OrangeBot.AI Digest — 2025-10-29

node of the decentralized oracle network, bridging on and off-chain computation

A free, open source, and extensible speech-to-text application that works completely offline.

API, CLI, and Web App for analyzing and finding a person's profile in 1000 social media \ websites

OpenTelemetry Collector

24 Lessons, 12 Weeks, Get Started as a Web Developer

Protocol Buffers - Google's data interchange format

Voice-to-text app for macOS to transcribe what you say to text almost instantly

an open source, extensible AI agent that goes beyond code suggestions - install, execute, edit, and test with any LLM

Toolkit for linearizing PDFs for LLM datasets/training

A reference .NET application implementing an eCommerce site

There can be more than Notion and Miro. AFFiNE(pronounced [ə‘fain]) is a next-gen knowledge base that brings planning, sorting and creating all together. Privacy first, open-source, customizable and ready to use.

The absolute trainer to light up AI agents.

Language agents have demonstrated remarkable potential in web search and information retrieval. However, these search agents assume user queries are complete and unambiguous, an assumption that diverges from reality where users begin with incomplete queries requiring clarification through interaction. Yet most agents lack interactive mechanisms during the search process, and existing benchmarks cannot assess this capability. To address this gap, we introduce InteractComp, a benchmark designed to evaluate whether search agents can recognize query ambiguity and actively interact to resolve it during search. Following the principle of easy to verify, interact to disambiguate, we construct 210 expert-curated questions across 9 domains through a target-distractor methodology that creates genuine ambiguity resolvable only through interaction. Evaluation of 17 models reveals striking failure: the best model achieves only 13.73% accuracy despite 71.50% with complete context, exposing systematic overconfidence rather than reasoning deficits. Forced interaction produces dramatic gains, demonstrating latent capability current strategies fail to engage. Longitudinal analysis shows interaction capabilities stagnated over 15 months while search performance improved seven-fold, revealing a critical blind spot. This stagnation, coupled with the immediate feedback inherent to search tasks, makes InteractComp a valuable resource for both evaluating and training interaction capabilities in search agents. The code is available at https://github.com/FoundationAgents/InteractComp.

We present Tongyi DeepResearch, an agentic large language model, which is specifically designed for long-horizon, deep information-seeking research tasks. To incentivize autonomous deep research agency, Tongyi DeepResearch is developed through an end-to-end training framework that combines agentic mid-training and agentic post-training, enabling scalable reasoning and information seeking across complex tasks. We design a highly scalable data synthesis pipeline that is fully automatic, without relying on costly human annotation, and empowers all training stages. By constructing customized environments for each stage, our system enables stable and consistent interactions throughout. Tongyi DeepResearch, featuring 30.5 billion total parameters, with only 3.3 billion activated per token, achieves state-of-the-art performance across a range of agentic deep research benchmarks, including Humanity's Last Exam, BrowseComp, BrowseComp-ZH, WebWalkerQA, xbench-DeepSearch, FRAMES and xbench-DeepSearch-2510. We open-source the model, framework, and complete solutions to empower the community.

LLM-based web agents show immense promise for information seeking, yet their effectiveness on long-horizon tasks is hindered by a fundamental trade-off in context management. Prevailing ReAct-based agents suffer from context saturation as they accumulate noisy, raw histories, while methods that fixedly summarize the full history at each step risk the irreversible loss of critical details. Addressing these, we introduce AgentFold, a novel agent paradigm centered on proactive context management, inspired by the human cognitive process of retrospective consolidation. AgentFold treats its context as a dynamic cognitive workspace to be actively sculpted, rather than a passive log to be filled. At each step, it learns to execute a `folding' operation, which manages its historical trajectory at multiple scales: it can perform granular condensations to preserve vital, fine-grained details, or deep consolidations to abstract away entire multi-step sub-tasks. The results on prominent benchmarks are striking: with simple supervised fine-tuning (without continual pre-training or RL), our AgentFold-30B-A3B agent achieves 36.2% on BrowseComp and 47.3% on BrowseComp-ZH. Notably, this performance not only surpasses or matches open-source models of a dramatically larger scale, such as the DeepSeek-V3.1-671B-A37B, but also surpasses leading proprietary agents like OpenAI's o4-mini.

Recent advances in Multimodal Large Language Models (MLLMs) have driven rapid progress in Vision-Language-Action (VLA) models for robotic manipulation. Although effective in many scenarios, current approaches largely rely on explicit instructions, whereas in real-world interactions, humans rarely issue instructions directly. Effective collaboration requires robots to infer user intentions proactively. In this work, we introduce cross-modal contextual instructions, a new setting where intent is derived from spoken dialogue, environmental sounds, and visual cues rather than explicit commands. To address this new setting, we present RoboOmni, a Perceiver-Thinker-Talker-Executor framework based on end-to-end omni-modal LLMs that unifies intention recognition, interaction confirmation, and action execution. RoboOmni fuses auditory and visual signals spatiotemporally for robust intention recognition, while supporting direct speech interaction. To address the absence of training data for proactive intention recognition in robotic manipulation, we build OmniAction, comprising 140k episodes, 5k+ speakers, 2.4k event sounds, 640 backgrounds, and six contextual instruction types. Experiments in simulation and real-world settings show that RoboOmni surpasses text- and ASR-based baselines in success rate, inference speed, intention recognition, and proactive assistance.

We present Game-TARS, a generalist game agent trained with a unified, scalable action space anchored to human-aligned native keyboard-mouse inputs. Unlike API- or GUI-based approaches, this paradigm enables large-scale continual pre-training across heterogeneous domains, including OS, web, and simulation games. Game-TARS is pre-trained on over 500B tokens with diverse trajectories and multimodal data. Key techniques include a decaying continual loss to reduce causal confusion and an efficient Sparse-Thinking strategy that balances reasoning depth and inference cost. Experiments show that Game-TARS achieves about 2 times the success rate over the previous sota model on open-world Minecraft tasks, is close to the generality of fresh humans in unseen web 3d games, and outperforms GPT-5, Gemini-2.5-Pro, and Claude-4-Sonnet in FPS benchmarks. Scaling results on training-time and test-time confirm that the unified action space sustains improvements when scaled to cross-game and multimodal data. Our results demonstrate that simple, scalable action representations combined with large-scale pre-training provide a promising path toward generalist agents with broad computer-use abilities.

Continuous-space video generation has advanced rapidly, while discrete approaches lag behind due to error accumulation and long-context inconsistency. In this work, we revisit discrete generative modeling and present Uniform discRete diffuSion with metric pAth (URSA), a simple yet powerful framework that bridges the gap with continuous approaches for the scalable video generation. At its core, URSA formulates the video generation task as an iterative global refinement of discrete spatiotemporal tokens. It integrates two key designs: a Linearized Metric Path and a Resolution-dependent Timestep Shifting mechanism. These designs enable URSA to scale efficiently to high-resolution image synthesis and long-duration video generation, while requiring significantly fewer inference steps. Additionally, we introduce an asynchronous temporal fine-tuning strategy that unifies versatile tasks within a single model, including interpolation and image-to-video generation. Extensive experiments on challenging video and image generation benchmarks demonstrate that URSA consistently outperforms existing discrete methods and achieves performance comparable to state-of-the-art continuous diffusion methods. Code and models are available at https://github.com/baaivision/URSA

LLM-based search agents are increasingly trained on entity-centric synthetic data to solve complex, knowledge-intensive tasks. However, prevailing training methods like Group Relative Policy Optimization (GRPO) discard this rich entity information, relying instead on sparse, outcome-based rewards. This critical limitation renders them unable to distinguish informative "near-miss" samples-those with substantially correct reasoning but a flawed final answer-from complete failures, thus discarding valuable learning signals. We address this by leveraging the very entities discarded during training. Our empirical analysis reveals a strong positive correlation between the number of ground-truth entities identified during an agent's reasoning process and final answer accuracy. Building on this insight, we introduce Entity-aware Group Relative Policy Optimization (E-GRPO), a novel framework that formulates a dense entity-aware reward function. E-GRPO assigns partial rewards to incorrect samples proportional to their entity match rate, enabling the model to effectively learn from these "near-misses". Experiments on diverse question-answering (QA) and deep research benchmarks show that E-GRPO consistently and significantly outperforms the GRPO baseline. Furthermore, our analysis reveals that E-GRPO not only achieves superior accuracy but also induces more efficient reasoning policies that require fewer tool calls, demonstrating a more effective and sample-efficient approach to aligning search agents.

With advances in decision-making and reasoning capabilities, multimodal agents show strong potential in computer application scenarios. Past evaluations have mainly assessed GUI interaction skills, while tool invocation abilities, such as those enabled by the Model Context Protocol (MCP), have been largely overlooked. Comparing agents with integrated tool invocation to those evaluated only on GUI interaction is inherently unfair. We present OSWorld-MCP, the first comprehensive and fair benchmark for assessing computer-use agents' tool invocation, GUI operation, and decision-making abilities in a real-world environment. We design a novel automated code-generation pipeline to create tools and combine them with a curated selection from existing tools. Rigorous manual validation yields 158 high-quality tools (covering 7 common applications), each verified for correct functionality, practical applicability, and versatility. Extensive evaluations of state-of-the-art multimodal agents on OSWorld-MCP show that MCP tools generally improve task success rates (e.g., from 8.3% to 20.4% for OpenAI o3 at 15 steps, from 40.1% to 43.3% for Claude 4 Sonnet at 50 steps), underscoring the importance of assessing tool invocation capabilities. However, even the strongest models have relatively low tool invocation rates, Only 36.3%, indicating room for improvement and highlighting the benchmark's challenge. By explicitly measuring MCP tool usage skills, OSWorld-MCP deepens understanding of multimodal agents and sets a new standard for evaluating performance in complex, tool-assisted environments. Our code, environment, and data are publicly available at https://osworld-mcp.github.io.

Recently, image editing based on Diffusion-in-Transformer models has undergone rapid development. However, existing editing methods often lack effective control over the degree of editing, limiting their ability to achieve more customized results. To address this limitation, we investigate the MM-Attention mechanism within the DiT model and observe that the Query and Key tokens share a bias vector that is only layer-dependent. We interpret this bias as representing the model's inherent editing behavior, while the delta between each token and its corresponding bias encodes the content-specific editing signals. Based on this insight, we propose Group Relative Attention Guidance, a simple yet effective method that reweights the delta values of different tokens to modulate the focus of the model on the input image relative to the editing instruction, enabling continuous and fine-grained control over editing intensity without any tuning. Extensive experiments conducted on existing image editing frameworks demonstrate that GRAG can be integrated with as few as four lines of code, consistently enhancing editing quality. Moreover, compared to the commonly used Classifier-Free Guidance, GRAG achieves smoother and more precise control over the degree of editing. Our code will be released at https://github.com/little-misfit/GRAG-Image-Editing.

Large Language Model (LLM)-based agents have emerged as a transformative approach for open-ended problem solving, with information seeking (IS) being a core capability that enables autonomous reasoning and decision-making. While prior research has largely focused on improving retrieval depth, we observe that current IS agents often suffer from low search efficiency, which in turn constrains overall performance. A key factor underlying this inefficiency is the sparsity of target entities in training tasks, which limits opportunities for agents to learn and generalize efficient search behaviors. To address these challenges, we propose WebLeaper, a framework for constructing high-coverage IS tasks and generating efficient solution trajectories. We formulate IS as a tree-structured reasoning problem, enabling a substantially larger set of target entities to be embedded within a constrained context. Leveraging curated Wikipedia tables, we propose three variants for synthesizing IS tasks, Basic, Union, and Reverse-Union, to systematically increase both IS efficiency and efficacy. Finally, we curate training trajectories by retaining only those that are simultaneously accurate and efficient, ensuring that the model is optimized for both correctness and search performance. Extensive experiments on both basic and comprehensive settings, conducted on five IS benchmarks, BrowserComp, GAIA, xbench-DeepSearch, WideSearch, and Seal-0, demonstrate that our method consistently achieves improvements in both effectiveness and efficiency over strong baselines.

Large language models (LLMs) have recently enabled coding agents capable of generating, executing, and revising visualization code. However, existing models often fail in practical workflows due to limited language coverage, unreliable execution, and lack of iterative correction mechanisms. Progress has been constrained by narrow datasets and benchmarks that emphasize single-round generation and single-language tasks. To address these challenges, we introduce three complementary resources for advancing visualization coding agents. VisCode-Multi-679K is a large-scale, supervised dataset containing 679K validated and executable visualization samples with multi-turn correction dialogues across 12 programming languages. VisPlotBench is a benchmark for systematic evaluation, featuring executable tasks, rendered outputs, and protocols for both initial generation and multi-round self-debug. Finally, we present VisCoder2, a family of multi-language visualization models trained on VisCode-Multi-679K. Experiments show that VisCoder2 significantly outperforms strong open-source baselines and approaches the performance of proprietary models like GPT-4.1, with further gains from iterative self-debug, reaching 82.4% overall execution pass rate at the 32B scale, particularly in symbolic or compiler-dependent languages.

Training large language model agents on tasks at the frontier of their capabilities is key to unlocking advanced reasoning. We introduce a data synthesis approach inspired by the educational theory of the Zone of Proximal Development (ZPD), which defines this frontier as tasks an LLM cannot solve alone but can master with guidance. To operationalize this, we present the AgentFrontier Engine, an automated pipeline that synthesizes high-quality, multidisciplinary data situated precisely within the LLM's ZPD. This engine supports both continued pre-training with knowledge-intensive data and targeted post-training on complex reasoning tasks. From the same framework, we derive the ZPD Exam, a dynamic and automated benchmark designed to evaluate agent capabilities on these frontier tasks. We train AgentFrontier-30B-A3B model on our synthesized data, which achieves state-of-the-art results on demanding benchmarks like Humanity's Last Exam, even surpassing some leading proprietary agents. Our work demonstrates that a ZPD-guided approach to data synthesis offers a scalable and effective path toward building more capable LLM agents.

While Multimodal Large Language Models (MLLMs) excel at visual understanding, they often struggle in complex scenarios that require visual planning and imagination. Inspired by how humans use sketching as a form of visual thinking to develop and communicate ideas, we introduce Latent Sketchpad, a framework that equips MLLMs with an internal visual scratchpad. The internal visual representations of MLLMs have traditionally been confined to perceptual understanding. We repurpose them to support generative visual thought without compromising reasoning ability. Building on frontier MLLMs, our approach integrates visual generation directly into their native autoregressive reasoning process. It allows the model to interleave textual reasoning with the generation of visual latents. These latents guide the internal thought process and can be translated into sketch images for interpretability. To realize this, we introduce two components: a Context-Aware Vision Head autoregressively produces visual representations, and a pretrained Sketch Decoder renders these into human-interpretable images. We evaluate the framework on our new dataset MazePlanning. Experiments across various MLLMs show that Latent Sketchpad delivers comparable or even superior reasoning performance to their backbone. It further generalizes across distinct frontier MLLMs, including Gemma3 and Qwen2.5-VL. By extending model's textual reasoning to visual thinking, our framework opens new opportunities for richer human-computer interaction and broader applications. More details and resources are available on our project page: https://latent-sketchpad.github.io/.

Parallel thinking expands exploration breadth, complementing the deep exploration of information-seeking (IS) agents to further enhance problem-solving capability. However, conventional parallel thinking faces two key challenges in this setting: inefficiency from repeatedly rolling out from scratch, and difficulty in integrating long-horizon reasoning trajectories during answer generation, as limited context capacity prevents full consideration of the reasoning process. To address these issues, we propose ParallelMuse, a two-stage paradigm designed for deep IS agents. The first stage, Functionality-Specified Partial Rollout, partitions generated sequences into functional regions and performs uncertainty-guided path reuse and branching to enhance exploration efficiency. The second stage, Compressed Reasoning Aggregation, exploits reasoning redundancy to losslessly compress information relevant to answer derivation and synthesize a coherent final answer. Experiments across multiple open-source agents and benchmarks demonstrate up to 62% performance improvement with a 10--30% reduction in exploratory token consumption.

Despite rapid progress in Multi-modal Large Language Models and Large Audio-Language Models, existing audio benchmarks largely test semantics that can be recovered from text captions, masking deficits in fine-grained perceptual reasoning. We formalize audio 4D intelligence that is defined as reasoning over sound dynamics in time and 3D space, and introduce STAR-Bench to measure it. STAR-Bench combines a Foundational Acoustic Perception setting (six attributes under absolute and relative regimes) with a Holistic Spatio-Temporal Reasoning setting that includes segment reordering for continuous and discrete processes and spatial tasks spanning static localization, multi-source relations, and dynamic trajectories. Our data curation pipeline uses two methods to ensure high-quality samples. For foundational tasks, we use procedurally synthesized and physics-simulated audio. For holistic data, we follow a four-stage process that includes human annotation and final selection based on human performance. Unlike prior benchmarks where caption-only answering reduces accuracy slightly, STAR-Bench induces far larger drops (-31.5\% temporal, -35.2\% spatial), evidencing its focus on linguistically hard-to-describe cues. Evaluating 19 models reveals substantial gaps compared with humans and a capability hierarchy: closed-source models are bottlenecked by fine-grained perception, while open-source models lag across perception, knowledge, and reasoning. Our STAR-Bench provides critical insights and a clear path forward for developing future models with a more robust understanding of the physical world.

Tor 浏览器项目释出了 v15.0 版本，该版本是首个基于 Firefox ESR 140 的稳定版本，整合了 Firefox 上游版本一年来的更新，包括垂直标签页，统一搜索按钮等新功能和可用性改进。Tor 开发者称他们审查并解决了约 200 个可能对 Tor 浏览器用户的隐私和安全造成负面影响的 Bugzilla 问题，移除了被认为不具有可审计性的 AI 功能。

Fedora 发行版项目宣布释出 Fedora Linux 43。新版的主要变化包括：GNOME 桌面环境仅支持 Wayland，移除了 X11 会话支持；但 KDE 桌面环境仍然支持 X11，并为 GNOME 用户提供了继续使用 X11 的权宜方法；如果字体配置文件 fontconfig`中缺少等宽字体，会默认设置一个备用等宽字体以免出现问题；gdk-pixbuf2 使用沙盒图像加载框架 Glycin 改进安全性；Noto Color Emoji 字体使用 COLRv1 格式；Python 3.14，gcc 15.2，Golang 1.25，LLVM 21，Ruby on Rails 8.0，等等。

本周三英伟达成为全球第一家市值突破 5 万亿美元的公司，三个月前该公司市值刚刚突破 4 万亿美元，超越苹果和微软等巨头，而三年前 AI 聊天机器人 ChatGPT 推出前英伟达市值约为 4000 亿美元。作为最大的 AI 芯片供应商，英伟达是受益于这一波 AI 技术发展的最主要公司，但这也引发了 AI 泡沫的担忧。英伟达 CEO 黄仁勋周二表示他不认为世界处于 AI 泡沫之中，认为我们正在使用各种模型，并乐于为此付费。黄仁勋在 GTC 大会上表示，英伟达预计其最新芯片的出货量将达到 2000 万颗，而上一代 Hopper 的总出货量仅为 400 万颗。

根据发表在《Cell Metabolism》期刊上的一项研究，小鼠实验显示头发能在 20 天内完全再生。这一突破为未来治愈脱发带来了希望，值得注意的是小鼠的头发周期比人类短得多，因此小鼠的结果能否在人类身上再现还有待观察。研究是基于毛囊再生的内部机制。当皮肤受伤或受到轻微刺激，免疫细胞会进入皮下脂肪组织发出信号促使脂肪细胞释放单不饱和脂肪酸。脂肪酸为毛囊干细胞提供营养，使其充满活力，促进新发生长。研究人员没有采用刺激疗法，而是直接使用了一种含有这些脂肪酸的精华液。

Segue 1 是一个矮星系，仅包含少数恒星——少到无法提供防止自身外散到太空中所需的重力。与其他矮星系一样，长久以来认为一种称为暗物质的引力是主要的束缚力。 一项新研究推翻了这一假设，挑战了天文学家对矮星系的理解。Segue 1 中心的一个巨大黑洞提供了所需的引力，而不是暗物质，使恒星被其引力束缚在一起。Segue 1 距离银河系仅 75,000 光年，是银河系的近邻，黑洞的质量是太阳质量的 450,000 倍，大约是 Segue 1 中所有恒星质量总和的 10 倍。在大多数星系中，中心黑洞的质量不会超过恒星的质量。一种可能解释是，它曾经是一个有更多恒星的更大星系。然而随着时间的推移，银河系可能已经攫取了大部分恒星，仅少数遗留下来。另一种可能性是，Segue 1 类似于一类新发现的星系，称为小红点，这些星系似乎是在巨大的黑洞和很少的恒星中发展起来的。这些早期星系位于宇宙最遥远的地方而难以研究。有了 Segue 1，天文学家现在在临近处有一个能提供他们观察小红点演变过程的的天体。

澳大利亚联邦警方正与微软合作开发 AI 工具解码 Z 世代俚语和表情符号以打击网络剥削和犯罪网红（crimefluencer）。联邦警察总长 Krissy Barrett 警告，以弱势少年少女为目标的年轻网络犯罪团伙正在兴起。她称这些人是犯罪网红，动机是制造混乱和伤害他人，而大多数受害者是少女。她说，他们的动机并非出于经济利益或性满足——纯粹是为了找乐子，或是为了博取关注，没有完全意识到其行为的后果。警方已经确认 59 名犯罪网红，逮捕了其中一部分人，他们的年龄都在 17-20 岁之间。

根据美国餐饮协会（National Restaurant Association）的统计数据，接近四分之三的餐厅订单不是堂食。2019-2024 年间外卖顾客比例翻了一倍多。一项民意调查显示，41% 受访者表示外卖已成为生活方式中不可或缺的一部分。这种转变从根本上改变了餐饮业的经济模式。外卖公司会向餐厅收取 5%-30% 的佣金，除此之外餐厅还需要付出处理支付的费用、广告费和搜索排名费。Shannon Orr 在西海岸经营着一个拥有八家餐厅的餐饮集团。她旗下的一家餐厅去年外卖销售收入达到 170 万美元，其中 40 万美元流向了外卖公司。她说这家餐厅曾是她最赚钱的餐厅，但 2024 年没有盈利。

在宣布强制性的开发者注册计划后，Google 发表多则声明声称侧载（sideloading）不会消失，但这是真的吗？FOSS 应用商店 F-droid 的开发者认为 Google 的说法并不正确。开发者验证要求实际上剥夺了个人选择在自己的设备上运行哪些软件的权利。而侧载这一术语本身就是人为制造出来的，我们通常称之为“安装”，无论软件是安装在手机上还是安装在电脑上。如果要区分传统方式获取软件和通过 Google Play Store 或 Apple App Store 等中间商平台获取软件，那么可以更准确的说是“直接安装”。侧载这个词带有负面含义，仿佛象征着用户绕过了旨在保护用户安全的机制。根据维基百科的定义：侧载是从非供应商批准的 Web 来源下载应用。根据该定义，Google 声称“侧载不会消失”的说法是完全错误的。供应商——以 Android 认证设备而言就是 Google——将会对应用来源进行审核。消费者选择 Android 很大程度上是源于 Google 做出的开放计算平台的承诺，用户可以自由选择运行任何软件。但从明年开始，Google 将剥夺用户的这一权利。

根据 ProtonDB 的数据，近九成 Windows 游戏现在能在 Linux 上运行。这一进步受益于 WINE 和 Proton 翻译层开发者的努力，以及对 Steam Deck 等 Linux 掌机的兴趣。ProtonDB 将游戏分为五类：白金级游戏无需任何调整即可完美运行；金级游戏需要进行小的调整；银级游戏可玩但并不完美；Borked 级游戏完全无法运行； 铜级游戏介于银级和 Borked 级之间。数据显示，白金级新游戏数量正在增长，而 Borked 级游戏数量则在减少。很多热门游戏不支持 Linux 主要是因为反作弊软件与 Linux 的不兼容性导致的。

哈佛本科教育办公室发表报告，对成绩膨胀问题发出警告。数据显示，本科课程 60% 的成绩被评为 A。十年前这一比例只有 40%，二十年前不到 25%。其它美国精英大学，包含常春藤盟校，也面临类似的成绩膨胀问题。报告作者、哈佛大学本科生院长 Amanda Claybaugh 敦促教师减少给大多数学生打高分的做法，称此举会破坏学术文化。报告指出，成绩膨胀的原因之一是教师担心自己比其他同行更严格，会导致选课人数减少。报告还指出，哈佛学生有时也会向教授施压要求更高的分数。

今年 1 月 Python 软件基金会（PSF）向美国国家科学基金会（National Science Foundation）的 Safety, Security, and Privacy of Open Source Ecosystems 项目递交了提案，首次申请政府拨款。PSF 为此投入了大量时间和精力。数个月后该提案获得了拨款建议，但有条件，要求推行特朗普政府的反 DEI(多元化、平等及包容)政策。反 DEI 条款不仅适用于申请的拨款项目，还适用于基金会的所有活动。违反条款将会导致拨款资金被追回，即使资金已经花掉了。PSF 认为这构成了巨大的无法估量的财务风险，而 DEI 是基金会的核心价值观。PSF 宣布放弃这笔 150 万美元的政府拨款，但在经济不确定的时代它迫切需要资金，因此 PSF 呼吁用户捐款和成为支持会员（Supporting Member）。

微软与 OpenAI 达成新协议，消除了不确定性，为 OpenAI 转型为营利性企业铺平道路。微软将获得 OpenAI 27% 的股权，价值约 1350 亿美元，保留访问其技术至 2032 年，包括实现 AGI 的模型。原来控制 OpenAI 公司的基金会将持有价值约 1300 亿美元的股权。微软是 OpenAI 最大的投资者，共投资约 137.5 亿美元。根据双方的协议，一旦 OpenAI 实现 AGI，经过独立专家小组验证后，微软将不再能 OpenAI 的收入中分得一杯羹。OpenAI 新的云基础设施业务也将不再优先购买微软的 Azure 云服务，不过它承诺会额外购买 250​0 亿美元的 Azure 服务。

根据发表在 PNAS 期刊上的一项研究，2008-2010 年之间社会极化加剧，与此同时社交圈亲密朋友的人数从两人增加到了四或五人。两者之间的关联或能从根本上解释世界各地的社会日益分裂成意识形态泡泡。数十年以来的社会学研究表明，人们平均拥有大约两位密友，而密友能影响他们对重要问题的看法。但从 2008 年起，密友的人数突然增加到了四到五名。更多的密友，以及由此带来的更紧密的社交网络，导致了网络的分裂，以及最后导致社会极化。研究人员利用基于真实数据的模型发现，当网络密度增加，内部的极化是不可避免的。研究人员说，彼此联系更紧密时，人们会更频繁的遭遇不同意见。不可避免的会导致更多冲突，从而加剧社会极化。

一项研究发现，人类的生物量迁移可能达到所有野生陆地哺乳类、鸟类和陆生节肢动物总和的 40 倍以上。而另一项研究发现，野生哺乳动物的生物量自 1850 年以来已减少逾半，海洋哺乳类生物量下降尤其多——约 70%，主要源于较大物种的衰退，如蓝鲸、座头鲸、长须鲸和抹香鲸等。这些发现为全球迁移和动物生物量经时变化的构成带来了新见解。迁移性是动物的一个本质特征，通过觅食、迁徙和营养物质运输塑造生态系统。人类同样会广泛迁移，无论是步行还是借助飞机、火车和汽车等交通手段。比较生物量的迁移——定义为体重与迁移距离的乘积——能直接地衡量人类和动物活动的规模。

统计数据显示，向勒索软件组织支付赎金的受害者数量创下了新低，23% 的受害公司屈服支付了赎金，而在 2024 年第一季度这一比例是 28%，此后比例略有上升，但到了 2025 年第三季度创新低。对这一现象的一种解释是企业加大了防御力度，以及政府也施加了压力要求拒绝支付赎金，因为只要支付赎金勒索软件组织就有足够的动机继续发动攻击。勒索软件组织通常在加密受害者系统的同时窃取数据，进行双重勒索。数据显示，2025 年第三季度逾 76% 的攻击涉及数据泄露。2025 年第三季度支持的赎金平均金额和中位数分别降至 37.7 万美元和 14 万美元。Akira 和 Qilin 等勒索软件组织占到了所有有记录攻击的 44%，它们的攻击目标已经转向更有可能支付赎金的中型企业。