OrangeBot.AI Digest — 2025-10-02

☁️ Nextcloud server, a safe home for all your data

A JAX-native LLM Post-Training Library

Python ETL framework for stream processing, real-time analytics, LLM pipelines, and RAG.

A free, open source, and extensible speech-to-text application that works completely offline.

📖 Runbooks that run

Asynchronous image downloader with cache support as a UIImageView category

Official repository for LTX-Video

An open source payments switch written in Rust to make payments fast, reliable and affordable

High performance self-hosted photo and video management solution.

[WIP] Resources for AI engineers. Also contains supporting materials for the book AI Engineering (Chip Huyen, 2025)

GitHub Actions runner images

利用AI大模型，一键生成高清短视频 Generate short videos with one click using AI LLM.

Open-source platform to build and deploy AI agent workflows.

Blazor Component Library based on Material Design principles with an emphasis on ease of use and extensibility

Although RLVR has become an essential component for developing advanced reasoning skills in LLMs, contemporary studies have documented training plateaus that emerge following thousands of optimization steps, demonstrating notable decreases in performance gains despite increased computational investment. This limitation stems from the sparse exploration patterns inherent in current RLVR practices, where models rely on limited rollouts that often miss critical reasoning paths and fail to provide systematic coverage of the solution space. We present DeepSearch, a framework that integrates Monte Carlo Tree Search directly into RLVR training. In contrast to existing methods that rely on tree search only at inference, DeepSearch embeds structured search into the training loop, enabling systematic exploration and fine-grained credit assignment across reasoning steps. Through training-time exploration, DeepSearch addresses the fundamental bottleneck of insufficient exploration, which leads to diminishing performance improvements over prolonged training steps. Our contributions include: (1) a global frontier selection strategy that prioritizes promising nodes across the search tree, (2) selection with entropy-based guidance that identifies confident paths for supervision, and (3) adaptive replay buffer training with solution caching for efficiency. Experiments on mathematical reasoning benchmarks show that DeepSearch achieves 62.95% average accuracy and establishes a new state-of-the-art for 1.5B reasoning models - using 5.7x fewer GPU hours than extended training approaches. These results highlight the importance of strategic exploration over brute-force scaling and demonstrate the promise of algorithmic innovation for advancing RLVR methodologies. DeepSearch establishes a new direction for scaling reasoning capabilities through systematic search rather than prolonged computation.

Vision-Language-Action (VLA) models enable embodied decision-making but rely heavily on imitation learning, leading to compounding errors and poor robustness under distribution shift. Reinforcement learning (RL) can mitigate these issues yet typically demands costly real-world interactions or suffers from sim-to-real gaps. We introduce VLA-RFT, a reinforcement fine-tuning framework that leverages a data-driven world model as a controllable simulator. Trained from real interaction data, the simulator predicts future visual observations conditioned on actions, allowing policy rollouts with dense, trajectory-level rewards derived from goal-achieving references. This design delivers an efficient and action-aligned learning signal, drastically lowering sample requirements. With fewer than 400 fine-tuning steps, VLA-RFT surpasses strong supervised baselines and achieves greater efficiency than simulator-based RL. Moreover, it exhibits strong robustness under perturbed conditions, sustaining stable task execution. Our results establish world-model-based RFT as a practical post-training paradigm to enhance the generalization and robustness of VLA models. For more details, please refer to https://vla-rft.github.io/.

The training paradigm for large language models (LLMs) is moving from static datasets to experience-based learning, where agents acquire skills via interacting with complex environments. To facilitate this transition we introduce GEM (General Experience Maker), an open-source environment simulator designed for the age of LLMs. Analogous to OpenAI-Gym for traditional reinforcement learning (RL), GEM provides a standardized framework for the environment-agent interface, including asynchronous vectorized execution for high throughput, and flexible wrappers for easy extensibility. GEM also features a diverse suite of environments, robust integrated tools, and single-file example scripts demonstrating using GEM with five popular RL training frameworks. Along with this, we also provide a set of baselines across 24 environments using REINFORCE with Return Batch Normalization (ReBN), which -- unlike GRPO -- is compatible with the full RL setting of dense per-turn rewards and offers better credit assignment. We further conduct apple-to-apple benchmarking of PPO, GRPO and REINFORCE in both single- and multi-turn settings using GEM to shed light on the algorithmic designs. Lastly, GEM also functions as a convenient evaluation toolkit besides a training environment. We hope this framework can help accelerate future agentic LLM research.

Large Language Models (LLMs) can self-improve through reinforcement learning, where they generate trajectories to explore and discover better solutions. However, this exploration process is computationally expensive, often forcing current methods to assign limited exploration budgets to each task. This uniform allocation creates problematic edge cases: easy tasks consistently succeed while difficult tasks consistently fail, both producing zero gradients during training updates for the widely used Group Relative Policy Optimization (GRPO). We address this problem from the lens of exploration budget allocation. Viewing each task's exploration as an "item" with a distinct "value" and "cost", we establish a connection to the classical knapsack problem. This formulation allows us to derive an optimal assignment rule that adaptively distributes resources based on the model's current learning status. When applied to GRPO, our method increases the effective ratio of non-zero policy gradients by 20-40% during training. Acting as a computational "free lunch", our approach could reallocate exploration budgets from tasks where learning is saturated to those where it is most impactful. This enables significantly larger budgets (e.g., 93 rollouts) for especially challenging problems, which would be computationally prohibitive under a uniform allocation. These improvements translate to meaningful gains on mathematical reasoning benchmarks, with average improvements of 2-4 points and peak gains of 9 points on specific tasks. Notably, achieving comparable performance with traditional homogeneous allocation would require about 2x the computational resources.

Environment setup-the process of configuring the system to work with a specific software project-represents a persistent challenge in Software Engineering (SE). Automated environment setup methods could assist developers by providing fully configured environments for arbitrary repositories without manual effort. This also helps SE researchers to scale execution-based benchmarks. However, recent studies reveal that even state-of-the-art Large Language Models (LLMs) achieve limited success in automating this task. To address this limitation, we tune a specialized model for environment setup. We combine supervised fine-tuning for generating correct Bash scripts and Reinforcement Learning with Verifiable Rewards (RLVR) to adapt it to the task of environment setup. On EnvBench-Python, our method enables Qwen3-8B (a model runnable on consumer hardware) to perform on par with larger models-Qwen3-32B and GPT-4o. The training code and model checkpoints are available online: https://github.com/JetBrains-Research/PIPer.

Post-training quantization has emerged as the most widely used strategy for deploying large language models at low precision. Still, current methods show perplexity degradation at bit-widths less than or equal to 4, partly because representing outliers causes precision issues in parameters that share the same scales as these outliers. This problem is especially pronounced for calibration-free, uniform quantization methods. We introduce SINQ to augment existing post-training quantizers with an additional second-axis scale factor and a fast Sinkhorn-Knopp-style algorithm that finds scales to normalize per-row and per-column variances, thereby minimizing a novel per-matrix proxy target for quantization: the matrix imbalance. Our method has no interactions between layers and can be trivially applied to new architectures to quantize any linear layers. We evaluate our method on the Qwen3 model family and DeepSeek-V2.5. SINQ improves WikiText2 and C4 perplexity significantly against uncalibrated uniform quantization baselines and can be further enhanced by combining it with calibration and non-uniform quantization levels. Code to reproduce the results of this work and to easily quantize models using SINQ is available at https://github.com/huawei-csl/SINQ.

Large language models (LLMs) are increasingly deployed as agents in dynamic, real-world environments, where success requires both reasoning and effective tool use. A central challenge for agentic tasks is the growing context length, as agents must accumulate long histories of actions and observations. This expansion raises costs and reduces efficiency in long-horizon tasks, yet prior work on context compression has mostly focused on single-step tasks or narrow applications. We introduce Agent Context Optimization (ACON), a unified framework that optimally compresses both environment observations and interaction histories into concise yet informative condensations. ACON leverages compression guideline optimization in natural language space: given paired trajectories where full context succeeds but compressed context fails, capable LLMs analyze the causes of failure, and the compression guideline is updated accordingly. Furthermore, we propose distilling the optimized LLM compressor into smaller models to reduce the overhead of the additional module. Experiments on AppWorld, OfficeBench, and Multi-objective QA show that ACON reduces memory usage by 26-54% (peak tokens) while largely preserving task performance, preserves over 95% of accuracy when distilled into smaller compressors, and enhances smaller LMs as long-horizon agents with up to 46% performance improvement.

While recent generative models advance pixel-space video synthesis, they remain limited in producing professional educational videos, which demand disciplinary knowledge, precise visual structures, and coherent transitions, limiting their applicability in educational scenarios. Intuitively, such requirements are better addressed through the manipulation of a renderable environment, which can be explicitly controlled via logical commands (e.g., code). In this work, we propose Code2Video, a code-centric agent framework for generating educational videos via executable Python code. The framework comprises three collaborative agents: (i) Planner, which structures lecture content into temporally coherent flows and prepares corresponding visual assets; (ii) Coder, which converts structured instructions into executable Python codes while incorporating scope-guided auto-fix to enhance efficiency; and (iii) Critic, which leverages vision-language models (VLM) with visual anchor prompts to refine spatial layout and ensure clarity. To support systematic evaluation, we build MMMC, a benchmark of professionally produced, discipline-specific educational videos. We evaluate MMMC across diverse dimensions, including VLM-as-a-Judge aesthetic scores, code efficiency, and particularly, TeachQuiz, a novel end-to-end metric that quantifies how well a VLM, after unlearning, can recover knowledge by watching the generated videos. Our results demonstrate the potential of Code2Video as a scalable, interpretable, and controllable approach, achieving 40% improvement over direct code generation and producing videos comparable to human-crafted tutorials. The code and datasets are available at https://github.com/showlab/Code2Video.

Group Relative Policy Optimization (GRPO) is a prominent reinforcement learning algorithm for post-training Large Language Models (LLMs). It is commonly believed that GRPO necessitates a large group size to ensure stable training via precise statistical estimation, which incurs substantial computational overhead. In this work, we challenge this assumption by reframing GRPO as a form of contrastive learning, which reveals a fundamental connection to Direct Preference Optimization (DPO). Motivated by DPO's empirical success, we investigate the minimal two-rollout case (2-GRPO), a configuration previously deemed infeasible. We provide a rigorous theoretical analysis to validate 2-GRPO and demonstrate empirically that it achieves performance on par with 16-GRPO, despite using only 1/8 of the rollouts and reducing training time by over 70%.

Language models are increasingly capable, yet still fail at a seemingly simple task of multi-digit multiplication. In this work, we study why, by reverse-engineering a model that successfully learns multiplication via implicit chain-of-thought, and report three findings: (1) Evidence of long-range structure: Logit attributions and linear probes indicate that the model encodes the necessary long-range dependencies for multi-digit multiplication. (2) Mechanism: the model encodes long-range dependencies using attention to construct a directed acyclic graph to ``cache'' and ``retrieve'' pairwise partial products. (3) Geometry: the model implements partial products in attention heads by forming Minkowski sums between pairs of digits, and digits are represented using a Fourier basis, both of which are intuitive and efficient representations that the standard fine-tuning model lacks. With these insights, we revisit the learning dynamics of standard fine-tuning and find that the model converges to a local optimum that lacks the required long-range dependencies. We further validate this understanding by introducing an auxiliary loss that predicts the ``running sum'' via a linear regression probe, which provides an inductive bias that enables the model to successfully learn multi-digit multiplication. In summary, by reverse-engineering the mechanisms of an implicit chain-of-thought model we uncover a pitfall for learning long-range dependencies in Transformers and provide an example of how the correct inductive bias can address this issue.

Existing studies on bias mitigation methods for large language models (LLMs) use diverse baselines and metrics to evaluate debiasing performance, leading to inconsistent comparisons among them. Moreover, their evaluations are mostly based on the comparison between LLMs' probabilities of biased and unbiased contexts, which ignores the gap between such evaluations and real-world use cases where users interact with LLMs by reading model responses and expect fair and safe outputs rather than LLMs' probabilities. To enable consistent evaluation across debiasing methods and bridge this gap, we introduce BiasFreeBench, an empirical benchmark that comprehensively compares eight mainstream bias mitigation techniques (covering four prompting-based and four training-based methods) on two test scenarios (multi-choice QA and open-ended multi-turn QA) by reorganizing existing datasets into a unified query-response setting. We further introduce a response-level metric, Bias-Free Score, to measure the extent to which LLM responses are fair, safe, and anti-stereotypical. Debiasing performances are systematically compared and analyzed across key dimensions: the prompting vs. training paradigm, model size, and generalization of different training strategies to unseen bias types. We will publicly release our benchmark, aiming to establish a unified testbed for bias mitigation research.

Recently, we have witnessed great progress in image editing with natural language instructions. Several closed-source models like GPT-Image-1, Seedream, and Google-Nano-Banana have shown highly promising progress. However, the open-source models are still lagging. The main bottleneck is the lack of a reliable reward model to scale up high-quality synthetic training data. To address this critical bottleneck, we built \mname, trained with our new large-scale human preference dataset, meticulously annotated by trained experts following a rigorous protocol containing over 200K preference pairs. \mname demonstrates superior alignment with human preferences in instruction-guided image editing tasks. Experiments show that \mname achieves state-of-the-art human correlation on established benchmarks such as GenAI-Bench, AURORA-Bench, ImagenHub, and our new \benchname, outperforming a wide range of VLM-as-judge models. Furthermore, we use \mname to select a high-quality subset from the existing noisy ShareGPT-4o-Image dataset. We train Step1X-Edit on the selected subset, which shows significant improvement over training on the full set. This demonstrates \mname's ability to serve as a reward model to scale up high-quality training data for image editing. Furthermore, its strong alignment suggests potential for advanced applications like reinforcement learning-based post-training and test-time scaling of image editing models. \mname with its training dataset will be released to help the community build more high-quality image editing training datasets.

Large language models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks when equipped with external tools. However, current frameworks predominantly rely on sequential processing, leading to inefficient execution particularly for tasks requiring extensive tool interaction. This paper introduces Flash-Searcher, a novel parallel agent reasoning framework that fundamentally reimagines the execution paradigm from sequential chains to directed acyclic graphs (DAGs). Flash-Searcher decomposes complex tasks into subtasks with explicit dependencies, enabling concurrent execution of independent reasoning paths while maintaining logical constraints. Through dynamic workflow optimization, our framework continuously refines the execution graph based on intermediate results, effectively integrating summary module. Comprehensive evaluations across multiple benchmarks demonstrate that Flash-Searcher consistently outperforms existing approaches. Specifically, it achieves 67.7% accuracy on BrowseComp and 83% on xbench-DeepSearch, while reducing agent execution steps by up to 35% compared to current frameworks. Furthermore, when distilling this parallel reasoning pipeline into single models, we observe substantial performance gains across diverse backbone architectures, underscoring the generalizability of our methodology. Our work thus represents a significant advance in agent architecture design, offering a more scalable and efficient paradigm for complex reasoning tasks.

Designing and optimizing task-specific quantum circuits are crucial to leverage the advantage of quantum computing. Recent large language model (LLM)-based quantum circuit generation has emerged as a promising automatic solution. However, the fundamental challenges remain unaddressed: (i) parameterized quantum gates require precise numerical values for optimal performance, which also depend on multiple aspects, including the number of quantum gates, their parameters, and the layout/depth of the circuits. (ii) LLMs often generate low-quality or incorrect quantum circuits due to the lack of quantum domain-specific knowledge. We propose QUASAR, an agentic reinforcement learning (RL) framework for quantum circuits generation and optimization based on tool-augmented LLMs. To align the LLM with quantum-specific knowledge and improve the generated quantum circuits, QUASAR designs (i) a quantum circuit verification approach with external quantum simulators and (ii) a sophisticated hierarchical reward mechanism in RL training. Extensive evaluation shows improvements in both syntax and semantic performance of the generated quantum circuits. When augmenting a 4B LLM, QUASAR has achieved the validity of 99.31% in Pass@1 and 100% in Pass@10, outperforming industrial LLMs of GPT-4o, GPT-5 and DeepSeek-V3 and several supervised-fine-tuning (SFT)-only and RL-only baselines.

Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a key ingredient for unlocking complex reasoning capabilities in large language models. Recent work ProRL has shown promise in scaling RL by increasing the number of training steps. However, performance plateaus after thousands of steps, with clear diminishing returns from allocating more computation to additional training. In this work, we investigate a complementary paradigm for scaling RL, BroR-Lincreasing the number of rollouts per example to hundreds to exhaustively Broaden exploration, which yields continuous performance gains beyond the saturation point observed in ProRL when scaling the number of training steps. Our approach is motivated by a mass balance equation analysis allowing us to characterize the rate of change in probability mass for correct and incorrect tokens during the reinforcement process. We show that under a one-step RL assumption, sampled rollout tokens always contribute to correct-mass expansion, while unsampled tokens outside rollouts may lead to gains or losses depending on their distribution and the net reward balance. Importantly, as the number of rollouts per example N increases, the effect of unsampled terms diminishes, ensuring overall correct-mass expansion. To validate our theoretical analysis, we conduct simulations under more relaxed conditions and find that a sufficiently large rollout size N-corresponding to ample exploration-guarantees an increase in the probability mass of all correct tokens. Empirically, BroRL revives models saturated after 3K ProRL training steps and demonstrates robust, continuous improvement, achieving state-of-the-art results for the 1.5B model across diverse benchmarks.

自称 Crimson Collective 的勒索组织声称入侵 Red Hat 的 GitHub 代码库，窃取了近 570GB 的数据。其中包括 800 份 Customer Engagement Reports(CERs)，可能包含了客户网络和平台的敏感信息。Red Hat 证实其咨询业务遭遇了安全事故，但拒绝证实黑客的说法。黑客组织在 Telegram 上公布了盗取的 GitHub 代码库的完整目录列表以及 2020-2025 年的 CER 列表。CER 列表中的知名组织包括了美国银行、T-Mobile、AT&T、富达、凯撒、梅奥诊所、​​沃尔玛、Costco、美国海军水面作战中心、FAA 和 众议院等。黑客表示他们尝试联络 Red Hat 提出勒索要求，但只收到一份模板回复，指示他们向其安全团队提交漏洞报告。

自 2000 年以来 15-49 岁人群癌症发病率增加了 10%，而老年人口的癌症发病率却略有下降。其中年轻女性的癌症率比同年龄段男性高 83%。美国癌症研究协会(American Association for Cancer Research)会议上发表的一项涉及 15 万人的研究发现，根据血液生物标志物，千禧一代的生物衰老速度看起来比前几代人更快。这种加速现象与肺癌、胃肠道肿瘤和子宫恶性肿瘤等癌症风险增加最高 42% 相关。研究人员将癌症发病率上升与怀孕期间服用的药物、摄入的超加工食品、人造光、轮班工作造成的昼夜节律紊乱，以及化学物质暴露联系起来。

正如城市居民所知，远离都市，奔赴海边，可以享受别样的风景或体验心灵的重启。发表在 ACS《环境科学与技术快报》上的一项研究为海边之旅又平添了一个新的理由。一项研究发现，城市空气潜藏致病性念珠酵母菌菌株，但在沿海空气样本中却没有发现这些菌株，揭示了其潜在的传播途径。念珠酵母菌是一组常见的微生物，存在于人体皮肤和内脏器官黏膜中，但不会造成危害。但是，在某些情况下，这些菌株可能会过度增殖，并导致阴道酵母菌感染或鹅口疮。已知这些感染可通过直接接触或体液传播。先前的研究发现空气中存在念珠菌 DNA，表明这种酵母菌可以通过空气传播。研究人员连续一整年每个月在香港及其附近的一个面向中国南海的人口稀疏地区收集一次空气样本。他们在 12 份城市空气样本中发现了三种被世界卫生组织归类为真菌病原体的念珠菌：白色念珠菌、近平滑念珠菌和热带念珠菌。而在沿海地区采集的样本中没有检测到念珠菌。这一地域差异让研究人员推测，空气中的酵母菌来源于工业或城市，例如污水处理厂。此外，一些城市空气样本中还含有对常见抗真菌药物具有耐药性的致病性念珠菌菌种。研究人员表示，抗真菌药物的过度使用、城市环境中的重金属等污染物或气温升高均可能是这种耐药性的促成因素。最后，空气中的其中一种念珠菌菌株的基因组成与先前从念珠菌感染者样本中提取的菌株密切相关，这表明空气中的菌株可能具有传染性。研究人员表示，这项研究挑战了长期以来存在的念珠菌主要通过直接接触传播的假设，将念珠菌描述为一种新兴的空气传播病原体。但是还需要开展更多的研究，以调查城市中念珠菌的来源，并充分了解这些空气中颗粒的潜在传染性。

著名动物学家、灵长类动物学家和人类学家珍·古道尔（Jane Goodall）去世，享年 91 岁。珍·古道尔以研究野外黑猩猩闻名，被认为是最重要的黑猩猩专家。古道尔于 1960 年在坦桑尼亚贡贝溪（Gombe Stream）国家公园的 Kasakela 黑猩猩社区开始研究黑猩猩的社会和家庭生活，她观察到黑猩猩的行为与人类十分相似。她的发现挑战了当时两大信念：只有人类才能制造和使用工具，黑猩猩是素食主义者。她在研究中与当地黑猩猩建立了紧密联系，成为黑猩猩社区唯一被接纳的人类。她后来投身于环境教育和公益事业，创办了著名民间动物保育机构珍·古道尔研究所。

亚马逊推出了三款 Kindle Scribe 新版本。Kindle Scribe 是专为笔记和阅读而设计的电子书阅读器，新版本配备了 11 英寸防眩光 E Ink 显示屏，而旧版本使用的是 10.2 英寸。Kindle Scribe 新版本包括了标准版，起售价 499.99 美元；无前置灯的版本售价 429.99 美元；彩色版售价 629.99 美元。新型号还改进了书写和翻译速度，比上一代提升 40%；支持 AI 驱动的笔记搜索和摘要，支持访问 Google Drive 和Microsoft OneDrive 中的文档，支持将可编辑的笔记文本导出到 OneNote。

流行图库 Imgur 有逾 1.3 亿用户，它因为母公司面临英国监管机构罚款而宣布屏蔽用户访问其服务。英国信息专员办公室(Information Commissioner's Office 简写 ICO)于今年三月启动了对 TikTok 和 Reddit 等公司的调查，调查重点是平台如何处理儿童数据和验证其年龄，上个月 ICO 对 Imgur 母公司 MediaLab 发出了罚款的意向通知书。ICO 临时执行董事 Tim Capel 对 Imgur 此举表示，即使屏蔽了英国用户访问，该机构仍然可能对 MediaLab 处以罚款，退出英国并不能逃避以前违反数据保护规定的责任。

微软正式宣布 Windows 11 25H2 GA。该版本的版本号为 26200.6584。Windows 11 24H2 用户将通过 enablement packages(eKB)安装最新更新，但 Windows 11 23H2 用户没有 eKB，最佳方法是 Windows Update。用户可选择现在下载 Installation Assistant 安装 Windows 11 25H2，或者等到 10 月 14 日通过 Windows Server Update Services (WSUS)安装更新。Windows 11 25H2 主要变化包括：移除 PowerShell 2.0 和 Windows Management Instrumentation 命令行工具；启用 AI 辅助安全编码——微软没有对此进行解释；Wi-Fi 7；通过组策略选择卸载预安装的 Microsoft Store 应用；等等。Windows 11 25H2 将一直支持到 2027 年 10 月。

今天的大部分浏览器使用 Google 的 Blink/Chromium、苹果的 WebKit 或 Mozilla 的 Gecko 渲染引擎，此外源自 Mozilla、使用 Rust 语言的 Servo 引擎还在开发之中。现在 Cloudflare 出人意料的宣布赞助 Ladybird 浏览器开源引擎项目。Ladybird 由 GitHub 联合创始人、前 CEO Chris Wanstrath 和 SerenityOS 项目创始人 Andreas Kling 发起，使用 C++ 语言开发，设计成为快速、标准兼容和无外部依赖的全新浏览器引擎。Chris Wanstrath 向该项目资助了 100 万美元。Cloudflare 的赞助意味着项目将有更多资源加速开发。

根据 Netblocks 的监测数据，阿富汗断网已超过 48 小时。互联网和移动电话服务全部中断，全国居民生活在通讯几乎完全中断的状况下。阿富汗的全面断网始于周一晚上，进入周二后互联网和电话服务继续中断。首都喀布尔一名 42 岁的店主 Najibullah 说，"没有电话和互联网我们都是盲人，所有业务都依赖于手机。送货是用手机。这一情况就像是假期：每个人都在家里。市场完全冻结。"这是塔利班政府首次切断全国的通信，官方没有对此做出解释。法新社在断网前曾收到一名政府官员的警告，称有八到九千个通信支柱（telecommunications pillars）将被关闭，通信中断将持续到另行通知为止。目前阿富汗有限的通信只能依靠无线电和少数卫星链路。更新：网络连接于 10 月 2 日恢复。

在 Linux 6.17 将 Bcachefs 文件系统列为由外部维护并且没有合并任何 Bcachefs 维护者 Kent Overstreet 递交的拉取请求之后，Linus Torvalds 在 Linux 6.18 中完全移除了 Bcachefs，总共删除了 11.7 万行代码。Torvalds 评论说，Bcachefs 现在是一个 DKMS 模块，内核代码过时了，删除内核中的代码以避免版本混淆。

贵州花江峡谷大桥正式通车。大桥桥面距水面625米，高度超过北盘江第一桥近 60 米，成为新的世界第一高桥；大桥主桥跨径 1420 米，居山区桥梁跨径世界第一。大桥全长 2890 米，可将两岸通行时间从两个多小时缩短到两分钟左右。从 2022 年开工到正式通车，这座“超级工程”的建造只花了三年多。花江峡谷大桥钢桁梁吊装有 93 个节段，总重达 2.1 万吨，需在 600 多米高空实现毫米级精准对接。建设团队借助研发的“智慧缆索吊装系统”，全部吊装仅用了 73 天就全面完成；3.8 万平方米的桥面，建设团队在 1 个多月里完成了 5 层铺装。

以研究数字取证和深度伪造而知名的加州伯克利计算机科学教授 Hany Farid 表示，计算机科学在极短时间内从经得住时间考验的职业变成了剧变中的行业。他说，计算机科学专业的学生通常会在前四年获得五份实习机会，毕业时会收到多份高薪的工作机会。但如今这种情况不会发生了，如果能收到一份工作邀约他们就很高兴了。Farid 教授认为 AI 只是因素之一。计算机科学行业正在发生某种变化。他现在给学生的建议是掌握多种技能，因为不知道未来会发生什么。他说，AI 不会让律师失业，但会用 AI 的律师会让不会用 AI 的律师失业。他认为每个职业都如此。

美国调查公司 Omdia 的数据显示，10～12 月服务器用 DRAM 的预测价格为 4.3 美元/GB，比 2023 年 10～12 月高出 2.4 美元。PC 用产品的预测价格为 2.8 美元，比 2023 年 10～12 月上涨 1.2 美元。这一趋势背后的原因是 AI 服务器的需求猛增。AI 服务器主要使用 HBM 内存。主要 DRAM 内存芯片制造商三星电子、SK 海力士及美光缩小产量或停产了上一代的 DDR4，转为生产和销售 HBM。AI 服务器的内存需求正在推动整个半导体市场。美国半导体行业协会（SIA）公布的全球半导体销售额7月达到了 620.7 亿美元，同比增长 20.6%，首次突破 600 亿美元。已连续 21 个月超过去年同期。

根据发表在《Osteoporosis International》上的一篇综述，研究人员分析了 62 项研究，发现微塑料会破坏骨髓干细胞，刺激破骨细胞——一种削弱骨组织的细胞，从而削弱骨骼。实验室实验发现，微塑料颗粒会降低细胞活性，诱导细胞过早衰老，改变基因表达，引发炎症反应。动物研究发现，微塑料的积累会降低白细胞数量，破坏骨骼微结构，导致细胞结构不规则，增加骨折风险。巴西 Campinas 州立大学的 Rodrigo Bueno de Oliveira 表示，这些影响会阻碍实验动物的骨骼生长。