B and 72B non-inferential versions.<\/p>","protected":false},"excerpt":{"rendered":"<p>On 28 July, at the World Congress of Artificial Intelligence, the ants count section of the ants group ' s scientific sub-company officially launched the Big Model of Financial Logging. According to the information received, Agentar-Fin-R1 is not just a new model, but also a \"professional hub\" for the financial scenes such as banking, securities, insurance and so on, where it is \u201creliable, manageable and optimized\u201d. In particular, Agentar-Fin-R1 is based on Qwen3 R &amp; D, which goes beyond the Deepseek-R1-Symmetric Open-Symmetry Generic Large Model and Financial Large Models in the assessment benchmarks of authoritative large financial models such as FinEval1.0 and FinanceIQ. Performance, Agentar-Fin-R<\/p>","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[146],"tags":[1538,7295,7296],"collection":[],"class_list":["post-40375","post","type-post","status-publish","format-standard","hentry","category-news","tag-1538","tag-7295","tag-7296"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/posts\/40375","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/comments?post=40375"}],"version-history":[{"count":0,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/posts\/40375\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/media?parent=40375"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/categories?post=40375"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/tags?post=40375"},{"taxonomy":"collection","embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/collection?post=40375"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}