OrangeBot.AI Digest — 2025-07-22

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Graphical User Interface (GUI) grounding maps natural language instructions to precise interface locations for autonomous interaction. Current reinforcement learning approaches use binary rewards that treat elements as hit-or-miss targets, creating sparse signals that ignore the continuous nature of spatial interactions. Motivated by human clicking behavior that naturally forms Gaussian distributions centered on target elements, we introduce GUI Gaussian Grounding Rewards (GUI-G^2), a principled reward framework that models GUI elements as continuous Gaussian distributions across the interface plane. GUI-G^2 incorporates two synergistic mechanisms: Gaussian point rewards model precise localization through exponentially decaying distributions centered on element centroids, while coverage rewards assess spatial alignment by measuring the overlap between predicted Gaussian distributions and target regions. To handle diverse element scales, we develop an adaptive variance mechanism that calibrates reward distributions based on element dimensions. This framework transforms GUI grounding from sparse binary classification to dense continuous optimization, where Gaussian distributions generate rich gradient signals that guide models toward optimal interaction positions. Extensive experiments across ScreenSpot, ScreenSpot-v2, and ScreenSpot-Pro benchmarks demonstrate that GUI-G^2, substantially outperforms state-of-the-art method UI-TARS-72B, with the most significant improvement of 24.7% on ScreenSpot-Pro. Our analysis reveals that continuous modeling provides superior robustness to interface variations and enhanced generalization to unseen layouts, establishing a new paradigm for spatial reasoning in GUI interaction tasks.

Large language models have recently evolved from fluent text generation to advanced reasoning across diverse domains, giving rise to reasoning language models. Among these domains, mathematical reasoning serves as a representative benchmark as it requires precise multi-step logic and abstract reasoning, which can be generalized to other tasks. While closed-source RLMs such as GPT-o3 demonstrate impressive reasoning capabilities, their proprietary nature limits transparency and reproducibility. Although many open-source projects aim to close this gap, most of them lack sufficient openness by omitting critical resources such as datasets and detailed training configurations, which hinders reproducibility. To contribute toward greater transparency in RLM development, we introduce the MiroMind-M1 series, a set of fully open-source RLMs built on the Qwen-2.5 backbone that match or exceed the performance of existing open-source RLMs. Specifically, our models are trained in two stages: SFT on a carefully curated corpus of 719K math-reasoning problems with verified CoT trajectories, followed by RLVR on 62K challenging and verifiable problems. To enhance the robustness and efficiency of the RLVR process, we introduce Context-Aware Multi-Stage Policy Optimization, an algorithm that integrates length-progressive training with an adaptive repetition penalty to encourage context-aware RL training. Our model achieves state-of-the-art or competitive performance and superior token efficiency among Qwen-2.5-based open-source 7B and 32B models on the AIME24, AIME25, and MATH benchmarks. To facilitate reproducibility, we release the complete stack: models (MiroMind-M1-SFT-7B, MiroMind-M1-RL-7B, MiroMind-M1-RL-32B); datasets (MiroMind-M1-SFT-719K, MiroMind-M1-RL-62K); and all training and evaluation configurations. We hope these resources will support further research and foster community advancement.

Recent advances in large reasoning models highlight Reinforcement Learning with Verifiable Rewards (RLVR) as a promising method for enhancing AI's capabilities, particularly in solving complex logical tasks. However, it remains unclear whether RLVR truly expands a model's reasoning boundary or merely amplifies high-reward outputs that the base model already knows for improved precision. This study presents a theoretical and empirical investigation that provides fresh insights into the potential limits of RLVR. First, we offer a new theoretical perspective that RLVR is constrained by the base model's support-unable to sample solutions with zero initial probability-and operates as a conservative reweighting mechanism that may restrict the discovery of entirely original solutions. We also identify an entropy-reward tradeoff: while RLVR reliably enhances precision, it may progressively narrow exploration and potentially overlook correct yet underrepresented solutions. Extensive empirical experiments validate that while RLVR consistently improves pass@1, the shrinkage of empirical support generally outweighs the expansion of empirical support under larger sampling budgets, failing to recover correct answers that were previously accessible to the base model. Interestingly, we also observe that while RLVR sometimes increases token-level entropy, resulting in greater uncertainty at each generation step, answer-level entropy declines, indicating that these seemingly more uncertain paths ultimately converge onto a smaller set of distinct answers. Taken together, these findings reveal potential limits of RLVR in extending reasoning horizons. Breaking this invisible leash may require future algorithmic innovations such as explicit exploration mechanisms or hybrid strategies that seed probability mass into underrepresented solution regions.

Recent advances in generative modeling enable image editing assistants that follow natural language instructions without additional user input. Their supervised training requires millions of triplets: original image, instruction, edited image. Yet mining pixel-accurate examples is hard. Each edit must affect only prompt-specified regions, preserve stylistic coherence, respect physical plausibility, and retain visual appeal. The lack of robust automated edit-quality metrics hinders reliable automation at scale. We present an automated, modular pipeline that mines high-fidelity triplets across domains, resolutions, instruction complexities, and styles. Built on public generative models and running without human intervention, our system uses a task-tuned Gemini validator to score instruction adherence and aesthetics directly, removing any need for segmentation or grounding models. Inversion and compositional bootstrapping enlarge the mined set by approximately 2.2x, enabling large-scale high-fidelity training data. By automating the most repetitive annotation steps, the approach allows a new scale of training without human labeling effort. To democratize research in this resource-intensive area, we release NHR-Edit: an open dataset of 358k high-quality triplets. In the largest cross-dataset evaluation, it surpasses all public alternatives. We also release Bagel-NHR-Edit, an open-source fine-tuned Bagel model, which achieves state-of-the-art metrics in our experiments.

The advent of Large Language Model (LLM)-powered agents has revolutionized artificial intelligence by enabling solutions to complex, open-ended tasks through web-based information-seeking (IS) capabilities. The scarcity of high-quality training data has limited the development of IS agents. Existing approaches typically adopt an information-driven paradigm that first collects web data and then generates questions based on the retrieval. However, this may lead to inconsistency between information structure and reasoning structure, question and answer. To mitigate, we propose a formalization-driven IS data synthesis framework WebShaper to construct a dataset. WebShaper systematically formalizes IS tasks through set theory. Central to the formalization is the concept of Knowledge Projections (KP), which enables precise control over reasoning structure by KP operation compositions. During synthesis, we begin by creating seed tasks, then use a multi-step expansion process. At each step, an agentic Expander expands the current formal question more complex with retrieval and validation tools based on our formalization. We train our model on the synthesized dataset. Experiment results demonstrate that WebShaper achieves state-of-the-art performance among open-sourced IS agents on GAIA and WebWalkerQA benchmarks.

Recent advances in 3D neural representations and instance-level editing models have enabled the efficient creation of high-quality 3D content. However, achieving precise local 3D edits remains challenging, especially for Gaussian Splatting, due to inconsistent multi-view 2D part segmentations and inherently ambiguous nature of Score Distillation Sampling (SDS) loss. To address these limitations, we propose RoMaP, a novel local 3D Gaussian editing framework that enables precise and drastic part-level modifications. First, we introduce a robust 3D mask generation module with our 3D-Geometry Aware Label Prediction (3D-GALP), which uses spherical harmonics (SH) coefficients to model view-dependent label variations and soft-label property, yielding accurate and consistent part segmentations across viewpoints. Second, we propose a regularized SDS loss that combines the standard SDS loss with additional regularizers. In particular, an L1 anchor loss is introduced via our Scheduled Latent Mixing and Part (SLaMP) editing method, which generates high-quality part-edited 2D images and confines modifications only to the target region while preserving contextual coherence. Additional regularizers, such as Gaussian prior removal, further improve flexibility by allowing changes beyond the existing context, and robust 3D masking prevents unintended edits. Experimental results demonstrate that our RoMaP achieves state-of-the-art local 3D editing on both reconstructed and generated Gaussian scenes and objects qualitatively and quantitatively, making it possible for more robust and flexible part-level 3D Gaussian editing. Code is available at https://janeyeon.github.io/romap.

We report our recent progress towards building generalist robot policies, the development of GR-3. GR-3 is a large-scale vision-language-action (VLA) model. It showcases exceptional capabilities in generalizing to novel objects, environments, and instructions involving abstract concepts. Furthermore, it can be efficiently fine-tuned with minimal human trajectory data, enabling rapid and cost-effective adaptation to new settings. GR-3 also excels in handling long-horizon and dexterous tasks, including those requiring bi-manual manipulation and mobile movement, showcasing robust and reliable performance. These capabilities are achieved through a multi-faceted training recipe that includes co-training with web-scale vision-language data, efficient fine-tuning from human trajectory data collected via VR devices, and effective imitation learning with robot trajectory data. In addition, we introduce ByteMini, a versatile bi-manual mobile robot designed with exceptional flexibility and reliability, capable of accomplishing a wide range of tasks when integrated with GR-3. Through extensive real-world experiments, we show GR-3 surpasses the state-of-the-art baseline method, pi_0, on a wide variety of challenging tasks. We hope GR-3 can serve as a step towards building generalist robots capable of assisting humans in daily life.

Video Object Segmentation (VOS) is a core task in computer vision, requiring models to track and segment target objects across video frames. Despite notable advances with recent efforts, current techniques still lag behind human capabilities in handling drastic visual variations, occlusions, and complex scene changes. This limitation arises from their reliance on appearance matching, neglecting the human-like conceptual understanding of objects that enables robust identification across temporal dynamics. Motivated by this gap, we propose Segment Concept (SeC), a concept-driven segmentation framework that shifts from conventional feature matching to the progressive construction and utilization of high-level, object-centric representations. SeC employs Large Vision-Language Models (LVLMs) to integrate visual cues across diverse frames, constructing robust conceptual priors. During inference, SeC forms a comprehensive semantic representation of the target based on processed frames, realizing robust segmentation of follow-up frames. Furthermore, SeC adaptively balances LVLM-based semantic reasoning with enhanced feature matching, dynamically adjusting computational efforts based on scene complexity. To rigorously assess VOS methods in scenarios demanding high-level conceptual reasoning and robust semantic understanding, we introduce the Semantic Complex Scenarios Video Object Segmentation benchmark (SeCVOS). SeCVOS comprises 160 manually annotated multi-scenario videos designed to challenge models with substantial appearance variations and dynamic scene transformations. In particular, SeC achieves an 11.8-point improvement over SAM 2.1 on SeCVOS, establishing a new state-of-the-art in concept-aware video object segmentation.

We introduce Being-H0, a dexterous Vision-Language-Action model (VLA) trained on large-scale human videos. Existing VLAs struggle with complex manipulation tasks requiring high dexterity and generalize poorly to novel scenarios and tasks, primarily due to their reliance on synthetic data with significant sim-to-real gaps or teleoperated demonstrations lacking scale and diversity. To address this data bottleneck, we propose leveraging human hands as a foundation manipulator, capitalizing on the rich dexterity and scalability present in web data. Our approach centers on physical instruction tuning, a novel training paradigm that combines large-scale VLA pretraining from human videos, physical space alignment for 3D reasoning, and post-training adaptation for robotic tasks. Additionally, we introduce a part-level motion tokenization method which achieves millimeter-level reconstruction accuracy to model precise hand trajectories for action learning. To support our proposed paradigm, we further develop a comprehensive data curation pipeline that integrates heterogeneous sources -- including motion capture, VR, and RGB-only videos -- into a large-scale dataset with millions of motion-based instructional instances. We empirically show the excellence of Being-H0 in hand motion generation and instruction following, and it also scales well with model and data sizes. Importantly, we observe the expected gains of Being-H0 in real-world robotic manipulation as physical instruction tuning is applied. More details are available at https://beingbeyond.github.io/Being-H0.

Reinforcement Learning with Verifiable Rewards (RLVR) has become an effective post-training method for improving the reasoning abilities of Large Language Models (LLMs), mainly by shaping higher-order behaviors such as reflection and planning. However, previous RLVR algorithms often apply uniform training signals to all tokens, without considering the different roles of low-entropy knowledge-related tokens and high-entropy reasoning-related tokens. Some recent methods try to separate these token types by gradient masking or asynchronous updates, but these approaches may break semantic dependencies in the model output and hinder effective learning. In this work, we propose Archer, an entropy-aware RLVR approach with dual-token constraints and synchronous updates. Specifically, our method applies weaker KL regularization and higher clipping thresholds to reasoning tokens to encourage exploration, while using stronger constraints on knowledge tokens to maintain factual knowledge. Experimental results on several mathematical reasoning and code generation benchmarks show that our approach significantly outperforms previous RLVR methods, reaching or exceeding state-of-the-art performance among models of comparable size. The code is available at https://github.com/wizard-III/ArcherCodeR.

We construct evaluation tasks where extending the reasoning length of Large Reasoning Models (LRMs) deteriorates performance, exhibiting an inverse scaling relationship between test-time compute and accuracy. Our evaluation tasks span four categories: simple counting tasks with distractors, regression tasks with spurious features, deduction tasks with constraint tracking, and advanced AI risks. We identify five distinct failure modes when models reason for longer: 1) Claude models become increasingly distracted by irrelevant information; 2) OpenAI o-series models resist distractors but overfit to problem framings; 3) models shift from reasonable priors to spurious correlations; 4) all models show difficulties in maintaining focus on complex deductive tasks; and 5) extended reasoning may amplify concerning behaviors, with Claude Sonnet 4 showing increased expressions of self-preservation. These findings suggest that while test-time compute scaling remains promising for improving model capabilities, it may inadvertently reinforce problematic reasoning patterns. Our results demonstrate the importance of evaluating models across diverse reasoning lengths to identify and address these failure modes in LRMs.

Human intelligence requires correctness and robustness, with the former being foundational for the latter. In video understanding, correctness ensures the accurate interpretation of visual content, and robustness maintains consistent performance in challenging conditions. Despite advances in video large language models (video LLMs), existing benchmarks inadequately reflect the gap between these models and human intelligence in maintaining correctness and robustness in video interpretation. We introduce the Video Thinking Test (Video-TT), to assess if video LLMs can interpret real-world videos as effectively as humans. Video-TT reflects genuine gaps in understanding complex visual narratives, and evaluates robustness against natural adversarial questions. Video-TT comprises 1,000 YouTube Shorts videos, each with one open-ended question and four adversarial questions that probe visual and narrative complexity. Our evaluation shows a significant gap between video LLMs and human performance.

3D Gaussian splatting (3DGS) has shown its detailed expressive ability and highly efficient rendering speed in the novel view synthesis (NVS) task. The application to inverse rendering still faces several challenges, as the discrete nature of Gaussian primitives makes it difficult to apply geometry constraints. Recent works introduce the signed distance field (SDF) as an extra continuous representation to regularize the geometry defined by Gaussian primitives. It improves the decomposition quality, at the cost of increasing memory usage and complicating training. Unlike these works, we introduce a discretized SDF to represent the continuous SDF in a discrete manner by encoding it within each Gaussian using a sampled value. This approach allows us to link the SDF with the Gaussian opacity through an SDF-to-opacity transformation, enabling rendering the SDF via splatting and avoiding the computational cost of ray marching.The key challenge is to regularize the discrete samples to be consistent with the underlying SDF, as the discrete representation can hardly apply the gradient-based constraints (\eg Eikonal loss). For this, we project Gaussians onto the zero-level set of SDF and enforce alignment with the surface from splatting, namely a projection-based consistency loss. Thanks to the discretized SDF, our method achieves higher relighting quality, while requiring no extra memory beyond GS and avoiding complex manually designed optimization. The experiments reveal that our method outperforms existing Gaussian-based inverse rendering methods. Our code is available at https://github.com/NK-CS-ZZL/DiscretizedSDF.

The rapid rise of Large Language Models (LLMs)-based intelligent agents underscores the need for robust, scalable evaluation frameworks. Existing methods rely on static benchmarks and labor-intensive data collection, limiting practical assessment. We introduce \oursystemname, an open-source Model Context Protocol (MCP)-based framework that automates end-to-end task generation and deep evaluation of LLM agents across diverse domains. MCPEval standardizes metrics, seamlessly integrates with native agent tools, and eliminates manual effort in building evaluation pipelines. Empirical results across five real-world domains show its effectiveness in revealing nuanced, domain-specific performance. We publicly release MCPEval https://github.com/SalesforceAIResearch/MCPEval to promote reproducible and standardized LLM agent evaluation.

Spoken Language Models (SLMs) are designed to take speech inputs and produce spoken responses. However, current SLMs lack the ability to perform an internal, unspoken thinking process before responding. In contrast, humans typically engage in complex mental reasoning internally, enabling them to communicate ideas clearly and concisely. Thus, integrating an unspoken thought process into SLMs is highly desirable. While naively generating a complete chain-of-thought (CoT) reasoning before starting to talk can enable thinking for SLMs, this induces additional latency for the speech response, as the CoT reasoning can be arbitrarily long. To solve this issue, we propose Stitch, a novel generation method that alternates between the generation of unspoken reasoning chunks and spoken response chunks. Since the audio duration of a chunk of spoken response is much longer than the time to generate the tokens in a chunk of spoken response, we use the remaining free time to generate the unspoken reasoning tokens. When a chunk of audio is played to the user, the model continues to generate the next unspoken reasoning chunk, achieving simultaneous thinking and talking. Remarkably, Stitch matches the latency of baselines that cannot generate unspoken CoT by design while outperforming those baselines by 15% on math reasoning datasets; Stitch also performs equally well on non-reasoning datasets as those baseline models. Some animations and demonstrations are on the project page: https://d223302.github.io/STITCH.

Arch Linux 项目发出安全警告，7 月 16 日晚 8 点左右（UTC+2）一个恶意软件包上传到了 Arch User Repository(AUR)，几个小时后同一位用户又上传了两个恶意包，这些软件包会安装来自同一个 GitHub 库的脚本，该脚本被识别为 RAT（远程访问木马）。三个恶意软件包都与 Firefox 或其分支相关： librewolf-fix-bin，firefox-patch-bin，zen-browser-patched-bin。开发者建议如果安装了这些恶意软件包，立即清除并采取安全防御措施。Reddit 用户随后报告了更多的恶意 AUR 包，相关软件包在被举报之后迅速移除了。

OpenAI 本应在上周发布自 GPT-2 以来首个开放权重模型，但 CEO Sam Altman 以安全审查的理由推迟了发布。美国迄今发布的性能最出色的开放模型是 Meta 的 Llama 4，除此之外微软发布了 Phi-4 14B，Google 发布了最多 270 亿参数的多模态模型 Gemma3。相比之下，中国的大模型明显比美国更为开放性能也更为出色。DeepSeek 发布了有 6710 亿个参数的 R1 模型；阿里巴巴发布了一系列通义千问模型 QwQ、Qwen3-235B-A22B 和 30B-A3B；MiniMax 在 Apache 2.0 下发布了有 4560 亿个参数的推理模型 M1，其上下文窗口一百万 token；百度开源了参数规模 470 亿到 4240 亿的文心模型；华为开源了盘古模型；北京月之暗面发布了 1 万亿参数的 Kimi 2 模型。美国最先进的模型都是闭源私有的，而中国最先进的模型开放了权重，公开了技术文档等细节。

加拿大公民 Harald Herchen 因涉嫌在台湾太鲁阁国家公园谋杀妻子、加州山景城教师 Alice Ku 而被判向其父母赔偿 2360 万美元。两人是在 2017 年 10 月秘密结婚，2019 年 11 月 29 日 Alice Ku 在游玩国家公园后失踪。Herchen 声称失踪前他送妻子去了火车站，但手机信号塔数据显示他们的手机直接回到了酒店。Herchen 还声称 Alice Ku 与年轻英俊的导游私奔了。她的一封电邮似乎可以证明他是清白的。但根据向 Google 发去的传票，Google 提供的 IP 证据显示这封邮件是从 Herchen 下榻酒店的 WiFi 发送出去的。Herchen 在旅游期间还发生了手部骨折，他对骨折的原因不同时间给出了不同的说法。陪审团裁决他需要对其妻子的死亡负责。由于谋杀发生在台湾，与美国没有引渡协议，因此美国无法起诉他，只能施加罚款。

2019 年离婚、自新冠疫情以来基本上一直住在游轮上的 Valve 联合创始人 Gabe Newell 罕见的接受了一位 YouTube 主播 Zalkar Saliev 的采访，谈论了他的个人生活。62 岁的 Newell 称他在卧室工作一周工作七天。他说，自己起床、工作，潜水，然后再工作，再潜水或去健身，然后再继续工作。他说自己不是被迫加班，而是做自己感觉有趣的东西。他的工作内容包括了 AI、Steam、研究气溶胶病原体检测装置、脑机接口等等。他控制了 Valve 50.1% 的股份，净资产大约 100 亿美元。

KDE 项目官方博客宣布了 KDE Plasma 6.5 的一项重大视觉更新：窗口支持圆角了。Plasma 6.5 预计将于 2025 年 10 月 21 日释出。开发者称，窗口圆角是用户期盼已久的功能，甚至社区有第三方插件 kde-rounded-corners 提供圆角支持。官方支持意味着对第三方方案需求的减少。在 Plasma 6.5 中，窗口圆角将默认启用，但为喜欢旧外观的用户提供了一个选项。

一项旨在预防线粒体 DNA 疾病遗传的开创性体外受精技术（IVF）——原核移植，已成功帮助 8 名婴儿健康出生。这些婴儿共有四男四女，其中一对为同卵双胞胎。他们由 7 名携带高风险线粒体 DNA 突变的女性所生，但均未表现出任何线粒体疾病迹象。该技术通过将母亲受精卵中的核 DNA，转移到一个健康捐赠者去核的卵子中，从而避免将母亲线粒体中的致病突变遗传给下一代。由此产生的胚胎，携带了父母的核 DNA 和捐赠者的线粒体DNA，因此被称为“三亲婴儿”。线粒体疾病，由线粒体中的基因突变引发，可能导致肌肉无力、癫痫、发育迟缓、器官衰竭乃至死亡。尽管常规体外受精检测可识别多数突变，但很多时候依然存在不确定性。这是“三亲婴儿”相关技术得以出现的原因。

在上周举行的 RISC-V 中国峰会上，英伟达宣布 CUDA 软件将支持 RISC-V 处理器。随着数据中心市场对 RISC-V 架构处理器的兴趣日益增长，英伟达为其 CUDA 软件加入 RISC-V 支持并不太出人意料。CUDA 目前主要支持 x86_64 和 AArch64 系统。英伟达的竞争对手 AMD 的内核计算驱动 AMDKFD 以及用户空间组件 ROCm 都支持在 RISC-V 上构建，AMDKFD/ROCm 甚至支持龙芯的 LoongArch 处理器。

2011 年 311 大地震以及福岛核事故之后，日本暂停了国内所有核电站的运营，甚至一度考虑退役现有的核电站。现在日本关西电力公司准备启动新一代核电站的建设，将在美浜核电站（福井县美浜町）的用地内开始地质调查工作。这是 14 年以来日本首次有核电站的新建或扩建计划进入具体化阶段。此举也标志着日本政府为实现脱碳目标而视为关键手段的核电利用开始迈出实质性步伐。日本上一次新建核电站是在 2009 年启用的北海道电力泊核电站 3 号机组。此次关西电力设想建设的是被认为具有较高安全性的“创新型轻水反应堆”等新一代核电站。

微软释出紧急补丁缓解正被利用的 Sharepoint 0day 漏洞。漏洞编号 CVE-2025-53770，攻击者正利用该漏洞攻击世界各地的政府机构和企业，安全专家警告有数以万计的 Sharepoint 服务器面临风险。安全公司已经跟踪到了数十起利用该漏洞的网络入侵事件，非营利机构 Center for Internet Security 称已经向一百个存在漏洞的机构发出了安全警告。Sharepoint 服务器通常被用于连接 Outlook、Teams 等核心服务，它遭到入侵之后会导致敏感信息泄漏。

Debian 发布团队宣布 Debian 13.0 "Trixie"计划于 8 月 9 日释出，7 月 27 日完全冻结。Debian Trixie 代表了为期两年的开发历程，内核采用 Linux 6.12 LTS，包含桌面环境 GNOME 48 、GCC 14.2 编译器、Python 3.13 等大量软件更新。Debian Trixie 将首次正式支持 64 位 RISC-V 架构。

对数据库 Dimensions 的分析发现，与 AI 相关的研究论文数量已从 2000 年的不到 8500 篇增长到 2024 年的 5.7 万多篇。2000 年，中国学者仅发表了 671 篇 AI 论文，但到 2024 年，他们发表了 23695 篇与 AI 相关的论文，超过了美国（6378篇）、英国（2747篇）和欧盟（10055篇）的总和。中国产生的海量AI论文也推动了创纪录的专利申请。2024 年中国研究人员提交了 35423 项与 AI 相关的专利申请，是美国、英国、加拿大、日本、韩国5国提交的专利申请总数（2678项）的 13 倍多。研究还显示，中国的 AI研 究正变得越来越独立。过去几年中，美国、英国和欧盟的科学家与中国学者共同撰写论文的频率比他们彼此间合著的频率更高。但在 4 个地区中，中国学者的国际合作率最低。随着中国庞大的 AI 研究队伍的成熟，国际合作可能会进一步减少。研究发现，中国拥有约 3 万名各个年龄段的 AI 研究人员，而美国约有 1 万名。中国的 AI 研究队伍也明显更年轻。

国际研究团队首次确定了太阳以外的恒星周围开始形成行星的时刻，这是人类首次观察到行星系统形成的早期阶段，并为我们探索自身太阳系的起源提供全新视角。这颗诞生中的行星系统围绕着一颗名为 HOPS 315 的原恒星运转，HOPS 315 距离我们约 1,300 光年，与新生的太阳类似。在太阳系中，最早在地球目前绕太阳位置附近凝结的固体物质被发现藏在古老的陨石中。天文学家对这些原始岩石进行年代测定，以确定太阳系形成的起始时间。这些陨石富含一氧化硅(SiO) 的晶体矿物，可以在年轻行星盘的极高温度下凝结。随着时间的推移，这些新凝结的固体会结合在一起，随着它们的体积和质量的增加，为行星的形成播下了种子。太阳系中第一批几千米大小的行星，最终发展成像地球或木星核心这样的行星，正是在这些晶体矿物凝结后形成的。天文学家在新的发现中，找到了这些热矿物在 HOPS-315 周围的圆盘中开始凝结的证据。研究结果显示，SiO 以气态存在于这颗宝宝恒星周围，也存在于这些结晶矿物中，这表示它才刚开始凝固。研究人员表示这个过程从未在原行星盘，甚至在我们太阳系以外的任何地方出现过。