OrangeBot.AI Digest — 2025-11-17

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An open-source, code-first Go toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control.

所有小初高、大学PDF教材。

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Node Version Manager - POSIX-compliant bash script to manage multiple active node.js versions

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

Send a phone call from AI agent, in an API call. Or, directly call the bot from the configured phone number!

Run Windows Subsystem For Android on your Windows 10 and Windows 11 PC using prebuilt binaries with Google Play Store (MindTheGapps) and/or Magisk or KernelSU (root solutions) built in.

Powerful web graphics runtime built on WebGL, WebGPU, WebXR and glTF

Collection of publicly available IPTV channels from all over the world

Rotary Position Embedding (RoPE) in Transformer models has inherent limits that weaken length extrapolation. We reinterpret the attention map with positional encoding as a noisy feature map, and propose Denoising Positional Encoding (DoPE), a training-free method based on truncated matrix entropy to detect outlier frequency bands in the feature map. Leveraging the noise characteristics of the feature map, we further reparameterize it with a parameter-free Gaussian distribution to achieve robust extrapolation. Our method theoretically reveals the underlying cause of the attention sink phenomenon and its connection to truncated matrix entropy. Experiments on needle-in-a-haystack and many-shot in-context learning tasks demonstrate that DoPE significantly improves retrieval accuracy and reasoning stability across extended contexts (up to 64K tokens). The results show that the denoising strategy for positional embeddings effectively mitigates attention sinks and restores balanced attention patterns, providing a simple yet powerful solution for improving length generalization. Our project page is Project: https://The-physical-picture-of-LLMs.github.io

Recent advances in unified multimodal models (UMMs) have enabled impressive progress in visual comprehension and generation. However, existing datasets and benchmarks focus primarily on single-turn interactions, failing to capture the multi-turn, context-dependent nature of real-world image creation and editing. To address this gap, we present WEAVE, the first suite for in-context interleaved cross-modality comprehension and generation. Our suite consists of two complementary parts. WEAVE-100k is a large-scale dataset of 100K interleaved samples spanning over 370K dialogue turns and 500K images, covering comprehension, editing, and generation tasks that require reasoning over historical context. WEAVEBench is a human-annotated benchmark with 100 tasks based on 480 images, featuring a hybrid VLM judger evaluation framework based on both the reference image and the combination of the original image with editing instructions that assesses models' abilities in multi-turn generation, visual memory, and world-knowledge reasoning across diverse domains. Experiments demonstrate that training on WEAVE-100k enables vision comprehension, image editing, and comprehension-generation collaboration capabilities. Furthermore, it facilitates UMMs to develop emergent visual-memory capabilities, while extensive evaluations on WEAVEBench expose the persistent limitations and challenges of current approaches in multi-turn, context-aware image generation and editing. We believe WEAVE provides a view and foundation for studying in-context interleaved comprehension and generation for multi-modal community.

The advent of Unified Multimodal Models (UMMs) signals a paradigm shift in artificial intelligence, moving from passive perception to active, cross-modal generation. Despite their unprecedented ability to synthesize information, a critical gap persists in evaluation: existing benchmarks primarily assess discriminative understanding or unconstrained image generation separately, failing to measure the integrated cognitive process of generative reasoning. To bridge this gap, we propose that geometric construction provides an ideal testbed as it inherently demands a fusion of language comprehension and precise visual generation. We introduce GGBench, a benchmark designed specifically to evaluate geometric generative reasoning. It provides a comprehensive framework for systematically diagnosing a model's ability to not only understand and reason but to actively construct a solution, thereby setting a more rigorous standard for the next generation of intelligent systems. Project website: https://opendatalab-raiser.github.io/GGBench/.

User interface (UI) programming is a core yet highly complex part of modern software development. Recent advances in visual language models (VLMs) highlight the potential of automatic UI coding, but current approaches face two key limitations: multimodal coding capabilities remain underdeveloped, and single-turn paradigms make little use of iterative visual feedback. We address these challenges with an interactive UI-to-code paradigm that better reflects real-world workflows and raises the upper bound of achievable performance. Under this paradigm, we present UI2Code^N, a visual language model trained through staged pretraining, fine-tuning, and reinforcement learning to achieve foundational improvements in multimodal coding. The model unifies three key capabilities: UI-to-code generation, UI editing, and UI polishing. We further explore test-time scaling for interactive generation, enabling systematic use of multi-turn feedback. Experiments on UI-to-code and UI polishing benchmarks show that UI2Code^N establishes a new state of the art among open-source models and achieves performance comparable to leading closed-source models such as Claude-4-Sonnet and GPT-5. Our code and models are available at https://github.com/zai-org/UI2Code_N.

The discovery of novel Ionic Liquids (ILs) is hindered by critical challenges in property prediction, including limited data, poor model accuracy, and fragmented workflows. Leveraging the power of Large Language Models (LLMs), we introduce AIonopedia, to the best of our knowledge, the first LLM agent for IL discovery. Powered by an LLM-augmented multimodal domain foundation model for ILs, AIonopedia enables accurate property predictions and incorporates a hierarchical search architecture for molecular screening and design. Trained and evaluated on a newly curated and comprehensive IL dataset, our model delivers superior performance. Complementing these results, evaluations on literature-reported systems indicate that the agent can perform effective IL modification. Moving beyond offline tests, the practical efficacy was further confirmed through real-world wet-lab validation, in which the agent demonstrated exceptional generalization capabilities on challenging out-of-distribution tasks, underscoring its ability to accelerate real-world IL discovery.

We introduce Virtual Width Networks (VWN), a framework that delivers the benefits of wider representations without incurring the quadratic cost of increasing the hidden size. VWN decouples representational width from backbone width, expanding the embedding space while keeping backbone compute nearly constant. In our large-scale experiment, an 8-times expansion accelerates optimization by over 2 times for next-token and 3 times for next-2-token prediction. The advantage amplifies over training as both the loss gap grows and the convergence-speedup ratio increases, showing that VWN is not only token-efficient but also increasingly effective with scale. Moreover, we identify an approximately log-linear scaling relation between virtual width and loss reduction, offering an initial empirical basis and motivation for exploring virtual-width scaling as a new dimension of large-model efficiency.

Diffusion Transformers, particularly for video generation, achieve remarkable quality but suffer from quadratic attention complexity, leading to prohibitive latency. Existing acceleration methods face a fundamental trade-off: dynamically estimating sparse attention patterns at each denoising step incurs high computational overhead and estimation errors, while static sparsity patterns remain fixed and often suboptimal throughout denoising. We identify a key structural property of diffusion attention, namely, its sparsity patterns exhibit strong temporal coherence across denoising steps. Tiles deemed non-essential at step t typically remain so at step t+δ. Leveraging this observation, we introduce LiteAttention, a method that exploits temporal coherence to enable evolutionary computation skips across the denoising sequence. By marking non-essential tiles early and propagating skip decisions forward, LiteAttention eliminates redundant attention computations without repeated profiling overheads, combining the adaptivity of dynamic methods with the efficiency of static ones. We implement a highly optimized LiteAttention kernel on top of FlashAttention and demonstrate substantial speedups on production video diffusion models, with no degradation in quality. The code and implementation details will be publicly released.

The landscape of video generation is shifting, from a focus on generating visually appealing clips to building virtual environments that support interaction and maintain physical plausibility. These developments point toward the emergence of video foundation models that function not only as visual generators but also as implicit world models, models that simulate the physical dynamics, agent-environment interactions, and task planning that govern real or imagined worlds. This survey provides a systematic overview of this evolution, conceptualizing modern video foundation models as the combination of two core components: an implicit world model and a video renderer. The world model encodes structured knowledge about the world, including physical laws, interaction dynamics, and agent behavior. It serves as a latent simulation engine that enables coherent visual reasoning, long-term temporal consistency, and goal-driven planning. The video renderer transforms this latent simulation into realistic visual observations, effectively producing videos as a "window" into the simulated world. We trace the progression of video generation through four generations, in which the core capabilities advance step by step, ultimately culminating in a world model, built upon a video generation model, that embodies intrinsic physical plausibility, real-time multimodal interaction, and planning capabilities spanning multiple spatiotemporal scales. For each generation, we define its core characteristics, highlight representative works, and examine their application domains such as robotics, autonomous driving, and interactive gaming. Finally, we discuss open challenges and design principles for next-generation world models, including the role of agent intelligence in shaping and evaluating these systems. An up-to-date list of related works is maintained at this link.

Multimodal large language models (MLLMs) have achieved remarkable progress in vision-language tasks, but they continue to struggle with spatial understanding. Existing spatial MLLMs often rely on explicit 3D inputs or architecture-specific modifications, and remain constrained by large-scale datasets or sparse supervision. To address these limitations, we introduce SpatialThinker, a 3D-aware MLLM trained with RL to integrate structured spatial grounding with multi-step reasoning. The model simulates human-like spatial perception by constructing a scene graph of task-relevant objects and spatial relations, and reasoning towards an answer via dense spatial rewards. SpatialThinker consists of two key contributions: (1) a data synthesis pipeline that generates STVQA-7K, a high-quality spatial VQA dataset, and (2) online RL with a multi-objective dense spatial reward enforcing spatial grounding. SpatialThinker-7B outperforms supervised fine-tuning and the sparse RL baseline on spatial understanding and real-world VQA benchmarks, nearly doubling the base-model gain compared to sparse RL, and surpassing GPT-4o. These results showcase the effectiveness of combining spatial supervision with reward-aligned reasoning in enabling robust 3D spatial understanding with limited data and advancing MLLMs towards human-level visual reasoning.

To provide a unified and flexible solution for daily communication among hearing-impaired individuals, we introduce the Omni-Model paradigm into assistive technology and present HI-TransPA, an instruction-driven audio-visual personal assistant. The model fuses indistinct speech with high-frame-rate lip dynamics, enabling both translation and dialogue within a single multimodal framework. To tackle the challenges of noisy and heterogeneous raw data and the limited adaptability of existing Omni-Models to hearing-impaired speech, we construct a comprehensive preprocessing and curation pipeline that detects facial landmarks, isolates and stabilizes the lip region, and quantitatively assesses multimodal sample quality. These quality scores guide a curriculum learning strategy that first trains on clean, high-confidence samples and progressively incorporates harder cases to strengthen model robustness. We further adopt a SigLIP encoder combined with a Unified 3D-Resampler to efficiently encode high-frame-rate lip motion. Experiments on our purpose-built HI-Dialogue dataset show that HI-TransPA achieves state-of-the-art performance in both literal accuracy and semantic fidelity. This work establishes a foundation for applying Omni-Models to assistive communication technology, providing an end-to-end modeling framework and essential processing tools for future research.

Recent progress in large language models (LLMs) has been propelled by reinforcement learning with verifiable rewards (RLVR) and test-time scaling. However, the limited output length of LLMs constrains the depth of reasoning attainable in a single inference process. Multi-agent reasoning systems offer a promising alternative by employing multiple agents including Solver, Verifier, and Corrector, to iteratively refine solutions. While effective in closed-source models like Gemini 2.5 Pro, they struggle to generalize to open-source models due to insufficient critic and correction capabilities. To address this, we propose MarsRL, a novel reinforcement learning framework with agentic pipeline parallelism, designed to jointly optimize all agents in the system. MarsRL introduces agent-specific reward mechanisms to mitigate reward noise and employs pipeline-inspired training to enhance efficiency in handling long trajectories. Applied to Qwen3-30B-A3B-Thinking-2507, MarsRL improves AIME2025 accuracy from 86.5% to 93.3% and BeyondAIME from 64.9% to 73.8%, even surpassing Qwen3-235B-A22B-Thinking-2507. These findings highlight the potential of MarsRL to advance multi-agent reasoning systems and broaden their applicability across diverse reasoning tasks.

The evaluation of discourse-level translation in expert domains remains inadequate, despite its centrality to knowledge dissemination and cross-lingual scholarly communication. While these translations demand discourse-level coherence and strict terminological precision, current evaluation methods predominantly focus on segment-level accuracy and fluency. To address this limitation, we introduce DiscoX, a new benchmark for discourse-level and expert-level Chinese-English translation. It comprises 200 professionally-curated texts from 7 domains, with an average length exceeding 1700 tokens. To evaluate performance on DiscoX, we also develop Metric-S, a reference-free system that provides fine-grained automatic assessments across accuracy, fluency, and appropriateness. Metric-S demonstrates strong consistency with human judgments, significantly outperforming existing metrics. Our experiments reveal a remarkable performance gap: even the most advanced LLMs still trail human experts on these tasks. This finding validates the difficulty of DiscoX and underscores the challenges that remain in achieving professional-grade machine translation. The proposed benchmark and evaluation system provide a robust framework for more rigorous evaluation, facilitating future advancements in LLM-based translation.

Enabling agentic AI systems to adapt their problem-solving approaches based on post-training interactions remains a fundamental challenge. While systems that update and maintain a memory at inference time have been proposed, existing designs only steer the system by modifying textual input to a language model or agent, which means that they cannot change sampling parameters, remove tools, modify system prompts, or switch between agentic and workflow paradigms. On the other hand, systems that adapt more flexibly require offline optimization and remain static once deployed. We present Experience-Guided Reasoner (EGuR), which generates tailored strategies -- complete computational procedures involving LLM calls, tools, sampling parameters, and control logic -- dynamically at inference time based on accumulated experience. We achieve this using an LLM-based meta-strategy -- a strategy that outputs strategies -- enabling adaptation of all strategy components (prompts, sampling parameters, tool configurations, and control logic). EGuR operates through two components: a Guide generates multiple candidate strategies conditioned on the current problem and structured memory of past experiences, while a Consolidator integrates execution feedback to improve future strategy generation. This produces complete, ready-to-run strategies optimized for each problem, which can be cached, retrieved, and executed as needed without wasting resources. Across five challenging benchmarks (AIME 2025, 3-SAT, and three Big Bench Extra Hard tasks), EGuR achieves up to 14% accuracy improvements over the strongest baselines while reducing computational costs by up to 111x, with both metrics improving as the system gains experience.

Open-vocabulary detectors achieve impressive performance on COCO, but often fail to generalize to real-world datasets with out-of-distribution classes not typically found in their pre-training. Rather than simply fine-tuning a heavy-weight vision-language model (VLM) for new domains, we introduce RF-DETR, a light-weight specialist detection transformer that discovers accuracy-latency Pareto curves for any target dataset with weight-sharing neural architecture search (NAS). Our approach fine-tunes a pre-trained base network on a target dataset and evaluates thousands of network configurations with different accuracy-latency tradeoffs without re-training. Further, we revisit the "tunable knobs" for NAS to improve the transferability of DETRs to diverse target domains. Notably, RF-DETR significantly improves on prior state-of-the-art real-time methods on COCO and Roboflow100-VL. RF-DETR (nano) achieves 48.0 AP on COCO, beating D-FINE (nano) by 5.3 AP at similar latency, and RF-DETR (2x-large) outperforms GroundingDINO (tiny) by 1.2 AP on Roboflow100-VL while running 20x as fast. To the best of our knowledge, RF-DETR (2x-large) is the first real-time detector to surpass 60 AP on COCO. Our code is at https://github.com/roboflow/rf-detr

Emotion plays a pivotal role in video-based expression, but existing video generation systems predominantly focus on low-level visual metrics while neglecting affective dimensions. Although emotion analysis has made progress in the visual domain, the video community lacks dedicated resources to bridge emotion understanding with generative tasks, particularly for stylized and non-realistic contexts. To address this gap, we introduce EmoVid, the first multimodal, emotion-annotated video dataset specifically designed for creative media, which includes cartoon animations, movie clips, and animated stickers. Each video is annotated with emotion labels, visual attributes (brightness, colorfulness, hue), and text captions. Through systematic analysis, we uncover spatial and temporal patterns linking visual features to emotional perceptions across diverse video forms. Building on these insights, we develop an emotion-conditioned video generation technique by fine-tuning the Wan2.1 model. The results show a significant improvement in both quantitative metrics and the visual quality of generated videos for text-to-video and image-to-video tasks. EmoVid establishes a new benchmark for affective video computing. Our work not only offers valuable insights into visual emotion analysis in artistically styled videos, but also provides practical methods for enhancing emotional expression in video generation.

根据 Mercury Research 的数据， 2025 年第三季度 AMD 占 x86 CPU 市场的份额突破四分之一。x86 CPU 的三季度出货量与二季度持平，但主要是英特尔出货量疲软，AMD 占 x86 客户端和服务器 CPU 出货量突破 25% 达到 25.6%，比上一季度的 24.2% 增加了 1.4%，英特尔仍然占 74.4%，AMD 的桌面 x86 CPU 出货量占比超过 33%。如果加上嵌入式系统、物联网和游戏机 SoC，AMD 占到了 30.9%，而去年第三季度只有 25%。

比特币币值一个月内跌掉了一年内的所有涨幅。10 月 6 日比特币币值创下了 126,251 美元的历史记录，但四天后特朗普的关税言论引发了全球市场暴跌，上周日比特币币值跌至了 93,714 美元，跌破了去年年底的币值水平，抹掉了过去一年的所有涨幅，下跌逾 3.2 万美元。加密货币资产管理公司 Bitwise Asset Management 的首席投资官 Matthew Hougan 称，过去一个月大卖家悄悄撤离了，市场失去了推动价格上涨的资金流动支撑。此次抛售是长期持有者获利了结、机构资金外流、宏观经济不确定性，以及杠杆多头头寸被清零等多种因素共同作用的结果。

根据 LayerX 的报告《Browser Security Report 2025》，企业数据外泄更常见源头如今是拷贝黏贴，原因是生成式 AI（GenAI）的流行，77% 的员工会将数据粘贴到 AI 提示框中，32% 的企业账户到非企业账户拷贝粘贴操作发生在 GenAI 中。LayerX CEO Or Eshed 表示传统上防止企业数据外泄是针对电子邮件、文件共享和批准的 SaaS 服务而构建的，未预料到拷贝粘贴到浏览器提示框会成为主要泄露途径。数据显示，GenAI 占企业应用使用量的 11%，45% 的员工经常使用 AI 工具，67% 的 AI 工具是通过个人账户访问的，而 ChatGPT 的使用量占所有使用量的 92%。

能源智库 Ember 的数据显示，太阳能和风能的新增发电量足以满足今年前三季度新增电力需求。Ember 预测化石能源发电量全年将持平，这将是疫情爆发以来化石能源发电量首次零增长。太阳能发电量比去年同期增长 498 TWh(+31%)，超过 2024 年全年太阳能发电量；风能发电量增加 137TWh（+7.6%）。两者提供了 635 TWh 的新增电力，超过全球新增电力需求 603 TWh（+2.7%)。太阳能和风能今年前三个季度再全球电力供应中占到 17.6%，高于去年同期的 15.2%。可再生能源（包括太阳能、风能、水力、生物质能和地热能）在全球电力供应中的占比达到 43%。化石燃料的占比则从 58.7% 下滑至 57.1%。这一能源转变部分由中国和印度驱动，中国的化石燃料发电量下降 52 TWh（-1.1%)，印度化石燃料发电量下降 34TWh（-3.3%）。

比特币矿场 Bitfarm 宣布计划在 2027 年前将其业务从加密货币挖矿转型为 AI 数据中心服务。Bitfarm 虽然不是美国最大的比特币矿场，但其运营规模仍然相当可观，拥有 12 个专门挖比特币的数据中心，拥有 341 MW 电力资源，足以部署数千英伟达 GB300 NVL72 服务器机架。Bitfarm CEO Ben Gagnon 认为 AI 数据中心能比挖比特币产生更高的营业收入。Bitfarm 在第三季度净亏损 4600 万美元，比去年同期增加近 91%。尽管比特币在 10 月初创下历史新高，但其波动性意味着该公司无法持续依赖比特币支付运营成本。这次转型被认为有巨大风险，因为 AI 行业被普遍认为存在泡沫。

WSJ 报道称，恐惧驱动了中美 AI 冷战。美国目前在 AI 领域拥有领先优势，拥有最先进最强大的 AI 模型，最先进的 AI 芯片，私人投资者仅仅今年上半年就向 AI 创业公司投资了 1040 亿美元。但中国拥有更多的 AI 工程师、更低的成本，更快的发展速度，以及更充足的能源，正利用国家主导优势在能源价格廉价的内蒙古等地加速建造计算集群，计划到 2028 年将数百个数据中心连接起来，建立一个称之为“国家云”的共享计算池。中国还向电网投入数千亿美元支持 AI 训练和普及。根据 Chatbot Arena 的数据，中国 AI 模型在从编码到视频生成的任务中都排名前列。前 OpenAI 董事 Helen Toner 指出，美国人并不知道通过更先进的芯片提升算力能持续产生更强大的 AI 模型。如果性能停滞不前，即便 OpenAI 等公司投入巨资，中国仍有机会与之竞争。

中国载人航天工程办公室通报，由于神舟二十号返回舱舷窗疑因空间碎片撞击出现微小裂缝，神舟二十号乘组选择搭乘神舟二十一号返回舱返回地面，而神舟二十二号飞船将择机发射。神舟二十号乘组原计划 11 月 5 日返回，推迟了 9 天于 11 月 14 日返回地面，神舟二十号的返回舱被留在轨道上。三名航天员陈冬、陈中瑞、王杰在轨驻留 204 天，刷新了中国航天员单个乘组在轨驻留时间最长纪录，期间完成了 4 次出舱活动和多次货物进出舱任务。

2019 年 NASA 宇航员 Anne McClain 被控盗窃身份访问了离异妻子 Summer Worden 的银行账号，但在 2020 年 Worden 被控对联邦调查机构做出虚假陈述。现年 50 岁的 Worden 女士本周认罪，她目前保释中，将于 2 月 12 日接受判决，可能面临最高五年监禁。Worden 是前美国空军情报官，于 2014 年与 McClain 女士结婚，2018 年申请离婚，2019 年她投诉当时还在国际空间站的 McClain 盗取其身份从太空访问了银行账号。McClain 承认有此事但否认了任何犯罪行为。她通过律师称，访问银行账号只是为了确保家庭的财务状况良好，有足够的钱支付账单和照顾好 Worden 女士的儿子——为体外受精代孕，两人正争夺其抚养权。McClain 称，银行账号是两人公用的，一直使用相同的密码，她从未被告知停止使用该账号。McClain 于 2018 年 12 月至 2019 年 6 月期间在国际空间站执行任务，今年 3 月担任 SpaceX Crew-10 载人任务指挥官驻扎国际空间站至 8 月。

根据 Climate Action Tracker 的最新报告，到本世纪末全球气温预计将比工业化前水平上升 2.6C。世界各国在减排上仍然做得不够多，而与此同时化石燃料排放量今年将增长约 1% 创历史新高，虽然其增长速度过去几年已下降逾 50%。过去十年煤炭、石油和天然气的排放量每年增长 0.8%，而前十年则为每年 2.0%。可再生能源的加速部署已接近满足全球能源需求的年增长，但尚未超过。新的分析还显示，地球的天然碳汇正在减弱。科学家表示，全球暖化和树木砍伐的双重影响使东南亚和南美洲大部分地区的热带森林从二氧化碳的吸收源转变为排放源。报告预测 2025 年大气中的二氧化碳浓度将达到 425ppm，而工业化前为 280ppm。如果碳汇没有减弱，二氧化碳浓度应能降低 8ppm。

围绕体重或体形的自我贬低式对话，被称为“肥胖谈话”。这类对话通常被视为拉近距离的社交调和剂，在社交场合中较为常见。然而研究显示，这类对话或增加个体进食障碍风险。青春期是体重体形快速变化、自我意识快速发展的阶段，也是进食障碍高发期。为探讨家庭中“肥胖谈话”与青少年进食障碍之间的关联，中国科学院心理研究所研究团队在 1049 个初中生家庭中开展了调查研究。研究发现，有 67.1% 的家庭存在“肥胖谈话”现象，这类谈话与青少年进食障碍风险存在中高度相关，且与他们的身体不满意度及负性情绪呈中高度相关。同时，研究显示，家庭“肥胖谈话”直接与青少年进食障碍症状之间存在关联，还可能通过体像不满和负性情绪，间接地与进食障碍症状关联在一起。尤其在女生中，家庭“肥胖谈话”经由体像不满和进食障碍症状之间的关联更需关注。

德国柏林的一家法院裁定，Google 滥用其市场支配地位，需要向德国比价平台 Idealo 赔偿 4.65 亿欧元。 除 Idealo 外，另一家德国比价网站 Producto 也将获得 1.07 亿欧元的赔偿。裁决公布后，Idealo 表示会继续对 Google 采取法律行动，而 Google 则表示强烈反对将提起上诉。Google 称，该公司于 2017 年进行了调整，确保竞争对手的比价购物服务与自家的 Google Shopping 一样在搜索结果页面上有同等机会展示广告。

2016 年 6 月 23 日英国举行脱离欧盟的全民公投，投票结果为脱离欧盟。之后英国开始启动脱欧程序，于 2020 年 1 月 31 日正式退出欧盟。这一事件被称为 Brexit。美国国家经济研究局(NBER)发表了一篇工作论文，讨论了英国脱欧的经济影响。研究人员利用脱欧近十年的数据估算，到 2025 年脱欧使得英国 GDP下降 6%-8%，且影响会随着时间的推移而逐渐累积。英国的投资减少了 12%-18%，就业减少了 3%-4%，生产率下降 3%-4%。这些负面影响是多种因素综合作用的结果，包括不确定性加剧、需求下降、管理时间被分散，以及漫长脱欧程序导致资源错配加剧。

亚马逊宣布其卫星宽带项目的名字从 Project Kuiper 改名为 Amazon Leo，其中 Leo 代表 low Earth orbit（低地球轨道）。亚马逊已经向低地球轨道发射了逾 150 颗宽带卫星，最终将建立一个拥有逾 3200 颗卫星的宽带星座。亚马逊称，宽带卫星项目最初只有几位工程师和几张设计图，项目的灵感来自于位于外太阳系的柯伊伯带（Kuiper Belt）。亚马逊表示一旦 Amazon Leo 网络覆盖范围足够大容量足够高后将会推出卫星宽带服务。

根据 Madison and Wall 的数据，2025 年第三季度美国家庭付费电视普及率降至 50.2%，预计到 12 月将进一步降至 50% 甚至更低。而十五年前近九成美国家庭都订阅了付费有线电视服务。这一趋势促使各大媒体公司剥离有线电视资产。Comcast、Warner Bros.、Discovery 和 A&E 正寻求出售或剥离其有线电视业务。派拉蒙表示不会出售其有线电视频道，但也承认“每个季度都在加速下滑”。

根据发表在《Science Translational Medicine》期刊上的一项研究，常见病毒 Epstein-Barr 可能是狼疮的病因。狼疮是人体免疫系统错误攻击身体健康组织而导致的一种慢性自体免疫疾病。其症状轻重因人而异，有疗法但无治愈之法，其单一病因一直没有找到。Epstein-Barr 病毒一种非常常见的病毒，95% 的人在其一生中的某个时间会被感染，它主要通过唾液传播，比如接吻或共用饮料、食物、餐具或牙刷。感染后病毒会永久潜伏在体内，通常处于非活跃状态，一般不会有症状。论文共同作者、斯坦福大学的 William Robinson 博士表示绝大多数感染该病毒的人不会发展成狼疮，只有特定毒株的病毒才会引发自身免疫反应。研究主要针对 B 细胞——一种帮助抵抗感染的白细胞。研究人员发现，狼疮患者体内携带 Epstein-Barr 病毒的 B 细胞数量是正常人的 25 倍。研究人员发现，病毒会感染并重编程 B 细胞，使其产生攻击自身组织的抗核抗体（antinuclear antibodies），从而导致狼疮。