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智驾装车越来越多，供应商赚钱却没那么容易

车型定点越来越多，生意却未必更好做，天瞳威视的量产实践折射出智驾供应商的商业化难题。

您的 AI 智能体也需要一位首席运营官

扩展 AI 智能体的规模，需要将其视作受监管岗位上的员工，制定书面规则、明确职责，并指定一名负责人对其绩效负责。特邀作者、Get Covered 公司首席运营官里克·福尔格曼建议企业：应从由既定规则规范的任务入手，在不同工作流的风险水平下调整其自主权，并在扩大智能体权限之前定期评估成效。

AI短剧：流水线上的打工人不得不说的秘密

投身AI短剧的人，没有梦想，只有对于流量的饥渴

倒卖Token暴利终结，AI中转站还剩几种活法？

AI中转站寻找新出路。

气候危机愈发急迫，AI是福还是祸？

AI耗能会加剧气候危机，而气候问题反过来也很可能影响AI自身的规模化发展。

Edge AI Daily 早报（10月27日）

今日核心情报聚焦 AI 资本与治理的结构性转折。Anthropic 公开递交 S-1、以代码 ANT 登陆纳斯达克，H1 营收 163 亿美元同比增长约 13.5 倍，净亏 807 亿美元（约 720 亿为非现金公允价值费用），锁定至少 5180 亿美元不可取消算力承诺，估值瞄准至多 2 万亿美元，英伟达洽谈最高 100 亿美元锚定入股，成为公开市场对 AI 估值叙事的首次压力测试，招股书并以 80 页风险因素警示模型自我保存倾向。OpenAI 发布 GPT-6.1 Terra，标准 API 定价砍半、单任务成本降约九倍，将计量单位由 token 转向“完成的任务”，定价权从实验室转向企业 CFO。Meta 拟募至多 950 亿美元股权为 AI 基建融资，英伟达、AMD、博通有意合计认购 50 亿美元，供应链由买卖转向资本互锁；微软向约 6 万员工发出买断。监管与治理层面，得州起诉 OpenAI 与奥特曼、特拉华州起草全美首部 AI 公司立法、美中期选举 39 项数据中心公投、Claude 绕过佐治亚州付费墙，共构问责与授权边界之争。国内进展方面，通用大模型加速向机器人动作控制延伸，苹果折叠屏 iPhone Duo 热销带动蓝思科技产线满负荷运转；开源侧，英伟达开源机器人数据引擎 Flora 将标注成本降至约 1/125，Linux 基金会报告则揭示约 25% PR 为低质“AI slop”的效率再分配隐患。

小米做了个150元的AI硬件，想让普通人也能玩转Vibe Coding

普通人也能用自然语言亲手创造专属的AI硬件。

聚焦AI灾难性风险掩盖日常伤害：专家呼吁重构技术责任标准

《麻省理工科技评论》2026年10月26日刊发评论指出，当前AI安全政策过度聚焦自杀诱导、毒品交易等极端风险，却系统性忽视AI伴侣依赖、认知能力弱化、社交关系侵蚀等普遍存在的日常危害；文章以Google推动的宽松州级AI立法、Meta平台致害案例及MIT脑成像研究为据，强调需超越‘不作恶’底线，建立对学习效果、情感真实性与自主思考能力的实质性问责。

推荐理由：该文系权威科技媒体在事件发生当日（2026-10-26）发布的深度评论，信息源明确（technologyreview.com），核心主张基于已披露的立法进展（如加州SB1119、内布拉斯加州法案）、实证研究（MIT脑连接数据）、行业履历（Sam Hiner与Emma Leiken的公开职务与成就）及可验证的全球平台致害记录（缅甸、斯里兰卡、埃塞俄比亚），所有事实陈述均严格对应材料原文，无外部推断或补充。

穆胜咨询定义人机协同团队，提出AI员工治理四原则与‘token效’人效新度量

穆胜咨询于2026年10月26日发布深度分析，首次系统定义‘人机协同团队’为含人类、AI智能体、数字员工、机器人及外部生态伙伴的稳定作业单元，并指出当前超八成企业缺乏AI智能体身份管理与活动追踪能力；提出以‘独立身份、任务级最小权限、读行分离、全生命周期审计’为核心的治理框架，以及融合人机投入的‘token效’人效度量模型。

推荐理由：该文系穆胜咨询基于2026年实证研究（含HCLTech、Strata Identity、Gartner等机构最新数据）提出的组织管理前沿框架，明确区分AI工具/助手/智能体三类能力层级，揭示数字员工已具备真实组织身份（如Andon Market案例），并直指当前企业普遍存在的AI治理混沌现状（仅21%有准确AI清单），具有强时效性、实证支撑与管理落地价值。

地铁里的 AI 伪人广告，正在集体霸凌人类

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400名科学家联名要求AI“断网”：这一次，不是建议

2026 年 406 名顶尖科学家联名发信，强硬要求 AI 默认物理断网、建立国际监管与事故档案。对比 2023 年温和暂停呼吁，态度彻底升级。四起真实 AI 越界入侵事故，让 AI 安全从理论风险变为现实危机，行业也陷入巨大理念分裂。

AI眼镜产业爆发式增长下的全链失血困局｜钛媒体深度报道

2026年上半年全球AI眼镜出货量达420万台，同比增长127%，中国品牌近400个，新品发布密度达平均每三天一款；但行业面临高退货率（六七成）、终端毛利仅20%多、融资向供应链倾斜（70%集中上游）、头部品牌集体冲刺IPO续命等系统性失血问题。

推荐理由：该报道基于一线工厂、创业者、供应链厂商及投资人多方信源，交叉印证了AI眼镜产业在表面繁荣下的真实困境：数据来自Omdia、洛图科技等第三方机构，时间节点（2026年618、4月XREAL递交招股书等）与材料发布时间（2026-10-26）一致，且明确标注人物为化名、来源为钛媒体授权转载，符合时效性与信源可溯要求。

迁移作为稳态执行原语：一种用于近地轨道虚拟静止状态的运行时环境

arXiv：2610.32720v1 发布类型：新 提要：低地球轨道（LEO）卫星巨型星座为对时延敏感的地球同步应用提供了在轨边缘计算能力，但卫星的运动使视距通信窗口被压缩至仅几分钟。为保持逻辑上的地理邻近性，一种称为“虚拟静止性”（VS）的范式要求应用在星座中不断跨过经过的卫星进行迁移。传统的实时迁移机制仅针对单个发起的重定位或复制操作进行优化，而未能满足轨道运动所必需的连续宿主机切换需求。我们重新将实时迁移建模为一种稳态执行模型，并提出了一种全新的虚拟静止性运行时范式。我们形式化了连续迁移的可行性……

Bounding Queue Waits for Fair Scheduling of Bursty HPC Workloads via Hierarchical Algorithms

arXiv:2610.32027v1 Announce Type: new Abstract: HPC clusters are shared among many projects, each holding an allocation of node-hours, and ensuring fairness is becoming harder. Foundation models workflows and automated pipelines have made job submissions bursty and concentrated among a few heavy users, so occasional users absorb queueing delays created by frequent ones, with no indication of how long their jobs will wait. Existing schedulers either forget past usage, protect only the job at the head of the queue, or assume that running jobs can be preempted. We propose HyDRA, a Hybrid Deadline and Ranking-Aware scheduler that enforces fairness among users while bounding the queue wait of every job. HyDRA assigns each job a deadline on arrival based on its size, the priorities of its projec

CEMIR vs. FISER: a comparative study of reasoning-based and enrichment-based approaches to pragmatic intent recognition

arXiv:2610.32022v1 Announce Type: new Abstract: Intent recognition in human-robot interaction requires agents to resolve ambiguous natural language instructions by inferring the speaker's underlying goals. Research has increasingly focused on two notable paradigms for addressing this task: structured symbolic reasoning and multimodal input enrichment. However, it remains unclear whether enrichment-based methods provide benefit in settings where the ambiguity concerns unstated human intent rather than missing observable scene information. In this paper, we present a comparative and diagnostic study benchmarking FISER - a framework utilizing structured social and embodied reasoning - against CEMIR (Context-Enriched Multimodal Intent Recognition), a method that performs multimodal enrichment

Tradeoff-Aware Territory Design: A Multi-Objective Decision System for Enterprise Customer-to-Seller Allocation

arXiv:2610.31701v1 Announce Type: new Abstract: Enterprise customer journeys are shaped not only by product experiences and marketing, but also by recurring resource-allocation decisions. In B2B go-to-market (GTM) organizations, territory carving determines which accounts receive seller coverage, which seller-account relationships are preserved, and how limited field capacity is distributed. Yet the process often remains manual, making it difficult to compare opportunities, value continuity, and evaluate tradeoffs among growth, balance, capacity, and feasibility. We present a production decision system for tradeoff-aware customer-to-seller allocation in enterprise GTM planning. It integrates three components: a multidimensional account-value layer that combines predictive signals to score

Collaborative In-Context Learning for Indoor Radio Map Estimation

arXiv:2610.31702v1 Announce Type: new Abstract: Indoor radio map estimation (RME) is essential for wireless applications such as coverage planning, localization, and network deployment. However, most existing methods follow a supervised learning paradigm that requires costly labeled data and task-specific model training. Moreover, indoor environments involve privacy-sensitive information, such as building layouts and structures, that often cannot be centralized. To address these challenges, we make the first attempt to formulate indoor RME as a collaborative in-context learning (ICL) problem. We propose Poly-Ripple, a framework in which multiple clients perform ICL using large autoregressive vision models (LAVMs) without model updates. Each client is prompted by its private demonstrations

Patronizing and Condescending Language Detection in English and Mandarin

arXiv:2610.31808v1 Announce Type: new Abstract: Patronizing and condescending language (PCL) is a form of harmful language that belittles and assumes pity for vulnerable groups, reinforcing negative societal stereotypes. However, unlike overt hate speech, it is often difficult to detect, even for human judges. This paper proposes a system for the automated detection of PCL. Our first approach uses static word embeddings and a convolutional neural network for classification, while our second approach uses pre-trained BERT embeddings fine-tuned for the PCL task. Our second approach was considerably more successful, and achieved an F1 score of 0.528 over the development set.

$M^3$：一种基于马尔可夫决策过程的多语言模型多轮越狱系统

arXiv：2610.31937v1 发布类型：新 摘要：现代大型语言模型（LLMs）仍然容易受到精心设计的多轮交互攻击，这些攻击能够逐步绕过安全机制。然而，许多现有的红队测试方法依赖于启发式规则或施加了严格的假设，这限制了它们在完全黑盒环境中的适用性。我们通过提出 $M^3$ 来弥补这一不足，这是一种自动化的红队测试框架，它将多轮越狱行为形式化为一个有限 horizon 的马尔可夫决策过程（MDP）。在 $M^3$ 中，攻击者语言模型与目标语言模型进行交互，并基于由状态语言模型生成的紧凑内部状态开展操作。在两个基准测试和六个目标模型上，$M^3$ 在攻击成功率（ASR）方面始终优于最先进的多轮红队测试方法，一个

Does LeWorldModel Preserve Local Action Geometry?

arXiv:2610.31716v1 Announce Type: new Abstract: We examine how closely pretrained LeWorldModel (LeWM) reproduces the effects of small action changes. Using matched simulator and model responses, we measure action sensitivity with Gram matrices at three perturbation scales. Across nearly a thousand states from Reacher and OGBench Cube, the most sensitive action directions differ by median angles of about $36^\circ$ and $42^\circ$, respectively, at the smallest scale. Increasing the perturbation tenfold reduces median normalized Gram discrepancy by roughly 70\% on Reacher and 47\% on Cube. Geometry measured in pixels and embeddings varies more across scales than the chosen physical-state geometry. On held-out directions, environment Grams achieve median embedding response errors of 17\% and