【钛媒体综合】据新华社，国务院总理李强11月14日主持召开国务院常务会议，部署了增强消费品供需 适配性进一步促进消费政策措施。 "增强供需适配性"意味着要通过精准对接消费需求与供给能力，减少供需错配带来的资源浪费，提升市 场效率。它不仅是释放消费潜力的关键，也是推动经济高质量发展的内在要求。中国城市发展研究院投 资部副主任袁帅表示，在当前政策体系中，增强供需适配性扮演着连接生产与消费、促进产业升级与消 费升级良性互动的桥梁角色，有助于形成需求牵引供给、供给创造需求的更高水平动态平衡。 为此，本次国务院常务会议作出一系列新部署：要以消费升级引领产业升级，以优质供给更好满足多元 需求，实现供需更高水平动态平衡。要加快新技术新模式创新应用，强化人工智能融合赋能，聚焦重点 行业、重点领域开发新产品和增值服务，持续拓展新的消费增量。要积极支持企业扩大特色、高品质消 费品供给，加快安全、性能、绿色等标准更新升级，完善认证体系。要围绕细分市场精准匹配不同人群 需求，发展一批柔性制造工厂，推动生产高效响应消费者个性化需要。要培育消费新场景新业态，丰富 消费金融产品和服务，营造良好消费环境。 伍超明认为，实现供给与需求之间更高 ...

生成式AI 一、openEuler发布全球首个超节点操作系统，专为AI打造 1. 开源欧拉社区正式开启新的5年发展之路，将于2025年底正式上线面向超节点的操作系统（openEuler 24.03 LTS SP3），成员单位超2100家，全球贡献者突破2.3万人； 2. 该操作系统具备全局资源抽象、异构资源融合和全局资源视图三大关键特征，旨在充分释放超节点算力潜能，加速 基于超节点的应用创新； 3. 灵衢互联协议2.0 由开 放 ，并将向社区贡献支持超节点的操作系统插件代码，提供内存统一编址、异构算力低时 延通信和全局资源池化等关键能力。 https://mp.weixin.qq.com/s/Xf31Wc8BYvdP_qEzcgcgsA 二、CEO劈柴两个神秘表情，或暗示Gemini 3.0等下周登场 1. 谷歌CEO劈柴用两个"若有所思"表情回应Gemini 3.0下周登场传闻，69%网友押注下一代AI模型即将发布，该模 型有望成为谷歌的重大转折点； 2. 提前流出的内测显示Gemini 3.0可一句话直出操作系统、动态构建Windows系统、5秒搭建网站，前端设计能力惊 艳，被称"前端工程师末日"； 3. ...

Core Insights - EverMind has launched EverMemOS, a world-class long-term memory operating system designed for AI agents, aiming to provide a persistent, coherent, and evolving "soul" for AI [1][10] - The system significantly outperforms previous models in long-term memory evaluations, establishing a new state-of-the-art (SOTA) benchmark [1][12] Memory Capability - Current AI models, particularly large language models (LLMs), face limitations due to fixed context windows, leading to frequent "forgetting" during long-term tasks, which undermines personalized and consistent knowledge [1][3] - The lack of a robust memory system is seen as a major barrier to the evolution of AI towards advanced intelligence, as it prevents the formation of consistent long-term behaviors and self-iteration [1][3] Industry Trends - Major industry players like Claude and ChatGPT have recognized the strategic importance of long-term memory, indicating a shift towards memory as a core competitive advantage in AI applications [3] - Existing solutions, such as traditional retrieval-augmented generation (RAG) methods, are often fragmented, highlighting the market's need for a comprehensive memory system that can cater to various scenarios [3][10] Design Inspiration - The EverMind team draws inspiration from human memory mechanisms, aiming to replicate the brain's encoding, indexing, and long-term storage processes in their design of EverMemOS [4][10] - This approach aligns with the vision of integrating brain science with AI, as emphasized by Shanda Group's founder, Chen Tianqiao [5][7] Technical Performance - EverMemOS has achieved high scores of 92.3% and 82% on the LoCoMo and LongMemEval-S long-term memory evaluation sets, respectively, surpassing previous benchmarks [12] - The system features a four-layer architecture that parallels key functions of the human brain, enhancing its memory capabilities [13][16] System Features - EverMemOS is not just a memory database but also an application processor, allowing memories to actively influence AI responses, thus providing a coherent and personalized interaction experience [15] - The system employs a hierarchical memory extraction method, organizing memories into structured units to improve context retrieval and application [15][18] - It introduces a modular memory framework that adapts to varying memory needs across different scenarios, from high-precision work environments to empathetic interactions [18] Availability - EverMind has released an open-source version of EverMemOS on GitHub for developers and AI teams to deploy and test, with plans for a cloud service version later this year [18]

机器之心发布 机器之心编辑部 EverMind 团队近日宣布正式发布其旗舰产品 EverMemOS，这是一款面向人工智能智能体的世界级长期记忆操作系统。它旨在成为未来智能体的数据基础 设施，为 AI 赋予 持久、连贯、可进化的 "灵魂"。 近期， EverMemOS 在 LoCoMo 和 LongMemEval-S 等最主流的长期记忆评测集上，其表现已显著超越此前工作，成为新的 SOTA。 记忆能力：决胜下一代 AI 的分水岭 灵感源自人类大脑的记忆机制 EverMind 团队来源于 盛大集团（Shanda Group） ，这一曾引领中国数字创新浪潮的科技和投资集团。他们的灵感来自人类大脑的记忆机制：从感官信 号编码、海马体索引到皮层长期存储，前额叶与海马体协同完成记忆的形成与提取。这种「类脑」理念，成为 EverMemOS 设计的核心，让 AI 能够像人类 一样 思考、记忆与成长。 受限于 LLMs 固定的上下文窗口，AI 在长时程任务中会频繁'失忆'。这不仅导致记忆断裂、事实矛盾，更让深度个性化和知识一致性成为空谈。AI 无法利 用历史交互数据来理解用户，也无法保留上下文的中间数据，这使其应用价值大打折扣 ...