OrangeBot.AI Digest — 2025-08-19

LLM agents built for control. Designed for real-world use. Deployed in minutes.

Beta release of Archon OS - the knowledge and task management backbone for AI coding assistants.

Supercharge Your LLM with the Fastest KV Cache Layer

BillionMail gives you open-source MailServer, NewsLetter, Email Marketing — fully self-hosted, dev-friendly, and free from monthly fees. Join the discord: https://discord.gg/asfXzBUhZr

Implement a ChatGPT-like LLM in PyTorch from scratch, step by step

Financial data platform for analysts, quants and AI agents.

🎉 一个简约的音乐播放器，支持逐字歌词，下载歌曲，展示评论区，音乐云盘及歌单管理，音乐频谱，移动端基础适配 | 网易云音乐 | A minimalist music player

Collection of awesome LLM apps with AI Agents and RAG using OpenAI, Anthropic, Gemini and opensource models.

Useful tool to track location or mobile number

High performance self-hosted photo and video management solution.

PixiEditor is a Universal Editor for all your 2D needs

🌐 The Internet OS! Free, Open-Source, and Self-Hostable.

AWS MCP Servers — helping you get the most out of AWS, wherever you use MCP.

Reach Inbox Zero by speaking with your email & calendar

Curiosity Lens: Your Visual Agent

First Music API trained on licensed data, commercial-ready

Production grade vibe coding

Your Product Hunt Filter

Easily transcribe & translate lectures and meetings

From endless scrolling to one-click bookmarks in GPT

Your camera roll is a mess, but TinyRoll let's you clean it

Finally on Android: the world’s favorite screen time app.

Learn any language or dialect through humanlike conversation

Activate your vagus nerve with cardiac coherence

The writing app that feels like a game

Transform every travel gem from TikTok/IG into pins on a map

Get Playwright tests from a Loom video

Chord recognition and beat tracking with llm

We present Ovis2.5, a successor to Ovis2 designed for native-resolution visual perception and strong multimodal reasoning. Ovis2.5 integrates a native-resolution vision transformer that processes images at their native, variable resolutions, avoiding the degradation from fixed-resolution tiling and preserving both fine detail and global layout -- crucial for visually dense content like complex charts. To strengthen reasoning, we train the model to move beyond linear chain-of-thought and perform reflection -- including self-checking and revision. This advanced capability is exposed as an optional "thinking mode" at inference time, allowing users to trade latency for enhanced accuracy on difficult inputs. The model is trained via a comprehensive five-phase curriculum that progressively builds its skills. The process begins with foundational visual and multimodal pretraining, advances through large-scale instruction tuning, and culminates in alignment and reasoning enhancement using DPO and GRPO. To scale these upgrades efficiently, we employ multimodal data packing and hybrid parallelism, yielding a significant end-to-end speedup. We release two open-source models: Ovis2.5-9B and Ovis2.5-2B. The latter continues the "small model, big performance" philosophy of Ovis2, making it ideal for resource-constrained, on-device scenarios. On the OpenCompass multimodal leaderboard, Ovis2.5-9B averages 78.3, marking a substantial improvement over its predecessor, Ovis2-8B, and achieving state-of-the-art results among open-source MLLMs in the sub-40B parameter range; Ovis2.5-2B scores 73.9, establishing SOTA for its size. Beyond aggregate scores, Ovis2.5 achieves leading results on STEM benchmarks, exhibits strong capabilities on grounding and video tasks, and achieves open-source SOTA at its scale for complex chart analysis.

Narrative comprehension on long stories and novels has been a challenging domain attributed to their intricate plotlines and entangled, often evolving relations among characters and entities. Given the LLM's diminished reasoning over extended context and high computational cost, retrieval-based approaches remain a pivotal role in practice. However, traditional RAG methods can fall short due to their stateless, single-step retrieval process, which often overlooks the dynamic nature of capturing interconnected relations within long-range context. In this work, we propose ComoRAG, holding the principle that narrative reasoning is not a one-shot process, but a dynamic, evolving interplay between new evidence acquisition and past knowledge consolidation, analogous to human cognition when reasoning with memory-related signals in the brain. Specifically, when encountering a reasoning impasse, ComoRAG undergoes iterative reasoning cycles while interacting with a dynamic memory workspace. In each cycle, it generates probing queries to devise new exploratory paths, then integrates the retrieved evidence of new aspects into a global memory pool, thereby supporting the emergence of a coherent context for the query resolution. Across four challenging long-context narrative benchmarks (200K+ tokens), ComoRAG outperforms strong RAG baselines with consistent relative gains up to 11% compared to the strongest baseline. Further analysis reveals that ComoRAG is particularly advantageous for complex queries requiring global comprehension, offering a principled, cognitively motivated paradigm for retrieval-based long context comprehension towards stateful reasoning. Our code is publicly released at https://github.com/EternityJune25/ComoRAG

We present 4DNeX, the first feed-forward framework for generating 4D (i.e., dynamic 3D) scene representations from a single image. In contrast to existing methods that rely on computationally intensive optimization or require multi-frame video inputs, 4DNeX enables efficient, end-to-end image-to-4D generation by fine-tuning a pretrained video diffusion model. Specifically, 1) to alleviate the scarcity of 4D data, we construct 4DNeX-10M, a large-scale dataset with high-quality 4D annotations generated using advanced reconstruction approaches. 2) we introduce a unified 6D video representation that jointly models RGB and XYZ sequences, facilitating structured learning of both appearance and geometry. 3) we propose a set of simple yet effective adaptation strategies to repurpose pretrained video diffusion models for 4D modeling. 4DNeX produces high-quality dynamic point clouds that enable novel-view video synthesis. Extensive experiments demonstrate that 4DNeX outperforms existing 4D generation methods in efficiency and generalizability, offering a scalable solution for image-to-4D modeling and laying the foundation for generative 4D world models that simulate dynamic scene evolution.

We propose a novel approach to image generation by decomposing an image into a structured sequence, where each element in the sequence shares the same spatial resolution but differs in the number of unique tokens used, capturing different level of visual granularity. Image generation is carried out through our newly introduced Next Visual Granularity (NVG) generation framework, which generates a visual granularity sequence beginning from an empty image and progressively refines it, from global layout to fine details, in a structured manner. This iterative process encodes a hierarchical, layered representation that offers fine-grained control over the generation process across multiple granularity levels. We train a series of NVG models for class-conditional image generation on the ImageNet dataset and observe clear scaling behavior. Compared to the VAR series, NVG consistently outperforms it in terms of FID scores (3.30 -> 3.03, 2.57 ->2.44, 2.09 -> 2.06). We also conduct extensive analysis to showcase the capability and potential of the NVG framework. Our code and models will be released.

Large Language Models (LLMs) have delivered impressive results in language understanding, generation, reasoning, and pushes the ability boundary of multimodal models. Transformer models, as the foundation of modern LLMs, offer a strong baseline with excellent scaling properties. However, the traditional transformer architecture requires substantial computations and poses significant obstacles for large-scale training and practical deployment. In this survey, we offer a systematic examination of innovative LLM architectures that address the inherent limitations of transformers and boost the efficiency. Starting from language modeling, this survey covers the background and technical details of linear and sparse sequence modeling methods, efficient full attention variants, sparse mixture-of-experts, hybrid model architectures incorporating the above techniques, and emerging diffusion LLMs. Additionally, we discuss applications of these techniques to other modalities and consider their wider implications for developing scalable, resource-aware foundation models. By grouping recent studies into the above category, this survey presents a blueprint of modern efficient LLM architectures, and we hope this could help motivate future research toward more efficient, versatile AI systems.

Large Language Models (LLMs) are highly sensitive to subtle, non-semantic variations in prompt phrasing and formatting. In this work, we present the first systematic evaluation of 5 methods for improving prompt robustness within a unified experimental framework. We benchmark these techniques on 8 models from Llama, Qwen and Gemma families across 52 tasks from Natural Instructions dataset. Our evaluation covers robustness methods from both fine-tuned and in-context learning paradigms, and tests their generalization against multiple types of distribution shifts. Finally, we extend our analysis to GPT-4.1 and DeepSeek V3 to assess frontier models' current robustness to format perturbations. Our findings offer actionable insights into the relative effectiveness of these robustness methods, enabling practitioners to make informed decisions when aiming for stable and reliable LLM performance in real-world applications. Code: https://github.com/AIRI-Institute/when-punctuation-matters.

Multi-modal models have achieved remarkable progress in recent years. Nevertheless, they continue to exhibit notable limitations in spatial understanding and reasoning, which are fundamental capabilities to achieving artificial general intelligence. With the recent release of GPT-5, allegedly the most powerful AI model to date, it is timely to examine where the leading models stand on the path toward spatial intelligence. First, we propose a comprehensive taxonomy of spatial tasks that unifies existing benchmarks and discuss the challenges in ensuring fair evaluation. We then evaluate state-of-the-art proprietary and open-source models on eight key benchmarks, at a cost exceeding one billion total tokens. Our empirical study reveals that (1) GPT-5 demonstrates unprecedented strength in spatial intelligence, yet (2) still falls short of human performance across a broad spectrum of tasks. Moreover, we (3) identify the more challenging spatial intelligence problems for multi-modal models, and (4) proprietary models do not exhibit a decisive advantage when facing the most difficult problems. In addition, we conduct a qualitative evaluation across a diverse set of scenarios that are intuitive for humans yet fail even the most advanced multi-modal models.

Large language models (LLMs) have shown remarkable capabilities in isolated step-by-step reasoning tasks such as mathematics and programming, but their proficiency in long-horizon planning, where solutions require extended, structured sequences of interdependent actions, remains underexplored. Existing benchmarks typically assess LLMs through abstract or low-dimensional algorithmic tasks, failing to capture the complexity of realistic planning environments. We introduce HeroBench, a novel benchmark designed specifically to evaluate long-horizon planning and structured reasoning within complex RPG-inspired virtual worlds. HeroBench provides a rigorously constructed dataset of tasks covering a wide range of difficulties, a simulated environment to execute and validate agent plans, and detailed analytical tools for evaluating model performance. Tasks challenge models to formulate strategic plans, efficiently gather resources, master necessary skills, craft equipment, and defeat adversaries, reflecting practical scenarios' layered dependencies and constraints. Our extensive evaluation of 25 state-of-the-art LLMs, spanning both open-source and proprietary models, including the GPT-5 family, reveals substantial performance disparities rarely observed in conventional reasoning benchmarks. Detailed error analysis further uncovers specific weaknesses in current models' abilities to generate robust high-level plans and reliably execute structured actions. HeroBench thus not only significantly advances the evaluation of LLM reasoning but also provides a flexible, scalable foundation for future research into advanced, autonomous planning in virtual environments.

Recent advances in interactive video generations have demonstrated diffusion model's potential as world models by capturing complex physical dynamics and interactive behaviors. However, existing interactive world models depend on bidirectional attention and lengthy inference steps, severely limiting real-time performance. Consequently, they are hard to simulate real-world dynamics, where outcomes must update instantaneously based on historical context and current actions. To address this, we present Matrix-Game 2.0, an interactive world model generates long videos on-the-fly via few-step auto-regressive diffusion. Our framework consists of three key components: (1) A scalable data production pipeline for Unreal Engine and GTA5 environments to effectively produce massive amounts (about 1200 hours) of video data with diverse interaction annotations; (2) An action injection module that enables frame-level mouse and keyboard inputs as interactive conditions; (3) A few-step distillation based on the casual architecture for real-time and streaming video generation. Matrix Game 2.0 can generate high-quality minute-level videos across diverse scenes at an ultra-fast speed of 25 FPS. We open-source our model weights and codebase to advance research in interactive world modeling.

We introduce G-CUT3R, a novel feed-forward approach for guided 3D scene reconstruction that enhances the CUT3R model by integrating prior information. Unlike existing feed-forward methods that rely solely on input images, our method leverages auxiliary data, such as depth, camera calibrations, or camera positions, commonly available in real-world scenarios. We propose a lightweight modification to CUT3R, incorporating a dedicated encoder for each modality to extract features, which are fused with RGB image tokens via zero convolution. This flexible design enables seamless integration of any combination of prior information during inference. Evaluated across multiple benchmarks, including 3D reconstruction and other multi-view tasks, our approach demonstrates significant performance improvements, showing its ability to effectively utilize available priors while maintaining compatibility with varying input modalities.

We introduce AuriStream, a biologically inspired model for encoding speech via a two-stage framework inspired by the human auditory processing hierarchy. The first stage transforms raw audio into a time-frequency representation based on the human cochlea, from which we extract discrete cochlear tokens. The second stage applies an autoregressive sequence model over the cochlear tokens. AuriStream learns meaningful phoneme and word representations, and state-of-the-art lexical semantics. AuriStream shows competitive performance on diverse downstream SUPERB speech tasks. Complementing AuriStream's strong representational capabilities, it generates continuations of audio which can be visualized in a spectrogram space and decoded back into audio, providing insights into the model's predictions. In summary, we present a two-stage framework for speech representation learning to advance the development of more human-like models that efficiently handle a range of speech-based tasks.

Video relighting is a challenging yet valuable task, aiming to replace the background in videos while correspondingly adjusting the lighting in the foreground with harmonious blending. During translation, it is essential to preserve the original properties of the foreground, e.g., albedo, and propagate consistent relighting among temporal frames. In this paper, we propose Lumen, an end-to-end video relighting framework developed on large-scale video generative models, receiving flexible textual description for instructing the control of lighting and background. Considering the scarcity of high-qualified paired videos with the same foreground in various lighting conditions, we construct a large-scale dataset with a mixture of realistic and synthetic videos. For the synthetic domain, benefiting from the abundant 3D assets in the community, we leverage advanced 3D rendering engine to curate video pairs in diverse environments. For the realistic domain, we adapt a HDR-based lighting simulation to complement the lack of paired in-the-wild videos. Powered by the aforementioned dataset, we design a joint training curriculum to effectively unleash the strengths of each domain, i.e., the physical consistency in synthetic videos, and the generalized domain distribution in realistic videos. To implement this, we inject a domain-aware adapter into the model to decouple the learning of relighting and domain appearance distribution. We construct a comprehensive benchmark to evaluate Lumen together with existing methods, from the perspectives of foreground preservation and video consistency assessment. Experimental results demonstrate that Lumen effectively edit the input into cinematic relighted videos with consistent lighting and strict foreground preservation. Our project page: https://lumen-relight.github.io/

Classifier-free Guidance (CFG) is a widely used technique in modern diffusion models for enhancing sample quality and prompt adherence. However, through an empirical analysis on Gaussian mixture modeling with a closed-form solution, we observe a discrepancy between the suboptimal results produced by CFG and the ground truth. The model's excessive reliance on these suboptimal predictions often leads to semantic incoherence and low-quality outputs. To address this issue, we first empirically demonstrate that the model's suboptimal predictions can be effectively refined using sub-networks of the model itself. Building on this insight, we propose S^2-Guidance, a novel method that leverages stochastic block-dropping during the forward process to construct stochastic sub-networks, effectively guiding the model away from potential low-quality predictions and toward high-quality outputs. Extensive qualitative and quantitative experiments on text-to-image and text-to-video generation tasks demonstrate that S^2-Guidance delivers superior performance, consistently surpassing CFG and other advanced guidance strategies. Our code will be released.

Traditional multimodal learning approaches require expensive alignment pre-training to bridge vision and language modalities, typically projecting visual features into discrete text token spaces. We challenge both fundamental assumptions underlying this paradigm by proposing Inverse-LLaVA, a novel approach that eliminates alignment pre-training entirely while inverting the conventional mapping direction. Rather than projecting visual features to text space, our method maps text embeddings into continuous visual representation space and performs fusion within transformer intermediate layers. Through selective additive components in attention mechanisms, we enable dynamic integration of visual and textual representations without requiring massive image-text alignment datasets. Comprehensive experiments across nine multimodal benchmarks demonstrate nuanced performance trade-offs: Inverse-LLaVA achieves notable improvements on reasoning-intensive and cognitive tasks (MM-VET: +0.2%, VizWiz: +1.8%, ScienceQA: +0.2%, cognitive reasoning: +27.2%), while showing expected decreases in perception tasks requiring memorized visual-text associations (celebrity recognition: -49.5%, OCR: -21.3%). These results provide the first empirical evidence that alignment pre-training is not necessary for effective multimodal learning, particularly for complex reasoning tasks. Our work establishes the feasibility of a new paradigm that reduces computational requirements by 45%, challenges conventional wisdom about modality fusion, and opens new research directions for efficient multimodal architectures that preserve modality-specific characteristics. Our project website with code and additional resources is available at https://inverse-llava.github.io.

Reinforcement Learning from Verifiable Rewards (RLVR) has emerged as a powerful paradigm for enhancing Large Language Models (LLMs), exemplified by the success of OpenAI's o-series. In RLVR, rewards are derived from verifiable signals-such as passing unit tests in code generation or matching correct answers in mathematical reasoning. While effective, this requirement largely confines RLVR to domains with automatically checkable outcomes. To overcome this, we extend the RLVR paradigm to open-ended tasks by integrating rubric-based rewards, where carefully designed rubrics serve as structured, model-interpretable criteria for automatic scoring of subjective outputs. We construct, to our knowledge, the largest rubric reward system to date, with over 10,000 rubrics from humans, LLMs, or a hybrid human-LLM collaboration. Implementing rubric-based RL is challenging; we tackle these issues with a clear framework and present an open-sourced Qwen-30B-A3B model with notable gains: 1) With only 5K+ samples, our system improves by +5.2% on open-ended benchmarks (especially humanities), outperforming a 671B DeepSeek-V3 model by +2.4%, while preserving general and reasoning abilities. 2) Our method provides fine-grained stylistic control, using rubrics as anchors to mitigate the "AI-like" tone and produce more human-like, expressive responses. We share key lessons in rubric construction, data selection, and training, and discuss limitations and future releases.

去年初震惊整个开源和网络安全社区的 XZ 后门事件并没有离我们而去。在 XZ 事件中，攻击者 Jia Tan（化名，未必是华人）潜伏 XZ Utils 项目长达两年多时间，最终获得信任成为项目的共同维护者，之后他或他们利用其权限悄悄在 xz-utils 包中植入了一个复杂的后门。在恶意版本大规模传播前，后门就被发现了，因此没有造成大问题。但 Binarly REsearch 的调查发现，在攻击期间构建的部分 Docker 镜像仍然包含有 XZ Utils 后门。安全研究人员从 DockerHub 上发现了超过 35 个含有后门的镜像。虽然数字不多，但研究人员只扫描了一小部分镜像，而且只针对 Debian 发行版，其它发行版如 Fedora 和 OpenSUSE 情况未知。

英国芯片设计公司 Arm 的大股东日本软银集团向陷入困境的英特尔公司投资 20 亿美元，入股芯片巨人。与此同时，美国政府也在与英特尔公司谈判入股 10%。软银将以每股 23 美元的价格收购英特尔的普通股，将持有约 2% 的英特尔股份。软银今年二季度是四年来首次盈利，英特尔现任 CEO 陈立武曾是软银董事。英特尔曾是最强大的半导体公司，如今已经落后英伟达和 AMD 等公司。它目前正在重组裁员。

MIT 发表报告《The GenAI Divide: State of AI in Business 2025》称，95% 的企业生成式 AI 试验失败了。虽然企业纷纷整合大模型，但只有 5% 的 AI 试点项目实现了收入的快速增长，大多数项目停滞，对损益表几乎没有产生可衡量的影响。研究基于对 150 名高管的访谈，350 名员工的调查以及对 300 个公开的 AI 部署项目的分析。报告主要作者 Aditya Challapally 解释说，95% 的企业部署生成式 AI 表现不佳不是因为大模型的质量，而是因为 ChatGPT 之类的通用工具因其灵活性对个人用户非常有用，但它们无法从工作流程中学习或适应工作流程，因此企业部署停滞不前。逾半数的生成式 AI 预算是投入在销售和营销工具上面，但研究显示后台业务自动化投资回报率最高——在后台部署 AI 有助于消除业务流程外包、削减外部营力成本和简化运营。

在美国登月计划受挫之际，中国的登月计划过去几个月则取得了显著进展，中国有可能在美国之前登陆月球。中国载人航天工程办公室于 8 月 6 日成功测试了 26 吨的揽月着陆器高保真模型。揽月月面着陆器是登月舱和推进舱组成，主要用于环月轨道和月球表面间的航天员运输，可搭载 2 名航天员往返，中国国家航天局在声明中再次确认计划在 2030 年前登月。中国还在上周完成了新一代载人运载火箭长征十号的首次系留点火试验，6 月份完成了梦舟载人飞船零高度逃逸试验。

英格兰儿童事务专员 Rachel de Souza 表示，政府需要阻止儿童使用 VPN 绕过色情网站的年龄验证。她表示该漏洞需要堵上，呼吁 VPN 服务也要验证年龄。在 PornHub、Reddit 和 X 等网站开始对访问成人内容的英国用户验证年龄后，VPN 成为英国苹果 App Store 下载量最多的应用。对于限制 VPN 的评论，英国科学、创新和科技部发言人回应称，VPN 是成人使用的合法工具，目前没有禁止的计划。但如果平台故意向儿童推销 VPN 作为绕过年龄验证方法，将面临严厉执法和巨额罚款。

天文学家利用 ALMA 观测 12P/Pons-Brooks 彗星，发现其中的水具有与地球海洋几乎相同的同位素组成比例。这一发现进一步支持了「彗星可能在年轻地球上带来水与部分生命所需分子原料」的假说。一般认为，地球上的水在数十亿年前由彗星、小行星及陨石撞击带来。然而，过去观测多数彗星的水同位素比例与地球差异明显。本次结果则提供了迄今最有力的证据，显示至少部分彗星携带的水与地球水有相同的化学「指纹」。在这项研究中，科学家首次绘制出 12P 彗星的彗发（包围彗核的气体）中普通水（H₂O）与重水（HDO，即水分子里的一个氢被带有额外中子的氘取代）的空间分布。观测时间点为 12P 彗星接近太阳之际，结合毫米／次毫米波与红外线数据，精确测定了氘氢比（D/H）。透过绘制 H₂O 与 HDO 的分布，研究团队判断这些气体确实来自彗核内的冰冻物质，而非在彗发中由化学作用或其他过程生成。测得的 D/H 比值为 (1.71 ± 0.44) × 10⁻⁴，这个数值在所有已观测的彗星中偏低，并与地球海洋完全一致。

上海长征医院骨科医生历经 3 小时成功为一例遭遇罕见严重颈椎骨折脱位、跨度之大几乎可以被称为“身首离断”的患者实施了复位固定手术。这名患者因此前颈部遭受机械臂重击，当场高位截瘫、心跳骤停，经紧急心肺复苏才勉强恢复微弱生命体征。影像学检查结果几乎为患者宣判了“死刑”——其颈椎发生了极其罕见的大跨度离断式脱位，脊髓严重挫伤、关键神经血管结构撕裂。患者于 6 月 18 日进行的手术，目前生命体征稳定，已脱离呼吸机大概半个月。患者双下肢功能永久损伤无法挽回，上肢功能在逐渐锻炼康复中。

在自然界中，多数雄性果蝇通过快速振动翅膀来创造声音模式或“求爱之歌”来求爱。然而 Drosophila subobscura 演化出了一种非常不同的策略：雄性提供食物，并在求偶期间将其作为礼物送给雌性。这种行为在近亲物种中并不存在，比如 D. melanogaster（黑腹果蝇）。两种果蝇大约在 3000 万到 3500 万年前分化。它们都有一个叫做“fruitless”或简称“fru”的基因，控制雄性的求偶行为，但它们使用不同的策略——一个物种唱歌，另一个物种送礼物。研究人员发现了这种差异的原因：在送礼物的果蝇（D. subbobscura）中，产生胰岛素的神经元与大脑中的求爱控制中心相连，而在唱歌的果蝇（D. melanogaster）中，这些细胞保持不连接。通过打开产生胰岛素的神经元中的一个基因，研究小组成功地让黑腹果蝇完成了它以前从未做过的送礼物求偶。

由 NASA 与法国国家空间研究中心（CNES）联合研制的地表水和海洋地形卫星（SWOT）所提供的数据，正助力改进海啸预报模型，为沿海社区带来福祉。当地震或水下滑坡等扰动的强度足以让从海底到海面的水体发生位移时，就会引发海啸。当地时间 7 月 30 日 11 时 25 分，俄罗斯堪察加半岛近海发生 8.8 级地震并引发海啸，SWOT 在地震发生约 70 分钟后记录下了此次海啸。SWOT 数据从多个维度呈现了堪察加地震引发的海啸前沿情况。测量数据包括超过 45 厘米的波高——在高亮轨迹中以红色显示，以及海啸前沿的形状和传播方向。在视觉图像中从西南向东北延伸的高亮区域所示的 SWOT 数据，与美国国家海洋和大气管理局（NOAA）海啸研究中心制作的海啸预报模型形成对比。将二者进行比对，有助于预报人员验证模型，确保准确性。

JetBrains 发布了第八次年度 Python 开发者调查报告。调查由 JetBrains 和 Python 软件基金会合作完成。结果显示，86% 的受访者表示 Python 是他们的主要语言，用于写程序、构建应用和创建 API 等；50% 受访者只有不到两年的专业编程经验，39% 的受访者只有不到两年的 Python 经验；83% 的 Python 开发者使用旧版本（v 3.12 或以下版本），主要理由是当前使用版本已能满足所有需求，报告指出从 Python 3.11 升级到 Python 3.13 能将代码速度提高 11% 内存占用减少 10-15%；51% 的受访者将 Python 用于数据探索和处理，最常用的工具是 Pandas 和 NumPy。

根据 Google Cloud 和 Harris Poll 的一项联合调查，87% 的游戏开发者在工作流程中使用 AI 智能体（AI agent）。这次调查于 2025 年 6 月底到 7 月初展开，询问了美国、韩国、挪威、芬兰和瑞典的 615 名游戏开发者关于 AI 在游戏行业现状以及未来发展方向等问题。结果显示，受访者普遍认同 AI 对创意工作、商业环境和内部工作流程产生积极影响，逾九成受访者表示 AI 正帮助应对一系列挑战，包括推动创新和提升玩家体验。97% 的受访者表示，生成式 AI 正重塑游戏行业，95% 的受访者表示 AI 正在减少工作流程中的重复性任务，94% 的受访者表示 AI 正推动创新。47% 的受访者表示，AI 加速游戏测试和机制平衡；45% 的受访者表示 AI 有助于游戏内容的本地化和翻译；44% 的受访者表示，AI 改进了代码生成和脚本支持。89% 的开发者认为 AI 的融入正在改变玩家的期望，37% 的受访者表示发现玩家正在寻求更真实的体验。63% 的开发者对数据所有权表达了担忧。

天文学家上个月报告发现了已知第三颗星际天体，前两颗分别是 'Oumuamua、彗星 2I/Borisov，第三颗 3I/ATLAS 也是星际彗星。哈佛大学天文学家提议使用现有的 NASA 探测器与这颗彗星会合，一探这颗神秘星际天体的面貌。根据他们的分析，NASA 朱诺号（Juno）探测器能于 2026 年 3 月 16 日在 3I/ATLAS 近距离飞过木星时对其进行拦截。

大众汽车正为其电动汽车 ID.3 推出提升引擎功率的订阅模式，如果司机想要从标准的 201 马力提升到 228 马力，他们需要每月支付约 16.50 英镑或每年 165 英镑，或一次性支付 649 英镑的终身费用。该费用是与汽车绑定而不是与司机绑定，如果司机卖掉了 ID.3，购买了另一辆 ID.3，那么如果想要获得更高的动力将需要再次支付。大众汽车辩解称，司机是在为更强的运动体验付费，他们无需为此购买更高引擎功率的型号。提升功率无需机械方面进行改变，主要是调整下软件，可能是修改下布尔值。

美国佐治亚大学开展的研究表明，水星这颗太阳系最内侧的行星，自 45 亿年前诞生以来，因热量流失在不断缩小，其半径已累计缩短 2.7—5.6 公里如同冷却后的芝士蛋糕表面会皲裂，水星岩石外壳也布满“皱纹”，科学家称之为冲断层。这些因地壳收缩形成的褶皱，成为测量行星“缩水”程度的天然标尺。此前研究认为，水星半径收缩幅度在 1—7 公里之间，但不同断层数据集得出的结论差异显著。研究团队通过聚焦最大断层的收缩效应，进而推演整体变化。通过对 5934 条、653 条及 100 条断层三个不同规模数据集的分析，新方法均得出水星半径缩短 2—3.5 公里的稳定估值。综合其他冷却效应后，他们最终将收缩范围精确锁定在 2.7—5.6 公里。

人类的语言是如何演化的？研究人员试图通过人类近亲黑猩猩找到答案。根据发表在《PLOS Biology》期刊上的论文，研究人员报告黑猩猩幼儿是通过母亲和母系亲属学会声音和视觉交流模式。这与人类幼儿从照顾者学习交流模式类似，这种能力可能可至少追溯到远古时代。研究人员在乌干达西部的 Kibale 国家公园考察了一个有大约 60 名黑猩猩的群体，它们会分成很多个小群体，其中至少包含一名母亲及其后代。小群体的数量在 2 到 9 只之间，它们都与母猩猩有血缘关系。对记录的视频和音频的分析显示，幼年黑猩猩跟随母亲学习了各种交流模式。