OrangeBot.AI Digest — 2025-11-27

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

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Multimodal large language models (MLLMs) are currently at the center of research attention, showing rapid progress in scale and capabilities, yet their intelligence, limitations, and risks remain insufficiently understood. To address these issues, particularly in the context of the Russian language, where no multimodal benchmarks currently exist, we introduce Mera Multi, an open multimodal evaluation framework for Russian-spoken architectures. The benchmark is instruction-based and encompasses default text, image, audio, and video modalities, comprising 18 newly constructed evaluation tasks for both general-purpose models and modality-specific architectures (image-to-text, video-to-text, and audio-to-text). Our contributions include: (i) a universal taxonomy of multimodal abilities; (ii) 18 datasets created entirely from scratch with attention to Russian cultural and linguistic specificity, unified prompts, and metrics; (iii) baseline results for both closed-source and open-source models; (iv) a methodology for preventing benchmark leakage, including watermarking and licenses for private sets. While our current focus is on Russian, the proposed benchmark provides a replicable methodology for constructing multimodal benchmarks in typologically diverse languages, particularly within the Slavic language family.

Multi-agent systems (MAS) extend large language models (LLMs) from independent single-model reasoning to coordinative system-level intelligence. While existing LLM agents depend on text-based mediation for reasoning and communication, we take a step forward by enabling models to collaborate directly within the continuous latent space. We introduce LatentMAS, an end-to-end training-free framework that enables pure latent collaboration among LLM agents. In LatentMAS, each agent first performs auto-regressive latent thoughts generation through last-layer hidden embeddings. A shared latent working memory then preserves and transfers each agent's internal representations, ensuring lossless information exchange. We provide theoretical analyses establishing that LatentMAS attains higher expressiveness and lossless information preservation with substantially lower complexity than vanilla text-based MAS. In addition, empirical evaluations across 9 comprehensive benchmarks spanning math and science reasoning, commonsense understanding, and code generation show that LatentMAS consistently outperforms strong single-model and text-based MAS baselines, achieving up to 14.6% higher accuracy, reducing output token usage by 70.8%-83.7%, and providing 4x-4.3x faster end-to-end inference. These results demonstrate that our new latent collaboration framework enhances system-level reasoning quality while offering substantial efficiency gains without any additional training. Code and data are fully open-sourced at https://github.com/Gen-Verse/LatentMAS.

World models serve as core simulators for fields such as agentic AI, embodied AI, and gaming, capable of generating long, physically realistic, and interactive high-quality videos. Moreover, scaling these models could unlock emergent capabilities in visual perception, understanding, and reasoning, paving the way for a new paradigm that moves beyond current LLM-centric vision foundation models. A key breakthrough empowering them is the semi-autoregressive (block-diffusion) decoding paradigm, which merges the strengths of diffusion and autoregressive methods by generating video tokens in block-applying diffusion within each block while conditioning on previous ones, resulting in more coherent and stable video sequences. Crucially, it overcomes limitations of standard video diffusion by reintroducing LLM-style KV Cache management, enabling efficient, variable-length, and high-quality generation. Therefore, Inferix is specifically designed as a next-generation inference engine to enable immersive world synthesis through optimized semi-autoregressive decoding processes. This dedicated focus on world simulation distinctly sets it apart from systems engineered for high-concurrency scenarios (like vLLM or SGLang) and from classic video diffusion models (such as xDiTs). Inferix further enhances its offering with interactive video streaming and profiling, enabling real-time interaction and realistic simulation to accurately model world dynamics. Additionally, it supports efficient benchmarking through seamless integration of LV-Bench, a new fine-grained evaluation benchmark tailored for minute-long video generation scenarios. We hope the community will work together to advance Inferix and foster world model exploration.

The synthesis of synchronized audio-visual content is a key challenge in generative AI, with open-source models facing challenges in robust audio-video alignment. Our analysis reveals that this issue is rooted in three fundamental challenges of the joint diffusion process: (1) Correspondence Drift, where concurrently evolving noisy latents impede stable learning of alignment; (2) inefficient global attention mechanisms that fail to capture fine-grained temporal cues; and (3) the intra-modal bias of conventional Classifier-Free Guidance (CFG), which enhances conditionality but not cross-modal synchronization. To overcome these challenges, we introduce Harmony, a novel framework that mechanistically enforces audio-visual synchronization. We first propose a Cross-Task Synergy training paradigm to mitigate drift by leveraging strong supervisory signals from audio-driven video and video-driven audio generation tasks. Then, we design a Global-Local Decoupled Interaction Module for efficient and precise temporal-style alignment. Finally, we present a novel Synchronization-Enhanced CFG (SyncCFG) that explicitly isolates and amplifies the alignment signal during inference. Extensive experiments demonstrate that Harmony establishes a new state-of-the-art, significantly outperforming existing methods in both generation fidelity and, critically, in achieving fine-grained audio-visual synchronization.

We investigate how well large language models (LLMs) generalize across different task difficulties, a key question for effective data curation and evaluation. Existing research is mixed regarding whether training on easier or harder data leads to better results, and whether those gains come on easier or harder test data. We address this question by conducting a systematic evaluation of LLMs' generalization across models, datasets, and fine-grained groups of example difficulty. We rank examples in six datasets using the outputs of thousands of different LLMs and Item Response Theory (IRT), a well-established difficulty metric in educational testing. Unlike prior work, our difficulty ratings are therefore determined solely by the abilities of many different LLMs, excluding human opinions of difficulty. With a more objective, larger-scale, and finer-grained analysis, we show that cross-difficulty generalization is often limited; training on either easy or hard data cannot achieve consistent improvements across the full range of difficulties. These results show the importance of having a range of difficulties in both training and evaluation data for LLMs, and that taking shortcuts with respect to difficulty is risky.

"Thinking with images" has emerged as an effective paradigm for advancing visual reasoning, extending beyond text-only chains of thought by injecting visual evidence into intermediate reasoning steps. However, existing methods fall short of human-like abstract visual thinking, as their flexibility is fundamentally limited by external tools. In this work, we introduce Monet, a training framework that enables multimodal large language models (MLLMs) to reason directly within the latent visual space by generating continuous embeddings that function as intermediate visual thoughts. We identify two core challenges in training MLLMs for latent visual reasoning: high computational cost in latent-vision alignment and insufficient supervision over latent embeddings, and address them with a three-stage distillation-based supervised fine-tuning (SFT) pipeline. We further reveal a limitation of applying GRPO to latent reasoning: it primarily enhances text-based reasoning rather than latent reasoning. To overcome this, we propose VLPO (Visual-latent Policy Optimization), a reinforcement learning method that explicitly incorporates latent embeddings into policy gradient updates. To support SFT, we construct Monet-SFT-125K, a high-quality text-image interleaved CoT dataset containing 125K real-world, chart, OCR, and geometry CoTs. Our model, Monet-7B, shows consistent gains across real-world perception and reasoning benchmarks and exhibits strong out-of-distribution generalization on challenging abstract visual reasoning tasks. We also empirically analyze the role of each training component and discuss our early unsuccessful attempts, providing insights for future developments in visual latent reasoning. Our model, data, and code are available at https://github.com/NOVAglow646/Monet.

We introduce Nemotron-Parse-1.1, a lightweight document parsing and OCR model that advances the capabilities of its predecessor, Nemoretriever-Parse-1.0. Nemotron-Parse-1.1 delivers improved capabilities across general OCR, markdown formatting, structured table parsing, and text extraction from pictures, charts, and diagrams. It also supports a longer output sequence length for visually dense documents. As with its predecessor, it extracts bounding boxes of text segments, as well as corresponding semantic classes. Nemotron-Parse-1.1 follows an encoder-decoder architecture with 885M parameters, including a compact 256M-parameter language decoder. It achieves competitive accuracy on public benchmarks making it a strong lightweight OCR solution. We release the model weights publicly on Huggingface, as well as an optimized NIM container, along with a subset of the training data as part of the broader Nemotron-VLM-v2 dataset. Additionally, we release Nemotron-Parse-1.1-TC which operates on a reduced vision token length, offering a 20% speed improvement with minimal quality degradation.

We propose Terminal Velocity Matching (TVM), a generalization of flow matching that enables high-fidelity one- and few-step generative modeling. TVM models the transition between any two diffusion timesteps and regularizes its behavior at its terminal time rather than at the initial time. We prove that TVM provides an upper bound on the 2-Wasserstein distance between data and model distributions when the model is Lipschitz continuous. However, since Diffusion Transformers lack this property, we introduce minimal architectural changes that achieve stable, single-stage training. To make TVM efficient in practice, we develop a fused attention kernel that supports backward passes on Jacobian-Vector Products, which scale well with transformer architectures. On ImageNet-256x256, TVM achieves 3.29 FID with a single function evaluation (NFE) and 1.99 FID with 4 NFEs. It similarly achieves 4.32 1-NFE FID and 2.94 4-NFE FID on ImageNet-512x512, representing state-of-the-art performance for one/few-step models from scratch.

Vision-Language Models (VLMs) still lack robustness in spatial intelligence, demonstrating poor performance on spatial understanding and reasoning tasks. We attribute this gap to the absence of a visual geometry learning process capable of reconstructing 3D space from 2D images. We present G^2VLM, a geometry grounded vision-language model that bridges two fundamental aspects of spatial intelligence: spatial 3D reconstruction and spatial understanding. G^2VLM natively leverages learned 3D visual geometry features to directly predict 3D attributes and enhance spatial reasoning tasks via in-context learning and interleaved reasoning. Our unified design is highly scalable for spatial understanding: it trains on abundant multi-view image and video data, while simultaneously leveraging the benefits of 3D visual priors that are typically only derived from hard-to-collect annotations. Experimental results demonstrate G^2VLM is proficient in both tasks, achieving comparable results to state-of-the-art feed-forward 3D reconstruction models and achieving better or competitive results across spatial understanding and reasoning tasks. By unifying a semantically strong VLM with low-level 3D vision tasks, we hope G^2VLM can serve as a strong baseline for the community and unlock more future applications, such as 3D scene editing.

Unified Multimodal Models (UMMs) have shown impressive performance in both understanding and generation with a single architecture. However, UMMs still exhibit a fundamental inconsistency: understanding favors compact embeddings, whereas generation favors reconstruction-rich representations. This structural trade-off produces misaligned decision boundaries, degraded cross-modal coherence, and heightened vulnerability under distributional and adversarial shifts. In this paper, we present UniGame, a self-adversarial post-training framework that directly targets the inconsistencies. By applying a lightweight perturber at the shared token interface, UniGame enables the generation branch to actively seek and challenge fragile understanding, turning the model itself into its own adversary. Experiments demonstrate that UniGame significantly improves the consistency (+4.6%). Moreover, it also achieves substantial improvements in understanding (+3.6%), generation (+0.02), out-of-distribution and adversarial robustness (+4.8% and +6.2% on NaturalBench and AdVQA). The framework is architecture-agnostic, introduces less than 1% additional parameters, and is complementary to existing post-training methods. These results position adversarial self-play as a general and effective principle for enhancing the coherence, stability, and unified competence of future multimodal foundation models. The official code is available at: https://github.com/AIFrontierLab/UniGame

Grounding natural-language instructions into continuous control for quadruped robots remains a fundamental challenge in vision language action. Existing methods struggle to bridge high-level semantic reasoning and low-level actuation, leading to unstable grounding and weak generalization in the real world. To address these issues, we present MobileVLA-R1, a unified vision-language-action framework that enables explicit reasoning and continuous control for quadruped robots. We construct MobileVLA-CoT, a large-scale dataset of multi-granularity chain-of-thought (CoT) for embodied trajectories, providing structured reasoning supervision for alignment. Built upon this foundation, we introduce a two-stage training paradigm that combines supervised CoT alignment with GRPO reinforcement learning to enhance reasoning consistency, control stability, and long-horizon execution. Extensive evaluations on VLN and VLA tasks demonstrate superior performance over strong baselines, with approximately a 5% improvement. Real-world deployment on a quadruped robot validates robust performance in complex environments. Code: https://github.com/AIGeeksGroup/MobileVLA-R1. Website: https://aigeeksgroup.github.io/MobileVLA-R1.

Timestep distillation is an effective approach for improving the generation efficiency of diffusion models. The Consistency Model (CM), as a trajectory-based framework, demonstrates significant potential due to its strong theoretical foundation and high-quality few-step generation. Nevertheless, current continuous-time consistency distillation methods still rely heavily on training data and computational resources, hindering their deployment in resource-constrained scenarios and limiting their scalability to diverse domains. To address this issue, we propose Trajectory-Backward Consistency Model (TBCM), which eliminates the dependence on external training data by extracting latent representations directly from the teacher model's generation trajectory. Unlike conventional methods that require VAE encoding and large-scale datasets, our self-contained distillation paradigm significantly improves both efficiency and simplicity. Moreover, the trajectory-extracted samples naturally bridge the distribution gap between training and inference, thereby enabling more effective knowledge transfer. Empirically, TBCM achieves 6.52 FID and 28.08 CLIP scores on MJHQ-30k under one-step generation, while reducing training time by approximately 40% compared to Sana-Sprint and saving a substantial amount of GPU memory, demonstrating superior efficiency without sacrificing quality. We further reveal the diffusion-generation space discrepancy in continuous-time consistency distillation and analyze how sampling strategies affect distillation performance, offering insights for future distillation research. GitHub Link: https://github.com/hustvl/TBCM.

Block-causal video generation faces a stark speed-quality trade-off: small 1.3B models manage only 16 FPS while large 14B models crawl at 4.5 FPS, forcing users to choose between responsiveness and quality. Block Cascading significantly mitigates this trade-off through training-free parallelization. Our key insight: future video blocks do not need fully denoised current blocks to begin generation. By starting block generation with partially denoised context from predecessors, we transform sequential pipelines into parallel cascades where multiple blocks denoise simultaneously. With 5 GPUs exploiting temporal parallelism, we achieve ~2x acceleration across all model scales: 1.3B models accelerate from 16 to 30 FPS, 14B models from 4.5 to 12.5 FPS. Beyond inference speed, Block Cascading eliminates overhead from KV-recaching (of ~200ms) during context switches for interactive generation. Extensive evaluations validated against multiple block-causal pipelines demonstrate no significant loss in generation quality when switching from block-causal to Block Cascading pipelines for inference. Project Page: https://hmrishavbandy.github.io/block_cascading_page/

Vision Foundation Models (VFMs) extract spatially downsampled representations, posing challenges for pixel-level tasks. Existing upsampling approaches face a fundamental trade-off: classical filters are fast and broadly applicable but rely on fixed forms, while modern upsamplers achieve superior accuracy through learnable, VFM-specific forms at the cost of retraining for each VFM. We introduce Neighborhood Attention Filtering (NAF), which bridges this gap by learning adaptive spatial-and-content weights through Cross-Scale Neighborhood Attention and Rotary Position Embeddings (RoPE), guided solely by the high-resolution input image. NAF operates zero-shot: it upsamples features from any VFM without retraining, making it the first VFM-agnostic architecture to outperform VFM-specific upsamplers and achieve state-of-the-art performance across multiple downstream tasks. It maintains high efficiency, scaling to 2K feature maps and reconstructing intermediate-resolution maps at 18 FPS. Beyond feature upsampling, NAF demonstrates strong performance on image restoration, highlighting its versatility. Code and checkpoints are available at https://github.com/valeoai/NAF.

Vision-Language-Action (VLA) policies excel in aligning language, perception, and robot control. However, most VLAs are trained purely by imitation, which overfits to demonstrations, and is brittle under distribution shift. Reinforcement learning (RL) directly optimizes task reward and thus addresses this misalignment, but real-robot interaction is expensive and conventional simulators are hard to engineer and transfer. We address both data efficiency and optimization stability in VLA post-training via a learned world model and an RL procedure tailored to flow-based action heads. Specifically, we introduce Prophet, a unified action-to-video robot actuation pretrained across large-scale, heterogeneous robot data to learn reusable action-outcome dynamics. It is able to few-shot adapt to new robots, objects, and environments, yielding a rollout-ready simulator. Upon Prophet, we reinforce action policies with Flow-action-GRPO (FA-GRPO), which adapts Flow-GRPO to operate on VLA actions, and with FlowScale, a stepwise reweighting that rescales per-step gradients in the flow head. Together, Prophet, FA-GRPO, and FlowScale constitute ProphRL, a practical, data- and compute-efficient path to VLA post-training. Experiments show 5-17% success gains on public benchmarks and 24-30% gains on real robots across different VLA variants.

德国研究人员回顾性对比了 648 名歌手的死亡风险，其中一半是明星，另一半则未成名。研究人员将 324 名明星与名气较小的同行在年龄、性别、国籍、种族、音乐流派以及是否为乐队独唱/主唱等方面进行了匹配。数据分析显示，著名歌手的平均寿命为 75 岁，而名气较小的歌手平均寿命为 79 岁。尽管与独唱艺人相比，乐队成员的死亡风险低 26%，但并未影响名气的整体效应——著名歌手的早逝风险仍比名气较小的同行高 33%。研究人员提出，一个可能解释是“名气带来的独特社会心理压力，例如强烈的公众审视、表演压力和隐私丧失”。“这些压力源可能引发心理困扰和有害的应对行为，使名气成为一种慢性负担，加剧了已有的职业风险。”

科学家可能首次观察到了“看不见”的暗物质。日本东京大学研究团队利用 NASA 费米伽马射线望远镜的最新观测数据探测到了与假想的“弱相互作用大质量粒子（WIMP）”碰撞湮灭时所预测的高能光子。科学家怀疑暗物质由 WIMP 构成。这些粒子被认为比质子重，与普通物质极少相互作用。理论预测，当两个 WIMP 粒子碰撞时，它们会相互湮灭并释放出包括伽马射线光子在内的高能粒子。东京大学的 Tomonori Totani 教授报告探测到了与理论预测结果相符的 20 GeV 伽马射线光晕。研究报告本周发表在《Journal of Cosmology and Astroparticle Physic》期刊上，预印本 7 月发表在 arXiv 上。

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。