OrangeBot.AI Digest — 2025-11-18

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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

Recent progress in large language models (LLMs) has moved the frontier from puzzle-solving to science-grade reasoning-the kind needed to tackle problems whose answers must stand against nature, not merely fit a rubric. Physics is the sharpest test of this shift, which binds symbols to reality in a fundamental way, serving as the cornerstone of most modern technologies. In this work, we manage to advance physics research by developing large language models with exceptional physics reasoning capabilities, especially excel at solving Olympiad-level physics problems. We introduce P1, a family of open-source physics reasoning models trained entirely through reinforcement learning (RL). Among them, P1-235B-A22B is the first open-source model with Gold-medal performance at the latest International Physics Olympiad (IPhO 2025), and wins 12 gold medals out of 13 international/regional physics competitions in 2024/2025. P1-30B-A3B also surpasses almost all other open-source models on IPhO 2025, getting a silver medal. Further equipped with an agentic framework PhysicsMinions, P1-235B-A22B+PhysicsMinions achieves overall No.1 on IPhO 2025, and obtains the highest average score over the 13 physics competitions. Besides physics, P1 models also present great performance on other reasoning tasks like math and coding, showing the great generalibility of P1 series.

We present Uni-MoE 2.0 from the Lychee family. As a fully open-source omnimodal large model (OLM), it substantially advances Lychee's Uni-MoE series in language-centric multimodal understanding, reasoning, and generating. Based on the Qwen2.5-7B dense architecture, we build Uni-MoE-2.0-Omni from scratch through three core contributions: dynamic-capacity Mixture-of-Experts (MoE) design, a progressive training strategy enhanced with an iterative reinforcement strategy, and a carefully curated multimodal data matching technique. It is capable of omnimodal understanding, as well as generating images, text, and speech. Architecturally, our new MoE framework balances computational efficiency and capability for 10 cross-modal inputs using shared, routed, and null experts, while our Omni-Modality 3D RoPE ensures spatio-temporal cross-modality alignment in the self-attention layer. For training, following cross-modal pretraining, we use a progressive supervised fine-tuning strategy that activates modality-specific experts and is enhanced by balanced data composition and an iterative GSPO-DPO method to stabilise RL training and improve reasoning. Data-wise, the base model, trained on approximately 75B tokens of open-source multimodal data, is equipped with special speech and image generation tokens, allowing it to learn these generative tasks by conditioning its outputs on linguistic cues. Extensive evaluation across 85 benchmarks demonstrates that our model achieves SOTA or highly competitive performance against leading OLMs, surpassing Qwen2.5-Omni (trained with 1.2T tokens) on over 50 of 76 benchmarks. Key strengths include video understanding (+7% avg. of 8), omnimodallity understanding (+7% avg. of 4), and audiovisual reasoning (+4%). It also advances long-form speech processing (reducing WER by 4.2%) and leads in low-level image processing and controllable generation across 5 metrics.

We present MiroThinker v1.0, an open-source research agent designed to advance tool-augmented reasoning and information-seeking capabilities. Unlike previous agents that only scale up model size or context length, MiroThinker explores interaction scaling at the model level, systematically training the model to handle deeper and more frequent agent-environment interactions as a third dimension of performance improvement. Unlike LLM test-time scaling, which operates in isolation and risks degradation with longer reasoning chains, interactive scaling leverages environment feedback and external information acquisition to correct errors and refine trajectories. Through reinforcement learning, the model achieves efficient interaction scaling: with a 256K context window, it can perform up to 600 tool calls per task, enabling sustained multi-turn reasoning and complex real-world research workflows. Across four representative benchmarks-GAIA, HLE, BrowseComp, and BrowseComp-ZH-the 72B variant achieves up to 81.9%, 37.7%, 47.1%, and 55.6% accuracy respectively, surpassing previous open-source agents and approaching commercial counterparts such as GPT-5-high. Our analysis reveals that MiroThinker benefits from interactive scaling consistently: research performance improves predictably as the model engages in deeper and more frequent agent-environment interactions, demonstrating that interaction depth exhibits scaling behaviors analogous to model size and context length. These findings establish interaction scaling as a third critical dimension for building next-generation open research agents, complementing model capacity and context windows.

Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse domains, but their training remains resource- and time-intensive, requiring massive compute power and careful orchestration of training procedures. Model souping-the practice of averaging weights from multiple models of the same architecture-has emerged as a promising pre- and post-training technique that can enhance performance without expensive retraining. In this paper, we introduce Soup Of Category Experts (SoCE), a principled approach for model souping that utilizes benchmark composition to identify optimal model candidates and applies non-uniform weighted averaging to maximize performance. Contrary to previous uniform-averaging approaches, our method leverages the observation that benchmark categories often exhibit low inter-correlations in model performance. SoCE identifies "expert" models for each weakly-correlated category cluster and combines them using optimized weighted averaging rather than uniform weights. We demonstrate that the proposed method improves performance and robustness across multiple domains, including multilingual capabilities, tool calling, and math and achieves state-of-the-art results on the Berkeley Function Calling Leaderboard.

We introduce Part-X-MLLM, a native 3D multimodal large language model that unifies diverse 3D tasks by formulating them as programs in a structured, executable grammar. Given an RGB point cloud and a natural language prompt, our model autoregressively generates a single, coherent token sequence encoding part-level bounding boxes, semantic descriptions, and edit commands. This structured output serves as a versatile interface to drive downstream geometry-aware modules for part-based generation and editing. By decoupling the symbolic planning from the geometric synthesis, our approach allows any compatible geometry engine to be controlled through a single, language-native frontend. We pre-train a dual-encoder architecture to disentangle structure from semantics and instruction-tune the model on a large-scale, part-centric dataset. Experiments demonstrate that our model excels at producing high-quality, structured plans, enabling state-of-the-art performance in grounded Q\&A, compositional generation, and localized editing through one unified interface. Project page: https://chunshi.wang/Part-X-MLLM/

While thinking-aware generation aims to improve performance on complex tasks, we identify a critical failure mode where existing sequential, autoregressive approaches can paradoxically degrade performance due to error propagation. To systematically analyze this issue, we propose ParaBench, a new benchmark designed to evaluate both text and image output modalities. Our analysis using ParaBench reveals that this performance degradation is strongly correlated with poor alignment between the generated reasoning and the final image. To resolve this, we propose a parallel multimodal diffusion framework, MMaDA-Parallel, that enables continuous, bidirectional interaction between text and images throughout the entire denoising trajectory. MMaDA-Parallel is trained with supervised finetuning and then further optimized by Parallel Reinforcement Learning (ParaRL), a novel strategy that applies semantic rewards along the trajectory to enforce cross-modal consistency. Experiments validate that our model significantly improves cross-modal alignment and semantic consistency, achieving a 6.9\% improvement in Output Alignment on ParaBench compared to the state-of-the-art model, Bagel, establishing a more robust paradigm for thinking-aware image synthesis. Our code is open-sourced at https://github.com/tyfeld/MMaDA-Parallel

Large Language Models have shown strong potential as rerankers to enhance the overall performance of RAG systems. However, existing reranking paradigms are constrained by a core theoretical and practical dilemma: Pointwise methods, while simple and highly flexible, evaluate documents independently, making them prone to the Ranking Myopia Trap, overlooking the relative importance between documents. In contrast, Listwise methods can perceive the global ranking context, but suffer from inherent List Rigidity, leading to severe scalability and flexibility issues when handling large candidate sets. To address these challenges, we propose Groupwise, a novel reranking paradigm. In this approach, the query and a group of candidate documents are jointly fed into the model, which performs within-group comparisons to assign individual relevance scores to each document. This design retains the flexibility of Pointwise methods while enabling the comparative capability of Listwise methods. We further adopt GRPO for model training, equipped with a heterogeneous reward function that integrates ranking metrics with a distributional reward aimed at aligning score distributions across groups. To overcome the bottleneck caused by the scarcity of high quality labeled data, we further propose an innovative pipeline for synthesizing high quality retrieval and ranking data. The resulting data can be leveraged not only for training the reranker but also for training the retriever. Extensive experiments validate the effectiveness of our approach. On two reasoning intensive retrieval benchmarks, BRIGHT and R2MED.

The rapid evolution of video generative models has shifted their focus from producing visually plausible outputs to tackling tasks requiring physical plausibility and logical consistency. However, despite recent breakthroughs such as Veo 3's chain-of-frames reasoning, it remains unclear whether these models can exhibit reasoning capabilities similar to large language models (LLMs). Existing benchmarks predominantly evaluate visual fidelity and temporal coherence, failing to capture higher-order reasoning abilities. To bridge this gap, we propose TiViBench, a hierarchical benchmark specifically designed to evaluate the reasoning capabilities of image-to-video (I2V) generation models. TiViBench systematically assesses reasoning across four dimensions: i) Structural Reasoning & Search, ii) Spatial & Visual Pattern Reasoning, iii) Symbolic & Logical Reasoning, and iv) Action Planning & Task Execution, spanning 24 diverse task scenarios across 3 difficulty levels. Through extensive evaluations, we show that commercial models (e.g., Sora 2, Veo 3.1) demonstrate stronger reasoning potential, while open-source models reveal untapped potential that remains hindered by limited training scale and data diversity. To further unlock this potential, we introduce VideoTPO, a simple yet effective test-time strategy inspired by preference optimization. By performing LLM self-analysis on generated candidates to identify strengths and weaknesses, VideoTPO significantly enhances reasoning performance without requiring additional training, data, or reward models. Together, TiViBench and VideoTPO pave the way for evaluating and advancing reasoning in video generation models, setting a foundation for future research in this emerging field.

3D modeling is shifting from static visual representations toward physical, articulated assets that can be directly used in simulation and interaction. However, most existing 3D generation methods overlook key physical and articulation properties, thereby limiting their utility in embodied AI. To bridge this gap, we introduce PhysX-Anything, the first simulation-ready physical 3D generative framework that, given a single in-the-wild image, produces high-quality sim-ready 3D assets with explicit geometry, articulation, and physical attributes. Specifically, we propose the first VLM-based physical 3D generative model, along with a new 3D representation that efficiently tokenizes geometry. It reduces the number of tokens by 193x, enabling explicit geometry learning within standard VLM token budgets without introducing any special tokens during fine-tuning and significantly improving generative quality. In addition, to overcome the limited diversity of existing physical 3D datasets, we construct a new dataset, PhysX-Mobility, which expands the object categories in prior physical 3D datasets by over 2x and includes more than 2K common real-world objects with rich physical annotations. Extensive experiments on PhysX-Mobility and in-the-wild images demonstrate that PhysX-Anything delivers strong generative performance and robust generalization. Furthermore, simulation-based experiments in a MuJoCo-style environment validate that our sim-ready assets can be directly used for contact-rich robotic policy learning. We believe PhysX-Anything can substantially empower a broad range of downstream applications, especially in embodied AI and physics-based simulation.

Large language model (LLM)-powered agents are transforming digital devices from passive tools into proactive intelligent collaborators. However, most existing frameworks remain confined to a single OS or device, making cross-device workflows brittle and largely manual. We present UFO^3, a system that unifies heterogeneous endpoints, desktops, servers, mobile devices, and edge, into a single orchestration fabric. UFO^3 models each user request as a mutable TaskConstellation: a distributed DAG of atomic subtasks (TaskStars) with explicit control and data dependencies (TaskStarLines). The TaskConstellation continuously evolves as results stream in from distributed devices, enabling asynchronous execution, adaptive recovery, and dynamic optimization. A Constellation Orchestrator} executes tasks safely and asynchronously while applying dynamic DAG updates, and the Agent Interaction Protocol (AIP) provides persistent, low-latency channels for reliable task dispatch and result streaming. These designs dissolve the traditional boundaries between devices and platforms, allowing agents to collaborate seamlessly and amplify their collective intelligence. We evaluate UFO^3 on NebulaBench, a benchmark of 55 cross-device tasks across 5 machines and 10 categories. UFO^3 achieves 83.3% subtask completion, 70.9% task success, exposes parallelism with an average width of 1.72, and reduces end-to-end latency by 31% relative to a sequential baseline. Fault-injection experiments demonstrate graceful degradation and recovery under transient and permanent agent failures. These results show that UFO^3 achieves accurate, efficient, and resilient task orchestration across heterogeneous devices, uniting isolated agents into a coherent, adaptive computing fabric that extends across the landscape of ubiquitous computing.

Automated red teaming frameworks for Large Language Models (LLMs) have become increasingly sophisticated, yet they share a fundamental limitation: their jailbreak logic is confined to selecting, combining, or refining pre-existing attack strategies. This binds their creativity and leaves them unable to autonomously invent entirely new attack mechanisms. To overcome this gap, we introduce EvoSynth, an autonomous framework that shifts the paradigm from attack planning to the evolutionary synthesis of jailbreak methods. Instead of refining prompts, EvoSynth employs a multi-agent system to autonomously engineer, evolve, and execute novel, code-based attack algorithms. Crucially, it features a code-level self-correction loop, allowing it to iteratively rewrite its own attack logic in response to failure. Through extensive experiments, we demonstrate that EvoSynth not only establishes a new state-of-the-art by achieving an 85.5\% Attack Success Rate (ASR) against highly robust models like Claude-Sonnet-4.5, but also generates attacks that are significantly more diverse than those from existing methods. We release our framework to facilitate future research in this new direction of evolutionary synthesis of jailbreak methods. Code is available at: https://github.com/dongdongunique/EvoSynth.

Today's denoising diffusion models do not "denoise" in the classical sense, i.e., they do not directly predict clean images. Rather, the neural networks predict noise or a noised quantity. In this paper, we suggest that predicting clean data and predicting noised quantities are fundamentally different. According to the manifold assumption, natural data should lie on a low-dimensional manifold, whereas noised quantities do not. With this assumption, we advocate for models that directly predict clean data, which allows apparently under-capacity networks to operate effectively in very high-dimensional spaces. We show that simple, large-patch Transformers on pixels can be strong generative models: using no tokenizer, no pre-training, and no extra loss. Our approach is conceptually nothing more than "Just image Transformers", or JiT, as we call it. We report competitive results using JiT with large patch sizes of 16 and 32 on ImageNet at resolutions of 256 and 512, where predicting high-dimensional noised quantities can fail catastrophically. With our networks mapping back to the basics of the manifold, our research goes back to basics and pursues a self-contained paradigm for Transformer-based diffusion on raw natural data.

Earth observation data presents a unique challenge: it is spatial like images, sequential like video or text, and highly multimodal. We present OlmoEarth: a multimodal, spatio-temporal foundation model that employs a novel self-supervised learning formulation, masking strategy, and loss all designed for the Earth observation domain. OlmoEarth achieves state-of-the-art performance compared to 12 other foundation models across a variety of research benchmarks and real-world tasks from external partners. When evaluating embeddings OlmoEarth achieves the best performance on 15 out of 24 tasks, and with full fine-tuning it is the best on 19 of 29 tasks. We deploy OlmoEarth as the backbone of an end-to-end platform for data collection, labeling, training, and inference of Earth observation models. The OlmoEarth Platform puts frontier foundation models and powerful data management tools into the hands of non-profits and NGOs working to solve the world's biggest problems. OlmoEarth source code, training data, and pre-trained weights are available at https://github.com/allenai/olmoearth_pretrain{https://github.com/allenai/olmoearth_pretrain}.

In-context learning (ICL) -- the capacity of a model to infer and apply abstract patterns from examples provided within its input -- has been extensively studied in large language models trained for next-token prediction on human text. In fact, prior work often attributes this emergent behavior to distinctive statistical properties in human language. This raises a fundamental question: can ICL arise organically in other sequence domains purely through large-scale predictive training? To explore this, we turn to genomic sequences, an alternative symbolic domain rich in statistical structure. Specifically, we study the Evo2 genomic model, trained predominantly on next-nucleotide (A/T/C/G) prediction, at a scale comparable to mid-sized LLMs. We develop a controlled experimental framework comprising symbolic reasoning tasks instantiated in both linguistic and genomic forms, enabling direct comparison of ICL across genomic and linguistic models. Our results show that genomic models, like their linguistic counterparts, exhibit log-linear gains in pattern induction as the number of in-context demonstrations increases. To the best of our knowledge, this is the first evidence of organically emergent ICL in genomic sequences, supporting the hypothesis that ICL arises as a consequence of large-scale predictive modeling over rich data. These findings extend emergent meta-learning beyond language, pointing toward a unified, modality-agnostic view of in-context learning.

Large Language Models (LLMs) are reshaping almost all industries, including software engineering. In recent years, a number of LLM agents have been proposed to solve real-world software problems. Such software agents are typically equipped with a suite of coding tools and can autonomously decide the next actions to form complete trajectories to solve end-to-end software tasks. While promising, they typically require dedicated design and may still be suboptimal, since it can be extremely challenging and costly to exhaust the entire agent scaffold design space. Recognizing that software agents are inherently software themselves that can be further refined/modified, researchers have proposed a number of self-improving software agents recently, including the Darwin-Gödel Machine (DGM). Meanwhile, such self-improving agents require costly offline training on specific benchmarks and may not generalize well across different LLMs or benchmarks. In this paper, we propose Live-SWE-agent, the first live software agent that can autonomously and continuously evolve itself on-the-fly during runtime when solving real-world software problems. More specifically, Live-SWE-agent starts with the most basic agent scaffold with only access to bash tools (e.g., mini-SWE-agent), and autonomously evolves its own scaffold implementation while solving real-world software problems. Our evaluation on the widely studied SWE-bench Verified benchmark shows that Live-SWE-agent can achieve an impressive solve rate of 75.4% without test-time scaling, outperforming all existing open-source software agents and approaching the performance of the best proprietary solution. Moreover, Live-SWE-agent outperforms state-of-the-art manually crafted software agents on the recent SWE-Bench Pro benchmark, achieving the best-known solve rate of 45.8%.

根据递交到 SEC 的文件，哈佛大学持有价值逾 4.42 亿美元的加密货币，持有形式是贝莱德（BlackRock）发行的 iShares Bitcoin Trust(IBIT) ETF。布朗大学也披露持有约 1400 万美元的加密货币 ETF。哈佛大学在比特币 ETF 上的投资超过了其持有的任何股票，包括其持有的英伟达、微软和亚马逊等主流公司的股份。4.42 亿美元仅占哈佛大学近 570 亿美元捐赠基金的不到 1%。尽管比特币币值近期下跌，但 IBIT 的总市值超过了 700 亿美元。哈佛的投资再次表明加密货币正日益被机构接受。

自 2022 年以来 Debian Live Images 都包含了非自由固件，Debian Libre Live Images 项目旨在让用户在不安装任何非自由软件的情况下运行和安装 Debian 操作系统。项目目前提供了 64 位 x86 CPU (amd64) 的 Live ISO 镜像。开发者表示，作为首个公开版本，镜像可能存在问题，建议用户在使用前查阅已知问题列表。

在亚马逊 AWS 和微软 Azure 之后，互联网再次体验到单点故障对整个互联网基础设施的影响：Cloudflare 宕机事故影响了整个互联网。Cloudflare 提供了多种服务，包括 DDoS 保护、网页应用防火墙、公共 DNS 解析器、反向代理和 CDN 等，它的服务和 AWS 以及 Azure 一样被广泛使用，它的故障也波及了整个互联网。Cloudflare 的状态页面显示它已经知道问题，并且处于“我们正在继续调查问题”之中。专家表示此类宕机事件凸显了现代互联网的脆弱性，突出了支持互联网的少数几家公司出现问题可能会造成严重破坏。

根据发表在《Frontiers in Zoology》期刊上的一项研究，生活在城市的浣熊显示出驯化的早期迹象。城里的残羹剩饭为动物提供了取之不尽的美食，但对动物而言城市和它们生活的野外环境有很大区别，为适应城市生活它们面临巨大的选择适应压力。驯化并非只是人类捕捉野生动物然后选择性繁殖，野生动物适应人类环境也是一种驯化。驯化的动物相比野生动物有些显著差异的生物特征，如脸短、头小、耳朵下垂以及白色皮毛斑块，这些特征被称为驯化综合征。生物学家 Raffaela Lesch 和同事分析了 iNaturalist 上的近两万张浣熊照片，发现城市浣熊的吻部比农村同类短 3.5%。研究人员计划接下来捕捉城市浣熊，观察它们是否比农村浣熊更友善。

你可能从儿时起一直被告诫要“坚持下去”，仿佛命悬一线时松手就会死亡。根据发表在《Nature Human Behaviour》期刊上的元分析研究，很多时候放弃其实是最明智的选择。研究人员分析了 235 项研究，涉及在遇到成功障碍后人们如何调整目标。研究作者 Hugh Riddell 称，坚持不可能实现的目标会造成严重影响，增加压力、幸福感下降，甚至引发健康问题。放弃旧目标重新转向新目标能恢复目标感和幸福感。研究还发现，放弃目标与压力、焦虑和抑郁的显著降低相关。

Alphabet CEO Sundar Pichai 在接受采访时称如果 AI 泡沫破裂没有公司能免受影响。他承认目前的 AI 热存在非理性因素。当被问及 Google 能否免受 AI 泡沫破裂的影响，Pichai 表示能承受但不可能免受影响。Alphabet 的股价在七个月内翻了一番达到 3.5 万亿美元。Pichai 表示 Google 独特的“全栈”技术模式——从芯片到YouTube 数据到模型和前沿科学——意味着它更有能力应对 AI 市场的任何动荡。他称 AI 是人类迄今创造的“最深刻的技术”，“我们将不得不应对社会变革，”也将“创造新的机遇”。

微软正进一步在 Windows 11 中整合 AI 功能。Windows 11 Build 26220.7262 的设置 > 系统 > AI 组件下新增了“实验性智能体”功能选项。该功能会启用智能体工作空间（Agent Workspace），但目前还无法使用。微软正在将 Windows 打造成一个 AI 原生操作系统。新功能允许 AI 智能体访问用户目录最常用的文件夹如桌面、音乐、图片和视频。如果启用该功能，AI 智能体还将拥有自己的运行时环境、桌面、用户帐户，并始终在后台运行。

Take-Two Interactive CEO Strauss Zelnick 在接受采访时表示，游戏行业正转向 PC 从封闭转向开放，但游戏机作为一种体验并不会消失。Zelnick 表示，游戏机和手游的市场份额产不多，但手游增长速度比游戏机更快。索尼 PS 和任天堂 Switch 的游戏机业务取得了成功，而竞争对手拥有 Xbox 的微软则暗示下一代硬件将更面向 PC 游戏。Valve 最近宣布的 Steam Machin 就是游戏机和 PC 的混合设备。

发表在《Proceedings of the Royal Society B: Biological Sciences》期刊上的一项研究揭示了对人脸过目不忘的秘密。研究人员招募了 37 名超级人脸识别者（super-recognizer）和 68 名识别能力一般的人，记录他们注视电脑屏幕上显示的人脸图片的位置和时长。团队此前进行的研究已经发现，超级人脸识别者会以类似拼图游戏的方式处理人脸：他们会将新面孔分成多部分，大脑再合成出完整图像。最新研究发现，超级人脸识别者会更专注于包含更多“线索”的特征。研究可能有助于改进人脸识别系统，研究人员表示，目前人类在人脸识别方面仍然优于人工智能，因为人类会在社交场合利用其他线索。

根据 Freedom House 的年度报告，全球互联网自由度连续 15 年下降。生活在被归类为“自由”国家的网民比例创历史新低。在评估的 72 个国家中，有 27 个国家的互联网自由度下降，其中肯尼亚恶化情况最严重——该国在镇压反腐败抗议期间网络中断长达 7 小时。美国的自由度排名也出现了下降，冰岛仍然是自由度最高的国家，而孟加拉国的进步最显著。

华盛顿邮报通知上万员工和合同工他们的个人和财务信息泄露。7 月 10 日至 8 月 22 日期间，攻击者入侵了华邮网络，利用甲骨文 E-Business Suite 的 0day 漏洞窃取了敏感数据。攻击者试图利用窃取的数据对华邮进行勒索。华邮随后在专家的帮助下进行详细调查，甲骨文则在期间披露其企业 ERP 平台 E-Business Suite 存在漏洞，允许未经授权客户应用。攻击者除了攻击华邮还有哈佛大学、美国航空 Envoy Air 以及日立的 GlobalLogic。

Google 将对高耗电量 Android 应用采取行动。如果一款应用后台活动频率超过阈值，Google Play 应用商店可能会将其标记为对电池性能产生负面影响，影响其曝光度。开发者需要在 2026 年 3 月 1 日之前更新其应用，以遵守名为“过度部分唤醒锁（excessive partial wake locks）”的指标。Google Play 的 Android Vitals 系统将跟踪应用阻止设备进入睡眠模式的后台活动时间，如果在 24 小时内累计非豁免唤醒锁时长逾两小时该应用将被认为过度。

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%。