OrangeBot.AI Digest — 2025-11-26

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The Cloud Native Application Proxy

[EMNLP2025] "LightRAG: Simple and Fast Retrieval-Augmented Generation"

A list of open source games.

verl: Volcano Engine Reinforcement Learning for LLMs

Open-Source Memory Engine for LLMs, AI Agents & Multi-Agent Systems

Curated coding interview preparation materials for busy software engineers

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Collection of publicly available IPTV channels from all over the world

Recent advances in LLM-guided evolutionary computation, particularly AlphaEvolve (Novikov et al., 2025; Georgiev et al., 2025), have demonstrated remarkable success in discovering novel mathematical constructions and solving challenging optimization problems. However, the high-level descriptions in published work leave many implementation details unspecified, hindering reproducibility and further research. In this report we present GigaEvo, an extensible open-source framework that enables researchers to study and experiment with hybrid LLM-evolution approaches inspired by AlphaEvolve. Our system provides modular implementations of key components: MAP-Elites quality-diversity algorithms, asynchronous DAG-based evaluation pipelines, LLM-driven mutation operators with insight generation and bidirectional lineage tracking, and flexible multi-island evolutionary strategies. In order to assess reproducibility and validate our implementation we evaluate GigaEvo on challenging problems from the AlphaEvolve paper: Heilbronn triangle placement, circle packing in squares, and high-dimensional kissing numbers. The framework emphasizes modularity, concurrency, and ease of experimentation, enabling rapid prototyping through declarative configuration. We provide detailed descriptions of system architecture, implementation decisions, and experimental methodology to support further research in LLM driven evolutionary methods. The GigaEvo framework and all experimental code are available at https://github.com/AIRI-Institute/gigaevo-core.

Preserving first-frame identity while ensuring precise motion control is a fundamental challenge in human image animation. The Image-to-Motion Binding process of the dominant Reference-to-Video (R2V) paradigm overlooks critical spatio-temporal misalignments common in real-world applications, leading to failures such as identity drift and visual artifacts. We introduce SteadyDancer, an Image-to-Video (I2V) paradigm-based framework that achieves harmonized and coherent animation and is the first to ensure first-frame preservation robustly. Firstly, we propose a Condition-Reconciliation Mechanism to harmonize the two conflicting conditions, enabling precise control without sacrificing fidelity. Secondly, we design Synergistic Pose Modulation Modules to generate an adaptive and coherent pose representation that is highly compatible with the reference image. Finally, we employ a Staged Decoupled-Objective Training Pipeline that hierarchically optimizes the model for motion fidelity, visual quality, and temporal coherence. Experiments demonstrate that SteadyDancer achieves state-of-the-art performance in both appearance fidelity and motion control, while requiring significantly fewer training resources than comparable methods.

Medical image segmentation is fundamental for biomedical discovery. Existing methods lack generalizability and demand extensive, time-consuming manual annotation for new clinical application. Here, we propose MedSAM-3, a text promptable medical segmentation model for medical image and video segmentation. By fine-tuning the Segment Anything Model (SAM) 3 architecture on medical images paired with semantic conceptual labels, our MedSAM-3 enables medical Promptable Concept Segmentation (PCS), allowing precise targeting of anatomical structures via open-vocabulary text descriptions rather than solely geometric prompts. We further introduce the MedSAM-3 Agent, a framework that integrates Multimodal Large Language Models (MLLMs) to perform complex reasoning and iterative refinement in an agent-in-the-loop workflow. Comprehensive experiments across diverse medical imaging modalities, including X-ray, MRI, Ultrasound, CT, and video, demonstrate that our approach significantly outperforms existing specialist and foundation models. We will release our code and model at https://github.com/Joey-S-Liu/MedSAM3.

Vision-language agents have achieved remarkable progress in a variety of multimodal reasoning tasks; however, their learning remains constrained by the limitations of human-annotated supervision. Recent self-rewarding approaches attempt to overcome this constraint by allowing models to act as their own critics or reward providers. Yet, purely text-based self-evaluation struggles to verify complex visual reasoning steps and often suffers from evaluation hallucinations. To address these challenges, inspired by recent advances in tool-integrated reasoning, we propose Agent0-VL, a self-evolving vision-language agent that achieves continual improvement with tool-integrated reasoning. Agent0-VL incorporates tool usage not only into reasoning but also into self-evaluation and self-repair, enabling the model to introspect, verify, and refine its reasoning through evidence-grounded analysis. It unifies two synergistic roles within a single LVLM: a Solver that performs multi-turn tool-integrated reasoning, and a Verifier that generates structured feedback and fine-grained self-rewards through tool-grounded critique. These roles interact through a Self-Evolving Reasoning Cycle, where tool-based verification and reinforcement learning jointly align the reasoning and evaluation distributions for stable self-improvement. Through this zero-external-reward evolution, Agent0-VL aligns its reasoning and verification behaviors without any human annotation or external reward models, achieving continual self-improvement. Experiments on geometric problem solving and visual scientific analysis show that Agent0-VL achieves an 12.5% improvement over the base model. Our code is available at https://github.com/aiming-lab/Agent0/Agent0-VL{this https URL}.

Pre-trained video models learn powerful priors for generating high-quality, temporally coherent content. While these models excel at temporal coherence, their dynamics are often constrained by the continuous nature of their training data. We hypothesize that by injecting the rich and unconstrained content diversity from image data into this coherent temporal framework, we can generate image sets that feature both natural transitions and a far more expansive dynamic range. To this end, we introduce iMontage, a unified framework designed to repurpose a powerful video model into an all-in-one image generator. The framework consumes and produces variable-length image sets, unifying a wide array of image generation and editing tasks. To achieve this, we propose an elegant and minimally invasive adaptation strategy, complemented by a tailored data curation process and training paradigm. This approach allows the model to acquire broad image manipulation capabilities without corrupting its invaluable original motion priors. iMontage excels across several mainstream many-in-many-out tasks, not only maintaining strong cross-image contextual consistency but also generating scenes with extraordinary dynamics that surpass conventional scopes. Find our homepage at: https://kr1sjfu.github.io/iMontage-web/.

Recent years have witnessed significant progress in Unified Multimodal Models, yet a fundamental question remains: Does understanding truly inform generation? To investigate this, we introduce UniSandbox, a decoupled evaluation framework paired with controlled, synthetic datasets to avoid data leakage and enable detailed analysis. Our findings reveal a significant understanding-generation gap, which is mainly reflected in two key dimensions: reasoning generation and knowledge transfer. Specifically, for reasoning generation tasks, we observe that explicit Chain-of-Thought (CoT) in the understanding module effectively bridges the gap, and further demonstrate that a self-training approach can successfully internalize this ability, enabling implicit reasoning during generation. Additionally, for knowledge transfer tasks, we find that CoT assists the generative process by helping retrieve newly learned knowledge, and also discover that query-based architectures inherently exhibit latent CoT-like properties that affect this transfer. UniSandbox provides preliminary insights for designing future unified architectures and training strategies that truly bridge the gap between understanding and generation. Code and data are available at https://github.com/PKU-YuanGroup/UniSandBox

The quadratic complexity of full attention limits efficient long-context processing in large language models (LLMs). Sparse attention mitigates this cost by restricting each query to attend to a subset of previous tokens; however, training-free approaches often lead to severe performance degradation. Native sparse-attention methods (e.g., NSA, MoBA) alleviate this issue, yet exhibit a critical paradox: they produce lower attention sparsity than full-attention models, despite aiming to approximate full attention, which may constrain their effectiveness. We attribute this paradox to gradient update deficiency: low-ranked key-value pairs excluded during sparse training receive neither forward contribution nor backward gradients, and thus never learn proper suppression. To overcome this limitation, we propose SSA (Sparse Sparse Attention), a unified training framework that considers both sparse and full attention and enforces bidirectional alignment at every layer. This design preserves gradient flow to all tokens while explicitly encouraging sparse-attention outputs to align with their full-attention counterparts, thereby promoting stronger sparsity. As a result, SSA achieves state-of-the-art performance under both sparse and full attention inference across multiple commonsense benchmarks. Furthermore, SSA enables models to adapt smoothly to varying sparsity budgets; performance improves consistently as more tokens are allowed to attend, supporting flexible compute-performance trade-offs at inference time. Finally, we show that native sparse-attention training surprisingly improves long-context extrapolation by mitigating the over-allocation of attention values in sink areas, with SSA demonstrating the strongest extrapolation capability.

World models are emerging as a foundational paradigm for scalable, data-efficient embodied AI. In this work, we present GigaWorld-0, a unified world model framework designed explicitly as a data engine for Vision-Language-Action (VLA) learning. GigaWorld-0 integrates two synergistic components: GigaWorld-0-Video, which leverages large-scale video generation to produce diverse, texture-rich, and temporally coherent embodied sequences under fine-grained control of appearance, camera viewpoint, and action semantics; and GigaWorld-0-3D, which combines 3D generative modeling, 3D Gaussian Splatting reconstruction, physically differentiable system identification, and executable motion planning to ensure geometric consistency and physical realism. Their joint optimization enables the scalable synthesis of embodied interaction data that is visually compelling, spatially coherent, physically plausible, and instruction-aligned. Training at scale is made feasible through our efficient GigaTrain framework, which exploits FP8-precision and sparse attention to drastically reduce memory and compute requirements. We conduct comprehensive evaluations showing that GigaWorld-0 generates high-quality, diverse, and controllable data across multiple dimensions. Critically, VLA model (e.g., GigaBrain-0) trained on GigaWorld-0-generated data achieve strong real-world performance, significantly improving generalization and task success on physical robots without any real-world interaction during training.

Reinforcement learning (RL) plays an increasingly important role in enhancing the reasoning capabilities of large language models (LLMs), yet stable and performant policy optimization remains challenging. Token-level importance ratios often exhibit high variance-a phenomenon exacerbated in Mixture-of-Experts models-leading to unstable updates. Existing group-based policy optimization methods, such as GSPO and GRPO, alleviate this problem via hard clipping, making it difficult to maintain both stability and effective learning. We propose Soft Adaptive Policy Optimization (SAPO), which replaces hard clipping with a smooth, temperature-controlled gate that adaptively attenuates off-policy updates while preserving useful learning signals. Compared with GSPO and GRPO, SAPO is both sequence-coherent and token-adaptive. Like GSPO, SAPO maintains sequence-level coherence, but its soft gating forms a continuous trust region that avoids the brittle hard clipping band used in GSPO. When a sequence contains a few highly off-policy tokens, GSPO suppresses all gradients for that sequence, whereas SAPO selectively down-weights only the offending tokens and preserves the learning signal from the near-on-policy ones, improving sample efficiency. Relative to GRPO, SAPO replaces hard token-level clipping with smooth, temperature-controlled scaling, enabling more informative and stable updates. Empirical results on mathematical reasoning benchmarks indicate that SAPO exhibits improved training stability and higher Pass@1 performance under comparable training budgets. Moreover, we employ SAPO to train the Qwen3-VL model series, demonstrating that SAPO yields consistent performance gains across diverse tasks and different model sizes. Overall, SAPO provides a more reliable, scalable, and effective optimization strategy for RL training of LLMs.

Despite advances, video diffusion transformers still struggle to generalize beyond their training length, a challenge we term video length extrapolation. We identify two failure modes: model-specific periodic content repetition and a universal quality degradation. Prior works attempt to solve repetition via positional encodings, overlooking quality degradation and achieving only limited extrapolation. In this paper, we revisit this challenge from a more fundamental view: attention maps, which directly govern how context influences outputs. We identify that both failure modes arise from a unified cause: attention dispersion, where tokens beyond the training window dilute learned attention patterns. This leads to quality degradation and repetition emerges as a special case when this dispersion becomes structured into periodic attention patterns, induced by harmonic properties of positional encodings. Building on this insight, we propose UltraViCo, a training-free, plug-and-play method that suppresses attention for tokens beyond the training window via a constant decay factor. By jointly addressing both failure modes, we outperform a broad set of baselines largely across models and extrapolation ratios, pushing the extrapolation limit from 2x to 4x. Remarkably, it improves Dynamic Degree and Imaging Quality by 233% and 40.5% over the previous best method at 4x extrapolation. Furthermore, our method generalizes seamlessly to downstream tasks such as controllable video synthesis and editing.

Generative models have excelled in RGB synthesis, but real-world applications require RGBA manipulation. This has led to a fragmented landscape: specialized, single-task models handle alpha but lack versatility, while unified multi-task frameworks are confined to the RGB domain. To bridge this critical gap, we propose OmniAlpha, the first unified, multi-task generative framework for sequence-to-sequence RGBA image generation and editing. Its architecture features MSRoPE-BiL, a novel RoPE method with a bi-directionally extendable layer axis for its Diffusion Transformer (DiT) backbone, enabling the concurrent processing of multiple input and target RGBA layers. To power this framework, we introduce AlphaLayers, a new dataset of 1,000 high-quality, multi-layer triplets, built via a novel automated synthesis and filter pipeline. Jointly training OmniAlpha on this dataset across a comprehensive suite of 21 diverse tasks, extensive experiments demonstrate that our unified approach consistently outperforms strong, specialized baselines. Most notably, OmniAlpha achieves a dramatic 84.8% relative reduction in SAD for mask-free matting on AIM-500 and wins over 90% of human preferences in layer-conditioned completion. Our work proves that a unified, multi-task model can learn a superior shared representation for RGBA, paving the way for more powerful, layer-aware generative systems.

We present ReDirector, a novel camera-controlled video retake generation method for dynamically captured variable-length videos. In particular, we rectify a common misuse of RoPE in previous works by aligning the spatiotemporal positions of the input video and the target retake. Moreover, we introduce Rotary Camera Encoding (RoCE), a camera-conditioned RoPE phase shift that captures and integrates multi-view relationships within and across the input and target videos. By integrating camera conditions into RoPE, our method generalizes to out-of-distribution camera trajectories and video lengths, yielding improved dynamic object localization and static background preservation. Extensive experiments further demonstrate significant improvements in camera controllability, geometric consistency, and video quality across various trajectories and lengths.

This paper presents HunyuanOCR, a commercial-grade, open-source, and lightweight (1B parameters) Vision-Language Model (VLM) dedicated to OCR tasks. The architecture comprises a Native Vision Transformer (ViT) and a lightweight LLM connected via an MLP adapter. HunyuanOCR demonstrates superior performance, outperforming commercial APIs, traditional pipelines, and larger models (e.g., Qwen3-VL-4B). Specifically, it surpasses current public solutions in perception tasks (Text Spotting, Parsing) and excels in semantic tasks (IE, Text Image Translation), securing first place in the ICDAR 2025 DIMT Challenge (Small Model Track). Furthermore, it achieves state-of-the-art (SOTA) results on OCRBench among VLMs with fewer than 3B parameters. HunyuanOCR achieves breakthroughs in three key aspects: 1) Unifying Versatility and Efficiency: We implement comprehensive support for core capabilities including spotting, parsing, IE, VQA, and translation within a lightweight framework. This addresses the limitations of narrow "OCR expert models" and inefficient "General VLMs". 2) Streamlined End-to-End Architecture: Adopting a pure end-to-end paradigm eliminates dependencies on pre-processing modules (e.g., layout analysis). This fundamentally resolves error propagation common in traditional pipelines and simplifies system deployment. 3) Data-Driven and RL Strategies: We confirm the critical role of high-quality data and, for the first time in the industry, demonstrate that Reinforcement Learning (RL) strategies yield significant performance gains in OCR tasks. HunyuanOCR is officially open-sourced on HuggingFace. We also provide a high-performance deployment solution based on vLLM, placing its production efficiency in the top tier. We hope this model will advance frontier research and provide a solid foundation for industrial applications.

This paper studies Visual Question-Visual Answering (VQ-VA): generating an image, rather than text, in response to a visual question -- an ability that has recently emerged in proprietary systems such as NanoBanana and GPT-Image. To also bring this capability to open-source models, we introduce VQ-VA World, a data-centric framework built around an agentic pipeline for large-scale, targeted data construction. Leveraging web-scale deployment, this pipeline crawls a massive amount of ~1.8M high-quality, interleaved image-text samples for model training. For evaluation, we further release IntelligentBench, a human-curated benchmark that systematically assesses VQ-VA along the aspects of world knowledge, design knowledge, and reasoning. Training with VQ-VA World data yields strong empirical gains: it helps LightFusion attain 53.06 on IntelligentBench, substantially surpassing the best prior open-source baselines (i.e., 7.78 from vanilla LightFusion; 1.94 from UniWorld-V1), and significantly narrowing the gap toward leading proprietary systems (e.g., 81.67 from NanoBanana; 82.64 from GPT-Image). By releasing the full suite of model weights, datasets, and pipelines, we hope to stimulate future research on VQ-VA.

Normalizing flows (NFs) are end-to-end likelihood-based generative models for continuous data, and have recently regained attention with encouraging progress on image generation. Yet in the video generation domain, where spatiotemporal complexity and computational cost are substantially higher, state-of-the-art systems almost exclusively rely on diffusion-based models. In this work, we revisit this design space by presenting STARFlow-V, a normalizing flow-based video generator with substantial benefits such as end-to-end learning, robust causal prediction, and native likelihood estimation. Building upon the recently proposed STARFlow, STARFlow-V operates in the spatiotemporal latent space with a global-local architecture which restricts causal dependencies to a global latent space while preserving rich local within-frame interactions. This eases error accumulation over time, a common pitfall of standard autoregressive diffusion model generation. Additionally, we propose flow-score matching, which equips the model with a light-weight causal denoiser to improve the video generation consistency in an autoregressive fashion. To improve the sampling efficiency, STARFlow-V employs a video-aware Jacobi iteration scheme that recasts inner updates as parallelizable iterations without breaking causality. Thanks to the invertible structure, the same model can natively support text-to-video, image-to-video as well as video-to-video generation tasks. Empirically, STARFlow-V achieves strong visual fidelity and temporal consistency with practical sampling throughput relative to diffusion-based baselines. These results present the first evidence, to our knowledge, that NFs are capable of high-quality autoregressive video generation, establishing them as a promising research direction for building world models. Code and generated samples are available at https://github.com/apple/ml-starflow.

欧洲议会周三呼吁欧盟设定儿童使用社交媒体的最低年龄限制，以应对青少年因过度接触社交媒体而导致的心理健康问题日益增多的现状。此前澳大利亚通过了全球首个针对 16 岁以下儿童的社交媒体禁令，丹麦和马来西亚也计划效仿。欧洲议会以 483 票赞成、92 票反对、86 票弃权通过决议呼吁欧盟范围内禁止 16 岁以下儿童在未经家长同意的情况下访问在线平台、视频分享网站和人工智能助手，并彻底禁止 13 岁以下儿童使用。决议还呼吁禁止“战利品箱”以及针对未成年人的基于用户参与度的推荐算法，并要求制定相关法律，规定内容设计必须符合儿童的年龄特点。

马来西亚柔佛州停止批准一级和二级数据中心，理由这些数据中心用水量太高。柔佛是东南亚数据中心枢纽之一。截至 2025 年 11 月它批准了 51 个数据中心项目，其中 17 个已投入运营，11 个正在建设中，23 个是今年新获批准的。一级和二级数据中心每天用水量在 4000-5000 万升之间。相比下三级和四级数据中心每天用水量约为 20 万升，与普通工业负荷相当。柔佛希望所有数据中心能达到更高、更可持续、更节能的标准，与国际标准接轨。马来西亚官员称，美国佐治亚州的一家数据中心投入运营后，居民区频繁遭遇供水中断；乌拉圭民众抗议数据中心可能会影响农田供水。

根据发表在《Nature Communications》期刊上的一项研究，对猕猴前额叶皮层神经元活动的分析揭示了大脑如何调配有限的工作记忆资源。分析发现，当记忆负荷增加时，大脑并不是不断招募新的神经元，而是反复“再利用”已有的神经元。这些神经元既能稳定保留早先的信息，又能灵活调整去编码新的信息，并尽量减少不同记忆项目之间的干扰。随着需要记住的项目增多，这些神经元在保留旧信息、编码新信息以及减少信息间干扰之间进行权衡，体现了前额叶皮层对有限资源的主动调配。

东南亚的泰国、越南、菲律宾和马来西亚都遭遇了创纪录降雨以及随之而来的洪灾。受灾人口数以百万计。气象专家警告称，未来几周暴雨将沿着马来半岛朝印尼和新加坡前进。东南亚异常强的降雨是两大气候系统——拉尼娜（La Niña）和负印度洋偶极（negative Indian Ocean Dipole）——同时作用的结果。两大气候系统通常不会同时达到峰值，因为它们源于不同的海盆，受不同的环流模式驱动，且季节性时间通常不相同。但今年两大气候系统的叠加为强降雨创造了理想条件。

Omar Rana 第一次驾驶中国造汽车是 2015 年海外旅行期间的租车，中国汽车给他的第一印象很糟糕。到了 2024年，一家他从未听说过的公司 DCar Studio 邀请他去洛杉矶试驾中国公司制造的电动汽车。由于邀请邮件的英语很生涩，他以为是 Spam。但当他得知其他汽车网红也收到类似邀请后，他克服了犹豫。这一次中国电动汽车给他留下了深刻印象。以吉利 Galaxy E5 为例：这是一款售价 2 万美元的紧凑型 SUV，配备了加热、通风和按摩座椅、数字仪表组、抬头显示器、可调节靠背的乘客座椅，以及 360 度影像。在北美市场上，这个价位上没有任何一款车能与之竞争。中国过去十年电动汽车的增长速度惊人，从全球汽车行业的中游力量一跃成为全球最大的汽车市场和最大的电动汽车出口国。由于高关税政策和软件限制，中国无法在美国市场销售电动汽车，但中国公司采用了另一种方法，邀请网红在美国市场宣传新车，提升其品牌的全球知名度。瑞士洛桑国际管理发展学院教授 Mark Greeven 称，美国网红仍然影响着整个西方世界的观点。

微软/动视旗下的《使命召唤》系列支配游戏行业已长达 20 年。上一次美国射击类游戏销量超过《使命召唤》还是 2006 年的《战争机器（Gears of War）》，上一次 FPS 游戏销量超过《使命召唤》是 2006 年的《星球大战：前线2》。过去 16 年《使命召唤》系列游戏有 13 年荣登年度销量榜首，例外是 2013 年的《侠盗猎车手5（GTA 5）》、2018 年的《荒野大镖客2（Red Dead Redemption II）》以及 2023 年的《霍格沃茨之遗（Hogwarts Legacy）》。但今年《使命召唤：黑色行动7》的销量明显大幅下滑，今年的射击和 FPS 游戏市场的竞争比往年更激烈，EA 旗下的《战地6》美国销量超过《黑色行动7》，另一款热门游戏《ARC Raiders》的销量也相当可观。

迪士尼旗下的 FX 将制作改编自育碧游戏《孤岛惊魂》系列的电视剧。《孤岛惊魂（Far Cry）》系列最早由德国 Crytek 工作室制作，之后由育碧旗下的工作室制作了五部续作和多部衍生作品，但每一部都有不同的背景和主角。它是最畅销的游戏系列之一，截至 2019 年的数据称销量超过 5000 万份。《孤岛惊魂》电视剧由 Noah Hawley 和 Rob Mac 担任制作人，也将采用和游戏类似的单元剧模式，每季有不同演员阵容和场景设置。Noah Hawley 是《Fargo》和《Alien: Earth》的制作人，而 Rob Mac 是 FX 喜剧片《Always Sunny in Philadelphia》的制作人，此前与育碧以及苹果合作推出了 Apple TV+ 原创情景喜剧《Mythic Quest》。

Cognitive Resonance 创始人 Benjamin Riley 认为 AI 并不能思考。今天的 AI 热是基于一个根本性的误解：语言的建模并不等同于智能。根据目前的神经学，人类的思维在很大程度上独立于人类语言，几乎没有理由相信，越来越复杂的语言建模能达到甚至超越人类水平的智能。人类用语言交流，用语言创造比喻去描述推理过程。失去语言能力的人仍然能展示出推理能力。当人类对现有的比喻不满时他们能创造出新的知识。爱因斯坦的相对论不是基于科学研究。他是在思想实验的基础上发展起来的，因为他不满足于当时的比喻。常识不过是一堆死气沉沉的比喻，而 AI 只会以有趣的方式重新排列旧的比喻，AI 永远不会对现有的数据或比喻感到不满。AI 基于的大语言模型使用的数据收集自互联网。互联网上使用的语言并不能代表现实世界上的语言。比如因纽特人的语言使用的比喻在欧洲语言中找不到。而如果互联网上没有这些比喻，那么 AI 也不可能创造出来。这并不意味着 AI 毫无用处。但它与人类智能相去甚远。

苹果公司及谷歌公司须在星期天（11月30日）前，遵守新加坡警察部队指令，采取措施防止用户通过 iMessage 及 Google Messages 冒充 gov.sg 及政府机构发短信。新加坡警方依据《网络刑事危害法》（Online Criminal Harms Act，OCHA）向这两家公司发出实施指令，要求在限期内落实防止冒充措施。为了保护公众免受虚假短信诈骗，新加坡政府已在 2022 年强制所有企业和机构加入新加坡短信发送者身份登记系统，但相关措施目前尚不适用于通过 iMessage 和 Google Messages 发送的消息。新警方称自 2024 年以来，已注意到至少 15 起通过iMessage 和 Google Messages 冒充政府机构，企图进行钓鱼诈骗的案件。

为应对 AI 热所引发的供应短缺，联想正在囤积内存等重要 PC 零部件。联想 CFO 郑孝明周一接受彭博社采访时表示，该公司目前的 PC 零部件库存比平常高 50%。随着 AI 热导致内存等零部件价格飙升，联想希望借助其库存优势获利。郑孝明表示联想已签订长期合同并拥有规模优势，将尽量避免在本季度将上涨的成本转移给客户，因为公司希望维持今年的强劲销售增长势头。联想目前拥有可满足 2026 年全年需求的内存库存。

根据发表在《Nature Communications》期刊上的一项研究，剑桥大学团队利用 MRI diffusion scans 数据集对比了年龄在 0-90 岁之间的 3802 个人的大脑，发现在人一生中大脑结构会经历四个转折点五个阶段。这四个转折点主要发生在 9 岁、32 岁、66 岁和 83 岁时。9 岁是儿童期，32 岁是进入长达 30 年的成年期的开始，66 岁是大脑结构进入早期衰老的开始，83 岁是晚期衰老的开始。

中国载人航天工程办公室宣布于 11 月 25 日 12 时 11 分在酒泉卫星发射中心发射了神舟二十二号飞船。这是中国首次发射应急飞船，原因是神舟二十号飞船原计划 11 月 5 日返回，但返回舱舷窗玻璃发现了疑似因空间碎片撞击产生的细裂纹，出于安全考虑神舟二十号宇航员改为搭乘神舟二十一号的返回舱返回地面，此举导致的结果是目前在空间站上的三名宇航员没有了可以在紧急情况下返回地面的飞船。神舟二十二号运载了备用物品、针对神舟二十号飞船舷窗玻璃的处置装置以及新鲜水果蔬菜前往天宫空间站。神舟二十号飞船目前仍靠在空间站上。神舟二十二号飞船将停留至 2026 年 4 月，届时将把神舟二十一号的宇航员送回地球。

制药公司诺和诺德（Novo Nordisk）表示，减肥注射剂 Wegovy 的活性成分司美格鲁肽（semaglutide）并不能延缓阿尔茨海默病的发展。在涉及逾 3800 人的大型试验中，已被用于治疗 2 型糖尿病和肥胖症的 GLP-1 药物的效果与安慰剂相差无几。参与试验的患者年龄在 55-85 岁之间，司美格鲁肽治疗改善了阿尔茨海默病相关的生物标志物，但未能延缓疾病的发展。

根据发表在《Nature Neuroscience》期刊上的一项研究，人脑最早的电活动是以结构化模式发生的，不需要外部经验，这一发现暗示人脑预配置了如何探索并与世界互动的指令。论文主要作者、UCSC 生物分子工程助理教授 Tal Sharf 称，大脑存在一个操作系统，它在原始状态下涌现。大脑类似计算机，运行在电信号——神经元放电——之上。电信号何时开始放电，人脑如何发育，是科学家面临的挑战性课题，因为早期的人脑是在子宫内受保护的情况下发育的。科学家利用类大脑器官在实验室环境下研究大脑如何发育。大脑被发现有一个默认的放电模式，即使没有接收到任何感觉输入，它们也会发出复杂的、基于时间的模式。Sharf 称，演化找到了一种方法，让中枢神经系统能构建一张地图，允许我们探索世界并与世界互动。

Heather Mitchell 手中的一部 Galaxy A71 智能手机已使用了六年之久，过去 26 年她一共使用了 5 部手机，每部都用到不能用为止。根据 Reviews.org 的调查，美国人每部智能手机的平均使用寿命从 2016 年的 22 个月延长到了今天的 29 个月。延长设备使用寿命在短期内可能省钱，但长期看可能损害经济。美联储发表的一项研究称，企业每推迟一年升级设备，生产率就会下降约0.33%。