OrangeBot.AI Digest — 2025-08-05

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Solidity, the Smart Contract Programming Language

This open-source curriculum introduces the fundamentals of Model Context Protocol (MCP) through real-world, cross-language examples in .NET, Java, TypeScript, JavaScript, and Python. Designed for developers, it focuses on practical techniques for building modular, scalable, and secure AI workflows from session setup to service orchestration.

Sim is an open-source AI agent workflow builder. Sim Studio's interface is a lightweight, intuitive way to quickly build and deploy LLMs that connect with your favorite tools.

A local-first personal finance app

A library for building applications in a consistent and understandable way, with composition, testing, and ergonomics in mind.

A collective list of free APIs

World's fastest and most advanced password recovery utility

Recommend new arxiv papers of your interest daily according to your Zotero libarary.

A hex editor for WeChat/QQ/TIM - PC版微信/QQ/TIM防撤回补丁（我已经看到了，撤回也没用了）

The easiest way to run WireGuard VPN + Web-based Admin UI.

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A platform for book lovers who value their collections

We present Qwen-Image, an image generation foundation model in the Qwen series that achieves significant advances in complex text rendering and precise image editing. To address the challenges of complex text rendering, we design a comprehensive data pipeline that includes large-scale data collection, filtering, annotation, synthesis, and balancing. Moreover, we adopt a progressive training strategy that starts with non-text-to-text rendering, evolves from simple to complex textual inputs, and gradually scales up to paragraph-level descriptions. This curriculum learning approach substantially enhances the model's native text rendering capabilities. As a result, Qwen-Image not only performs exceptionally well in alphabetic languages such as English, but also achieves remarkable progress on more challenging logographic languages like Chinese. To enhance image editing consistency, we introduce an improved multi-task training paradigm that incorporates not only traditional text-to-image (T2I) and text-image-to-image (TI2I) tasks but also image-to-image (I2I) reconstruction, effectively aligning the latent representations between Qwen2.5-VL and MMDiT. Furthermore, we separately feed the original image into Qwen2.5-VL and the VAE encoder to obtain semantic and reconstructive representations, respectively. This dual-encoding mechanism enables the editing module to strike a balance between preserving semantic consistency and maintaining visual fidelity. Qwen-Image achieves state-of-the-art performance, demonstrating its strong capabilities in both image generation and editing across multiple benchmarks.

Retrieval-augmented generation (RAG) over long documents typically involves splitting the text into smaller chunks, which serve as the basic units for retrieval. However, due to dependencies across the original document, contextual information is often essential for accurately interpreting each chunk. To address this, prior work has explored encoding longer context windows to produce embeddings for longer chunks. Despite these efforts, gains in retrieval and downstream tasks remain limited. This is because (1) longer chunks strain the capacity of embedding models due to the increased amount of information they must encode, and (2) many real-world applications still require returning localized evidence due to constraints on model or human bandwidth. We propose an alternative approach to this challenge by representing short chunks in a way that is conditioned on a broader context window to enhance retrieval performance -- i.e., situating a chunk's meaning within its context. We further show that existing embedding models are not well-equipped to encode such situated context effectively, and thus introduce a new training paradigm and develop the situated embedding models (SitEmb). To evaluate our method, we curate a book-plot retrieval dataset specifically designed to assess situated retrieval capabilities. On this benchmark, our SitEmb-v1 model based on BGE-M3 substantially outperforms state-of-the-art embedding models, including several with up to 7-8B parameters, with only 1B parameters. Our 8B SitEmb-v1.5 model further improves performance by over 10% and shows strong results across different languages and several downstream applications.

Virtual cell modeling represents an emerging frontier at the intersection of artificial intelligence and biology, aiming to predict quantities such as responses to diverse perturbations quantitatively. However, autonomously building computational models for virtual cells is challenging due to the complexity of biological systems, the heterogeneity of data modalities, and the need for domain-specific expertise across multiple disciplines. Here, we introduce CellForge, an agentic system that leverages a multi-agent framework that transforms presented biological datasets and research objectives directly into optimized computational models for virtual cells. More specifically, given only raw single-cell multi-omics data and task descriptions as input, CellForge outputs both an optimized model architecture and executable code for training virtual cell models and inference. The framework integrates three core modules: Task Analysis for presented dataset characterization and relevant literature retrieval, Method Design, where specialized agents collaboratively develop optimized modeling strategies, and Experiment Execution for automated generation of code. The agents in the Design module are separated into experts with differing perspectives and a central moderator, and have to collaboratively exchange solutions until they achieve a reasonable consensus. We demonstrate CellForge's capabilities in single-cell perturbation prediction, using six diverse datasets that encompass gene knockouts, drug treatments, and cytokine stimulations across multiple modalities. CellForge consistently outperforms task-specific state-of-the-art methods. Overall, CellForge demonstrates how iterative interaction between LLM agents with differing perspectives provides better solutions than directly addressing a modeling challenge. Our code is publicly available at https://github.com/gersteinlab/CellForge.

Large language models (LLMs) have shown remarkable success across many domains, yet their integration into cybersecurity applications remains limited due to a lack of general-purpose cybersecurity data, representational complexity, and safety and regulatory concerns. To address this gap, we previously introduced Foundation-Sec-8B, a cybersecurity-focused LLM suitable for fine-tuning on downstream tasks. That model, however, was not designed for chat-style interactions or instruction-following. In this report, we release Foundation-Sec-8B-Instruct: a model specifically trained for general-purpose cybersecurity dialogue. Built on Foundation-Sec-8B, it combines domain-specific knowledge with instruction-following, conversational capabilities, and alignment with human preferences to produce high-quality, relevant responses. Comprehensive evaluations show that Foundation-Sec-8B-Instruct outperforms Llama 3.1-8B-Instruct on a range of cybersecurity tasks while matching its instruction-following performance. It is also competitive with GPT-4o-mini on cyber threat intelligence and instruction-following tasks. We envision Foundation-Sec-8B-Instruct becoming an indispensable assistant in the daily workflows of cybersecurity professionals. We release the model publicly at https://huggingface.co/fdtn-ai/Foundation-Sec-8B-Instruct.

Reasoning models excel in complex problem solving but exhibit a concerning trade off between reasoning capabilities and instruction following abilities. Existing approaches for improving instruction following rely on stronger external models, creating methodological bottlenecks and practical limitations including increased costs and accessibility constraints. We propose a self-supervised RL framework that leverages reasoning models' own internal signals to improve instruction following capabilities without external supervision. Extensive experiments demonstrate that our framework significantly improves instruction following capabilities while maintaining reasoning performance, offering a scalable and cost-effective approach to enhance instruction following in reasoning models. The data and code are publicly available at https://github.com/Rainier-rq/verl-if.

Recent advances in large language models (LLMs) have driven impressive progress in omni-modal understanding and generation. However, training omni-modal LLMs remains a significant challenge due to the heterogeneous model architectures required to process diverse modalities, necessitating sophisticated system design for efficient large-scale training. Existing frameworks typically entangle model definition with parallel logic, incurring limited scalability and substantial engineering overhead for end-to-end omni-modal training. % We present \veomni, a modular and efficient training framework to accelerate the development of omni-modal LLMs. \veomni introduces model-centric distributed recipes that decouples communication from computation, enabling efficient 3D parallelism on omni-modal LLMs. \veomni also features a flexible configuration interface supporting seamless integration of new modalities with minimal code change. % Using \veomni, a omni-modal mixture-of-experts (MoE) model with 30B parameters can be trained with over 2,800 tokens/sec/GPU throughput and scale to 160K context lengths via 3D parallelism on 128 GPUs, showcasing its superior efficiency and scalability for training large omni-modal LLMs.

To operate effectively in the real world, robots must integrate multimodal reasoning with precise action generation. However, existing vision-language-action (VLA) models often sacrifice one for the other, narrow their abilities to task-specific manipulation data, and suffer catastrophic forgetting of pre-trained vision-language capabilities. To bridge this gap, we introduce InstructVLA, an end-to-end VLA model that preserves the flexible reasoning of large vision-language models (VLMs) while delivering leading manipulation performance. InstructVLA introduces a novel training paradigm, Vision-Language-Action Instruction Tuning (VLA-IT), which employs multimodal training with mixture-of-experts adaptation to jointly optimize textual reasoning and action generation on both standard VLM corpora and a curated 650K-sample VLA-IT dataset. On in-domain SimplerEnv tasks, InstructVLA achieves 30.5% improvement over SpatialVLA. To evaluate generalization, we introduce SimplerEnv-Instruct, an 80-task benchmark requiring closed-loop control and high-level instruction understanding, where it outperforms a fine-tuned OpenVLA by 92% and an action expert aided by GPT-4o by 29%. Additionally, InstructVLA surpasses baseline VLMs on multimodal tasks and exhibits inference-time scaling by leveraging textual reasoning to boost manipulation performance in both simulated and real-world settings. These results demonstrate InstructVLA's potential for bridging intuitive and steerable human-robot interaction with efficient policy learning.

Visual token compression is critical for Large Vision-Language Models (LVLMs) to efficiently process high-resolution inputs. Existing methods that typically adopt fixed compression ratios cannot adapt to scenes of varying complexity, often causing imprecise pruning that discards informative visual tokens and results in degraded model performance. To address this issue, we introduce a dynamic pruning framework, GlimpsePrune, inspired by human cognition. It takes a data-driven ''glimpse'' and prunes irrelevant visual tokens in a single forward pass before answer generation. This approach prunes 92.6% of visual tokens while on average fully retaining the baseline performance on free-form VQA tasks. The reduced computational cost also enables more effective fine-tuning: an enhanced GlimpsePrune+ achieves 110% of the baseline performance while maintaining a similarly high pruning rate. Our work paves a new way for building more powerful and efficient LVLMs.

Most human proteins remain undrugged, over 96% of human proteins remain unexploited by approved therapeutics. While structure-based virtual screening promises to expand the druggable proteome, existing methods lack atomic-level precision and fail to predict binding fitness, limiting translational impact. We present AuroBind, a scalable virtual screening framework that fine-tunes a custom atomic-level structural model on million-scale chemogenomic data. AuroBind integrates direct preference optimization, self-distillation from high-confidence complexes, and a teacher-student acceleration strategy to jointly predict ligand-bound structures and binding fitness. The proposed models outperform state-of-the-art models on structural and functional benchmarks while enabling 100,000-fold faster screening across ultra-large compound libraries. In a prospective screen across ten disease-relevant targets, AuroBind achieved experimental hit rates of 7-69%, with top compounds reaching sub-nanomolar to picomolar potency. For the orphan GPCRs GPR151 and GPR160, AuroBind identified both agonists and antagonists with success rates of 16-30%, and functional assays confirmed GPR160 modulation in liver and prostate cancer models. AuroBind offers a generalizable framework for structure-function learning and high-throughput molecular screening, bridging the gap between structure prediction and therapeutic discovery.

We present Voxlect, a novel benchmark for modeling dialects and regional languages worldwide using speech foundation models. Specifically, we report comprehensive benchmark evaluations on dialects and regional language varieties in English, Arabic, Mandarin and Cantonese, Tibetan, Indic languages, Thai, Spanish, French, German, Brazilian Portuguese, and Italian. Our study used over 2 million training utterances from 30 publicly available speech corpora that are provided with dialectal information. We evaluate the performance of several widely used speech foundation models in classifying speech dialects. We assess the robustness of the dialectal models under noisy conditions and present an error analysis that highlights modeling results aligned with geographic continuity. In addition to benchmarking dialect classification, we demonstrate several downstream applications enabled by Voxlect. Specifically, we show that Voxlect can be applied to augment existing speech recognition datasets with dialect information, enabling a more detailed analysis of ASR performance across dialectal variations. Voxlect is also used as a tool to evaluate the performance of speech generation systems. Voxlect is publicly available with the license of the RAIL family at: https://github.com/tiantiaf0627/voxlect.

Text-to-image diffusion models have revolutionized visual content generation, but current safety mechanisms apply uniform standards that often fail to account for individual user preferences. These models overlook the diverse safety boundaries shaped by factors like age, mental health, and personal beliefs. To address this, we propose Personalized Safety Alignment (PSA), a framework that allows user-specific control over safety behaviors in generative models. PSA integrates personalized user profiles into the diffusion process, adjusting the model's behavior to match individual safety preferences while preserving image quality. We introduce a new dataset, Sage, which captures user-specific safety preferences and incorporates these profiles through a cross-attention mechanism. Experiments show that PSA outperforms existing methods in harmful content suppression and aligns generated content better with user constraints, achieving higher Win Rate and Pass Rate scores. Our code, data, and models are publicly available at https://torpedo2648.github.io/PSAlign/.

Large-scale datasets are foundational for research and development in natural language processing. However, current approaches face three key challenges: (1) reliance on ambiguously licensed sources restricting use, sharing, and derivative works; (2) static dataset releases that prevent community contributions and diminish longevity; and (3) quality assurance processes restricted to publishing teams rather than leveraging community expertise. To address these limitations, we introduce two contributions: the Dynaword approach and Danish Dynaword. The Dynaword approach is a framework for creating large-scale, open datasets that can be continuously updated through community collaboration. Danish Dynaword is a concrete implementation that validates this approach and demonstrates its potential. Danish Dynaword contains over four times as many tokens as comparable releases, is exclusively openly licensed, and has received multiple contributions across industry and research. The repository includes light-weight tests to ensure data formatting, quality, and documentation, establishing a sustainable framework for ongoing community contributions and dataset evolution.

Diffusion Large Language Models (dLLMs) enable breakthroughs in reasoning and parallel decoding but suffer from prohibitive quadratic computational complexity and memory overhead during inference. Current caching techniques accelerate decoding by storing full-layer states, yet impose substantial memory usage that limit long-context applications. Our analysis of attention patterns in dLLMs reveals persistent cross-layer sparsity, with pivotal tokens remaining salient across decoding steps and low-relevance tokens staying unimportant, motivating selective cache eviction. We propose Sparse-dLLM, the first training-free framework integrating dynamic cache eviction with sparse attention via delayed bidirectional sparse caching. By leveraging the stability of token saliency over steps, it retains critical tokens and dynamically evicts unimportant prefix/suffix entries using an attention-guided strategy. Extensive experiments on LLaDA and Dream series demonstrate Sparse-dLLM achieves up to 10times higher throughput than vanilla dLLMs, with comparable performance and similar peak memory costs, outperforming previous methods in efficiency and effectiveness.

The attribution of artworks in general and of paintings in particular has always been an issue in art. The advent of powerful artificial intelligence models that can generate and analyze images creates new challenges for painting attribution. On the one hand, AI models can create images that mimic the style of a painter, which can be incorrectly attributed, for example, by other AI models. On the other hand, AI models may not be able to correctly identify the artist for real paintings, inducing users to incorrectly attribute paintings. In this paper, both problems are experimentally studied using state-of-the-art AI models for image generation and analysis on a large dataset with close to 40,000 paintings from 128 artists. The results show that vision language models have limited capabilities to: 1) perform canvas attribution and 2) to identify AI generated images. As users increasingly rely on queries to AI models to get information, these results show the need to improve the capabilities of VLMs to reliably perform artist attribution and detection of AI generated images to prevent the spread of incorrect information.

We present RoboMemory, a brain-inspired multi-memory framework for lifelong learning in physical embodied systems, addressing critical challenges in real-world environments: continuous learning, multi-module memory latency, task correlation capture, and infinite-loop mitigation in closed-loop planning. Grounded in cognitive neuroscience, it integrates four core modules: the Information Preprocessor (thalamus-like), the Lifelong Embodied Memory System (hippocampus-like), the Closed-Loop Planning Module (prefrontal lobe-like), and the Low-Level Executer (cerebellum-like) to enable long-term planning and cumulative learning. The Lifelong Embodied Memory System, central to the framework, alleviates inference speed issues in complex memory frameworks via parallelized updates/retrieval across Spatial, Temporal, Episodic, and Semantic submodules. It incorporates a dynamic Knowledge Graph (KG) and consistent architectural design to enhance memory consistency and scalability. Evaluations on EmbodiedBench show RoboMemory outperforms the open-source baseline (Qwen2.5-VL-72B-Ins) by 25% in average success rate and surpasses the closed-source State-of-the-Art (SOTA) (Claude3.5-Sonnet) by 5%, establishing new SOTA. Ablation studies validate key components (critic, spatial memory, long-term memory), while real-world deployment confirms its lifelong learning capability with significantly improved success rates across repeated tasks. RoboMemory alleviates high latency challenges with scalability, serving as a foundational reference for integrating multi-modal memory systems in physical robots.

日本京都大学的科学家研发出一种效力与吗啡相当但无严重副作用的止痛药。吗啡常被癌症患者使用，它有呼吸困难和成瘾等严重副作用。新药物 Adrian 的工作原理与吗啡和现有的合成阿片类药物完全不同，研究团队声称有望彻底改变医学领域的疼痛控制，有助于解决阿片类药物滥用问题。当人遭遇危及生命的情况时，大脑会分泌去甲肾上腺素(norepinephrine)去抑制疼痛。新研究集中在是人体调节去甲肾上腺素过度分泌的机制。研究团队通过引入新技术首次成功研发出一种能阻断这种调控的药物。科学家计划 2026 年在美国开展临床试验，2028 年投入实用。

科学家理解大脑运作主要使用两种工具，它们都有各自的优点和缺点：脑电图 (EEG)廉价且轻便，但无法读取大脑外皮层之外的信息；功能性核磁共振成像 (fMRI) 昂贵且体积庞大但可以深入大脑。现在格拉斯哥(Glasgow)大学研究团队找到了一种能集两者于一身的技术：像 EEG 那样廉价且轻便，像 fMRI 那样能读取大脑深层的信息。他们使用激光器从大脑一侧发射数以百万的光子，然后测量到达另一侧的时间。由于只有极少数光子能完全穿过大脑，因此研究的一大难点是降低背景噪音。这项技术离真正实用还有一段距离，研究人员还需要克服更多障碍。

英国科学家发现，超加工饮食对减重和降低心血管代谢疾病风险的效果可能不如最少加工的饮食，即使这两种饮食都遵循相同的国家饮食指南。研究结果基于一项对英国 55 名成年人开展的社群水平的临床试验，揭示了在整体营养构成之外，食品加工程度对特定健康结局的可能影响。全球超加工食物消耗量在近几十年里快速增加，而肥胖症以及2型糖尿病和心血管疾病这类慢性病的发病率也在同期上升。研究人员开展了一项随机交叉试验，比较了以超加工食品为主和以最少加工食品为主的饮食，两种饮食结构都遵循了英国《健康饮食指南》——一组促进健康均衡营养的国家饮食建议。试验中的 55 名成人或接受预制的超加工食品，如早餐谷物或即食千层意面；或接受预制的最少加工食品，如隔夜燕麦或自制肉酱意面，这些食品在 8 周内分别配送到家。休息 4 周后，受试者换成另一种饮食再继续 8 周，从而能在受试者本人身上比较超加工食品和最少加工食品在 6 个月期间的影响。50 名受试者至少完成了一种饮食。研究者发现，遵循英国《健康饮食指南》的两种饮食都能在 8 周内显著减重。不过，最少加工饮食的平均减重量为 2%，而超加工饮食只有 1%。除了减重，最少加工饮食能更有效地改善与心血管代谢健康指标相关的身体成分，如降低脂肪总量、内脏脂肪和甘油三酯水平，但超加工饮食后的低密度脂蛋白胆固醇更低。

陪审团上周裁决特斯拉对一起牵涉到 Autopilot 的车祸过失死亡事件负有部分责任。庭审记录显示，特斯拉试图将所有责任都归罪于司机，主动隐瞒 Autopilot 在事故前后表现的关键证据。在车祸发生三分钟内，特斯拉汽车将碰撞快照（collision snapshot）——视频、CAN‑bus streams、EDR 数据等——上传到特斯拉公司的服务器上，然后删除了本地拷贝，使得特斯拉公司成为唯一一个能访问关键证据的实体。警方在多年之后才让特斯拉承认碰撞快照的存在。专家通过从车载电脑上取证恢复数据确认特斯拉一直拥有该“碰撞快照”。而特斯拉一直宣称快照数据并不存在。

就算没有母星相伴，部分质量与木星差不多的漂流行星，也可能孕育出属于自己的微型行星系统。这些流浪天体可能跟恒星一样，是从巨大气体分子云塌缩形成的；也有可能原本属于某个行星系统的成员，后来被其他大型行星的重力扰动踢出来，变成在星际空间中漂流的行星。研究团队运用韦伯望远镜上两套高灵敏度的红外线相机，从 2024年 8 月到 10 月间，对这些天体进行详细的光谱测量，并分析其结构与组成。结果显示它们的质量确实与木星相当，在其中 6 颗行星周围还发射出较为多量的红外线，显示它们身边环绕着温暖的气体尘埃圆盘，这正是行星系统形成时常见的特征。观测结果还发现这些尘埃盘中含有矽酸盐颗粒，不但有逐渐成长的迹象，还出现结晶化现象，这正是行星系统中形成岩石质行星形成的第一个步骤。过去只有在恒星或棕矮星周围的气体尘埃圆盘中发现这种现象，如今却首度在质量小得多，与木星质量相近的漂流行星中被侦测出来。

CDN 服务商 Cloudflare 指责 AI 搜索引擎公司 Perplexity 使用隐蔽策略绕过网站禁止抓取的指令。Cloudflare 称它收到了客户的投诉，客户通过 robots.txt 以及 Web 应用防火墙屏蔽了 Perplexity 的搜索爬虫，然而尽管采取了这些措施 Perplexity 的爬虫仍然继续访问网站内容。Cloudflare 随后展开了调查，发现当 Perplexity 注意到 robots.txt 或防火墙规则屏蔽其爬虫后，它会使用一个隐蔽的机器人爬虫，使用一系列策略掩盖其活动。此举意味着 Perplexity 违反了实施了 30 多年的互联网规范。

乌克兰的无人机完成了一项非同寻常的任务：快递电动自行车救出士兵。被称为 Rubizh 的乌克兰国民警卫队第四快速反应旅分享了一则长 16 分钟的相关视频，显示无人机吊起了一辆电动自行车，然后士兵骑着电动车返回安全地带。乌克兰方面称，这名士兵坚守的前线阵地遭到了袭击，多名战友阵亡，他发现自己与安全区隔离，不得不独自坚守阵地数日。为了营救这名士兵，参谋制定了用大型无人机运送电动自行车的计划，第一架无人机被击落，第二架因为过重而失败，第三架成功了。

Mozilla 警告针对 Firefox 扩展开发者的钓鱼攻击，督促开发者对冒充 Mozilla 或 AMO (addons.mozilla.org) 发件人的邮件提高警惕。攻击者可能是利用钓鱼邮件劫持开发者的账号，然后向 Firefox 用户推送包含恶意代码的扩展更新，发动供应链攻击。安全研究人员称，目前针对 Firefox 的恶意插件旨在窃取加密货币钱包的凭证。

根据发表在柳叶刀期刊上的一则评论，专家警告全球塑料危机，认为塑料每年造成的健康相关损失达到至少 1.5 万亿美元。自 1950 年以来，塑料产量增长了 200 多倍，到 2060 年塑料产量将达到每年 10 亿吨以上。结果是整个地球从从珠穆朗玛峰顶到最深的海沟都被塑料污染，全世界目前的塑料垃圾重达 80 亿吨。而不到 10% 的塑料被回收。塑料会导致空气污染和接触有毒化学物质，而微塑料则能渗透进人体。塑料污染甚至会增加携带疾病的蚊子，因为散落各地的塑料捕获的水为蚊子提供了良好的繁殖场所。石油公司和塑料行业认为，对抗塑料污染的重点应放在回收塑料而不是削减产量上。

加密货币波场（TRON）的创始人孙宇晨周日搭乘贝佐斯（Jeff Bezos）旗下太空公司 Blue Origin 的飞船完成了亚轨道太空飞行。这次任务是 Blue Origin 飞船 New Shepard 的第 34 次飞行，因此代号为 NS-34。孙宇晨于 2021 年以 2800 万美元拍下了 New Shepard 首次载人飞行的座位，但由于时间安排上的冲突，孙未能参与此次任务。参与 NS-34 任务的共有六人，除了最受瞩目的孙宇晨外，还有印度裔美国房地产投资人 Arvinder (Arvi) Singh Bahal，土耳其企业家 Gökhan Erdem、波多黎各气象学家兼记者 Deborah Martorell、在尼泊尔经营孤儿院三十年的英国人 Lionel Pitchford，以及美国企业家 James (J.D.) Russell。

根据发表在《Nature Human Behaviour》期刊上的一项研究，22% 的 CS 论文可能含有 AI 生成内容。研究分析了 2020-2024 年之间发表的逾百万篇论文和预印本，主要集中在摘要和引言上，寻找常见于 AI 生成文本的高频词汇如“regenerate response”或“my knowledge cutoff”，以及 pivotal、intricate 和 showcase 等 AI 更可能使用而人类不太可能用的单词。研究人员称，在 CS 等领域，大模型修改文本的痕迹更为普遍。分析显示，在 2022 年 11 月 ChatGPT 发布后仅几个月时间，大模型修改内容的数量就急剧上升。最接近 AI 的领域，大模型使用的比例越高。到 2024 年 9 月，22.5% 的 CS 论文摘要存在大模型修改的证据，电气系统和工程学论文紧随其后，而数学论文摘要使用大模型修改的比例只有 7.7%。生物医学和物理学等的比例也相对较小。研究人员认为实际比例可能更高，因为论文作者可能会有意删除大模型的高频词汇，比如 delve 在 ChatGPT 诞生之后使用频率大幅提升，但在它成为 AI 生成文本的公认标志之后，使用率又逐渐下降。

地球轨道上出现了越来越多的卫星宽带星座，其中包括了 SpaceX 的 Starlink、AST 的 BlueBird、亚马逊的 Kuiper 、欧洲的 OneWeb、中国的千帆与国网。未来几年轨道上的宽带卫星数量将会超过数万。卫星会发射太阳光，在长时间曝光的天文相片上留下光迹，影响天文观测。国际天文联合会（IAU）为此成立了保护黑暗与宁静夜空免受卫星星系干扰中心（Centre for the Protection of the Dark and Quiet Sky from Satellite Constellation Interference, CPS）进行相关研究与政策倡议活动。CPS 于近期发表的回顾与观测报告显示，有些公司并未遵守他们去年所建议的亮度限制。其中 BlueBird 卫星最明亮，但数量尚不足以形成规模，SpaceX 的 Starlink Mini 第二代卫星的体积是第一代的四倍多，但亮度与第一代版本大致相同，证明 SpaceX 降低亮度措施有效。中国的千帆和国网虽然亮度与 Starlink 差不多，但它们目前都部署在约 1000 公里的高轨道上，未来若继续在 300-500 公里范围内进行部署，卫星的反光亮度恐将比现在再亮 1-2 个星等。

高分辨率成像显示，距今 2500 年的西伯利亚冰木乃伊身上有复杂纹身。木乃伊为女性，约 50 岁，生活在中国和欧洲之间上的广阔草原上，属于帕兹里克文明（Pazyryk）。木乃伊是 19 世纪西伯利亚阿尔泰山脉的冰墓中发现的，此前纹身难以发现，现在利用近红外数字摄影技术才发现。她的右前臂纹了鹿头和豹，左臂是神话中的狮鹫与鹿对打，拇指上纹了公鸡。研究人员估计纹身使用了多点的针状工具和单点针，前者用动物角或骨头制成。使用的涂料可能是烧焦的植物材料或烟灰。

GitHub 发言人披露，微软的 AI 编程助手 GitHub Copilot 目前有 2000 万“历史用户（all-time users）”。2025 年 4 月该公司披露 GitHub Copilot 的用户有 1500 万，这意味着过去三个月增加了 500 万新用户。但用户在试用之后就放弃还是一直高频使用，微软没有对此做出进一步说明。微软称，GitHub Copilot 是目前最受欢迎的 AI 辅助编程工具之一，被九成的财富百强企业使用。该产品在企业客户中的使用率比上季度增长了约 75%。

2024 年第 28 届国际 Ｃ 语言混乱代码大赛（IOCCC, The International Obfuscated C Code Contest）公布了获奖作品，其中包括 OpenRISC 32 位 CPU 模拟器、能运行 Doom 的虚拟机，号称世界最小的大模型推理引擎（基于 70 亿参数的 Meta 开源模型 LLaMA 2），等等。IOCCC 是一项国际程序设计赛事，旨在写出最有创意和最让人难以理解的 C 语言代码。从 1984 年开始，该赛事基本每年举办一次，但有过多次中断，IOCCC28 是为了纪念比赛创办 40 周年。