OrangeBot.AI Digest — 2026-09-30

Your Mac’s battery as a living, breathing landscape

Your second brain for meetings.

Produce pitch perfect launch videos with precise control

NotchDodo turns your notch into a full-blown dashboard.

Your SEO work, done from your AI agent

Idea to physical product, engineer anything you can imagine

Open source local AI voice-to-text for Mac for private STT

Create and edit Product videos with your AI Agent

Open-source voice notes that file your tasks and reminders

Give every AI agent real identities, inboxes, and a phone

Explore dental anatomy in 3D, tooth by tooth

One source of truth for your team and its AI agents

A context aware FOSS voice assistant and dictation for macOS

Meet Ace, an agentic teammate for work

One voice for all your coding agents, from your phone

General-purpose agents can plan, reason, and act over long horizons, yet their production capabilities remain fragmented across text, images, audio, video, documents, 3D assets, and code. Extending a foundation model to additional modalities ties capability growth to costly model updates, while assembling specialist models and tools leaves unresolved how procedures, dependencies, intermediate assets, and cross-turn revisions should be coordinated. We present Omni-IO Skills, a plug-and-play Agent Harness that makes existing agents omni-native through hierarchical Skills, a standardized multimodal execution interface, dependency-aware orchestration, and a persistent Asset Registry. Multi-asset workflows are represented as Declare Execution Graphs, which schedule independent operations concurrently and register successful outputs for downstream and cross-turn reuse across replaceable execution backends. Its 27 Skills cover 38 representative tasks spanning seven artifact modalities and four capability families: understanding, generation, reasoning, and retrieval. On UniM-90, the harness raises the input-support rates of GPT-5.6 Sol and Claude Sonnet 5 from 40.00% and 38.89% to 100%, while increasing relative Semantic--Quality Coupled Score from 26.99 to 74.94 and from 27.82 to 77.78, respectively; Strict Structure Score reaches 100.00 and 99.78. These results establish harness-level capability composition as a practical route to broad, evolvable Omni systems without changing the host agent's reasoning core.

Spoken conversational systems must recover information from prior interactions (i.e., memory), yet relevant information in speech extends beyond what was said to who said it, how it was spoken, and what was audible, information that exists only in the audio signal and cannot be recovered from a transcript. Beyond what to remember, memory also demands diverse operations: retrieving a single fact, integrating evidence across turns, tracking an evolving state. Real interactions further unfold across sessions, meaning information accumulates across distinct episodes rather than a single continuous recording. Existing benchmarks fall short on all three dimensions: they focus primarily on lexical content, adopt limited and ad hoc memory operations, and treat memory as a single-session problem. We argue that principled memory evaluation requires jointly characterizing the acoustic evidence to be retained and the operations applied to it, and introduce a taxonomy along these two axes. Building on this taxonomy, we present VoxMem: 3,196 evaluation instances over 34,743 spoken sessions (177 hours) crossing four acoustic evidence types (speech semantics, speaker identity, paralinguistic cues, environmental sound) with four memory operations (information extraction, multi-session reasoning, temporal tracking, and answer refusal), grounded in multi-session histories and stratified across context budgets from 8K to 64K tokens. Evaluating 15 LALMs, no model exceeds 40% at 32K. Models retain what was said far better than who said it, how, or what was audible, a gap that widens for complex operations, grows with history length, and manifests as qualitatively distinct failure modes across evidence types. VoxMem aims to provide a foundation to measure and drive progress on the full scope of spoken conversational memory.

*Reinforcement learning (RL)* can induce substantial reasoning capabilities in large language models (LLMs), but how much of this capability transfers across model scales, and how quickly, remains unclear. We study the scaling properties of *on-policy distillation (OPD)* across *weak-to-strong*, *same-base*, and *strong-to-weak* teacher--student setups. We find that early OPD training dynamics uniformly exhibit a regular *useful-transfer* regime, in which held-out accuracy (the *gold score*, G) rises approximately linearly in d=mathrm{KL(π_θVert π_{ref})}, the square root of token-level reverse KL divergence from the student initialization. In every observed weak-to-strong pair, the student's peak gold score exceeds its teacher's own, so a compact RL expert can transfer capability to a much larger student via OPD. To estimate OPD outcomes, we fit *power laws* for how G_{peak} and the slope of the useful-transfer regime scale with student and teacher parameter counts and with teacher gold score. These laws show that peak gold score improves with teacher scale only up to roughly the student's scale, and that at a matched gold score smaller teachers transfer better, so a teacher's score alone does not define its supervision value. We also study the scaling effects of two OPD variants, bootstrapping weak-to-strong OPD, and the degree of on-policy supervision.

Chunked KV-cache compression reduces the memory and attention costs of long-context inference by compressing windows of consecutive tokens into fewer cache entries at a fixed stride. Such compression also introduces a new positional coordinate: a token's phase, or its position relative to compression-window boundaries. We uncover a systematic asymmetry in models using such compression: the same information can be easy to retrieve at one phase and difficult at another. We call this periodic variation in retrieval performance phase sensitivity. In large open-weight models with such compression, long-context retrieval accuracy can differ by up to 40 percentage points across phases, revealing periodic weak spots that average benchmark scores can conceal. To investigate this behavior, we pretrain a family of transformers from scratch across multiple KV-compression designs, reproducing phase sensitivity across the variants. Mechanistic analysis using causal interventions in these models reveals phase specialization: different attention components contribute asymmetrically to retrieving information at different source phases. We further analyze idealized retrieval models, showing how gradient flow dynamics may favor sharp phase specialization. Evaluating models with chunked KV-cache compression thus requires measuring across compression phases: high average accuracy can coexist with systematic positional failures.

Answering questions and completing tasks over large document collections often requires connecting evidence spread across multiple documents, such as a project's approval recorded in one, its requirements in another, and its latest status in a third. Recent LLM agents approach this by iteratively searching the full corpus rather than reading only a fixed set of top-ranked documents. However, when the corpus is exposed only as a flat collection of files, a relevant document gives no indication of how it relates to others, so the agent must rediscover these relationships for every query, often missing complementary evidence while simultaneously consuming substantial additional tokens. To address this, we introduce CorpusMap, a navigation layer that organizes the corpus around its recurring entities, which are identifiable from the documents themselves and can link a single document to many others across sources. Specifically, CorpusMap represents each recurring entity as an Entity Page that aggregates information about it and links to every document that refers to it, forming a graph between entities and documents that the agent can traverse to gather otherwise disconnected evidence. Moreover, since CorpusMap is constructed offline by resolving mentions of the same entity across documents, its links are shared across queries rather than rediscovered repeatedly at inference time. Using 7 different models with 3 benchmark datasets, we show that CorpusMap improves both evidence discovery and answer quality over raw-corpus agentic search while using fewer tokens on average, and further outperforms 4 alternative navigation layers, suggesting that entities serve as effective anchors for navigating large document collections.

Long-term memory enables agents to accumulate information and reason across sessions, yet existing research primarily focuses on dyadic text or image-text conversations, leaving long-term memory for multi-party spoken conversations underexplored. This setting requires preserving conversational content, identifying participants across sessions, and retaining who speaks to whom. To this end, we propose VoxPolyMem, an interaction-aware multimodal memory framework combining incremental speaker identification with a memory hierarchy comprising interaction memory, fact memory, and participant profiles. We formulate retrieval as sequential decision-making, where an agent rewrites queries and selects retrieval tools and memory layers based on accumulated evidence to address information gaps. We further introduce Evidence-Gain GRPO (EG-GRPO), which uses round-wise credit assignment to encourage complementary evidence acquisition. We also construct VoxPolyBench to evaluate memory evolution, personalized answering, memory retrieval and reasoning, and interaction reasoning and attribution in multi-party spoken conversations. VoxPolyMem achieves an overall score of 85.0 on VoxPolyBench, surpassing the strongest evaluated baseline by 23.6 points. On Mem-Gallery and H2HMem-Multi, it scores 89.6 and 74.4, respectively, exceeding the strongest evaluated public memory baselines by over 8 points each. These results highlight its potential for persistent, personalized assistance in multi-party multimodal interactions. Code and datasets are available at https://voxpolymem.github.io/VoxPolyBench/demo/

Modern LLMs are increasingly capable as autonomous agents, but they follow sequential interaction cycles: read, think, reply or call tools, repeat. Many real-world use cases are not sequential: voice assistants, embodied agents, and monitoring systems receive new inputs while they think or perform another task. Modern LLMs address this with specialized architectures for voice interaction and video streams, VLAs for robot control, asynchronous tool calling for API usage, and others. In this work, we generalize from different asynchronous tasks to general asynchronous agents that can adapt to different types of concurrency. To achieve this, we develop an asynchronous LLM framework that lets users (or the agents themselves) define inference coroutines with overlapping memory states. We showcase that Qwen 3.x models are capable of asynchronous operation for streaming video understanding, videogames, and monitoring, without task-specific training.

Autonomous agents have made rapid progress in general-purpose computer use, but reliable automation of professional industrial engineering remains out of reach, as engineering workflows demand reasoning over geometric and physical constraints and dependencies preserved across software and design stages. We present EngiWorld, the first benchmark structured around the complete design loop: 1,301 expert-curated tasks spanning 6 engineering domains (CAD, CAE, CAM, BIM, EDA, and 3D visualization) and 26 professional software platforms, with both GUI and CLI interfaces and 6 task types ranging from software-selection to open-ended tasks. We further introduce an artifact-centric evaluation methodology built on a unified domain-verifier suite, which programmatically checks the geometric validity, physical feasibility, and rule compliance of final and intermediate artifacts, and scores quantitative design tasks continuously by specification attainment rather than binary success. Evaluation of seven frontier models reveals a substantial capability gap: the strongest model achieves an EngiScore of only 44.3, and just 3.6% of multi-software attempts succeed. EngiWorld provides the first rigorous foundation for measuring progress toward agents that operate professional engineering software end to end.

We present LongCat-DeepResearch, a deep research system that combines an enhanced LongCat model with a multi-agent workflow for producing comprehensive, evidence-grounded reports. The workflow separates global planning from detailed investigation and coordinates revision at the section level. Multiple planning agents first explore external sources and refine an actionable research plan, termed ResearchSpec. Research agents then investigate and draft their assigned sections in parallel, gathering additional evidence in separate contexts as their analyses develop. Once the sections are assembled, global review guides targeted local revisions, reducing reliance on repeated full-report rewriting. This workflow also supports the construction of research tasks and trajectories for the mid-training and post-training of LongCat's general-purpose models. LongCat-DeepResearch achieves 55.25 on DeepResearchBench, 51.35 on DeepResearchBench II, and 79.83 on ResearchRubrics. On an in-house benchmark, it scores 76.04, ranking second among four compared systems. Development-set analyses show benefits from combining planning perspectives, while further planning refinement has mixed effects. Additional editing improves average automatic readability preference across two benchmarks, with different trends on each.

To serve as real-world personal assistants, streaming video models need persistent memory that retains past experiences for later use. Yet existing streaming benchmarks and methods often focus on individual continuous videos or short clips, overlooking that real-world interactions are often intermittent and require memory to persist across interruptions. To fill this gap, we introduce APM-Bench, which reformulates real-world streaming interaction as multi-session life trajectories. It contains 549 sessions, 104 trajectories, and 2,719 candidates, spanning both objective and open-ended questions. Each session is a video with fine-grained annotations, and sessions within a trajectory revolve around related activities. Models then use persistent memory to answer questions about past sessions and provide proactive responses while maintaining real-time interaction. This raises challenges: persistent memory must be storable, selectively retain information, be injected at the right time, and remain efficient. Moreover, finite storage may leave required evidence unavailable, so assistants should recognize missing evidence. Therefore, we systematically evaluate general video models under different memory protocols and diverse specialized streaming memory systems, and test whether models acknowledge insufficient evidence. Our evaluation reveals a clear utility--latency--storage trade-off: existing methods still struggle to simultaneously achieve reliable long-term recall, low overhead, and effective proactive assistance across sessions. APM-Bench provides a comprehensive testbed for developing and comparing persistent memory systems under realistic streaming conditions. We hope it encourages future work that jointly considers utility, latency, and storage toward more practical persistent memory for real-world streaming assistants.

An agent that uses tools typically responds to what the user explicitly asks, yet completing the task may require information the user never requested. Work on proactive agents mainly studies whether and when an agent should act on its own, not what information it should pursue. We study a distinct axis of proactivity: its content. Horizontal proactivity pursues unstated information that the current context already identifies, and vertical proactivity pursues needs that only earlier evidence reveals. A need graph, recovered from a benchmark's own decomposition, records which needs depend on which, so both forms, and whether the agent stops at the right time, can be scored from a transcript without a model judge. To learn this behavior, we propose Q&D (questioner and drafter), which trains a questioner to prefer the question whose continuation retrieves more of the required evidence, with no reward model or judge. On held-out splits of three multi-hop question-answering benchmarks, at equal retrieval spend, the trained questioner improves both forms of proactivity over the same model, prompted, and outperforms a prompted model 15times larger in the same role on two of the three, and the gain persists after controlling for question volume and length. Without further training, we place the questioner in an interactive customer-service agent with a simulated customer, where it completes more tasks while asking fewer questions, and in retail it outperforms the 15times larger model with fewer follow-up turns from the customer. These results show that proactivity depends not only on whether an agent acts without being asked, but also on what it chooses to pursue and when it stops.

High-resolution video generation is expensive, as its cost grows rapidly with the number of spatiotemporal tokens. A practical alternative first generates a lower-resolution video and then applies a refiner, but conventional multi-step refinement introduces a second sampling bottleneck. We present SoL-Refiner, a one-step video refiner that transforms low-resolution model outputs into 4K videos with a single denoising step. Our three-stage recipe combines high-resolution continual training, reinforcement learning (RL) post-training, and a final one-step distillation. We introduce Refiner-Bench, a video refinement benchmark constructed from the outputs of different video generators, and use a shared-input protocol to compare refiners at approximately 2K output resolution. At 2K, the one-step SoL-Refiner outperforms all external refiners on the VBench and UniPercept averages, while at 3840!times!2176 it improves both metrics over the three-step LTX-2.3 Refiner. With the complete acceleration stack, SoL-Refiner achieves an 8.91times speedup in refinement latency over the same baseline in our 2K latency setting.

We introduce Context Language Models (CLMs), language models that natively manage their own context. We implement this by treating the context as a file and allowing the model to make unrestricted updates to this file. This allows the model to learn what is most important to maintain in context, and naturally extends to multi-agent systems where multiple agent contexts coexist as files. Building CLMs zero-shot with existing models outperforms SOTA context management strategies across a variety of tasks: 11.4% higher accuracy with 21.5% fewer FLOPs on BrowseComp-Plus, 5% higher scores with 59% fewer FLOPs on 12-hour EdgeBench, and 65% greater improvement with the same compute on a 24-hour multi-repository agent-swarm task. Moreover, by shifting context management from external harness control to intrinsic model behavior, CLMs naturally enable both in-context and parametric learning of context-management strategies. We show that CLMs can be steered with natural-language instructions evolved through a standard skill-optimization loop, improving held-out accuracy by up to 35.9 points on a context-management task while reducing compute. We also introduce an online reinforcement learning method for CLMs, improving Qwen3.5-9B performance on BrowseComp-Plus by 47.6% while using 12% fewer FLOPs. Finally, we co-design Suffix Cache Reuse for CLM serving, further reducing server-side compute by 35% relative to standard SGLang at matched performance.

Long-context reasoning in large language models incurs substantial computation and memory costs. Visual text compression (VTC) reduces input length by rendering text as images, but fixed-resolution rendering creates a compression-performance trade-off: low DPI saves tokens at the expense of legibility, whereas high DPI spends tokens on irrelevant content. We introduce FocusVTC, which breaks this trade-off through adaptive resolution while preserving general multimodal capabilities. It combines compressed low-DPI global views with selective region enhancement, integrating enhanced views into ongoing reasoning. We construct 29.4K high-quality Reasoning-Evidence Localization (REL) chain-of-thought examples (REL-CoT) that link reasoning traces to page indices and bounding boxes. Multi-resolution REL supervised fine-tuning (REL-SFT) teaches the model to localize relevant regions, and Group Relative Policy Optimization learns when to enhance resolution and how to use the resulting observations, without a separate continual-pretraining stage. At 72 DPI on RULER v1, FocusVTC scores 87.4 at 2.9times input compression, including tool observations, versus 57.5 for Glyph at 3.0times input compression. It surpasses its text-input backbone on LongBench (56.40 versus 55.86), improves the MRCR macro-average by 13.91 points, and achieves a 51.19 macro-average on VTCBench. The MRCR latency evaluation also shows a 2.79times online end-to-end speedup over Text. General multimodal capabilities are preserved, with MMMU increasing from 65.12 to 66.73 and MME from 2424.02 to 2457.62.

Omni-modal agents must seek evidence across video, audio, web pages, and computation to answer questions. Their main bottleneck is planning: noisy multimodal observations accumulate in conversation history and disrupt later decisions, while multimodal models have limited capacity for multi-step planning. Controlled backend replacements support this diagnosis: replacing the planner causes a much larger performance loss than replacing the perception backend. We present Omni-Decision, an omni-modal agent built on evidence-ledger planning: it replaces the growing dialogue history with an explicit evidence ledger that records what evidence is still missing, what has been confirmed, and where records conflict. A critic reads each noisy observation and passes only the usable content to the ledger, discarding the rest, so the planner works from a compact context throughout the task. Each run records the state, action, and verdict at every step, and supervised fine-tuning and decision-level reinforcement learning on these trajectories further improve the planner. Omni-Decision achieves state-of-the-art accuracy of 81.4% on OmniGAIA at approximately 43% of Gemini-3.1-Pro's cost per question, and 65.0% on WorldSense long-video understanding, level with the strongest end-to-end model.

Rya Jetha / Business Insider : Factory CEO Matan Grinberg alleges Cognition's new CRO Chris Degnan had been confiding with Cognition executives while advising Factory as a board observer —  Factory AI and Cognition, two high-profile San Francisco-based AI startups building coding agents, are publicly feuding over the departure …

CNBC : Micron reports Q4 revenue up 379% YoY to $54.23B, vs. $51.07B est., net income up 1,078% to $37.7B, and forecasts Q1 revenue above estimates —  Micron reported better-than-expected quarterly results on Wednesday as the memory maker continues to benefit from soaring demand for AI infrastructure.

Luke Fountain / CNBC : DOD taps Elon Musk, Palmer Luckey, and former House Speaker Newt Gingrich for Project Meridian, a 120-day study of capabilities the US may need in future wars —  The Pentagon is tapping Tesla and SpaceX CEO Elon Musk, defense technology entrepreneur and Anduril co-founder Palmer Luckey …

Bloomberg : Sources: some Google employees say Gemini 4 performs well on benchmarks but struggles with some real-world coding tasks; Google disputes that characterization —  Alphabet Inc.'s Google has begun rolling out Gemini 4 Argon, its long-awaited flagship artificial intelligence model …

Madison Mills / Axios : Google rolls out Gemini 4 Argon to a small group of cybersecurity partners and says it outperforms GPT-6 Astra on certain coding and knowledge work benchmarks —  Google is unveiling its long-awaited next-generation AI model, Gemini 4 Argon, to a small group of cybersecurity partners, the company said Wednesday.

Sarah Perez / TechCrunch : Reddit says it is ending support for RSS feeds on November 13 because the feeds are a target for abuse by AI bots and is ending its public API access in March —  Amid a number of updates for moderators and developers announced Wednesday comes bad news for supporters of a more open web: Reddit is ending support for RSS feeds.

Maggie Eastland / Bloomberg : OpenAI says individuals associated with Moonshot AI played a significant role in a coordinated model-distillation campaign that began in early July —  OpenAI accused its Chinese rival Moonshot AI of being responsible for a wide-scale effort to extract data from its GPT artificial intelligence systems …

Ben Weiss / Bloomberg : Open Standard launches its OUSD stablecoin; founding partners Visa, Stripe, Mastercard, and Coinbase will offer initial access to businesses and developers —  Open Standard, a company backed by more than 100 corporations, including Visa Inc., Stripe Inc., and Mastercard Inc. …

Edward Ludlow / Bloomberg : Document: SpaceXAI plans a unified subscription for Grok and X with four tiers, including a $100/month Ultra plan, an $8/month Lite plan, and a free offering —  SpaceXAI, the artificial intelligence unit of Elon Musk's SpaceX, is considering an overhaul of its subscription pricing options …

Chris Metinko / Axios : Miter, which provides workforce management tools for the construction industry, raised a $40M Series B led by Battery Ventures, taking its total funding to $78M —  Miter, a construction workforce management platform, raised a $40 million Series B led by Battery Ventures, CEO Connor Watumull tells Axios Pro exclusively.

Dean Takahashi / GamesBeat : CScale, which is developing optical interconnect for accelerators, emerges from stealth with a $145M Series C led by Atreides, Valor Equity, and Premji Invest —  CScale exited stealth today and announced $145 million in funding to accelerate development and commercialization of optical interconnect for gigawatt-scale AI.

John Timmer / Ars Technica : Google DeepMind introduces SynthID Bio, a family of watermarking methods for AI-designed proteins to help with biosecurity and scientific integrity —  AI-based tools seem to be causing security threats on a nearly daily basis, in part because we've been slow to recognize potential threats.

Marina Temkin / TechCrunch : Berlin-based Restate, which provides a durable execution engine to make software workflows resilient to crashes, raised a $20M Series A led by Singular —  When Stephen Ewan co-founded Restate in 2022 to provide durable workflow infrastructure, he couldn't have anticipated how valuable …

Sam Sabin / Axios : Alex Stamos is joining Cognition as its new chief security officer; he most recently oversaw security at AI security startup Corridor and SentinelOne —  Alex Stamos has joined Cognition as the AI coding unicorn's new chief security officer. … The hire suggests security has become a major focus for the fast-growing company.

Chris Metinko / Axios : Metaview, which uses AI agents to automate recruiting workflows, raised a $60M Series C led by Insight Partners, bringing its total funding to $110M —  Metaview, an agentic recruiting platform, raised a $60 million Series C led by Insight Partners, CEO Siadhal Magos tells Axios Pro exclusively.

由于索尼频繁更新 PS5 的固件，而大部分 PS5 越狱方法只针对特定固件版本的漏洞，因而这些越狱方法实用性相当有限。但情况在本周二发生了变化，名为 Relapse 的漏洞利用方法适用于最高固件版本 v13.6 的 PS5 游戏机，而 v13.6 是在今年 7 月释出的，意味着 PS5 只要不更新最新固件，就能成功越狱。Relapse 利用了 PS5 浏览器的一个已知的 WebKit 漏洞，提权获取内核的写入访问权限，安装 ELF 加载器去简化任意代码的运行。越狱后的 PS5 除了能备份游戏外还能运行模拟器以及 PS4 游戏的非官方 60 帧 MOD。

中国互联网络信息中心（CNNIC）发布了《生成式人工智能应用发展报告（2026）》，截至 2026 年上半年，我国生成式人工智能用户规模突破 7亿 人，普及率超 50%。76.0%的 用户表示自己会让生成式人工智能回答问题；使用生成式人工智能处理图片/视频、文本、工作总结/会议纪要/PPT的用户占比分别为 47.8%、37.6% 和 32.5%。数据显示，38.7%的网民近半年在网上购买过智能硬件设备。其中可穿戴设备和3C数码产品是我国网民接触智能硬件设备的首要入口。购买过智能可穿戴设备的网民比例为 20.2%；购买智能手机、平板电脑等3C数码产品的网民比例为18.2%。报告称，深度求索、月之暗面等本土企业先后发布多个万亿级参数开源大模型，全球主流大模型调用榜单上排名前六的模型全部来自中国团队。

新奥集团发表新闻稿，称其“玄龙-50U”装置实现氢硼聚变反应。新闻稿称：氢硼聚变具有无中子、燃料丰富易得、低成本等商业化优势，产物是氦（α粒子），但相对于氘氚聚变，反应温度及三乘积要求更高，反应条件更苛刻。本次新奥聚变团队通过高能中性束注入与射频波的协同，大幅提高了氢硼反应第一共振峰的非热平衡快质子份额，实现了大于 10^8/秒的氢硼聚变反应率，表明新奥氢硼聚变迈入燃烧等离子体相关实验阶段，是中国多路径聚变能发展的重大突破。来自全球多个国家科研院所与知名高校的十余位聚变权威专家就本次实验成果召开专题论证会，一致认为：本次实验实现了球形环装置中质子能谱及氢硼聚变反应产物α粒子的有效、可重复测量，探测方法可靠，可支撑氢硼聚变反应验证，是球形环氢硼聚变创新探索实践的里程碑突破，对全球磁约束氢硼反应的科学研究具有重要价值贡献。

过去几年极端气候频发，美国佛蒙特州每年都会因此发生十几次持续数小时的断电事故。当地电力公司 Green Mountain Power(GMP)记录到的 10 场最具有破坏性的飓风有 7 场发生在过去十年，造成了逾 2.25 亿美元的损失。为了应对气候变化导致的断电，该公司推出了分布式电池储能网络，向参与该网络的家庭出租两块电池，租期十年，每月费用为 55 美元。该州有超过 5,500 人参与了该家庭电池网络，半数家庭还安装了太阳能电池板。该项目目前提供约 110 MW 的电力，相当于一座中小型天然气发电厂的装机容量。美国其他州也有类似的电池储能网络。

文学家团队借助韦伯望远镜在距地球 500 光年的巨行星 HATS-6 b 大气中探测到水、甲烷、氨，同时发现这颗行星温度可能比标准推算温度低得多。这是透射光谱技术第二次在系外行星大气中检出水汽之外的氨信号。由于氨这类含氮分子在较冷的巨行星中本应比在炽热类木星行星中更为丰富，这一发现支持了一个判断：围绕 M 型矮星运行的行星可能在化学上构成独特群体。HATS-6 b 体积大致相当于木星，每 3 天绕一颗体积小的低温红矮星公转一周。按现有认识，小恒星周围的气体尘埃盘既缺乏足够物质，也缺乏足够时间积聚出木星、土星级别的行星。目前人类已在太阳系外发现 6000 多颗行星，多数与太阳系内行星毫无相似之处。团队表示，弄清它们由什么构成、怎样形成，是判断其他星系是否与太阳系有共同起源的前提。

微软宣布 WSL Containers GA，该工具为 Windows 11 开发者提供了一种通过 Windows Subsystem for Linux 构建、运行和部署 Linux 容器的内置方案。微软同时提供了容器管理工具 wslc.exe，GPU 支持、网络改进、健康检查、存储挂载、与 Microsoft Defender for Endpoint 和 Intune 的集成。微软还声称，当 Linux 环境访问存储在 Windows 中的文件时，性能最多可提升一倍。

日本人的身高在 1896～1996 年的 100 年间，男性增长约 14.6 厘米，女性增长了约 16 厘米。但 1990 年代之后升高增长日益乏力，相比下中韩平均身高则在快速增长。2019 年日本 19 岁男性平均身高约 172.1 厘米，女性约 158.5 厘米。韩国男性为 175.5 厘米，女性为 163.2 厘米，中国男性为 175.7 厘米，女性为 163.5 厘米。日本人口身高增长停滞有三种解释：其一是能量摄入减少，日本人均每日能量摄入量在 1995 年至 2023 年间缓慢下降并趋于停滞，中国和韩国的能量摄入则比日本高出约 760-800 千卡。其二是能量摄入问题可能对正在怀孕的女性产生影响，日本每 10 名新生儿中就有 1 人出生体重不足 2500 克，高于中韩。其三可能与婴幼儿时期的睡眠时长相关，日本婴儿（11.62 小时）显著低于中国（12.49小时）和韩国（11.9小时）。

日本总务省公布的 2025 年人口普查确定值显示，截至 2025 年 10 月 1 日，包含外国人在内的日本总人口为 122,972,528 人，较 2020 年的上次调查减少 3,173,571 人，降幅为 2.5%。自 2015 年的调查起连续3次负增长。65 岁以上人口在总人口中的占比（老龄化率）为 29.4%，创历史新高。较上次调查上升 0.9 个百分点。总人口中的日本人减少 3.5% 至 119,131,935 人，自 2010 年的调查起连续 4 次减少。居住在日本国内的外国人增加 39.8% 至 3,840,039 人，创历史新高。从国籍来看，越南和尼泊尔增幅显著。日本整体的家庭数量比上一次 2020 年调查增加了 149 万 4465 户，达到 5732 万 4619 户，创出有可比数据以来的新高。每户平均人数降至 2.10 人，降至最低水平。一人家庭达到 2368 万 9024 户，增加了 253 万 7982 户。其中增长最多的是 65 岁以上的老年人，占一人家庭总数的 34.4%。一人家庭中，男性有 24.6% 为 65 岁以上，女性有 44.7% 为 65 岁以上。一人家庭的比例达到 41.4%，首次超过4成。

世界正日益城市化，夜空的亮度每年都在增加10%。全球八成的人口生活在受光污染影响的夜空之下，真正的黑暗日益稀缺，对大部分人而言正变得遥不可及。享受黑暗的意义不止于观星。黑夜本身就是一个独特的生态环境。人造光会干扰依靠月光导航的动物：无论是误将停车场当成大海而迷途的幼龟，还是绕着灯泡团团转的飞蛾，都深受其害。萤火虫和青​​蛙需要黑暗环境完成求偶仪式；候鸟常因大城市的强光照射而偏离迁徙路线。研究表明，光污染正在破坏植物与授粉昆虫之间的关系，改变树木的开花时节，扰乱包括人类在内的所有生物的昼夜节律。无论是室内的灯光，还是夜间透过窗户射入的光线，研究都证实我们需要黑暗环境休息、恢复体力和保持健康。大多数光污染源自彻夜长明的路灯、建筑物、停车场和运动场，但来自天空本身的光污染威胁也日益增加。地球轨道上的卫星越来越多。

FBI 与荷兰合作逮捕了疑似 ShinyHunters 组织领导成员、24 岁的 Pepijn van der Stap。他是在 9 月 16 日左右被捕的，发生在 ShinyHunters 入侵 FBI 招聘网站 apply.fbijobs.gov 纂改网页窃取逾 2TB 雇员数据之前——有一种解释是 ShinyHunters 组织的领导者换人了，该组织在新领导人的管理下变得更激进，并试图将入侵 FBI 的行动嫁祸给被捕的 van der Stap。van der Stap 曾使用化名 Umbreon，其头像就是宝可梦 Umbreon。入侵 FBI 的 ShinyHunters 黑客在纂改网页时植入了宝可梦 Umbreon 的 ASCII 艺术图。接管 ShinyHunters 的据称是约旦的少年黑客 Rey。

据清华大学经济管理学院官方微信公号称，今年清华经管学院顾问委员会新增委员三人，新增接任委员五人。三位新增委员分别是黄仁勋、苏姿丰，以及瑞士百达集团高级管理合伙人百达铭（Marc Pictet）。五位新增接任委员分别是宝马集团董事长聂科维（Milan Nedeljković）、沃尔玛公司总裁兼首席执行官方威翰（John Furner）、可口可乐公司首席执行官柏瑞凯（Henrique Braun）、泛大西洋资本集团联席总裁、全球成长型股权投资负责人马丁·埃斯科瓦里（Martín Escobari），以及bp集团首席执行官梅格·奥尼尔（Meg O’Neill）。清华经管学院称，这五位接任委员所在公司的前任都曾是学院顾问委员会委员，因工作变动不再担任。清华经管学院现任顾问委员会主席为苹果董事会主席库克。马斯克（Elon Musk）、扎克伯格（Mark Zuckerberg）以及微软 CEO 纳德拉(Satya Nadella)都是委员。

加州州长 Gavin Newsom 签署了 AB 2409 法案，禁止加州公职人员发行模因币（memecoin），也禁止企业使用公职人员的肖像或形象发行模因币。这项新法规的背景是美国总统特朗普及第一夫人在正式上任前夕发行了自己的模因币，据报道百万购买特朗普模因币的投资者损失了 38 亿美元——模因币的币值与热度密切相关，因此币值波动巨大，最知名的模因币是狗币（Dogecoin）。Newsom 表示：“任何公职人员都不应利用其职位牟利——我们正在实施更强有力的保护措施，以确保此类事件不会在本州发生。”

和美国 AI 模型一样，中国公司的 AI 智能体也会撒谎和欺骗。在今年 3 月进行的一次商业招标实验中， 北航、北大、宁波诺丁汉和 360 AI 安全实验室的研究人员让多个智能体参与模拟客户合同的竞标，每个智能体都被告知其产品的功能及客户的需求，随后被要求进行报价。阿里巴巴 Qwen3-Max-Preview 模型 88% 的会话至少出现一次虚假陈述，DeepSeek-V3.2-Exp 模型的比例为 84%，月之暗面 Kimi-K2 模型为 88%。研究人员允许智能体在再次尝试前从之前的竞标轮中学习。研究显示，三款模型的欺骗行为增加了 12-20 个百分点。测试中包含的美国公司 AI 模型也产生了类似的结果。复旦大学研究人员在 2025 年 3 月报告称，一个由阿里巴巴Qwen2.5-72B-Instruct 模型驱动的 AI 系统在知道自己将被替换后，在未接到复制指令的情况下，在另一个计算环境中创建了自己的副本。对逾 200 份技术文件的分析发现，自 2025 年以来，至少有 20 项研究或评估记录了中国 AI 智能体表现出欺骗、自我复制及挑战边界等行为的案例。

根据本周发表在《The Lancet Oncology》期刊上的一项研究，全世界八分之一癌症病例由感染引起。研究发现，2024 年感染性病原体相关的新癌症病例有 230 万例，占到了总新癌症病例的 12%。其中幽门螺杆菌会增加胃癌风险，这种病菌导致了 76 万例新癌症病例，数量最多，主要集中在东亚地区。其次是人乳头瘤病毒（HPV）导致了近 75 万例新癌症病例，撒哈拉以南非洲地区最多，这种病毒与宫颈癌、与肛门癌、外阴癌、阴道癌、阴茎癌等相关。乙肝和丙肝病毒则与肝癌病例相关。Epstein-Barr 病毒导致了 26 万例癌症病例，该病毒会导致鼻咽癌、胃癌以及霍奇金淋巴瘤。

意大利最大银行 Intesa Sanpaolo 旗下私人银行业务部门 Fideuram 的总裁 Paolo Molesini 今年 2 月成为一起组合利用 WhatsApp 假信息和 Deepfake 语音的诈骗行动目标，导致逾 1 亿美元被骗走，他本人则在 3 月份辞职。Molesini 首先是收到了看起来是母公司 CEO Carlo Messina 发来的 WhatsApp 信息，要求他帮助处理一笔巨额海外交易，接着他收到了一家大型律师事务所高级合伙人的电话。但打电话的不是真人，而是骗子利用 AI 模型模仿该律师的声音（即 Deepfake）。这通电话让 Molesini 相信之前的信息确实是其上司 Messina 发送的。Fideuram 随后向中国大陆以及香港的账号转了 9500 万欧元（约 1.08 亿美元）。在意大利、葡萄牙和中国有关部门及银行机构的协助下，Fideuram 追回了 5300 万欧元，其余款项则被通过海外复杂账号网络兑换成了加密货币。