OrangeBot.AI Digest — 2025-08-26

Collection of extracted System Prompts from popular chatbots like ChatGPT, Claude & Gemini

开源白板工具（SaaS），一体化白板，包含思维导图、流程图、自由画等。All in one open-source whiteboard tool with mind, flowchart, freehand and etc.

Verifiers for LLM Reinforcement Learning

"DeepCode: Open Agentic Coding (Paper2Code & Text2Web & Text2Backend)"

All Algorithms implemented in Java

Open Source Alternative to NotebookLM / Perplexity, connected to external sources such as Search Engines, Slack, Linear, Jira, ClickUp, Confluence, Notion, YouTube, GitHub, Discord and more. Join our discord: https://discord.gg/ejRNvftDp9

The Fully Customizable Desktop Environment for Windows 10/11.

🤱🏻 Turn any webpage into a desktop app with Rust. 🤱🏻 利用 Rust 轻松构建轻量级多端桌面应用

坚持分享 GitHub 上高质量、有趣实用的开源技术教程、开发者工具、编程网站、技术资讯。A list cool, interesting projects of GitHub.

A Model Context Protocol (MCP) Gateway & Registry. Serves as a central management point for tools, resources, and prompts that can be accessed by MCP-compatible LLM applications. Converts REST API endpoints to MCP, composes virtual MCP servers with added security and observability, and converts between protocols (stdio, SSE, Streamable HTTP).

Collection of Summer 2026 tech internships!

OpenProject is the leading open source project management software.

The Cursor for Designers • An Open-Source AI-First Design tool • Visually build, style, and edit your React App with AI

Official code repo for the O'Reilly Book - "Hands-On Large Language Models"

Split SaaS revenue with partners, sell without headaches

Draw a fish, and watch it swim with the world

Auto-replies for Instagram DMs, comments, and stories

One AI suite to replace all your design tools

Build your idea from prompt to product to pipeline

The agentic code review experience.

From words to workflows in seconds

Your pocket-sized AI pet companion

Tracking AI usage and cost by customer

10x your vibe-coding workflow writing visual annotations

Turn applications into interviews with confidence.

Tasks. Focus. Routines. All in one simple app

Turn your readme into beautiful documentation website

Instantly create & deploy MCP servers from any API spec

The fastest way to learn German articles

We introduce InternVL 3.5, a new family of open-source multimodal models that significantly advances versatility, reasoning capability, and inference efficiency along the InternVL series. A key innovation is the Cascade Reinforcement Learning (Cascade RL) framework, which enhances reasoning through a two-stage process: offline RL for stable convergence and online RL for refined alignment. This coarse-to-fine training strategy leads to substantial improvements on downstream reasoning tasks, e.g., MMMU and MathVista. To optimize efficiency, we propose a Visual Resolution Router (ViR) that dynamically adjusts the resolution of visual tokens without compromising performance. Coupled with ViR, our Decoupled Vision-Language Deployment (DvD) strategy separates the vision encoder and language model across different GPUs, effectively balancing computational load. These contributions collectively enable InternVL3.5 to achieve up to a +16.0\% gain in overall reasoning performance and a 4.05times inference speedup compared to its predecessor, i.e., InternVL3. In addition, InternVL3.5 supports novel capabilities such as GUI interaction and embodied agency. Notably, our largest model, i.e., InternVL3.5-241B-A28B, attains state-of-the-art results among open-source MLLMs across general multimodal, reasoning, text, and agentic tasks -- narrowing the performance gap with leading commercial models like GPT-5. All models and code are publicly released.

Despite the promising progress of recent autoregressive models in text-to-image (T2I) generation, their ability to handle multi-attribute and ambiguous prompts remains limited. To address these limitations, existing works have applied chain-of-thought (CoT) to enable stage-aware visual synthesis and employed reinforcement learning (RL) to improve reasoning capabilities. However, most models provide reward signals only at the end of the generation stage. This monolithic final-only guidance makes it difficult to identify which stages contribute positively to the final outcome and may lead to suboptimal policies. To tackle this issue, we propose a Visual-Chain of Guidance (Visual-CoG) paradigm consisting of three stages: semantic reasoning, process refining, and outcome evaluation, with stage-aware rewards providing immediate guidance throughout the image generation pipeline. We further construct a visual cognition benchmark, VisCog-Bench, which comprises four subtasks to evaluate the effectiveness of semantic reasoning. Comprehensive evaluations on GenEval, T2I-CompBench, and the proposed VisCog-Bench show improvements of 15%, 5%, and 19%, respectively, demonstrating the superior performance of the proposed Visual-CoG. We will release all the resources soon.

Text-to-3D generation approaches have advanced significantly by leveraging pretrained 2D diffusion priors, producing high-quality and 3D-consistent outputs. However, they often fail to produce out-of-domain (OOD) or rare concepts, yielding inconsistent or inaccurate results. To this end, we propose MV-RAG, a novel text-to-3D pipeline that first retrieves relevant 2D images from a large in-the-wild 2D database and then conditions a multiview diffusion model on these images to synthesize consistent and accurate multiview outputs. Training such a retrieval-conditioned model is achieved via a novel hybrid strategy bridging structured multiview data and diverse 2D image collections. This involves training on multiview data using augmented conditioning views that simulate retrieval variance for view-specific reconstruction, alongside training on sets of retrieved real-world 2D images using a distinctive held-out view prediction objective: the model predicts the held-out view from the other views to infer 3D consistency from 2D data. To facilitate a rigorous OOD evaluation, we introduce a new collection of challenging OOD prompts. Experiments against state-of-the-art text-to-3D, image-to-3D, and personalization baselines show that our approach significantly improves 3D consistency, photorealism, and text adherence for OOD/rare concepts, while maintaining competitive performance on standard benchmarks.

We propose T2I-ReasonBench, a benchmark evaluating reasoning capabilities of text-to-image (T2I) models. It consists of four dimensions: Idiom Interpretation, Textual Image Design, Entity-Reasoning and Scientific-Reasoning. We propose a two-stage evaluation protocol to assess the reasoning accuracy and image quality. We benchmark various T2I generation models, and provide comprehensive analysis on their performances.

Recent advances in Large Language Models (LLMs) have underscored the potential of Reinforcement Learning (RL) to facilitate the emergence of reasoning capabilities. Despite the encouraging results, a fundamental dilemma persists as RL improvement relies on learning from high-quality samples, yet the exploration for such samples remains bounded by the inherent limitations of LLMs. This, in effect, creates an undesirable cycle in which what cannot be explored cannot be learned. In this work, we propose Rubric-Scaffolded Reinforcement Learning (RuscaRL), a novel instructional scaffolding framework designed to break the exploration bottleneck for general LLM reasoning. Specifically, RuscaRL introduces checklist-style rubrics as (1) explicit scaffolding for exploration during rollout generation, where different rubrics are provided as external guidance within task instructions to steer diverse high-quality responses. This guidance is gradually decayed over time, encouraging the model to internalize the underlying reasoning patterns; (2) verifiable rewards for exploitation during model training, where we can obtain robust LLM-as-a-Judge scores using rubrics as references, enabling effective RL on general reasoning tasks. Extensive experiments demonstrate the superiority of the proposed RuscaRL across various benchmarks, effectively expanding reasoning boundaries under the best-of-N evaluation. Notably, RuscaRL significantly boosts Qwen-2.5-7B-Instruct from 23.6 to 50.3 on HealthBench-500, surpassing GPT-4.1. Furthermore, our fine-tuned variant on Qwen3-30B-A3B-Instruct achieves 61.1 on HealthBench-500, outperforming leading LLMs including OpenAI-o3.

Reasoning is a core capability of large language models, yet understanding how they learn and perform multi-step reasoning remains an open problem. In this study, we explore how different architectures and training methods affect model multi-step reasoning capabilities within a cellular automata framework. By training on state sequences generated with random Boolean functions for random initial conditions to exclude memorization, we demonstrate that most neural architectures learn to abstract the underlying rules. While models achieve high accuracy in next-state prediction, their performance declines sharply if multi-step reasoning is required. We confirm that increasing model depth plays a crucial role for sequential computations. We demonstrate that an extension of the effective model depth with recurrence, memory, and test-time compute scaling substantially enhances reasoning capabilities.

Vision-Language Models (VLMs) demonstrate impressive performance in understanding visual content with language instruction by converting visual input to vision tokens. However, redundancy in vision tokens results in the degenerated inference efficiency of VLMs. While many algorithms have been proposed to reduce the number of vision tokens, most of them apply only unimodal information (i.e., vision/text) for pruning and ignore the inherent multimodal property of vision-language tasks. Moreover, it lacks a generic criterion that can be applied to different modalities. To mitigate this limitation, in this work, we propose to leverage both vision and text tokens to select informative vision tokens by the criterion of coverage. We first formulate the subset selection problem as a maximum coverage problem. Afterward, a subset of vision tokens is optimized to cover the text tokens and the original set of vision tokens, simultaneously. Finally, a VLM agent can be adopted to further improve the quality of text tokens for guiding vision pruning. The proposed method MMTok is extensively evaluated on benchmark datasets with different VLMs. The comparison illustrates that vision and text information are complementary, and combining multimodal information can surpass the unimodal baseline with a clear margin. Moreover, under the maximum coverage criterion on the POPE dataset, our method achieves a 1.87x speedup while maintaining 98.7% of the original performance on LLaVA-NeXT-13B. Furthermore, with only four vision tokens, it still preserves 87.7% of the original performance on LLaVA-1.5-7B. These results highlight the effectiveness of coverage in token selection.

Multi-agent systems built upon large language models (LLMs) have demonstrated remarkable capabilities in tackling complex compositional tasks. In this work, we apply this paradigm to the paper-to-poster generation problem, a practical yet time-consuming process faced by researchers preparing for conferences. While recent approaches have attempted to automate this task, most neglect core design and aesthetic principles, resulting in posters that require substantial manual refinement. To address these design limitations, we propose PosterGen, a multi-agent framework that mirrors the workflow of professional poster designers. It consists of four collaborative specialized agents: (1) Parser and Curator agents extract content from the paper and organize storyboard; (2) Layout agent maps the content into a coherent spatial layout; (3) Stylist agents apply visual design elements such as color and typography; and (4) Renderer composes the final poster. Together, these agents produce posters that are both semantically grounded and visually appealing. To evaluate design quality, we introduce a vision-language model (VLM)-based rubric that measures layout balance, readability, and aesthetic coherence. Experimental results show that PosterGen consistently matches in content fidelity, and significantly outperforms existing methods in visual designs, generating posters that are presentation-ready with minimal human refinements.

Benchmarks shape progress in AI research. A useful benchmark should be both difficult and realistic: questions should challenge frontier models while also reflecting real-world usage. Yet, current paradigms face a difficulty-realism tension: exam-style benchmarks are often made artificially difficult with limited real-world value, while benchmarks based on real user interaction often skew toward easy, high-frequency problems. In this work, we explore a radically different paradigm: assessing models on unsolved questions. Rather than a static benchmark scored once, we curate unsolved questions and evaluate models asynchronously over time with validator-assisted screening and community verification. We introduce UQ, a testbed of 500 challenging, diverse questions sourced from Stack Exchange, spanning topics from CS theory and math to sci-fi and history, probing capabilities including reasoning, factuality, and browsing. UQ is difficult and realistic by construction: unsolved questions are often hard and naturally arise when humans seek answers, thus solving them yields direct real-world value. Our contributions are threefold: (1) UQ-Dataset and its collection pipeline combining rule-based filters, LLM judges, and human review to ensure question quality (e.g., well-defined and difficult); (2) UQ-Validators, compound validation strategies that leverage the generator-validator gap to provide evaluation signals and pre-screen candidate solutions for human review; and (3) UQ-Platform, an open platform where experts collectively verify questions and solutions. The top model passes UQ-validation on only 15% of questions, and preliminary human verification has already identified correct answers among those that passed. UQ charts a path for evaluating frontier models on real-world, open-ended challenges, where success pushes the frontier of human knowledge. We release UQ at https://uq.stanford.edu.

We present Hermes 4, a family of hybrid reasoning models that combine structured, multi-turn reasoning with broad instruction-following ability. We describe the challenges encountered during data curation, synthesis, training, and evaluation, and outline the solutions employed to address these challenges at scale. We comprehensively evaluate across mathematical reasoning, coding, knowledge, comprehension, and alignment benchmarks, and we report both quantitative performance and qualitative behavioral analysis. To support open research, all model weights are published publicly at https://huggingface.co/collections/NousResearch/hermes-4-collection-68a731bfd452e20816725728

Recent advancements in large vision-language models (VLMs) have primarily focused on English, with limited attention given to other languages. To address this gap, we introduce MEENA (also known as PersianMMMU), the first dataset designed to evaluate Persian VLMs across scientific, reasoning, and human-level understanding tasks. Our dataset comprises approximately 7,500 Persian and 3,000 English questions, covering a wide range of topics such as reasoning, mathematics, physics, diagrams, charts, and Persian art and literature. Key features of MEENA include: (1) diverse subject coverage spanning various educational levels, from primary to upper secondary school, (2) rich metadata, including difficulty levels and descriptive answers, (3) original Persian data that preserves cultural nuances, (4) a bilingual structure to assess cross-linguistic performance, and (5) a series of diverse experiments assessing various capabilities, including overall performance, the model's ability to attend to images, and its tendency to generate hallucinations. We hope this benchmark contributes to enhancing VLM capabilities beyond English.

Compositional visual reasoning has emerged as a key research frontier in multimodal AI, aiming to endow machines with the human-like ability to decompose visual scenes, ground intermediate concepts, and perform multi-step logical inference. While early surveys focus on monolithic vision-language models or general multimodal reasoning, a dedicated synthesis of the rapidly expanding compositional visual reasoning literature is still missing. We fill this gap with a comprehensive survey spanning 2023 to 2025 that systematically reviews 260+ papers from top venues (CVPR, ICCV, NeurIPS, ICML, ACL, etc.). We first formalize core definitions and describe why compositional approaches offer advantages in cognitive alignment, semantic fidelity, robustness, interpretability, and data efficiency. Next, we trace a five-stage paradigm shift: from prompt-enhanced language-centric pipelines, through tool-enhanced LLMs and tool-enhanced VLMs, to recently minted chain-of-thought reasoning and unified agentic VLMs, highlighting their architectural designs, strengths, and limitations. We then catalog 60+ benchmarks and corresponding metrics that probe compositional visual reasoning along dimensions such as grounding accuracy, chain-of-thought faithfulness, and high-resolution perception. Drawing on these analyses, we distill key insights, identify open challenges (e.g., limitations of LLM-based reasoning, hallucination, a bias toward deductive reasoning, scalable supervision, tool integration, and benchmark limitations), and outline future directions, including world-model integration, human-AI collaborative reasoning, and richer evaluation protocols. By offering a unified taxonomy, historical roadmap, and critical outlook, this survey aims to serve as a foundational reference and inspire the next generation of compositional visual reasoning research.

Speech tokenizers serve as foundational components for speech language models, yet current designs exhibit several limitations, including: 1) dependence on multi-layer residual vector quantization structures or high frame rates, 2) reliance on auxiliary pre-trained models for semantic distillation, and 3) requirements for complex two-stage training processes. In this work, we introduce the Text-aware Diffusion Transformer Speech Codec (TaDiCodec), a novel approach designed to overcome these challenges. TaDiCodec employs end-to-end optimization for quantization and reconstruction through a diffusion autoencoder, while integrating text guidance into the diffusion decoder to enhance reconstruction quality and achieve optimal compression. TaDiCodec achieves an extremely low frame rate of 6.25 Hz and a corresponding bitrate of 0.0875 kbps with a single-layer codebook for 24 kHz speech, while maintaining superior performance on critical speech generation evaluation metrics such as Word Error Rate (WER), speaker similarity (SIM), and speech quality (UTMOS). Notably, TaDiCodec employs a single-stage, end-to-end training paradigm, and obviating the need for auxiliary pre-trained models. We also validate the compatibility of TaDiCodec in language model based zero-shot text-to-speech with both autoregressive modeling and masked generative modeling, demonstrating its effectiveness and efficiency for speech language modeling, as well as a significantly small reconstruction-generation gap. We will open source our code and model checkpoints. Audio samples are are available at https:/tadicodec.github.io/. We release code and model checkpoints at https:/github.com/HeCheng0625/Diffusion-Speech-Tokenizer.

Surface reconstruction has been widely studied in computer vision and graphics. However, existing surface reconstruction works struggle to recover accurate scene geometry when the input views are extremely sparse. To address this issue, we propose MeshSplat, a generalizable sparse-view surface reconstruction framework via Gaussian Splatting. Our key idea is to leverage 2DGS as a bridge, which connects novel view synthesis to learned geometric priors and then transfers these priors to achieve surface reconstruction. Specifically, we incorporate a feed-forward network to predict per-view pixel-aligned 2DGS, which enables the network to synthesize novel view images and thus eliminates the need for direct 3D ground-truth supervision. To improve the accuracy of 2DGS position and orientation prediction, we propose a Weighted Chamfer Distance Loss to regularize the depth maps, especially in overlapping areas of input views, and also a normal prediction network to align the orientation of 2DGS with normal vectors predicted by a monocular normal estimator. Extensive experiments validate the effectiveness of our proposed improvement, demonstrating that our method achieves state-of-the-art performance in generalizable sparse-view mesh reconstruction tasks. Project Page: https://hanzhichang.github.io/meshsplat_web

This paper investigates the limitations of the normalization in attention mechanisms. We begin with a theoretical framework that enables the identification of the model's selective ability and the geometric separation involved in token selection. Our analysis includes explicit bounds on distances and separation criteria for token vectors under softmax scaling. Through experiments with pre-trained GPT-2 model, we empirically validate our theoretical results and analyze key behaviors of the attention mechanism. Notably, we demonstrate that as the number of selected tokens increases, the model's ability to distinguish informative tokens declines, often converging toward a uniform selection pattern. We also show that gradient sensitivity under softmax normalization presents challenges during training, especially at low temperature settings. These findings advance current understanding of softmax-based attention mechanism and motivate the need for more robust normalization and selection strategies in future attention architectures.

马斯克(Elon Musk')旗下 AI 初创公司 xAI 起诉苹果和 OpenAI，指控两家公司非法合谋阻碍 AI 领域的竞争。由于 xAI 的 AI 聊天机器人 Grok 在苹果 App Store 应用排行榜中排名低于 OpenAI 的 ChatGPT，马斯克此前通过社交媒体公开抨击了苹果和 OpenAI。xAI 在德州联邦法庭起诉苹果和 OpenAI 合谋打压 AI 领域的竞争对手。OpenAI 发言人表示此举符合马斯克一贯的骚扰模式。

根据发表在《nature climate change》期刊上的一项研究，暴露在热浪下会加速衰老。研究人员分析了 24,922 名台湾成年人 15 年（2008–2022）的健康数据，发现暴露在两年的热浪下可能会加速生物衰老 8-12 天。论文主要作者、香港大学助理教授 Cui Guo 表示，数字虽小但意义重大，因为全世界的热浪已经持续了几十年。今天世界各地都面临创记录的高温。

英特尔周一警告，美国政府控制 10% 股份可能会在其它国家引发负面反应。美国政府上周与英特尔达成协议，将 88.7 亿美元的联邦芯片补贴变为投资，换取芯片巨人 10% 股份。然而英特尔 76% 的收入来自国际市场，其中中国的收入占到了英特尔总收入的 29%。英特尔在递交到证券监管机构的证券申报文件中警告，在美国政府持有部分股份之后，外国政府可能会英特尔施加额外监管，可能会阻止其它国家向其提供补贴，此举也可能限制其战略灵活性。

苹果指控前 Apple Watch 员工 Chen Shi 博士为新雇主 Oppo 窃取了其智能手表的商业机密，而 Oppo 则否认有任何不当行为。苹果在诉讼文件中称，加盟 Oppo 之前 Chen Shi 参加了数十场 Apple Watch 团队技术人员的会议，了解其工作，并从一个受保护的 Box 文件夹中下载了 63 份文档，传输到 U 盘。Chen Shi 曾向 Oppo 发送消息表示自己正在尽可能的收集信息。离职前他使用苹果发的 MacBook 笔记本电脑搜索“如何清除 MacBook 数据”，“他人能否看到我打开了共享驱动器上的文件？”等关键词。Chen Shi 担任过苹果公司传感器系统架构师，目前领导着 Oppo 的一个传感技术团队。在给苹果的辞职信中，Shi 表示他是因为个人和家庭原因而离职。通过提供给 Shi 的工作用 iPhone 手机上留下的信息，苹果发现 Oppo 鼓励 Shi 收集苹果的商业机密。

Android 正以安全的名义变得日益封闭。Google 宣布将验证所有 Android 应用开发者的身份，不再局限于在 Play Store 应用商店发布应用的开发者。从明年开始，Google 将屏蔽未经身份验证的开发者的 Android 应用的侧载（sideload）。Google 给出的理由是安全。Google 称，它从 2023 年要起求所有 Google Play 应用开发者验证身份，此后恶意软件和欺诈大幅下降。于是搜索巨人得出一个结论：验证所有开发者的身份将有助于增强 Android 生态系统的安全性。

Twitch 打击机器人账号，部分主播频道的观看者减少了一半甚至更多。TwitchTracker 和 StreamsCharts 等的分析显示，知名主播如 Tectone 和 LydiaViolet 的观看人数约减少了一半，Asmongold 的观看人数比平时减少了 1.5 万到 2 万。Mira 的观看人数从平时的 2,000 多人降至 150-200 人。YourRageGaming 的观看人数从 20,000-30,000 的峰值降至 4,000-7,000 人。Agent00、Lacy 和 Plaqueboymax 都出现了类似程度的下滑。机器人刷屏（viewbotting）多年来一直困扰着 Twitch。机器人账号会人为夸大观看人数以提高频道排名。更高的排名意味着更多真实观众会发现该直播频道。更多的观众意味着更好的赞助协议和更高的广告费率。经济动机显而易见。

美国太空军的神秘轨道飞行器 X-37B 将测试量子惯性传感器。传统的 GPS 导航在很多条件下会无法使用，比如外太空和水下，在地面上也容易受到干扰或欺骗或根本无法使用（如战争中）。量子惯性传感器可能彻底改变飞行器、飞机、舰船和潜艇在 GPS 无法使用环境中导航的方式。在绝对零度附近，原子的行为类似于波，会同时存在于多种状态，这两个特性是量子惯性传感器的核心。相比传统惯性传感器，量子惯性传感器的灵敏度高出几个数量级，无需外部参考即可实现长时间高精度导航。

如果你想减肥，最好在家里做饭，多接触下食物气味。根据发表在《nature communications 》期刊上的一项小鼠研究，短暂接触食物气味会引发饥饿感，但长时间接触会抑制食物摄入，原因是连接嗅觉和食欲的大脑回路。研究人员在侧海马下托(vSub)发现了一组神经元会被食物气味激活，激活的神经元会接收来自嗅球 (OB) 的兴奋性输入，将谷氨酸能投射到腹内侧下丘脑 (VMH)。激活 OB → vSub → VMH 回路会抑制食物摄入减轻体重，但抑制该回路则会消除对食物摄入的影响。

普渡大学研究显示，一次典型的高温美发过程可能释放超百亿纳米颗粒。研究表明，卷发棒、直发器等造型工具温度超过 150℃ 时，护发产品中的环状硅氧烷等低挥发性成分会迅速挥发、成核并生成大量新纳米颗粒，大多数颗粒直径小于 100 纳米。一次 10-20 分钟的高温美发过程，可能让人体吸入超百亿纳米颗粒。这些颗粒会直接沉积在肺部，其中肺泡区的沉积剂量最高。研究人员建议，尽量减少或避免使用需配合高温工具的产品，特别是标榜“耐热”的免洗型发胶、发霜、发凝胶。如果必须使用，应确保室内通风良好。

英伟达已通知半导体后工序大企业美国 Amkor Technology 和韩国三星电子停止涉及 H20 的相关业务。英伟达也向台湾鸿海精密工业提出了类似请求。英伟达之所以对 H20 的生产持犹豫态度，是因为中国市场的需求预期正在迅速恶化。中国相关部门于 7 月对 H20 存在安全方面的漏洞提出了担忧。对于英伟达来说，H20 本应是开拓中国市场的王牌产品。在困境之下，英伟达正在摸索的方案是投放新型芯片。H20 基于上一代 Hopper 架构，英伟达据报道正基于最新一代的 Blackwell 架构开发面向中国市场的半导体。

Bluesky 拒绝遵守美国密西西比州一项要求所有社交媒体用户验证年龄的新法律，决定屏蔽密西西比州用户访问其服务。Bluesky 通过官方博客解释说，作为一个小团队它没有足够的资源执行法律所要求的重大技术修改，同时对该法律的范围及其对隐私的影响表达了担忧。密西西比州的 HB 1126 法案要求社交网络在所有用户访问前验证年龄。美国最高法院法官周四决定阻止一项紧急上诉，上诉原本会阻止法律在面临挑战期间生效。Bluesky 不得不决定如何合规。HB 1126 不只是要求用户访问成人内容前验证年龄，而是要求所有用户验证年龄。意味着社交网络如 Bluesky 必须验证每位用户的年龄，而 18 岁以下用户访问服务还需要征得其父母同意。Bluesky 指出，不遵守规定的可能处罚非常严厉——最高每位用户 1 万美元。Bluesky 强调该法律超出了保护儿童安全的范围。

谷神星是小行星带最大的天体，NASA 黎明号（Dawn）探测器在 2015 年进入谷神星的公转轨道，为科学家提供小行星的近距离的观测资料。研究人员通过重建谷神星内部热模型和化学模型，模拟了谷神星内部温度和成分随时间的变化。他们发现大约 25 亿年前谷神星内部放射性元素衰变产生的热能不仅足以使地下水库存在，还能不断供应热水给地下水库。而热水中含有溶解的气体，气体便从岩石核心的变质岩中向上流动，相当类似地球深海的海底热泉。研究估计谷神星最有可能的宜居时期是它形成后的 5 亿到 20 亿年之间，也就是大约 25 亿到 40 亿年前。尽管谷神星以前对于微生物来说可能很宜居，现在的谷神星早已耗尽了它的热能。不但水大量结冰，残留的液体也已经变成了浓缩的盐水。

反疫苗的美国卫生部长小肯尼迪（Robert F. Kennedy Jr）要求《Annals of Internal Medicine》期刊撤回丹麦研究人员发表的一篇论文《Aluminum-Adsorbed Vaccines and Chronic Diseases in Childhood: A Nationwide Cohort Study》，对丹麦过去逾 20年来出生的 120 万名儿童的分析发现，疫苗中的铝化合物并不显著增加罹患自身免疫性、过敏性或神经发育障碍的风险。小肯尼迪对研究结论提出了质疑。疫苗怀疑论者曾宣称铝化合物 与自闭症等儿童疾病发病率上升有关，而 WHO 等机构早就驳斥过此类观点。以盐形式存在的铝广泛用于疫苗，没有证据表明疫苗中少量的铝会引起严重的副作用。对于小肯尼迪的要求，期刊表示无意撤稿。Retraction Watch 联合创始人 Ivan Oransky 指出，公共卫生官员很少要求撤稿，小肯尼迪此举是想要科学期刊屈服于他的意志。

新西兰空管系统上周末因软件故障罢工一小时，干扰了机场的正常运作，有五架飞机在空中盘旋，四架飞机无法起飞。新西兰唯一的空管服务商 Airways 表示问题是因为软件故障导致飞行数据无法在系统之间传输。Airways CEO James Young 表示，在发现问题之后，空中交通管制员立即采取了措施，飞机要么在地面等待，要么在空中等待。Airways 的空管系统有备份系统，但 Young 表示无法即时切换到备份系统，验证飞行信息数据需要时间。故障持续了大约一个小时，期间在空中盘旋的飞机有两架继续飞行，三架重返了起飞地。

数学家张益唐称他是因为政治气候从美国回到中国。张益唐是在今年六月离开加州圣巴巴拉，受聘于中山大学香港高等研究院，在大湾区定居和工作。张证明了存在无穷多对间隙小于 7000 万的相邻素数对，在数学史上第一次实质性推进解决著名数论难题“孪生素数猜想”，并在与黎曼猜想有关的朗道-西格尔零点猜想上取得重要进展。他表示有很多华裔学者和教授回到了中国。他说自己所处的数学领域没有受到多少政治气候的影响，但计算机、芯片或任何与军工相关的研究人员需要小心。他称，数学，尤其是理论数学，一大优势是展开研究不必局限于特定地点。