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Gate Q3 institutional business growth accelerated: CrossEx's assets surged by 902%, with trading volume and the number of trading users increasing by 63% and 315% respectively

According to the "Q3 2026 Report" released by Gate, the platform's institutional business continues to grow, with cross-market trading, asset custody, and professional fund services further expanding.Among them, CrossEx has seen significant improvements in several core indicators, with quarterly trading volume increasing by 63% month-on-month, asset custody increasing by 902% month-on-month, the number of trading users growing by approximately 315% compared to Q2, and the number of fund users increasing by 95%.In terms of products and infrastructure, CrossEx has added mainstream exchanges, Lighter contracts, real stocks, and ETF trading scenarios, and launched features such as RPI Orders and arbitrage opportunity displays. CrossEx Colo services have officially launched, with order placement concurrency and multiple transaction reporting delays optimized by about 50%, and the highest fees for certain trading scenarios reduced by 50%. During the same period, Gate's institutional cumulative borrowing volume increased by 412.02% month-on-month.In addition, the Gate SuperLink ecosystem has added two banking partners, ABN AMRO and Pave Bank, and deepened cooperation with custodians such as Copper and BitGo. OES clients can now access CrossEx, participating in trading across multiple exchanges through a unified margin pool under an isolated custody model, further expanding institutional clients' cross-market asset allocation and trading options.

first_img Google DeepMind's First Audit: AI Did Not Accelerate Its Own Development, 75% of Code Written by AI

Google DeepMind released the first comprehensive empirical assessment of automation in machine learning research and development in the Gemini 4 Argon security report. The conclusion is that AI has not accelerated the R&D of AI itself, despite the fact that AI has written most of the code delivered by the lab's engineers. Researcher Haydn Belfield, who participated in the assessment, stated on X that there is currently no significant end-to-end ML R&D acceleration, so the risks of AI-driven ML R&D are currently low.The team built an internal index, GCI v1, which synthesizes the scores of 123 AI models across 92 benchmarks into a single value. The cutting-edge Gemini capabilities are improving at a stable rate of about 0.1 GCI points per month, with Gemini 1.5 Pro approaching 0.5 points by the end of 2024, Gemini 3.8 Flash slightly above 2.9 points by September 2026, and Argon about 3.3 points above the trend line. However, Belfield noted that this is due to increased computing power and organizational investment, rather than AI-driven acceleration.Over 75% of the code produced by DeepMind is written by AI, and the proportion of fully delegated code merge requests increased from 7.5% to 11.9% between April and September, but about 88% of requests still require multiple rounds of human guidance or manual writing. A survey of 12 senior researchers showed that automation is highest in clearly defined, verifiable execution tasks and lowest in areas such as open science concepts, new experimental designs, and unstable diagnostic training.
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