{"id":214707,"date":"2026-02-12T17:13:00","date_gmt":"2026-02-12T22:13:00","guid":{"rendered":"https:\/\/news-you-need.com\/index.php\/2026\/02\/12\/is-this-agi-googles-gemini-3-deep-think-shatters-humanitys-last-exam-and-hits-84-6-on-arc-agi-2-performance-today\/"},"modified":"2026-02-17T23:55:10","modified_gmt":"2026-02-18T04:55:10","slug":"is-this-agi-googles-gemini-3-deep-think-shatters-humanitys-last-exam-and-hits-84-6-on-arc-agi-2-performance-today","status":"publish","type":"post","link":"https:\/\/news-you-need.com\/index.php\/2026\/02\/12\/is-this-agi-googles-gemini-3-deep-think-shatters-humanitys-last-exam-and-hits-84-6-on-arc-agi-2-performance-today\/","title":{"rendered":"Is This AGI? Google\u2019s Gemini 3 Deep Think Shatters Humanity\u2019s Last Exam And Hits 84.6% On ARC-AGI-2 Performance Today"},"content":{"rendered":"

Is This AGI? Google\u2019s Gemini 3 Deep Think Shatters Humanity\u2019s Last Exam And Hits 84.6% On ARC-AGI-2 Performance Today<\/a><\/p>\n

https:\/\/www.marktechpost.com\/2026\/02\/12\/is-this-agi-googles-gemini-3-deep-think-shatters-humanitys-last-exam-and-hits-84-6-on-arc-agi-2-performance-today\/<\/a><\/p>\n

Publish Date: 2026-02-12 17:13:00<\/a><\/p>\n

Source Domain: www.marktechpost.com<\/a><\/p>\n

Google announced a major update to Gemini 3 Deep Think<\/strong> today. This update is specifically built to accelerate modern science, research, and engineering. This seems to be more than just another model release. It represents a pivot toward a \u2018reasoning mode\u2019 that uses internal verification to solve problems that previously required human expert intervention.<\/p>\n

The updated model is hitting benchmarks that redefine the frontier of intelligence. By focusing on test-time compute<\/strong>\u2014the ability of a model to \u2018think\u2019 longer before generating a response\u2014Google is moving beyond simple pattern matching. <\/p>\n

\"\"https:\/\/blog.google\/innovation-and-ai\/models-and-research\/gemini-models\/gemini-3-deep-think\/<\/p>\n

Redefining AGI with 84.6% on ARC-AGI-2<\/strong><\/h3>\n

The ARC-AGI<\/strong> benchmark is an ultimate test of intelligence. Unlike traditional benchmarks that test memorization, ARC-AGI measures a model\u2019s ability to learn new skills and generalize to novel tasks it has never seen. Google team reported that Gemini 3 Deep Think achieved 84.6%<\/strong> on ARC-AGI-2<\/strong>, a result verified by the ARC Prize Foundation<\/strong>.<\/p>\n

A score of 84.6%<\/strong> is a massive leap for the industry. To put this in perspective, humans average about 60%<\/strong> on these visual reasoning puzzles, while previous AI models often struggled to break 20%<\/strong>. This means the model is no longer just predicting the most likely next word. It is developing a flexible internal representation of logic. This capability is critical for R&D<\/strong> environments where engineers deal with messy, incomplete, or novel data that does not exist in a training set.<\/p>\n

Passing \u2018Humanity\u2019s Last Exam<\/strong>\u2018<\/h3>\n

Google also set a new standard on Humanity\u2019s Last Exam (HLE)<\/strong>, scoring 48.4%<\/strong> (without tools). HLE is a benchmark consisting of 1000s of questions designed by subject matter experts to be easy for humans but nearly impossible for current AI. These questions span specialized academic topics where data is scarce and logic is dense.<\/p>\n

Achieving 48.4%<\/strong> without…<\/p>\n

Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

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