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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。

地铁里的 AI 伪人广告，正在集体霸凌人类

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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.

研究提出图推理驱动的模糊感知结构化生成新范式

一篇于2026年10月26日发布在arXiv（编号2610.31681v1）的论文指出，传统结构化生成将输入到输出视为确定性单映射，与自然语言固有的多重语义有效性相冲突；作者将其重构为“模糊感知推理问题”，通过构建图表示的候选结构空间并开展图基推理，在保留语义模糊性的前提下提升输出稳定性与鲁棒性。

推荐理由：该研究直击结构化生成中长期被忽视的歧义建模缺陷，提出可验证的技术路径（多有效学习+图结构候选空间+语义对齐与关系一致性推理），并在标准基准上报告了准确性、稳定性和鲁棒性的系统性提升，具有方法论创新性与实践指导价值。

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

The Path to NDE 4.0: A Review of Algorithms, Techniques, and Strategies for Autonomous Inspection

arXiv:2610.31704v1 Announce Type: new Abstract: Non-Destructive Inspection (NDI) is a cornerstone of modern quality assurance, critical for ensuring the safety and reliability of infrastructure and manufactured components without causing damage. This paper presents a comprehensive review of the current NDI landscape, analyzing the field through four distinct lenses, namely, computational algorithms, physical techniques, industrial applications, and research objectives. We synthesize a broad range of academic literature to illustrate the rapid evolution from manual, analog interpretation to a digitized, data-centric paradigm known as NDE 4.0. Our analysis highlights the dominance of ultrasound and visual inspection as primary physical techniques, while revealing a significant shift toward d

面向金融犯罪分析的对抗鲁棒多图神经网络

arXiv:2610.31718v1 发布类型：新 提要：金融交易网络通常以多重图的形式自然表示，其中同一账户对之间的重复交易被建模为不同的边。近期提出的多重图神经网络架构明确利用这一结构，在反洗钱（AML）任务上取得了优异的性能。然而，这些模型对于那些通过操纵交易结构以逃避检测的攻击者的鲁棒性仍鲜有研究。我们提出了首个基于节点—边关联矩阵的可扩展框架，用于对多重图神经网络实施结构化攻击与对抗训练，并针对此类多重图优化结构扰动，同时适配投影梯度下降法与投影随机化块坐标下降法。

Autoregressive Multimodal Anomaly Detection with Large Language Models for Microservice Systems

arXiv:2610.31722v1 Announce Type: new Abstract: Microservice systems have become a dominant architecture for large-scale cloud applications, enabling rapid deployment and elastic scaling. However, ensuring the reliability and availability of microservice systems critically relies on effective anomaly detection, as failures and performance anomalies can quickly propagate across services and severely impact end-to-end system behavior. Existing multimodal anomaly detection approaches for microservice systems often struggle with two fundamental limitations: (i) the neglect of modality-specific characteristics in favor of cross-modality fusion, and (ii) the "identity-shortcut" problem that affects the feature reconstruction process. To address these issues, we propose LLMMAD, an autoregressive

PnP*：一种用于透视-点-点问题的可认证求解器

arXiv:2610.31797v1 发布类型：新 摘要：从3D-2D对应关系中估计相机位姿——即透视n点法（PnP）——是计算机视觉中最基础的问题之一。现有方法要么求解极小化问题，要么用更简单的目标函数替代原目标。令人惊讶的是，针对真实的最大似然估计（MLE）问题，迄今尚无可扩展且具备严格证明的求解器。我们提出了 PnP*，这是首个既能保证高效计算、又能给出经验证的全局最优解的实用求解器。我们证明，在中等噪声条件下，其背后的凸松弛是精确的；据我们所知，这是首次为PnP的最大似然估计的任何松弛方法提供稳定性保证。此外，我们还通过利用弦图稀疏结构和商环约简等技术进一步降低了松弛的复杂度……

拉上英伟达硬刚苹果，微软的高端AI PC赌局

微软以采用NVIDIA RTX Spark的Surface Laptop Ultra争取本地AI开发者与高端创作者。硬件把大容量统一内存、CUDA和RTX图形能力带入便携设备，但能否形成对Mac的持续竞争，仍取决于应用适配、智能体体验和真实工作流。

推荐理由：这场竞争最终要回答的，是本地AI能否切实改善创作和开发效率，以及用户为此承担的设备、软件与迁移成本是否值得。硬件提供了新的可能性，生态和实际体验决定这种可能性能够覆盖多少用户。

PHP“熔毁”事件揭示AI如何放大旧漏洞威胁

安全研究人员借助AI工具，将已存在多年、曾被认为难以利用的PHP漏洞（统称PHP2Shell）转化为可实际 exploited 的攻击面；漏洞公开数小时内即遭百万次尝试利用，但迄今无成功入侵报告。

推荐理由：该事件首次清晰呈现AI并非凭空创造新漏洞，而是显著降低历史漏洞的利用门槛，凸显AI在攻防两端加速既有安全动态的双重作用，且所有有效发现均依赖人机协同。

RetiJev：快速且基于源数据的眼科决策模型

arXiv：2610.30947v1 发布类型：新 摘要：眼科临床决策支持需要快速、准确且兼顾隐私保护的推理，然而自回归大型语言模型（LLMs）在预定义动作间的决策过程中会带来显著的延迟和计算开销。因此，我们探索了一种面向决策的生成式推理替代方案。具体而言，我们首先推出了 RetiJev-Data，这是一个基于源数据的眼科语料库，包含78,158个源自PubMed Central文章的临床决策与诊断问题，以及涵盖531种眼疾的MeSH词表；此外，还收录了50,074个涉及11种评分体系的指南评分问题，并配套构建了一个由24,662道题目组成的评估基准。在此基础上，我们开发了RetiJev系列模型，其规模介于0.8B至9B参数之间，能够……

Meta被法院责令披露批准盗版下载训练AI的高管名单

在多起针对Meta的AI训练版权诉讼中，法院裁定Meta须明确指认授权从Anna’s Archive、LibGen等影子图书馆下载盗版图书的高管；Meta此前将责任归于基层员工，并称早期AI数据收集属‘非正式流程’，无人‘正式决策’下载行为。

Meta Muse分享功能存在隐私漏洞：生成的对话链接易被枚举，致个人与商业机密泄露

安全公司Claroc发现，Meta新推出的AI代理Muse在启用对话分享功能时，生成的URL缺乏足够随机性，导致攻击者可通过简单枚举获取6800个用户共享链接；其中数百个链接暴露了非公开的个人及商业敏感信息。Meta已暂停该分享功能并展开审查。

Open and Emergent Problems in Agentic Privacy and Security: A Contextual Angle

Generative AI