Federal Reserve Governor Jefferson: No Need to Hurry to Further Cut Interest Rates, Strong Economy Makes Caution Appropriate

U.S. President Donald Trump gave a media interview on November 2 local time. When asked whether he believed regime change was needed in Iran, Trump said: "In a sense, we have already overthrown the Iranian regime. We’ve done it twice before. Whether a deal with Iran will be reached remains to be seen; it can be said that Iran is very eager to strike an agreement." When asked if he was surprised by Iran’s demonstrated resilience, Trump stated: "I don’t know what you mean by 'resilience.' I have been relatively polite to Iran because they want to negotiate and have been talking, but one day we will have to take action. I know some people are angry with me for not completely eliminating Iran." (Jinshi)

Blockchain analytics firm Elliptic has disclosed that North Korea-linked hackers, after stealing approximately $6.43 billion in crypto assets from the Binance exchange, are conducting sophisticated money laundering operations in a highly automated manner. As of the article’s update, the stolen funds have been moved across 11 blockchains via roughly 12,000 wallets, with over 40,000 transactions executed in less than four days—an average of one new wallet created every 25 seconds. Elliptic noted that approximately $3.24 billion remains in addresses linked to the heist, primarily in ETH and BTC; around $2.85 billion has been frozen or seized, with negligible amounts confirmed to have been cashed out. The money laundering trail is dominated by Tron, with funds split into roughly 7,700 addresses and bridged cross-chain to Ethereum; at least $147 million has been moved out of Tron via bridges. Since November 1, portions of ETH, SOL, NEAR, and XRP have begun to be moved; approximately 28 BTC (valued at around $2.2 million) has entered a Wasabi-style coinjoin mixing operation for the first time. Elliptic believes the operations show a fixed rhythm and batch characteristics, pointing to standard automated scripts rather than AI-driven autonomous decision-making. The firm also predicts the attackers will follow the path of the Bybit case in subsequent steps: converting ETH to BTC in batches, then cashing out via channels like over-the-counter (OTC) brokers.

According to official announcements from Bitget, the platform has released its 2026 Q3 Transparency Report. The report shows Bitget’s combined trading volume in Q3 reached $1.86 trillion, with a cumulative monthly growth of 21.2% from July to September. Specifically, crypto derivatives trading volume hit $409.19 billion, up 31.5% quarter-over-quarter; crypto spot trading volume rose 65.7%; and CFD trading volume reached $376.32 billion, growing 39.7%. In terms of product layout, Bitget launched the Cross-Asset Unified Account (UTA), integrating over 370 assets into a unified margin system—including more than 100 tokenized US stocks—with a collateral valuation ratio of up to 95%, further optimizing cross-asset trading and fund management experiences. The platform also extended the application of its AI trading tool GetAgent Playbook to tokenized US stocks, and launched the stablecoin yield product Cash Plus, enriching users’ asset management and income options. On the security and reserve front, Bitget has published monthly reserve reports since December 2022, with 47 consecutive issues released as of September 2026, and all asset reserve ratios have remained above 100%. This quarter, Bitget upgraded its Proof of Reserves (PoR) mechanism, expanding reserve verification coverage from the original 4 cryptocurrencies to 19 major assets. The upgrade also broadened platform-level reserve disclosures and individual asset verification scopes: users can check each asset’s reserve ratio, reserve size, and on-chain distribution via the PoR page, and independently verify whether their personal positions are included in the reserve snapshot through Merkle Tree, further enhancing the transparency and verifiability of reserve information.

According to TradingBeats monitoring, a long-term NEAR whale today liquidated approximately 2.4863 million NEAR long positions in one go, worth around $10.276 million, locking in a profit of roughly $4.4266 million. The account’s original average entry price for NEAR long positions was only about $2.35, while the average liquidation price for this round stood at approximately $4.13. The whale has been reducing its position over the past six hours and has now fully cleared all its NEAR holdings. During the liquidation of NEAR long positions, the whale has shifted to shorting other altcoins. The same account opened short positions in AAVE, and also shorted MEGA and BNB. As of press time, the new short positions include: ~$1.4701 million in AAVE, with an average entry price of $131.92; ~$326,800 in BNB, average entry price $706.94; ~$190,500 in MEGA, average entry price $0.0413.

According to HTX market data, Bitcoin is currently trading at $78,025.63, down 0.02% over 24 hours; Ethereum is at $2,285.78, up 0.65% in the same period. Per CoinGlass data, BTC’s open interest-weighted and volume-weighted funding rates are 0.0075% and 0.0071% respectively; ETH’s two weighted rates stand at 0.0062% and 0.0064%, both in the neutral range. Compared to its previous negative levels, ETH’s bearish sentiment has eased notably, though overall rates remain lower than BTC’s. BlockBeats Note: CoinGlass converts funding rates from all platforms to an 8-hour timeframe. The funding rate is a mechanism designed to align perpetual contract prices with spot prices, involving fund exchanges between long and short positions; the benchmark rate is 0.01%, with rates above 0.01% signaling a bullish market and rates below 0.005% indicating a bearish market.

Beating AI News: Tencent’s Hunyuan has released research agent Hyra 2.0, shifting its focus from trial-and-error for individual problems to developing reusable scientific models, algorithms, and tools. To enable AI to conduct research over days or even weeks, Hyra 2.0 divides work into three agent layers: Overall (resource coordination), Mission (research planning), and Worker (experiment execution). Tasks are persistently stored; while waiting for training results, other research can proceed. Each conclusion must be accompanied by evidence such as code and logs, and is reviewed by an independent agent when necessary. Valid experiences are also accumulated as Skills for subsequent tasks. Tencent disclosed that Hyra 2.0 has trained two protein models from scratch: HyInv, a 1.96 million-parameter model that designs protein sequences based on target backbones, generating sequences 57 times faster than the widely used ProteinMPNN. In a warm-up test on a single H20 GPU, generating 128 sequences of ~400 residues takes only ~50 milliseconds. The other model, HyPLM-Flash, has 650 million parameters and was trained on 726 billion tokens. The weights, inference code, and technical reports of both models are now public. Hyra 2.0 also participated in optimizing the open-source RISC-V processor Xiangshan, boosting its SPEC CPU2006 performance per GHz by 5.79% while only increasing chip area by 0.3%. It also designed a five-finger "spiral ladder hand" with incrementally longer fingers; initially, this hand was ~11% slower than the best existing design, but after training with the same budget control algorithm, its simulated rotation speed increased by 28.6%. Currently, Hyra 2.0 is also involved in pre-training, post-training, and inference system optimization for Tencent’s next-generation Hunyuan model, aiming to help develop a stronger next-generation AI by improving R&D tools.