OrangeBot.AI Digest — 2025-12-16

Open-source platform to build and deploy AI agent workflows.

A book for Learning the Foundations of LLMs

An AI Hedge Fund Team

A Claude Code plugin that automatically captures everything Claude does during your coding sessions, compresses it with AI (using Claude's agent-sdk), and injects relevant context back into future sessions.

Feature packed AUR helper

Jellyfin Desktop Client

Backup automation for self-hosters

A plain text-based spaced repetition system.

Lightweight coding agent that runs in your terminal

💾 Self-hosted online file converter. Supports 1000+ formats ⚙️

The official source for MDN Web Docs content. Home to over 14,000 pages of documentation about HTML, CSS, JS, HTTP, Web APIs, and more.

A simple, lightweight PowerShell script to remove pre-installed apps, disable telemetry, as well as perform various other changes to customize, declutter and improve your Windows experience. Win11Debloat works for both Windows 10 and Windows 11.

Autoregressive models (ARMs) are hindered by slow sequential inference. While masked diffusion models (MDMs) offer a parallel alternative, they suffer from critical drawbacks: high computational overhead from precluding Key-Value (KV) caching, and incoherent generation arising from learning dependencies over an intractable space of token combinations. To address these limitations, we introduce ReFusion, a novel masked diffusion model that achieves superior performance and efficiency by elevating parallel decoding from the token level to a higher slot level, where each slot is a fixed-length, contiguous sub-sequence. This is achieved through an iterative ``plan-and-infill'' decoding process: a diffusion-based planning step first identifies a set of weakly dependent slots, and an autoregressive infilling step then decodes these selected slots in parallel. The slot-based design simultaneously unlocks full KV cache reuse with a unified causal framework and reduces the learning complexity from the token combination space to a manageable slot-level permutation space. Extensive experiments on seven diverse benchmarks show that ReFusion not only overwhelmingly surpasses prior MDMs with 34% performance gains and an over 18times speedup on average, but also bridges the performance gap to strong ARMs while maintaining a 2.33times average speedup.

The quality of the latent space in visual tokenizers (e.g., VAEs) is crucial for modern generative models. However, the standard reconstruction-based training paradigm produces a latent space that is biased towards low-level information, leading to a foundation flaw: better pixel-level accuracy does not lead to higher-quality generation. This implies that pouring extensive compute into visual tokenizer pre-training translates poorly to improved performance in generation. We identify this as the ``pre-training scaling problem`` and suggest a necessary shift: to be effective for generation, a latent space must concisely represent high-level semantics. We present VTP, a unified visual tokenizer pre-training framework, pioneering the joint optimization of image-text contrastive, self-supervised, and reconstruction losses. Our large-scale study reveals two principal findings: (1) understanding is a key driver of generation, and (2) much better scaling properties, where generative performance scales effectively with compute, parameters, and data allocated to the pretraining of the visual tokenizer. After large-scale pre-training, our tokenizer delivers a competitive profile (78.2 zero-shot accuracy and 0.36 rFID on ImageNet) and 4.1 times faster convergence on generation compared to advanced distillation methods. More importantly, it scales effectively: without modifying standard DiT training specs, solely investing more FLOPS in pretraining VTP achieves 65.8\% FID improvement in downstream generation, while conventional autoencoder stagnates very early at 1/10 FLOPS. Our pre-trained models are available at https://github.com/MiniMax-AI/VTP.

Memory has emerged, and will continue to remain, a core capability of foundation model-based agents. As research on agent memory rapidly expands and attracts unprecedented attention, the field has also become increasingly fragmented. Existing works that fall under the umbrella of agent memory often differ substantially in their motivations, implementations, and evaluation protocols, while the proliferation of loosely defined memory terminologies has further obscured conceptual clarity. Traditional taxonomies such as long/short-term memory have proven insufficient to capture the diversity of contemporary agent memory systems. This work aims to provide an up-to-date landscape of current agent memory research. We begin by clearly delineating the scope of agent memory and distinguishing it from related concepts such as LLM memory, retrieval augmented generation (RAG), and context engineering. We then examine agent memory through the unified lenses of forms, functions, and dynamics. From the perspective of forms, we identify three dominant realizations of agent memory, namely token-level, parametric, and latent memory. From the perspective of functions, we propose a finer-grained taxonomy that distinguishes factual, experiential, and working memory. From the perspective of dynamics, we analyze how memory is formed, evolved, and retrieved over time. To support practical development, we compile a comprehensive summary of memory benchmarks and open-source frameworks. Beyond consolidation, we articulate a forward-looking perspective on emerging research frontiers, including memory automation, reinforcement learning integration, multimodal memory, multi-agent memory, and trustworthiness issues. We hope this survey serves not only as a reference for existing work, but also as a conceptual foundation for rethinking memory as a first-class primitive in the design of future agentic intelligence.

We introduce QwenLong-L1.5, a model that achieves superior long-context reasoning capabilities through systematic post-training innovations. The key technical breakthroughs of QwenLong-L1.5 are as follows: (1) Long-Context Data Synthesis Pipeline: We develop a systematic synthesis framework that generates challenging reasoning tasks requiring multi-hop grounding over globally distributed evidence. By deconstructing documents into atomic facts and their underlying relationships, and then programmatically composing verifiable reasoning questions, our approach creates high-quality training data at scale, moving substantially beyond simple retrieval tasks to enable genuine long-range reasoning capabilities. (2) Stabilized Reinforcement Learning for Long-Context Training: To overcome the critical instability in long-context RL, we introduce task-balanced sampling with task-specific advantage estimation to mitigate reward bias, and propose Adaptive Entropy-Controlled Policy Optimization (AEPO) that dynamically regulates exploration-exploitation trade-offs. (3) Memory-Augmented Architecture for Ultra-Long Contexts: Recognizing that even extended context windows cannot accommodate arbitrarily long sequences, we develop a memory management framework with multi-stage fusion RL training that seamlessly integrates single-pass reasoning with iterative memory-based processing for tasks exceeding 4M tokens. Based on Qwen3-30B-A3B-Thinking, QwenLong-L1.5 achieves performance comparable to GPT-5 and Gemini-2.5-Pro on long-context reasoning benchmarks, surpassing its baseline by 9.90 points on average. On ultra-long tasks (1M~4M tokens), QwenLong-L1.5's memory-agent framework yields a 9.48-point gain over the agent baseline. Additionally, the acquired long-context reasoning ability translates to enhanced performance in general domains like scientific reasoning, memory tool using, and extended dialogue.

Building video world models upon pretrained video generation systems represents an important yet challenging step toward general spatiotemporal intelligence. A world model should possess three essential properties: controllability, long-term visual quality, and temporal consistency. To this end, we take a progressive approach-first enhancing controllability and then extending toward long-term, high-quality generation. We present LongVie 2, an end-to-end autoregressive framework trained in three stages: (1) Multi-modal guidance, which integrates dense and sparse control signals to provide implicit world-level supervision and improve controllability; (2) Degradation-aware training on the input frame, bridging the gap between training and long-term inference to maintain high visual quality; and (3) History-context guidance, which aligns contextual information across adjacent clips to ensure temporal consistency. We further introduce LongVGenBench, a comprehensive benchmark comprising 100 high-resolution one-minute videos covering diverse real-world and synthetic environments. Extensive experiments demonstrate that LongVie 2 achieves state-of-the-art performance in long-range controllability, temporal coherence, and visual fidelity, and supports continuous video generation lasting up to five minutes, marking a significant step toward unified video world modeling.

We introduce a finance & accounting benchmark (Finch) for evaluating AI agents on real-world, enterprise-grade professional workflows -- interleaving data entry, structuring, formatting, web search, cross-file retrieval, calculation, modeling, validation, translation, visualization, and reporting. Finch is sourced from authentic enterprise workspaces at Enron (15,000 spreadsheets and 500,000 emails from 150 employees) and other financial institutions, preserving in-the-wild messiness across multimodal artifacts (text, tables, formulas, charts, code, and images) and spanning diverse domains such as budgeting, trading, and asset management. We propose a workflow construction process that combines LLM-assisted discovery with expert annotation: (1) LLM-assisted, expert-verified derivation of workflows from real-world email threads and version histories of spreadsheet files, and (2) meticulous expert annotation for workflows, requiring over 700 hours of domain-expert effort. This yields 172 composite workflows with 384 tasks, involving 1,710 spreadsheets with 27 million cells, along with PDFs and other artifacts, capturing the intrinsically messy, long-horizon, knowledge-intensive, and collaborative nature of real-world enterprise work. We conduct both human and automated evaluations of frontier AI systems including GPT 5.1, Claude Sonnet 4.5, Gemini 3 Pro, Grok 4, and Qwen 3 Max, and GPT 5.1 Pro spends 48 hours in total yet passes only 38.4% of workflows, while Claude Sonnet 4.5 passes just 25.0%. Comprehensive case studies further surface the challenges that real-world enterprise workflows pose for AI agents.

Recent advances in coding agents suggest rapid progress toward autonomous software development, yet existing benchmarks fail to rigorously evaluate the long-horizon capabilities required to build complete software systems. Most prior evaluations focus on localized code generation, scaffolded completion, or short-term repair tasks, leaving open the question of whether agents can sustain coherent reasoning, planning, and execution over the extended horizons demanded by real-world repository construction. To address this gap, we present NL2Repo Bench, a benchmark explicitly designed to evaluate the long-horizon repository generation ability of coding agents. Given only a single natural-language requirements document and an empty workspace, agents must autonomously design the architecture, manage dependencies, implement multi-module logic, and produce a fully installable Python library. Our experiments across state-of-the-art open- and closed-source models reveal that long-horizon repository generation remains largely unsolved: even the strongest agents achieve below 40% average test pass rates and rarely complete an entire repository correctly. Detailed analysis uncovers fundamental long-horizon failure modes, including premature termination, loss of global coherence, fragile cross-file dependencies, and inadequate planning over hundreds of interaction steps. NL2Repo Bench establishes a rigorous, verifiable testbed for measuring sustained agentic competence and highlights long-horizon reasoning as a central bottleneck for the next generation of autonomous coding agents.

Linear-time attention and State Space Models (SSMs) promise to solve the quadratic cost bottleneck in long-context language models employing softmax attention. We introduce Error-Free Linear Attention (EFLA), a numerically stable, fully parallelism and generalized formulation of the delta rule. Specifically, we formulate the online learning update as a continuous-time dynamical system and prove that its exact solution is not only attainable but also computable in linear time with full parallelism. By leveraging the rank-1 structure of the dynamics matrix, we directly derive the exact closed-form solution effectively corresponding to the infinite-order Runge-Kutta method. This attention mechanism is theoretically free from error accumulation, perfectly capturing the continuous dynamics while preserving the linear-time complexity. Through an extensive suite of experiments, we show that EFLA enables robust performance in noisy environments, achieving lower language modeling perplexity and superior downstream benchmark performance than DeltaNet without introducing additional parameters. Our work provides a new theoretical foundation for building high-fidelity, scalable linear-time attention models.

Avatar video generation models have achieved remarkable progress in recent years. However, prior work exhibits limited efficiency in generating long-duration high-resolution videos, suffering from temporal drifting, quality degradation, and weak prompt following as video length increases. To address these challenges, we propose KlingAvatar 2.0, a spatio-temporal cascade framework that performs upscaling in both spatial resolution and temporal dimension. The framework first generates low-resolution blueprint video keyframes that capture global semantics and motion, and then refines them into high-resolution, temporally coherent sub-clips using a first-last frame strategy, while retaining smooth temporal transitions in long-form videos. To enhance cross-modal instruction fusion and alignment in extended videos, we introduce a Co-Reasoning Director composed of three modality-specific large language model (LLM) experts. These experts reason about modality priorities and infer underlying user intent, converting inputs into detailed storylines through multi-turn dialogue. A Negative Director further refines negative prompts to improve instruction alignment. Building on these components, we extend the framework to support ID-specific multi-character control. Extensive experiments demonstrate that our model effectively addresses the challenges of efficient, multimodally aligned long-form high-resolution video generation, delivering enhanced visual clarity, realistic lip-teeth rendering with accurate lip synchronization, strong identity preservation, and coherent multimodal instruction following.

Mental health disorders affect hundreds of millions globally, and the Web now serves as a primary medium for accessing support, information, and assessment. Large language models (LLMs) offer scalable and accessible assistance, yet their deployment in mental-health settings remains risky when their reasoning is incomplete, inconsistent, or ungrounded. Existing psychological LLMs emphasize emotional understanding or knowledge recall but overlook the step-wise, clinically aligned reasoning required for appraisal, diagnosis, intervention planning, abstraction, and verification. To address these issues, we introduce MentraSuite, a unified framework for advancing reliable mental-health reasoning. We propose MentraBench, a comprehensive benchmark spanning five core reasoning aspects, six tasks, and 13 datasets, evaluating both task performance and reasoning quality across five dimensions: conciseness, coherence, hallucination avoidance, task understanding, and internal consistency. We further present Mindora, a post-trained model optimized through a hybrid SFT-RL framework with an inconsistency-detection reward to enforce faithful and coherent reasoning. To support training, we construct high-quality trajectories using a novel reasoning trajectory generation strategy, that strategically filters difficult samples and applies a structured, consistency-oriented rewriting process to produce concise, readable, and well-balanced trajectories. Across 20 evaluated LLMs, Mindora achieves the highest average performance on MentraBench and shows remarkable performances in reasoning reliability, demonstrating its effectiveness for complex mental-health scenarios.

The 2025 BEHAVIOR Challenge is designed to rigorously track progress toward solving long-horizon tasks by physical agents in simulated environments. BEHAVIOR-1K focuses on everyday household tasks that people most want robots to assist with and these tasks introduce long-horizon mobile manipulation challenges in realistic settings, bridging the gap between current research and real-world, human-centric applications. This report presents our solution to the 2025 BEHAVIOR Challenge in a very close 2nd place and substantially outperforms the rest of the submissions. Building on π_{0.5}, we focus on systematically building our solution by studying the effects of training techniques and data. Through careful ablations, we show the scaling power in pre-training and post-training phases for competitive performance. We summarize our practical lessons and design recommendations that we hope will provide actionable insights for the broader embodied AI community when adapting powerful foundation models to complex embodied scenarios.

Vision-Language-Action (VLA) models provide a promising paradigm for robot learning by integrating visual perception with language-guided policy learning. However, most existing approaches rely on 2D visual inputs to perform actions in 3D physical environments, creating a significant gap between perception and action grounding. To bridge this gap, we propose a Spatial-Aware VLA Pretraining paradigm that performs explicit alignment between visual space and physical space during pretraining, enabling models to acquire 3D spatial understanding before robot policy learning. Starting from pretrained vision-language models, we leverage large-scale human demonstration videos to extract 3D visual and 3D action annotations, forming a new source of supervision that aligns 2D visual observations with 3D spatial reasoning. We instantiate this paradigm with VIPA-VLA, a dual-encoder architecture that incorporates a 3D visual encoder to augment semantic visual representations with 3D-aware features. When adapted to downstream robot tasks, VIPA-VLA achieves significantly improved grounding between 2D vision and 3D action, resulting in more robust and generalizable robotic policies.

LLM-based agents often operate in a greedy, step-by-step manner, selecting actions solely based on the current observation without considering long-term consequences or alternative paths. This lack of foresight is particularly problematic in web environments, which are only partially observable-limited to browser-visible content (e.g., DOM and UI elements)-where a single misstep often requires complex and brittle navigation to undo. Without an explicit backtracking mechanism, agents struggle to correct errors or systematically explore alternative paths. Tree-search methods provide a principled framework for such structured exploration, but existing approaches lack mechanisms for safe backtracking, making them prone to unintended side effects. They also assume that all actions are reversible, ignoring the presence of irreversible actions-limitations that reduce their effectiveness in realistic web tasks. To address these challenges, we introduce WebOperator, a tree-search framework that enables reliable backtracking and strategic exploration. Our method incorporates a best-first search strategy that ranks actions by both reward estimates and safety considerations, along with a robust backtracking mechanism that verifies the feasibility of previously visited paths before replaying them, preventing unintended side effects. To further guide exploration, WebOperator generates action candidates from multiple, varied reasoning contexts to ensure diverse and robust exploration, and subsequently curates a high-quality action set by filtering out invalid actions pre-execution and merging semantically equivalent ones. Experimental results on WebArena and WebVoyager demonstrate the effectiveness of WebOperator. On WebArena, WebOperator achieves a state-of-the-art 54.6% success rate with gpt-4o, underscoring the critical advantage of integrating strategic foresight with safe execution.

While many vision-language models (VLMs) are developed to answer well-defined, straightforward questions with highly specified targets, as in most benchmarks, they often struggle in practice with complex open-ended tasks, which usually require multiple rounds of exploration and reasoning in the visual space. Such visual thinking paths not only provide step-by-step exploration and verification as an AI detective but also produce better interpretations of the final answers. However, these paths are challenging to evaluate due to the large exploration space of intermediate steps. To bridge the gap, we develop an evaluation suite, ``Visual Reasoning with multi-step EXploration (V-REX)'', which is composed of a benchmark of challenging visual reasoning tasks requiring native multi-step exploration and an evaluation protocol. V-REX covers rich application scenarios across diverse domains. V-REX casts the multi-step exploratory reasoning into a Chain-of-Questions (CoQ) and disentangles VLMs' capability to (1) Planning: breaking down an open-ended task by selecting a chain of exploratory questions; and (2) Following: answering curated CoQ sequentially to collect information for deriving the final answer. By curating finite options of questions and answers per step, V-REX achieves a reliable quantitative and fine-grained analysis of the intermediate steps. By assessing SOTA proprietary and open-sourced VLMs, we reveal consistent scaling trends, significant differences between planning and following abilities, and substantial room for improvement in multi-step exploratory reasoning.

Although multi-modal large language models (MLLMs) have shown strong capabilities across diverse domains, their application in generating fine-grained 3D perception and prediction outputs in autonomous driving remains underexplored. In this paper, we propose DrivePI, a novel spatial-aware 4D MLLM that serves as a unified Vision-Language-Action (VLA) framework that is also compatible with vision-action (VA) models. Our method jointly performs spatial understanding, 3D perception (i.e., 3D occupancy), prediction (i.e., occupancy flow), and planning (i.e., action outputs) in parallel through end-to-end optimization. To obtain both precise geometric information and rich visual appearance, our approach integrates point clouds, multi-view images, and language instructions within a unified MLLM architecture. We further develop a data engine to generate text-occupancy and text-flow QA pairs for 4D spatial understanding. Remarkably, with only a 0.5B Qwen2.5 model as MLLM backbone, DrivePI as a single unified model matches or exceeds both existing VLA models and specialized VA models. Specifically, compared to VLA models, DrivePI outperforms OpenDriveVLA-7B by 2.5% mean accuracy on nuScenes-QA and reduces collision rate by 70% over ORION (from 0.37% to 0.11%) on nuScenes. Against specialized VA models, DrivePI surpasses FB-OCC by 10.3 RayIoU for 3D occupancy on OpenOcc, reduces the mAVE from 0.591 to 0.509 for occupancy flow on OpenOcc, and achieves 32% lower L2 error than VAD (from 0.72m to 0.49m) for planning on nuScenes. Code will be available at https://github.com/happinesslz/DrivePI

2010 年代以来流媒体支配了媒体消费，实体媒介如 DVD 和 CD 的销量随之下滑。但实体媒介仍然在流通，最近几年部分类型的实体媒介的销量还出现了反弹。推动这一趋势的驱动力量之一是 Z 世代，流媒体服务日益昂贵，而 3-5 美元的实体 DVD 可能比购买数字版更便宜，而且能真正拥有所有权。实体 CD 交易平台 Discogs 今年的销量比去年同期增长 8%。行业组织 Digital Entertainment Group 的数据显示，DVD、蓝光和 4K 超高清蓝光光盘的销量在第三季度比去年同期下降 3%，而去年同期则下降了近 26%。美国唱片行业协会 RIAA 预测，2024 年 CD 销量将同比增长 1.5%。黑胶唱片的销量则在 2023 年就超过了 CD。

地球气候在数百万年间于冰河期与温暖期之间反覆摆荡，主要原因来自于地球轨道参数与自转轴倾角的微小变化。这类长期变动在地球科学上统称为米兰科维奇循环（Milankovitch cycles），反映了地球持续受到其他行星的引力扰动。行星间的引力交互作用，会缓慢改变地球的轨道离心率、自转轴倾角以及岁差方向，进而调节地表接收的太阳辐射量，塑造大尺度的气候模式。过往研究已确认木星与金星在此过程中扮演关键角色。最新的精细数值分析显示，质量相对较小的火星，对地球气候模式同样具有显著且先前被低估的影响。研究团队透过计算机模拟，系统性地将火星质量由零变化至现值的十倍，并追踪其对地球轨道参数在数百万年尺度上的气候影响，结果显示火星是决定地球季节性与气候变化的重要成员。模拟显示，主导冰河期与温暖期转换约 10 万年循环直接受到火星影响。地球自转轴倾角亦受火星重力扰动的直接影响。地质纪录中常见的 4.1 万年倾角循环，随火星质量增加而显著延长；若火星质量为现值的十倍，倾角循环的周期将延长至约 4.5～5.5 万年，足以大幅改变南、北半球冰盖的生成与消融时序。

与动物不同，植物的性别并非与生俱来，而是受基因、激素水平、环境信号的调控，复杂性远超动物。性别决定在农业生产中有广泛应用价值。对于以种子和果实为收获对象的作物，增加雌花可以提高产量；对于观赏园艺作物，如银杏树，可通过控制雌雄比例来满足不同需求；在杂交育种中，利用纯雌系可以避免去雄工序，节约成本。中国农业大学的科学家发现关键基因 CsARF3 在生长素和乙烯激素之间搭建桥梁，精准调控黄瓜的性别决定。实验发现当 CsARF3 被编辑突变后，黄瓜植株不再产生雌花，全部变为雄花；当该基因过表达时，雌花数量显著增加。更重要的是，即使外施生长素也无法挽回突变体的表型。这证明 CsARF3 是生长素信号通路中不可或缺的关键环节。

随着 AI 取代入门级工作，中印等国 IT 业入门级工作大幅减少。印度一所顶尖工程学院的一名学生称，400 名同学中不到四分之一获得了工作机会，校园弥漫着恐慌气氛。中国、迪拜和肯尼亚的工程学院学生面临类似的现象。曾由应届生承担的任务如调试、测试和日常软件维护，如今正日益自动化。SignalFire 的一份报告显示，过去三年大型科技公司招聘的应届生人数减少半数以上。尽管 2024 年招聘人数有回升但新员工只有 7% 是应届生。安永上月发布的报告显示，由于自动化和 AI 印度 IT 服务公司将入门级职位减少了 20%-25%。LinkedIn、Indeed 和 Eures 等求职平台显示 2024 年欧盟主要国家初级技术职位减少 35%。

内存、固态硬盘之后，机械硬盘过去几个月也开始涨价。10～12 月用于台式电脑和监控摄像头的 3.5 英寸 1TB 产品比前一季度上涨 4% 达到 53.0 美元左右。用于笔记本电脑的 2.5 英寸 1TB 产品也上涨 3% 至 50.0 美元左右。涨幅创 2023 年 10～12 月以来的新高。中国加速采购 PC 用 HDD，主要用于监控摄像头。机械硬盘价格上涨预计会持续一段时间。

由于涌入了大量 AI 生成的 GNOME Shell 扩展，GNOME 项目宣布将拒绝接受此类扩展。开发者表示将 AI 用于辅助学习编程或作为代码补全等开发工具使用并不禁止，但扩展开发者应在合理范围内解释和说明其提交的代码。如果提交的代码包含大量不必要的代码、不一致代码风格和虚构 API 使用等任何表明代码由 AI 生成的迹象都将被拒绝。GNOME 开发者称部分开发者在使用 AI 时并不理解代码。

《时代》今年的年度人物是 AI 时代的主要建筑师——英伟达 CEO 黄仁宇、AMD CEO 苏姿丰、xAI CEO 马斯克(Elon Musk)、Meta CEO 扎克伯格、OpenAI CEO 奥特曼(Sam Altman)、有 AI 教母之称的李飞飞、Anthropic CEO Dario Amodei 以及 Google AI CEO Demis Hassabis。《时代》称，不管好坏，这些人主导了今年的新闻头条，他们开启了机器智能时代，令世人惊叹担忧，他们改变了现状和超越了可能。

在澳大利亚之后，丹麦计划严格限制 15 岁以下青少年使用社交媒体。丹麦政府已与议会中三个执政联盟和两个反对党达成协议，计划最早在 2026 年中期成为法律。拟议措施将赋予部分家长允许其子女从 13 岁起使用社交媒体的权利，但完整计划尚未公布。丹麦数字事务部上个月宣布推出名为“数字证据（digital evidence）”的全新应用，预计明年春季上线，很可能是计划的核心。这款应用将显示年龄证明确保用户遵守社交媒体的年龄限制。马来西亚和挪威也在采取措施。

扫地机器人 Roomba 的制造商 iRobot 申请破产重组。根据重组协议，iRobot 的控制权将转交给其主要代工厂 深圳杉川机器人公司（Shenzhen PICEA Robotics）及其子公司香港杉川（Santrum Hong Kong）。iRobot 的主营业务因中国制造的扫地机器人的竞争而陷入困境，该公司曾试图出售给电商巨人亚马逊，但没有获得欧盟监管机构的批准。2021 年它的估值曾高达 35.6 亿美元，如今仅为 1.4 亿美元。iRobot 大部分销往美国的产品在越南制造，而美国对越南商品征收 46% 的进口关税，导致其今年的成本增加了 2300 万美元。iRobot 创办 35 年以来制造了逾 5000 万台扫地机器人。

咨询公司 Teneo 调查了逾 350 位上市公司 CEO。这些上市公司的年收入都超过 10 亿美元。调查显示，68% 的CEO 计划在 2026 年增加 AI 支出，受访者同时表示目前的 AI 项目只有不到一半产生了超过支出的回报。CEO 们称，AI 在市场营销和客服领域应用最成功，在安全、法律和 HR 等高风险领域面临挑战。Teneo 还调查了约 400 家机构投资者，53% 预计 AI 项目将在六个月内投资开始产生回报。84% 的大型公司——年收入 100 亿美元或以上——CEO 认为 AI 项目的投资需要逾六个月时间才能产生回报。此外 67% 的 CEO 认为 AI 将增加公司入门级员工人数，58% 的 CEO 认为 AI 将增加领导层人数。

微软看起来正向电视推广其 AI 应用。用户在 Reddit 上报告，其 LG 电视的 webOS 操作系统在更新之后加入了微软的 Copilot AI，而且该 AI 应用无法卸载。暂时不清楚 Copilot AI 能在电视上做什么。除 Copilot AI 外 LG 可能还在 webOS 中加入了其它 AI 功能——它还提供了名为 Live Plus 的设置，启用该功能之后电视能识别屏幕上显示的内容，将这些观影信息用于个性化推荐和广告，不过该功能可以关闭——Settings > All Settings > General > Additional Settings。

天文学家成功直接拍摄到一颗如《星球大战》中塔图因星球般绕行双星运转的系外行星。能拍到太阳系外的行星本就极为罕见，而能拍到一颗同时绕行两颗恒星的行星，更是少之又少。令人惊讶的是，这颗名为 HD 143811 AB b的系外行星距离其双母恒星约 64AU，是目前已知以直接成像方式发现、且绕行双星系统的行星中，距离母恒星最近的一颗，其轨道半径比过去同类型行星小了约六倍。这颗新行星 HD 143811 AB b 其实隐藏在多年以前的观测资料中。其质量约为木星的 6 倍，年龄约为 1,300 万年，温度高于太阳系内任何行星。 HD 143811 这个系统的结构同样引人注目：两颗恒星彼此紧密环绕（0.18 AU），每 18 天完成一次公转；而行星则以轨道半长轴约 64AU、330 年的周期，绕着这对恒星运行，与冥王星绕行太阳的时间尺度相近。

Benchmark Mineral Intelligence 报告，2025 年 11 月全球电动汽车销量 200 万辆，今年迄今全球电动汽车销量 1850 万辆，比 2024 年同期增长 21%。欧洲 11 月的电动汽车销量增幅最高，同比增长 36%，其中纯电增长 35%，插电混动 39%，今年至今电动汽车总销量 380 万辆，比 2024 年同期增长 33%。北美因电动汽车减税政策于 9 月 30 日结束而销量下滑，今年至今电动汽车销量比 2024 年同期下降 1%。中国电动汽车销量仍然远超世界其它地区，今年至今销量增长 19% 达 1160 万辆，比亚迪 11 月电动汽车出口量创 131,935 辆的纪录，今年在欧洲销量达到 20 万辆，东南亚销量翻一番，南美销量增长逾 50%。除中国、欧洲和北美外，其它地区今年的电动汽车销量比 2024 年同期增长 48% 达到 150 万辆。

Linus Torvalds 通常在周日释出新版内核的 RC 版本，而美国时间的周日是北京时间的周一。Torvalds 生活在北美，因此他通常是在北京时间的周一发布新内核 RC 版本。然而本周 Torvalds 在日本参加 Linux Plumbers 大会和 Linux 内核维护者峰会，而日本的周日相当于美国的周六，他在当地时间的周日释出 Linux 6.19-rc1。Torvalds 表示这可能会让那些习惯于最后一刻递交 pull request 的人措手不及。Linux 6.19 包含了驱动、子系统和架构更新，其中一个更新是龙芯加入了 32 位架构 LoongArch32 支持。大部分 CPU 架构都从 32 位过渡到 64 位，而龙芯则反其道而行之，从 64 位过渡到 32 位。