3.【宇树科技推出人形机器人App Store，用户可下载动作预设】12月13日，宇树科技宣布首发人形 机器人App Store，用户可在其中下载动作和预设，一键完成复杂操作。目前公测已上线搞笑动作、 扭扭舞、李小龙三大预设。（钛媒体） 1.【甲骨文：与OpenAI的合作安排"没有延迟"】当地时间12月12日，甲骨文公司否认为OpenAI建造 的数据中心完工时间从2027年推迟至2028年的报道。甲骨文发言人称，选址和交付时间表是在协议 签署后与OpenAI密切协调后确定的，并经双方共同同意。所有需要满足合同承诺的节点均未出现延 误，所有里程碑仍按计划推进。（界面新闻） 2.【谷歌称将Gemini的翻译功能引入谷歌的文本翻译】谷歌称将Gemini的翻译功能引入谷歌的文本 翻译，并推出耳机实时语音转语音翻译的测试版体验。（财联社） 4.【天桥脑科学研究院成立尖峰智能实验室，主攻类脑大模型和脉冲神经网络研发】12月13日，界面 新闻记者获悉，天桥脑科学研究院成立尖峰智能实验室（Spiking Intelligence Lab, SIL）。天桥脑 科学研究院创始人雒芊芊介绍，该实验室由中国科学院自动化所李国齐教授领 ...

Core Insights - The article discusses the limitations of Spiking Neural Networks (SNNs) and introduces a new architecture called Max-Former that addresses these limitations by enhancing high-frequency information processing [5][24]. Group 1: Performance Limitations of SNNs - SNNs have been traditionally viewed as inferior to Artificial Neural Networks (ANNs) due to their binary pulse transmission, which was believed to cause significant information loss [5][6]. - The research indicates that the real issue lies in the frequency bias of SNNs, where spiking neurons act as low-pass filters, suppressing high-frequency components and favoring low-frequency information [4][8][19]. - This frequency imbalance leads to a degradation in the feature representation capabilities of SNNs, limiting their performance [10][23]. Group 2: Introduction of Max-Former - The Max-Former architecture is designed to counteract the inherent low-frequency preference of SNNs by incorporating two lightweight "frequency-enhancing lenses" [24][28]. - The architecture includes an additional Max-Pool operation in the Patch Embedding stage to actively inject high-frequency signals at the input source [28]. - It also replaces early-stage self-attention with deep convolution (DWC), which retains local high-frequency details while being computationally efficient [28]. Group 3: Performance Metrics and Results - Max-Former achieved a Top-1 accuracy of 82.39% on ImageNet with fewer parameters compared to Spikformer, demonstrating a significant performance improvement [27]. - The architecture also reduced energy consumption by over 30% while achieving performance breakthroughs [30]. - The findings suggest that optimizing SNNs with high-pass operators can lead to improvements in both performance and energy efficiency [31]. Group 4: Broader Implications - The insights gained from the Max-Former architecture are applicable beyond Transformer models, as demonstrated by the Max-ResNet architecture, which also benefited from the addition of high-frequency operations [33]. - The research provides a new perspective on the performance bottlenecks of SNNs, suggesting that their optimization should not merely mimic successful designs from ANNs [35].

Group 1: Gold Market Insights - Spot gold prices surpassed $3,300 per ounce for the first time since May 9, driven by rising geopolitical tensions and negative GDP growth in the U.S., which increased safe-haven demand [1] - Domestic gold consumption remains strong, with retail sales of gold and silver jewelry in April up 25.3% year-on-year and 14.7% month-on-month, indicating that domestic demand is independent of international gold price fluctuations [1] - There is a divergence in institutional views on gold; bullish arguments include inflation risks and a potential Fed rate cut, while cautious signals highlight the current high price levels and the possibility of profit-taking due to eased trade tensions [1] Group 2: Solar Industry Impact from Tariffs - The U.S. plans to impose extreme tariffs on Southeast Asian solar equipment, with Cambodia facing a 3,521% tariff due to non-cooperation in investigations, while Malaysia faces only 34% [2] - The U.S. heavily relies on Southeast Asia for solar imports, with 80% of imports coming from four countries, leading to a potential shift in procurement to domestic or third-party manufacturers [2] - U.S. solar project developers are facing increased costs due to these tariffs, which may delay installation progress and create cash flow pressures for EPC companies [2] Group 3: Humanoid Robots Development - The commercialization of humanoid robots depends on their ability to create actual value by addressing real-life challenges, with a long-term development cycle similar to that of autonomous driving, estimated at 10-20 years [3] - The industry is entering an accelerated phase due to supportive policies and the presence of a significant talent pool in the field of embodied intelligence, with a focus on practical applications [3] - Early application scenarios have been validated in sectors like power and chemical inspections, indicating a potential for successful technology-commercialization loops [3] Group 4: AI Agent Development - The AI agent market is rapidly evolving, with diverse technical paths and a focus on expanding application scenarios, although a unified standard has yet to be established [4] - There are significant differences between the North American and Chinese markets, with both targeting enterprise-level markets as a core breakthrough point [4] - Current challenges include high token consumption during interactions and the need for robust computational infrastructure, which remains a key limiting factor for commercial scalability [4] Group 5: Public Fund Regulation Changes - New regulations for public funds are driving a shift in strategy, with a focus on core asset pricing and a potential systemic adjustment in strategy paradigms [5] - The easing of U.S.-China tariffs has improved market risk appetite, with a focus on opportunities in the export chain [5] - Social financing growth is supported by low base effects and monetary policy, although potential impacts from tariff shocks should be monitored [5]