OrangeBot.AI Digest — 2025-07-08

What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers?

🌐 WebAgent for Information Seeking bulit by Tongyi Lab: WebWalker & WebDancer & WebSailor https://arxiv.org/pdf/2507.02592

YouTube Music Desktop App bundled with custom plugins

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

24 channel, 100Msps logic analyzer hardware and software

Anthropic's Interactive Prompt Engineering Tutorial

🚀 High-performance distributed object storage for MinIO alternative.

openpilot is an operating system for robotics. Currently, it upgrades the driver assistance system on 300+ supported cars.

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A curated list of awesome computer vision resources

macOS inside a Docker container.

A Collection of application ideas which can be used to improve your coding skills.

This repository provides tutorials and implementations for various Generative AI Agent techniques, from basic to advanced. It serves as a comprehensive guide for building intelligent, interactive AI systems.

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Large Language Models (LLMs) have become an essential infrastructure for Artificial General Intelligence (AGI), yet their lack of well-defined memory management systems hinders the development of long-context reasoning, continual personalization, and knowledge consistency.Existing models mainly rely on static parameters and short-lived contextual states, limiting their ability to track user preferences or update knowledge over extended periods.While Retrieval-Augmented Generation (RAG) introduces external knowledge in plain text, it remains a stateless workaround without lifecycle control or integration with persistent representations.Recent work has modeled the training and inference cost of LLMs from a memory hierarchy perspective, showing that introducing an explicit memory layer between parameter memory and external retrieval can substantially reduce these costs by externalizing specific knowledge. Beyond computational efficiency, LLMs face broader challenges arising from how information is distributed over time and context, requiring systems capable of managing heterogeneous knowledge spanning different temporal scales and sources. To address this challenge, we propose MemOS, a memory operating system that treats memory as a manageable system resource. It unifies the representation, scheduling, and evolution of plaintext, activation-based, and parameter-level memories, enabling cost-efficient storage and retrieval. As the basic unit, a MemCube encapsulates both memory content and metadata such as provenance and versioning. MemCubes can be composed, migrated, and fused over time, enabling flexible transitions between memory types and bridging retrieval with parameter-based learning. MemOS establishes a memory-centric system framework that brings controllability, plasticity, and evolvability to LLMs, laying the foundation for continual learning and personalized modeling.

Learning high-quality text representations is fundamental to a wide range of NLP tasks. While encoder pretraining has traditionally relied on Masked Language Modeling (MLM), recent evidence suggests that decoder models pretrained with Causal Language Modeling (CLM) can be effectively repurposed as encoders, often surpassing traditional encoders on text representation benchmarks. However, it remains unclear whether these gains reflect an inherent advantage of the CLM objective or arise from confounding factors such as model and data scale. In this paper, we address this question through a series of large-scale, carefully controlled pretraining ablations, training a total of 30 models ranging from 210 million to 1 billion parameters, and conducting over 15,000 fine-tuning and evaluation runs. We find that while training with MLM generally yields better performance across text representation tasks, CLM-trained models are more data-efficient and demonstrate improved fine-tuning stability. Building on these findings, we experimentally show that a biphasic training strategy that sequentially applies CLM and then MLM, achieves optimal performance under a fixed computational training budget. Moreover, we demonstrate that this strategy becomes more appealing when initializing from readily available pretrained CLM models (from the existing LLM ecosystem), reducing the computational burden needed to train best-in-class encoder models. We release all project artifacts at https://hf.co/MLMvsCLM to foster further research.

Reconstructing fast-dynamic scenes from multi-view videos is crucial for high-speed motion analysis and realistic 4D reconstruction. However, the majority of 4D capture systems are limited to frame rates below 30 FPS (frames per second), and a direct 4D reconstruction of high-speed motion from low FPS input may lead to undesirable results. In this work, we propose a high-speed 4D capturing system only using low FPS cameras, through novel capturing and processing modules. On the capturing side, we propose an asynchronous capture scheme that increases the effective frame rate by staggering the start times of cameras. By grouping cameras and leveraging a base frame rate of 25 FPS, our method achieves an equivalent frame rate of 100-200 FPS without requiring specialized high-speed cameras. On processing side, we also propose a novel generative model to fix artifacts caused by 4D sparse-view reconstruction, as asynchrony reduces the number of viewpoints at each timestamp. Specifically, we propose to train a video-diffusion-based artifact-fix model for sparse 4D reconstruction, which refines missing details, maintains temporal consistency, and improves overall reconstruction quality. Experimental results demonstrate that our method significantly enhances high-speed 4D reconstruction compared to synchronous capture.

Recent advances in vision-language-action (VLA) models have shown promise in integrating image generation with action prediction to improve generalization and reasoning in robot manipulation. However, existing methods are limited to challenging image-based forecasting, which suffers from redundant information and lacks comprehensive and critical world knowledge, including dynamic, spatial and semantic information. To address these limitations, we propose DreamVLA, a novel VLA framework that integrates comprehensive world knowledge forecasting to enable inverse dynamics modeling, thereby establishing a perception-prediction-action loop for manipulation tasks. Specifically, DreamVLA introduces a dynamic-region-guided world knowledge prediction, integrated with the spatial and semantic cues, which provide compact yet comprehensive representations for action planning. This design aligns with how humans interact with the world by first forming abstract multimodal reasoning chains before acting. To mitigate interference among the dynamic, spatial and semantic information during training, we adopt a block-wise structured attention mechanism that masks their mutual attention, preventing information leakage and keeping each representation clean and disentangled. Moreover, to model the conditional distribution over future actions, we employ a diffusion-based transformer that disentangles action representations from shared latent features. Extensive experiments on both real-world and simulation environments demonstrate that DreamVLA achieves 76.7% success rate on real robot tasks and 4.44 average length on the CALVIN ABC-D benchmarks.

We offer a novel perspective on reward modeling by formulating it as a policy discriminator, which quantifies the difference between two policies to generate a reward signal, guiding the training policy towards a target policy with desired behaviors. Based on this conceptual insight, we propose a scalable pre-training method named Policy Discriminative Learning (POLAR), which trains a reward model (RM) to discern identical policies and discriminate different ones. Unlike traditional reward modeling methods relying on absolute preferences, POLAR captures the relative difference between one policy and an arbitrary target policy, which is a scalable, high-level optimization objective suitable for modeling generic ranking relationships. Leveraging the POLAR pre-training paradigm, we present a series of RMs with parameter scales from 1.8B to 7B. Empirical results show that POLAR substantially outperforms traditional non-pre-trained methods, significantly enhancing RM performance. For instance, POLAR-7B could improve preference accuracy from 54.8% to 81.0% on STEM tasks and from 57.9% to 85.5% on creative writing tasks compared to SOTA baselines. POLAR also shows robust generalization capabilities in RLHF using Reinforcement Fine-tuning (RFT), providing reliable reward signals and markedly enhancing policy performance--improving LLaMa3.1-8B from an average of 47.36% to 56.33% and Qwen2.5-32B from 64.49% to 70.47% on 20 benchmarks. Moreover, scaling experiments reveal a clear power-law relationship between computation and performance, supported by linear correlation coefficients approaching 0.99. The impressive performance, strong generalization, and scaling properties suggest that POLAR is a promising direction for developing general and strong reward models.

In this paper, we introduce BMMR, a large-scale bilingual, multimodal, multi-disciplinary reasoning dataset for the community to develop and evaluate large multimodal models (LMMs). BMMR comprises 110k college-level questions spanning 300 UNESCO-defined subjects, spanning diverse formats-multiple-choice, fill-in-the-blank, and open-ended QA-and sourced from both print and digital media such as books, exams, and quizzes. All data are curated and filtered via a human-in-the-loop and scalable framework, and each instance is paired with a high-quality reasoning path. The dataset is organized into two parts: BMMR-Eval that comprises 20,458 high-quality instances to comprehensively assess LMMs' knowledge and reasoning across multiple disciplines in both Chinese and English; and BMMR-Train that contains 88,991 instances to support further research and development, extending the current focus on mathematical reasoning to diverse disciplines and domains. In addition, we propose the process-based multi-discipline verifier (i.e., BMMR-Verifier) for accurate and fine-grained evaluation of reasoning paths. Extensive experiments on 24 models reveal that (i) even SOTA models (e.g., o3 and Gemini-2.5-Pro) leave substantial headroom on BMMR-Eval; (ii) reasoning models exhibit discipline bias and outperform LMMs only on specific subjects; (iii) open-source models still trail their proprietary counterparts; and (iv) fine-tuning on BMMR-Train narrows this gap. Additionally, we conduct reasoning-chain analyses using BMMR-Verifier and other in-depth studies, uncovering the challenges LMMs currently face in multidisciplinary reasoning. We will release the data, and we hope our work can offer insights and contributions to the community.

We introduce RoboBrain 2.0, our latest generation of embodied vision-language foundation models, designed to unify perception, reasoning, and planning for complex embodied tasks in physical environments. It comes in two variants: a lightweight 7B model and a full-scale 32B model, featuring a heterogeneous architecture with a vision encoder and a language model. Despite its compact size, RoboBrain 2.0 achieves strong performance across a wide spectrum of embodied reasoning tasks. On both spatial and temporal benchmarks, the 32B variant achieves leading results, surpassing prior open-source and proprietary models. In particular, it supports key real-world embodied AI capabilities, including spatial understanding (e.g., affordance prediction, spatial referring, trajectory forecasting) and temporal decision-making (e.g., closed-loop interaction, multi-agent long-horizon planning, and scene graph updating). This report details the model architecture, data construction, multi-stage training strategies, infrastructure and practical applications. We hope RoboBrain 2.0 advances embodied AI research and serves as a practical step toward building generalist embodied agents. The code, checkpoint and benchmark are available at https://superrobobrain.github.io.

The foundational capabilities of large language models (LLMs) are deeply influenced by the quality of their pre-training corpora. However, enhancing data quality at scale remains a significant challenge, primarily due to the trade-off between refinement effectiveness and processing efficiency. While rule-based filtering remains the dominant paradigm, it typically operates at the document level and lacks the granularity needed to refine specific content within documents. Inspired by emerging work such as ProX, we propose RefineX, a novel framework for large-scale, surgical refinement of pre-training data through programmatic editing tasks. RefineX enables efficient and fine-grained data refinement while reliably preserving the diversity and naturalness of raw text. The core strength of RefineX lies in distilling high-quality, expert-guided end-to-end refinement results into minimal edit-based deletion programs. This high-precision distillation pipeline is used to train an efficient and reliable refine model that can systematically improve every instance in the corpus at scale. We evaluate RefineX across from-scratch pre-training at multiple model scales and find that it consistently outperforms models trained on raw, filtered, or alternatively refined data across diverse downstream tasks. On the 750M model, RefineX yields 2.6%-7.2% average gains on lighteval tasks, and achieves comparable performance using significantly fewer training tokens. Further analysis shows that RefineX reliably enhances text quality with both high efficiency and precision, outperforming prior approaches such as end-to-end generation and Prox-C. These results position RefineX as a scalable, effective, and reliable solution for optimizing pre-training data in modern LLM pipelines.

Large language models (LLMs) have shown impressive performance on general-purpose tasks, yet adapting them to specific domains remains challenging due to the scarcity of high-quality domain data. Existing data synthesis tools often struggle to extract reliable fine-tuning data from heterogeneous documents effectively. To address this limitation, we propose Easy Dataset, a unified framework for synthesizing fine-tuning data from unstructured documents via an intuitive graphical user interface (GUI). Specifically, Easy Dataset allows users to easily configure text extraction models and chunking strategies to transform raw documents into coherent text chunks. It then leverages a persona-driven prompting approach to generate diverse question-answer pairs using public-available LLMs. Throughout the pipeline, a human-in-the-loop visual interface facilitates the review and refinement of intermediate outputs to ensure data quality. Experiments on a financial question-answering task show that fine-tuning LLMs on the synthesized dataset significantly improves domain-specific performance while preserving general knowledge. The source code and installable package are available at https://github.com/ConardLi/easy-dataset and have garnered over 9,000 GitHub stars.

Recently, great progress has been achieved in text-to-video (T2V) generation by scaling transformer-based diffusion models to billions of parameters, which can generate high-quality videos. However, existing models typically produce only short clips offline, restricting their use cases in interactive and real-time applications. This paper addresses these challenges by proposing StreamDiT, a streaming video generation model. StreamDiT training is based on flow matching by adding a moving buffer. We design mixed training with different partitioning schemes of buffered frames to boost both content consistency and visual quality. StreamDiT modeling is based on adaLN DiT with varying time embedding and window attention. To practice the proposed method, we train a StreamDiT model with 4B parameters. In addition, we propose a multistep distillation method tailored for StreamDiT. Sampling distillation is performed in each segment of a chosen partitioning scheme. After distillation, the total number of function evaluations (NFEs) is reduced to the number of chunks in a buffer. Finally, our distilled model reaches real-time performance at 16 FPS on one GPU, which can generate video streams at 512p resolution. We evaluate our method through both quantitative metrics and human evaluation. Our model enables real-time applications, e.g. streaming generation, interactive generation, and video-to-video. We provide video results and more examples in our project website: <a href="https://cumulo-autumn.github.io/StreamDiT/">this https URL.</a>

Historical documents represent an invaluable cultural heritage, yet have undergone significant degradation over time through tears, water erosion, and oxidation. Existing Historical Document Restoration (HDR) methods primarily focus on single modality or limited-size restoration, failing to meet practical needs. To fill this gap, we present a full-page HDR dataset (FPHDR) and a novel automated HDR solution (AutoHDR). Specifically, FPHDR comprises 1,633 real and 6,543 synthetic images with character-level and line-level locations, as well as character annotations in different damage grades. AutoHDR mimics historians' restoration workflows through a three-stage approach: OCR-assisted damage localization, vision-language context text prediction, and patch autoregressive appearance restoration. The modular architecture of AutoHDR enables seamless human-machine collaboration, allowing for flexible intervention and optimization at each restoration stage. Experiments demonstrate AutoHDR's remarkable performance in HDR. When processing severely damaged documents, our method improves OCR accuracy from 46.83\% to 84.05\%, with further enhancement to 94.25\% through human-machine collaboration. We believe this work represents a significant advancement in automated historical document restoration and contributes substantially to cultural heritage preservation. The model and dataset are available at https://github.com/SCUT-DLVCLab/AutoHDR.

Speculative decoding generally dictates having a small, efficient draft model that is either pretrained or distilled offline to a particular target model series, for instance, Llama or Qwen models. However, within online deployment settings, there are two major challenges: 1) usage of a target model that is incompatible with the draft model; 2) expectation of latency improvements over usage and time. In this work, we propose OmniDraft, a unified framework that enables a single draft model to operate with any target model and adapt dynamically to user data. We introduce an online n-gram cache with hybrid distillation fine-tuning to address the cross-vocabulary mismatch across draft and target models; and further improve decoding speed by leveraging adaptive drafting techniques. OmniDraft is particularly suitable for on-device LLM applications where model cost, efficiency and user customization are the major points of contention. This further highlights the need to tackle the above challenges and motivates the ``one drafter for all'' paradigm. We showcase the proficiency of the OmniDraft framework by performing online learning on math reasoning, coding and text generation tasks. Notably, OmniDraft enables a single Llama-68M model to pair with various target models including Vicuna-7B, Qwen2-7B and Llama3-8B models for speculative decoding; and additionally provides up to 1.5-2x speedup.

We study whether visual embedding models capture continuous, ordinal attributes along linear directions, which we term _rank axes_. We define a model as _rankable_ for an attribute if projecting embeddings onto such an axis preserves the attribute's order. Across 7 popular encoders and 9 datasets with attributes like age, crowd count, head pose, aesthetics, and recency, we find that many embeddings are inherently rankable. Surprisingly, a small number of samples, or even just two extreme examples, often suffice to recover meaningful rank axes, without full-scale supervision. These findings open up new use cases for image ranking in vector databases and motivate further study into the structure and learning of rankable embeddings. Our code is available at https://github.com/aktsonthalia/rankable-vision-embeddings.

The generative capabilities of Large Language Models (LLMs) are rapidly expanding from static code to dynamic, interactive visual artifacts. This progress is bottlenecked by a critical evaluation gap: established benchmarks focus on algorithmic correctness and are blind to the visual fidelity and interactive integrity that define modern user experiences. To bridge this gap, we introduce ArtifactsBench, a new benchmark and paradigm for the automated, multimodal evaluation of visual code generation. Our framework programmatically renders each generated artifact and captures its dynamic behavior through temporal screenshots. This visual evidence, alongside the source code, is then assessed by a Multimodal LLM (MLLM)-as-Judge, which is rigorously guided by a fine-grained, per-task checklist to ensure holistic and reproducible scoring. We construct a new benchmark of 1,825 diverse tasks and evaluate over 30 leading LLMs. Our automated evaluation achieves a striking 94.4% ranking consistency with WebDev Arena, the gold-standard for human preference in web development, and over 90% pairwise agreement with human experts. This establishes ArtifactsBench as the first framework to reliably automate the assessment of human-perceived quality at scale. Our analysis provides a high-resolution map of the current SOTA, revealing that generalist models often outperform domain-specific ones. We open-source ArtifactsBench, including the benchmark, evaluation harness, and baseline results at https://artifactsbenchmark.github.io/, to provide the community with a scalable and accurate tool to accelerate the development of user-centric generative models.

Neural Radiance Field (NeRF)-based segmentation methods focus on object semantics and rely solely on RGB data, lacking intrinsic material properties. This limitation restricts accurate material perception, which is crucial for robotics, augmented reality, simulation, and other applications. We introduce UnMix-NeRF, a framework that integrates spectral unmixing into NeRF, enabling joint hyperspectral novel view synthesis and unsupervised material segmentation. Our method models spectral reflectance via diffuse and specular components, where a learned dictionary of global endmembers represents pure material signatures, and per-point abundances capture their distribution. For material segmentation, we use spectral signature predictions along learned endmembers, allowing unsupervised material clustering. Additionally, UnMix-NeRF enables scene editing by modifying learned endmember dictionaries for flexible material-based appearance manipulation. Extensive experiments validate our approach, demonstrating superior spectral reconstruction and material segmentation to existing methods. Project page: https://www.factral.co/UnMix-NeRF.

虽然太空船能藉由恒星辨识方位，但要准确掌握其离开地球多远、行经何处，通常仍需仰赖地面以电波进行精密追踪。NASA 新视野号（New Horizo​​ns）任务团队的成员利用这艘目前已距地球超过 88 亿公里的飞船，成功示范仅透过星野影像即可判定方向与位置的导航方法。随着太空船深入太空，从其所在位置所见的恒星位置会开始偏离地球所见的位置。一艘航行至银河系深处的太空船可藉由这种因视差效应产生的偏移，来定位自己相对于邻近恒星的位置。而新视野号已飞行至足够遥远的距离，得以首次真实示范星际导航的可行性。自 2006 年发射以来，新视野号飞越冥王星与柯伊伯带天体 Arrokoth，并将在未来十年间逐步脱离太阳系，进入星际空间。2020 年新视野号科学团队同时从地球与太空中观测并拍摄了邻近恒星比邻星（距离地球4.2光年）与沃夫359（距离7.86光年）周围的星野。这项实验生动呈现出新视野号从内太阳系飞往外太阳系时的视角变化。而针对 2020 年影像中两颗恒星精确位置的更进一步分析，新视野号团队成员及成功推算出新视野号相对于邻近恒星的三维空间位置，精度达约 660 万公里。

日本总务省公布的 2025 年《信息通信白皮书》中发布调查结果称，使用生成式 AI 的个人仅占 26.7%。与上次调查相比增加至约 3 倍，但与进行对比调查的中国（81.2%）、美国（68.8%）和德国（59.2%）仍存在较大差距。关于不使用的理由，比例最高的是“生活和业务上没有需要”，超过 4 成，“不知道使用方法”也接近 4 成。使用率存在明显的年龄差异。使用率最高的 20~29岁人群为 44.7%，其次是 40~49 岁（29.6%）、30~39 岁（23.8%）、50~59 岁（19.9%）。最低的 60~69 岁仅为15.5%。日本国内企业的利用率为 55.2%，而中国（95.8%）、美国（90.6%）和德国（90.3%）均超过 9 成。

Netflix 加大了对动漫的投资，公布了其全球订户的动漫观看数据，凸显了日本动漫从小众市场成长为全球主流内容市场的过程。Netflix 称，其全球订户——逾 1.5 亿家庭约 3 亿用户——在观看动漫。过去五年，该平台动漫收视率增长了两倍，2024 年有 33 部动漫作品登上了它的 Global Top 10 (Non-English)排行榜，是 2021 年的两倍多。2024 年全球动漫内容的观看次数逾 10 亿次，其中 80% 至 90% 的用户选择观看配音版。为满足这一需求，Netflix 开始为动漫作品提供最多 33 种语言的配音和解说。

施乐发表新闻稿，宣布完成了对美国打印机制造商利盟（Lexmark）的收购。利盟最初是 IBM 的打印机部门，1991 年独立成立利盟国际，它一度是财富 500 强之一，2016 年珠海艾派克科技(现纳思达）、香港太盟投资（PAG）及君联资本组成的财团以每股 40.5 美元的现金斥资 25.4 亿美元收购利盟。2023 年美国因利盟在产品生产中使用强迫劳动而对其实施制裁。此举意味着利盟在美国市场的销售面临困境。施乐是在去年 12 月宣布以 15 亿美元从中国财团手中收购利盟，它表示这笔交易有助于增强其产品组合。

印度外包巨头 Infosys 通知员工，警告他们每天工作时间不得超过 9 小时 15 分钟。该公司将监控员工工作时间，此举旨在防止员工工作倦怠，但这与公司联合创始人、英国前首相 Rishi Sunak 的岳父 Narayana Murthy 呼吁印度人每周工作 70 小时的立场相悖。

中国电影基金会等组织计划利用AI技术，对包括《警察故事》《黄飞鸿》和《精武门》等在内的 100 部经典功夫影片进行“重焕”。该基金会表示，将与上海灿星文化传媒股份有限公司等企业合作，向 AI 公司授权调用电影素材，以在全球范围内重新推出这些电影，吸引年轻观众。参与功夫片“重焕”项目的官员表示，AI 将用于为电影添加“令人惊叹的真实感”。他们正计划打造“身临其境的观看体验”，例如在竹林决斗，“感受动与静的哲学”。功夫电影的“重焕”将扩展到其他领域，包括创建武术视频游戏。行业观察人士表示，中国重新挖掘经典功夫电影作品的举措是明智的，这些作品多年来一直是美国动作电影的灵感来源。

南极洲的变暖速度是世界其他地区的两倍，但过去十年南极洲周围海冰面积缩小的程度超过了气候模型的估计。发表在 PNAS 期刊上的一项研究给出了一种解释，认为可能发生了危险的反馈循环。海水因密度不同而分成不同层的现象被称为分层，其中冷淡水层位于较深较温暖和较咸的水层之上，将热量困在海洋深处，使表层海水保持较凉的状态，有助于海冰的形成。海水密度越大重量也越大。当表层海水盐度升高时，它们更容易下沉，搅动了海洋的不同层，使深层的热量上升。这种热涌在冬季也会融化海冰，使得海冰更难形成，从而形成了一个反馈循环：高盐海水将更多热量带到海洋表面，融化了更多冰，吸收了更多热，循环加剧。

欧盟的新能效标签要求厂商标明电池的额定充电次数。那么根据充电次数，今天哪些手机品牌的电池更耐用？数据显示， 三星手机电池遥遥领先。Google Pixel 系列手机电池充电次数基本上是一千次；三星基本上是 2000 次（少数几款 1200 次）；Fairphone 5 1200 次，Fairphone 6 降至 1000 次；摩托罗拉 Edge 50 系列为 1200 次，G55 800 次，其它型号基本上是 1000 次；Nothing 系列手机为 1400 次；OnePlus OnePlus 13R 1200 次，OnePlus 13 1000 次；索尼 Xperia 1 VII 为 1400 次，苹果 iPhone 16 系列都是 1000 次。

根据 Internet Society 的统计数字，自 2018 年以来它记录到了 863 次断网事件，其中印度一国就占了近半多达 411 次，其次是伊拉克的 140 次，叙利亚的 66 次，苏丹的 33 次，巴基斯坦和阿尔及利亚的 17 次，伊朗的 16 次。印度频繁断网的一个原因是法律授予官员以维护公共次序的名义切断互联网，地方官员有法定权力能命令电信公司手动关闭网络服务。要断网时，官员只需写信和发邮件给所有在当地有办事处的 ISP，ISP 随后屏蔽所有进出数据。 伊拉克断网则主要是因为考试。

根据发表在《Journal of Comparative Psychology》期刊上的一项研究，世界各地经常观察到的虎鲸（或杀人鲸）朝人类扔鱼或其它猎物的现象可归因于它们想和我们交朋友。研究人员在过去 20 年记录了 34 次此类遭遇，即使人类拒绝了它们的礼物，虎鲸仍然会满怀期待的继续逗留，有时候还会再次尝试送礼，表明了它们奇特的建立关系的动机。论文主要作者、加拿大不列颠哥伦比亚 Bay Cetology 的 Jared Towers 说，虎鲸经常互相分享食物，这是一种亲社会行为，是它们建立关系的一种方式。它们与人类分享食物，可能表明它们也有兴趣与人类建立联系。

Moderna 公布了基于 mRNA 的季节性流感疫苗的试验结果：mRNA 疫苗 mRNA-1010 有效性比标准疫苗高 27%。有大约 4.1 万 50 岁及以上人群参与试验，他们被随机分配接受 mRNA-1010 或标准疫苗接种，在流感季节进行约六个月的随访。相比标准疫苗，mRNA 疫苗的总体效力高出 26.6%，在 65 岁及以上参与者中高出 27.4%。早先的试验数据显示，mRNA-1010 在参与者体内产生的免疫反应比标准流感疫苗和高剂量疫苗要高。由于美国现任卫生部部长 Robert F. Kennedy Jr. 的反疫苗立场， mRNA-1010 的未来命运不确定。Kennedy Jr.已经取消了上届拜登政府授予 Moderna 的 mRNA 流感疫苗拨款。

美元正经历其现代史上最糟糕的一年，美元今年已下跌逾 7%，摩根士丹利预测下半年可能再下跌 10%。美元走弱可能增强美国的出口竞争力，推动特朗普政府的美国贸易再平衡计划，但也会使进口商品更昂贵，加剧关税带来的冲击。未来的问题是，美元是否不仅会失去其价值，还会失去其在全球金融体系中的核心地位。各国央行去美元化的努力是转向黄金，而不是转向另一种货币如人民币。

气候变化已对全球各地的企业产生影响。根据摩根士丹利的一份报告，被调查的企业逾半数过去一年经历了与气候相关的运营中断，包括成本增加、员工中断工作和收入损失。极端高温和风暴是造成运营中断最频繁的因素，其次是野火和烟雾、水资源短缺和洪水或海平面上升。彭博智库的一项分析发现，仅美国过去一年就花费了近万亿美元用于灾难恢复和气候相关的其它需求。近九成南美企业估计，到本十年末气候变化将对其商业模式构成风险。北美最主要风险则被认为不是气候变化而是政治动荡。

天文学家首次发现证据，证实一颗 Ia 型超新星是由「双重爆炸」机制产生：白矮星在尚未达到临界质量的情况下，先由表层的氦引发第一次爆炸，再触发核心的第二次爆炸。Ia 型超新星是源自双星系统中的白矮星爆炸事件。当白矮星从伴星吸积足够物质、达到所谓「钱卓极限」（Chandrasekhar limit）时，便会发生剧烈的热核爆炸，产生稳定且明亮的超新星光度。触发这类超新星的精确机制至今仍有许多未解之谜。模拟研究显示，至少部分 Ia 型超新星可能源自于尚未达到临界质量就发生的「双重爆炸」机制。在这一模型中，白矮星表面首先积聚一层由吸积而来的氦，当氦层变得不稳定时会率先引爆，产生一道向内传递的冲击波，进而触发核心的第二次爆炸，造成整体超新星事件。科学家观测了位于大麦哲伦星系内的超新星遗迹 SNR 0509-67.5，发现了两层明显的钙元素壳层，正是双重爆炸所留下的指纹。这是首次在观测中清楚辨识出这种结构，证实双重爆炸机制确实存在，也显示白矮星可在未达临界质量前即发生爆炸。这一成果有助于我们理解 Ia 型超新星的形成多样性，并进一步提升对宇宙距离测量与重元素起源的掌握。

根据 StatCounter 截至 7 月的统计数据，距离微软停止支持 Windows 10 仅剩三个月时间，其市场份额终于被 Windows 11 超过。Windows 11 的市场份额为 50.24%，Windows 10 的市场份额为 46.84%。相比一年前这是巨大的飞跃：一年前 Windows 10 市场份额为 66.04%，而 Windows 11 仅为 29.75%。