{"id":92521,"date":"2025-12-22T17:50:20","date_gmt":"2025-12-22T14:50:20","guid":{"rendered":"https:\/\/forklog.com\/en\/?p=92521"},"modified":"2025-12-22T17:55:15","modified_gmt":"2025-12-22T14:55:15","slug":"tether-unveils-open-dataset-for-ai-training","status":"publish","type":"post","link":"https:\/\/forklog.com\/en\/tether-unveils-open-dataset-for-ai-training\/","title":{"rendered":"Tether Unveils Open Dataset for AI Training"},"content":{"rendered":"

Tether Data’s AI division, QVAC, has significantly expanded<\/a> the “world’s largest publicly available synthetic dataset” for artificial intelligence training.<\/p>\n

QVAC Genesis II has added 107 billion new tokens, bringing the total to 148 billion across 19 educational fields. This “substantially increases” the scale, depth, and quality of reasoning.<\/p>\n

The second version builds on the foundation of the first. It covers 10 new areas, including chemistry, computer science, statistics, machine learning, astronomy, geography, econometrics, and electrical engineering.<\/p>\n

QVAC Genesis II recreates university-level physics and, together with Genesis I, forms “the most comprehensive synthetic educational dataset ever made available to the public.”<\/p>\n

The release is based on a new approach to information generation\u2014Option-Level Reasoning. It is designed to extract structured reasoning from model errors and correct answers.<\/p>\n

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“The result is training data that emphasizes clarity, causality, and decision-making, rather than just superficial correctness,” the company’s blog states.<\/p>\n<\/blockquote>\n

Tether emphasized that QVAC focuses on training the model to think, reason, and explain, rather than mimic.<\/p>\n

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“Today, most programs are optimized for fluency rather than understanding. With this release, we move beyond volume to structure, reasoning, and clarity,” stated the firm’s CEO, Paolo Ardoino.<\/p>\n<\/blockquote>\n

In May, Tether announced<\/a> a new QVAC platform for developing “infinite and ubiquitous intelligence,” which envisions “launching and evolving” AI agents on user devices instead of large company data centers.<\/p>\n

In June, Ardoino stated<\/a> that within 15 years, a trillion AI agents will emerge, using Bitcoin and USDT for settlements and transactions.<\/p>\n","protected":false},"excerpt":{"rendered":"

Tether Data’s AI division, QVAC, has significantly expanded the “world’s largest publicly available synthetic dataset” for artificial intelligence training.<\/p>\n","protected":false},"author":1,"featured_media":92522,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"select":"1","news_style_id":"1","cryptorium_level":"","_short_excerpt_text":"Tether Data's AI division, QVAC, has expanded the world's largest synthetic dataset for AI training.","creation_source":"","_metatest_mainpost_news_update":false,"footnotes":""},"categories":[3],"tags":[438,1245],"class_list":["post-92521","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news-and-analysis","tag-artificial-intelligence","tag-tether-usdt"],"aioseo_notices":[],"amp_enabled":true,"views":"172","promo_type":"1","layout_type":"1","short_excerpt":"Tether Data's AI division, QVAC, has expanded the world's largest synthetic dataset for AI training.","is_update":"","_links":{"self":[{"href":"https:\/\/forklog.com\/en\/wp-json\/wp\/v2\/posts\/92521","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/forklog.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/forklog.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/forklog.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/forklog.com\/en\/wp-json\/wp\/v2\/comments?post=92521"}],"version-history":[{"count":1,"href":"https:\/\/forklog.com\/en\/wp-json\/wp\/v2\/posts\/92521\/revisions"}],"predecessor-version":[{"id":92523,"href":"https:\/\/forklog.com\/en\/wp-json\/wp\/v2\/posts\/92521\/revisions\/92523"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/forklog.com\/en\/wp-json\/wp\/v2\/media\/92522"}],"wp:attachment":[{"href":"https:\/\/forklog.com\/en\/wp-json\/wp\/v2\/media?parent=92521"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/forklog.com\/en\/wp-json\/wp\/v2\/categories?post=92521"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/forklog.com\/en\/wp-json\/wp\/v2\/tags?post=92521"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}