CBS: U.S. Treasury Secretary and Trump Once Had Heated Debate on Tariff Policy

According to GMGN market data, the Robinhood ecosystem meme coin SHROOM briefly surpassed a $35 million market cap, posting a 24-hour gain of over 340% and trading volume of $10.5 million in the same period. The project official stated that SHROOM is building a liquidity network for stock tokens on Robinhood Chain. SHROOM holders receive regular MU rewards, distributed by Pons and subject to its terms. SHROOM provides liquidity (LP) by pairing with other stocks and tokens, enabling routing between any paired tokens and generating fees from fluctuations in any asset. It automatically compounds fees generated from LP to deepen liquidity, thereby earning more fees over time. BlockBeats reminds users: Most meme coins lack practical use cases and are highly volatile. Please protect your assets and avoid FOMO.

According to GMGN market data, popular meme coins in the Robinhood ecosystem have seen broad declines, with details as follows: · CASHCAT fell over 19% in 24 hours, dropping to a market cap of $214 million; · AI dropped nearly 5% in 24 hours, hitting a market cap of $227 million; · BONER plummeted over 12% in 24 hours, with a market cap of $42.69 million; · microduck fell more than 28% in 24 hours, reaching a market cap of $23.55 million; · Newly launched meme coin MEME plunged over 50% in 6 hours, falling to a market cap of $39 million. Robinhood Chain was reportedly hit by an outage yesterday, but Arbitrum later issued a clarification stating that the chain did not experience an outage today. Due to Blob activity in the L1 market, Robinhood Chain previously faced batch transaction delays, though user-initiated direct transactions were not affected. Over the past 24 hours, Robinhood Chain recorded a total net outflow of $21 million, making it the largest net outflow party of the day. BlockBeats reminds users: Most meme coins lack practical use cases and are highly volatile. Please protect your assets and avoid FOMO.

Beating AI News Brief: Microsoft AI has launched MAI-Image-2.6-Flash, now available in public preview on Microsoft Foundry. The model is an accelerated variant of its flagship MAI-Image-2.6, supporting text-to-image, image editing, multi-image reference, real-time web information access, and automatic aspect ratio selection, with a core focus on cutting generation latency and costs. Speed is its key selling point: MAI-Image-2.6-Flash generates images 2.8 times faster than GPT-Image-2 Medium, with 72% higher GPU utilization efficiency. That said, it does not trade off image quality solely for speed. In Artificial Analysis’s current image editing leaderboard, MAI-Image-2.6-Flash ranks third with an Elo score of 1311, slightly higher than GPT Image 2 high’s 1309; it places eighth in text-to-image with an Elo of 1297. The flagship MAI-Image-2.6 performs better overall, currently ranking first in image editing and second in text-to-image respectively. On Microsoft Foundry, MAI-Image-2.6-Flash is priced at $19 per million tokens for image output, while the flagship MAI-Image-2.6 costs $38 per million tokens. After adjusting for representative images per Artificial Analysis, Flash’s cost is roughly $19.5 per thousand images, compared to GPT Image 2 high’s approximately $211 per thousand images.

Beating AI Insight Flash News: GitHub has added a multi-model orchestration system called HydraFusion to Copilot. The system first determines how to handle a task before calling different models, operating in three modes: simple tasks are assigned to a single model for direct completion; complex tasks start with a lower-cost model, which is escalated to a more powerful model if the result is unsatisfactory; for tasks requiring review, one model completes the work first, while another model from a different family is tasked with identifying errors, after which the first model revises its output. GitHub compared HydraFusion with Claude Opus 5 across three programming agent benchmarks: it scored 4.9 percentage points higher than Claude Opus 5 on TerminalBench 2.1, 1.5 points lower on DeepSWE, and only 0.1 points lower on CheckpointBench (nearly on par); meanwhile, cost reductions reached 67%, 36%, and 65% respectively across the three benchmarks. This approach is very similar to Sakana AI’s Fugu. Both shift the focus from "which model to choose" to "how to organize multiple models". The key difference is that Fugu is a trained orchestrator model that learns to call different agents and can even recursively invoke itself; HydraFusion, by contrast, currently only selects among three fixed execution modes: Single, Cascade, and Critique, and is essentially a multi-model scheduler integrated directly into Copilot. HydraFusion is now available as a research preview for all GitHub Copilot plans, and can be enabled via the experimental features in Copilot CLI. GitHub also noted that the system is currently best suited for single-turn programming tasks that can be clearly defined at once, with multi-turn long tasks still under optimization.

Per Farside's monitoring, U.S. spot Bitcoin ETFs recorded a net inflow of $174.6 million yesterday, extending their streak to three consecutive trading days of net inflows.

Beating AI Express: Since OpenAI launched GPT-6 Astra on September 3, multiple model benchmark evaluation metrics have been continuously adjusted. Some changes have boosted Astra’s performance, while scores of some competing models have dropped, sparking external skepticism about AI "benchmark manipulation" and evaluation transparency. Specifically, Astra’s hallucination rate was once lowered from 4.2% to 2%, while GPT-5.6 Sol’s rate fell from 12.2% to 9.4%, before both rates returned to 4.2% and 12.2% respectively. In math evaluations, Anthropic’s Fable 5.1 score also dropped from 87.8% to 78%, and has since rebounded to 83%; GPT-5.6 Sol’s score fell from 83% to 80.5%, then returned to 83%. Additionally, Astra’s score in the ARC-AGI-3 benchmark rose from 98.6% in its pre-release draft to 99.99% on the final page, while its programming evaluation score was also slightly adjusted upward from 57.7% to 57.9%. OpenAI stated that evaluation results are influenced by factors including model version, tool configuration, inference level, and test runs, adding that the adjustments were made to ensure the data more accurately reflects the model’s optimal performance. However, Stanford University researchers argue that frequent re-running of evaluations may involve so-called "benchmaxxing" – the practice of adjusting test conditions to maximize benchmark scores. Industry insiders note that as competition among AI models intensifies, evaluation data has become a key tool for measuring model capabilities and competing for market share, with improving the transparency and reproducibility of benchmark tests drawing growing attention.