OrangeBot.AI Digest — 2025-11-03

微舆：人人可用的多Agent舆情分析助手，打破信息茧房，还原舆情原貌，预测未来走向，辅助决策！从0实现，不依赖任何框架。

Nano vLLM

"DeepCode: Open Agentic Coding (Paper2Code & Text2Web & Text2Backend)"

Render markdown on the CLI, with pizzazz! 💅🏻

The AI coding agent built for the terminal.

The only AI app builder that knows backend

Tensors and Dynamic neural networks in Python with strong GPU acceleration

Fully Local Manus AI. No APIs, No $200 monthly bills. Enjoy an autonomous agent that thinks, browses the web, and code for the sole cost of electricity. 🔔 Official updates only via twitter @Martin993886460 (Beware of fake account)

🤖 The free, Open Source alternative to OpenAI, Claude and others. Self-hosted and local-first. Drop-in replacement for OpenAI, running on consumer-grade hardware. No GPU required. Runs gguf, transformers, diffusers and many more. Features: Generate Text, Audio, Video, Images, Voice Cloning, Distributed, P2P and decentralized inference

🔥 MaxKB is an open-source platform for building enterprise-grade agents. MaxKB 是强大易用的开源企业级智能体平台。

Collection of Cyber Threat Intelligence sources from the deep and dark web

📄🧠 PageIndex: Document Index for Reasoning-based RAG

real time face swap and one-click video deepfake with only a single image

OpenTUI is a library for building terminal user interfaces (TUIs)

A context-aware AI assistant for your desktop. Ready to respond intelligently, seamlessly integrating multiple LLMs and MCP tools.

Computer-using agents powered by Vision-Language Models (VLMs) have demonstrated human-like capabilities in operating digital environments like mobile platforms. While these agents hold great promise for advancing digital automation, their potential for unsafe operations, such as system compromise and privacy leakage, is raising significant concerns. Detecting these safety concerns across the vast and complex operational space of mobile environments presents a formidable challenge that remains critically underexplored. To establish a foundation for mobile agent safety research, we introduce MobileRisk-Live, a dynamic sandbox environment accompanied by a safety detection benchmark comprising realistic trajectories with fine-grained annotations. Built upon this, we propose OS-Sentinel, a novel hybrid safety detection framework that synergistically combines a Formal Verifier for detecting explicit system-level violations with a VLM-based Contextual Judge for assessing contextual risks and agent actions. Experiments show that OS-Sentinel achieves 10%-30% improvements over existing approaches across multiple metrics. Further analysis provides critical insights that foster the development of safer and more reliable autonomous mobile agents.

Multimodal reasoning requires iterative coordination between language and vision, yet it remains unclear what constitutes a meaningful interleaved chain of thought. We posit that text and image thoughts should function as complementary, rather than isomorphic, modalities that mutually advance reasoning. Guided by this principle, we build ThinkMorph, a unified model fine-tuned on 24K high-quality interleaved reasoning traces spanning tasks with varying visual engagement. ThinkMorph learns to generate progressive text-image reasoning steps that concretely manipulate visual content while maintaining coherent verbal logic. It delivers large gains on vision-centric benchmarks (averaging 34.7% over the base model) and generalizes to out-of-domain tasks, matching or surpassing larger and proprietary VLMs. Beyond performance, ThinkMorph exhibits emergent multimodal intelligence, including unseen visual manipulation skills, adaptive switching between reasoning modes, and better test-time scaling through diversified multimodal thoughts.These findings suggest promising directions for characterizing the emergent capabilities of unified models for multimodal reasoning.

Modern AI hardware, such as Nvidia's Blackwell architecture, is increasingly embracing low-precision floating-point (FP) formats to handle the pervasive activation outliers in Large Language Models (LLMs). Despite this industry trend, a unified comparison of FP and integer (INT) quantization across varying granularities has been missing, leaving algorithm and hardware co-design without clear guidance. This paper fills that gap by systematically investigating the trade-offs between FP and INT formats. We reveal a critical performance crossover: while FP excels in coarse-grained quantization, the comparison at fine-grained (block-wise) levels is more nuanced. Our comprehensive comparison demonstrates that for popular 8-bit fine-grained formats (e.g., MX with block size 32), MXINT8 is superior to its FP counterpart in both algorithmic accuracy and hardware efficiency. However, for 4-bit formats, FP (e.g., MXFP4, NVFP4) often holds an accuracy advantage , though we show that NVINT4 can surpass NVFP4 when outlier-mitigation techniques like Hadamard rotation are applied. We also introduce a symmetric clipping method that resolves gradient bias in fine-grained low-bit INT training, enabling nearly lossless performance for MXINT8 training. These findings challenge the current hardware trajectory, demonstrating that a one-size-fits-all FP approach is suboptimal and advocating that fine-grained INT formats, particularly MXINT8, offer a better balance of accuracy, power, and efficiency for future AI accelerators.

Vision-Language-Action (VLA) models enable robots to understand and perform complex tasks from multimodal input. Although recent work explores using reinforcement learning (RL) to automate the laborious data collection process in scaling supervised fine-tuning (SFT), applying large-scale RL to flow-based VLAs (e.g., pi_0, pi_{0.5}) remains challenging due to intractable action log-likelihoods from iterative denoising. We address this challenge with pi_{RL}, an open-source framework for training flow-based VLAs in parallel simulation. pi_{RL} implements two RL algorithms: (1) {Flow-Noise} models the denoising process as a discrete-time MDP with a learnable noise network for exact log-likelihood computation. (2) {Flow-SDE} integrates denoising with agent-environment interaction, formulating a two-layer MDP that employs ODE-to-SDE conversion for efficient RL exploration. We evaluate pi_{RL} on LIBERO and ManiSkill benchmarks. On LIBERO, pi_{RL} boosts few-shot SFT models pi_0 and pi_{0.5} from 57.6% to 97.6% and from 77.1% to 98.3%, respectively. In ManiSkill, we train pi_{RL} in 320 parallel environments, improving pi_0 from 41.6% to 85.7% and pi_{0.5} from 40.0% to 84.8% across 4352 pick-and-place tasks, demonstrating scalable multitask RL under heterogeneous simulation. Overall, pi_{RL} achieves significant performance gains and stronger generalization over SFT-models, validating the effectiveness of online RL for flow-based VLAs.

The efficiency of large language models (LLMs) is fundamentally limited by their sequential, token-by-token generation process. We argue that overcoming this bottleneck requires a new design axis for LLM scaling: increasing the semantic bandwidth of each generative step. To this end, we introduce Continuous Autoregressive Language Models (CALM), a paradigm shift from discrete next-token prediction to continuous next-vector prediction. CALM uses a high-fidelity autoencoder to compress a chunk of K tokens into a single continuous vector, from which the original tokens can be reconstructed with over 99.9\% accuracy. This allows us to model language as a sequence of continuous vectors instead of discrete tokens, which reduces the number of generative steps by a factor of K. The paradigm shift necessitates a new modeling toolkit; therefore, we develop a comprehensive likelihood-free framework that enables robust training, evaluation, and controllable sampling in the continuous domain. Experiments show that CALM significantly improves the performance-compute trade-off, achieving the performance of strong discrete baselines at a significantly lower computational cost. More importantly, these findings establish next-vector prediction as a powerful and scalable pathway towards ultra-efficient language models. Code: https://github.com/shaochenze/calm. Project: https://shaochenze.github.io/blog/2025/CALM.

Spatial understanding remains a weakness of Large Vision-Language Models (LVLMs). Existing supervised fine-tuning (SFT) and recent reinforcement learning with verifiable rewards (RLVR) pipelines depend on costly supervision, specialized tools, or constrained environments that limit scale. We introduce Spatial-SSRL, a self-supervised RL paradigm that derives verifiable signals directly from ordinary RGB or RGB-D images. Spatial-SSRL automatically formulates five pretext tasks that capture 2D and 3D spatial structure: shuffled patch reordering, flipped patch recognition, cropped patch inpainting, regional depth ordering, and relative 3D position prediction. These tasks provide ground-truth answers that are easy to verify and require no human or LVLM annotation. Training on our tasks substantially improves spatial reasoning while preserving general visual capabilities. On seven spatial understanding benchmarks in both image and video settings, Spatial-SSRL delivers average accuracy gains of 4.63% (3B) and 3.89% (7B) over the Qwen2.5-VL baselines. Our results show that simple, intrinsic supervision enables RLVR at scale and provides a practical route to stronger spatial intelligence in LVLMs.

Reinforcement learning (RL) fine-tuning of large language models (LLMs) often suffers from instability due to the numerical mismatch between the training and inference policies. While prior work has attempted to mitigate this issue through algorithmic corrections or engineering alignments, we show that its root cause lies in the floating point precision itself. The widely adopted BF16, despite its large dynamic range, introduces large rounding errors that breaks the consistency between training and inference. In this work, we demonstrate that simply reverting to FP16 effectively eliminates this mismatch. The change is simple, fully supported by modern frameworks with only a few lines of code change, and requires no modification to the model architecture or learning algorithm. Our results suggest that using FP16 uniformly yields more stable optimization, faster convergence, and stronger performance across diverse tasks, algorithms and frameworks. We hope these findings motivate a broader reconsideration of precision trade-offs in RL fine-tuning.

Autonomous Graphical User Interface (GUI) agents rely on accurate GUI grounding, which maps language instructions to on-screen coordinates, to execute user commands. However, current models, whether trained via supervised fine-tuning (SFT) or reinforcement fine-tuning (RFT), lack self-awareness of their capability boundaries, leading to overconfidence and unreliable predictions. We first systematically evaluate probabilistic and verbalized confidence in general and GUI-specific models, revealing a misalignment between confidence and actual accuracy, which is particularly critical in dynamic GUI automation tasks, where single errors can cause task failure. To address this, we propose HyperClick, a novel framework that enhances reliable GUI grounding through uncertainty calibration. HyperClick introduces a dual reward mechanism, combining a binary reward for correct actions with a truncated Gaussian-based spatial confidence modeling, calibrated using the Brier score. This approach jointly optimizes grounding accuracy and confidence reliability, fostering introspective self-criticism. Extensive experiments on seven challenge benchmarks show that HyperClick achieves state-of-the-art performance while providing well-calibrated confidence. By enabling explicit confidence calibration and introspective self-criticism, HyperClick reduces overconfidence and supports more reliable GUI automation.

Distribution Matching Distillation (DMD) distills score-based generative models into efficient one-step generators, without requiring a one-to-one correspondence with the sampling trajectories of their teachers. However, limited model capacity causes one-step distilled models underperform on complex generative tasks, e.g., synthesizing intricate object motions in text-to-video generation. Directly extending DMD to multi-step distillation increases memory usage and computational depth, leading to instability and reduced efficiency. While prior works propose stochastic gradient truncation as a potential solution, we observe that it substantially reduces the generation diversity of multi-step distilled models, bringing it down to the level of their one-step counterparts. To address these limitations, we propose Phased DMD, a multi-step distillation framework that bridges the idea of phase-wise distillation with Mixture-of-Experts (MoE), reducing learning difficulty while enhancing model capacity. Phased DMD is built upon two key ideas: progressive distribution matching and score matching within subintervals. First, our model divides the SNR range into subintervals, progressively refining the model to higher SNR levels, to better capture complex distributions. Next, to ensure the training objective within each subinterval is accurate, we have conducted rigorous mathematical derivations. We validate Phased DMD by distilling state-of-the-art image and video generation models, including Qwen-Image (20B parameters) and Wan2.2 (28B parameters). Experimental results demonstrate that Phased DMD preserves output diversity better than DMD while retaining key generative capabilities. We will release our code and models.

Multimodal large language models (MLLMs) have advanced embodied agents by enabling direct perception, reasoning, and planning task-oriented actions from visual inputs. However, such vision driven embodied agents open a new attack surface: visual backdoor attacks, where the agent behaves normally until a visual trigger appears in the scene, then persistently executes an attacker-specified multi-step policy. We introduce BEAT, the first framework to inject such visual backdoors into MLLM-based embodied agents using objects in the environments as triggers. Unlike textual triggers, object triggers exhibit wide variation across viewpoints and lighting, making them difficult to implant reliably. BEAT addresses this challenge by (1) constructing a training set that spans diverse scenes, tasks, and trigger placements to expose agents to trigger variability, and (2) introducing a two-stage training scheme that first applies supervised fine-tuning (SFT) and then our novel Contrastive Trigger Learning (CTL). CTL formulates trigger discrimination as preference learning between trigger-present and trigger-free inputs, explicitly sharpening the decision boundaries to ensure precise backdoor activation. Across various embodied agent benchmarks and MLLMs, BEAT achieves attack success rates up to 80%, while maintaining strong benign task performance, and generalizes reliably to out-of-distribution trigger placements. Notably, compared to naive SFT, CTL boosts backdoor activation accuracy up to 39% under limited backdoor data. These findings expose a critical yet unexplored security risk in MLLM-based embodied agents, underscoring the need for robust defenses before real-world deployment.

Multimodal position encoding is essential for vision-language models, yet there has been little systematic investigation into multimodal position encoding. We conduct a comprehensive analysis of multimodal Rotary Positional Embedding (RoPE) by examining its two core components: position design and frequency allocation. Through extensive experiments, we identify three key guidelines: positional coherence, full frequency utilization, and preservation of textual priors-ensuring unambiguous layout, rich representation, and faithful transfer from the pre-trained LLM. Based on these insights, we propose Multi-Head RoPE (MHRoPE) and MRoPE-Interleave (MRoPE-I), two simple and plug-and-play variants that require no architectural changes. Our methods consistently outperform existing approaches across diverse benchmarks, with significant improvements in both general and fine-grained multimodal understanding. Code will be avaliable at https://github.com/JJJYmmm/Multimodal-RoPEs.

The verbosity of Chain-of-Thought (CoT) reasoning hinders its mass deployment in efficiency-critical applications. Recently, implicit CoT approaches have emerged, which encode reasoning steps within LLM's hidden embeddings (termed ``implicit reasoning'') rather than explicit tokens. This approach accelerates CoT by reducing the reasoning length and bypassing some LLM components. However, existing implicit CoT methods face two significant challenges: (1) they fail to preserve the semantic alignment between the implicit reasoning (when transformed to natural language) and the ground-truth reasoning, resulting in a significant CoT performance degradation, and (2) they focus on reducing the length of the implicit reasoning; however, they neglect the considerable time cost for an LLM to generate one individual implicit reasoning token. To tackle these challenges, we propose a novel semantically-aligned implicit CoT framework termed SemCoT. In particular, for the first challenge, we design a contrastively trained sentence transformer that evaluates semantic alignment between implicit and explicit reasoning, which is used to enforce semantic preservation during implicit reasoning optimization. To address the second challenge, we introduce an efficient implicit reasoning generator by finetuning a lightweight language model using knowledge distillation. This generator is guided by our sentence transformer to distill ground-truth reasoning into semantically aligned implicit reasoning, while also optimizing for accuracy. SemCoT is the first approach that enhances CoT efficiency by jointly optimizing token-level generation speed and preserving semantic alignment with ground-truth reasoning. Extensive experiments demonstrate the superior performance of SemCoT compared to state-of-the-art methods in both efficiency and effectiveness. Our code can be found at https://github.com/YinhanHe123/SemCoT/.

The quadratic cost of scaled dot-product attention is a central obstacle to scaling autoregressive language models to long contexts. Linear-time attention and State Space Models (SSMs) provide scalable alternatives but are typically restricted to first-order or kernel-based approximations, which can limit expressivity. We introduce Higher-order Linear Attention (HLA), a causal, streaming mechanism that realizes higher interactions via compact prefix sufficient statistics. In the second-order case, HLA maintains a constant-size state and computes per-token outputs in linear time without materializing any n times n matrices. We give closed-form streaming identities, a strictly causal masked variant using two additional summaries, and a chunk-parallel training scheme based on associative scans that reproduces the activations of a serial recurrence exactly. We further outline extensions to third and higher orders. Collectively, these results position HLA as a principled, scalable building block that combines attention-like, data-dependent mixing with the efficiency of modern recurrent architectures. Project Page: https://github.com/yifanzhang-pro/HLA.

Recently, augmenting Vision-Language-Action models (VLAs) with world modeling has shown promise in improving robotic policy learning. However, it remains challenging to jointly predict next-state observations and action sequences because of the inherent difference between the two modalities. To address this, we propose DUal-STream diffusion (DUST), a world-model augmented VLA framework that handles the modality conflict and enhances the performance of VLAs across diverse tasks. Specifically, we propose a multimodal diffusion transformer architecture that explicitly maintains separate modality streams while still enabling cross-modal knowledge sharing. In addition, we introduce independent noise perturbations for each modality and a decoupled flow-matching loss. This design enables the model to learn the joint distribution in a bidirectional manner while avoiding the need for a unified latent space. Based on the decoupling of modalities during training, we also introduce a joint sampling method that supports test-time scaling, where action and vision tokens evolve asynchronously at different rates. Through experiments on simulated benchmarks such as RoboCasa and GR-1, DUST achieves up to 6% gains over baseline methods, while our test-time scaling approach provides an additional 2-5% boost. On real-world tasks with the Franka Research 3, DUST improves success rates by 13%, confirming its effectiveness beyond simulation. Furthermore, pre-training on action-free videos from BridgeV2 yields significant transfer gains on RoboCasa, underscoring DUST's potential for large-scale VLA pretraining.

We present Denario, an AI multi-agent system designed to serve as a scientific research assistant. Denario can perform many different tasks, such as generating ideas, checking the literature, developing research plans, writing and executing code, making plots, and drafting and reviewing a scientific paper. The system has a modular architecture, allowing it to handle specific tasks, such as generating an idea, or carrying out end-to-end scientific analysis using Cmbagent as a deep-research backend. In this work, we describe in detail Denario and its modules, and illustrate its capabilities by presenting multiple AI-generated papers generated by it in many different scientific disciplines such as astrophysics, biology, biophysics, biomedical informatics, chemistry, material science, mathematical physics, medicine, neuroscience and planetary science. Denario also excels at combining ideas from different disciplines, and we illustrate this by showing a paper that applies methods from quantum physics and machine learning to astrophysical data. We report the evaluations performed on these papers by domain experts, who provided both numerical scores and review-like feedback. We then highlight the strengths, weaknesses, and limitations of the current system. Finally, we discuss the ethical implications of AI-driven research and reflect on how such technology relates to the philosophy of science. We publicly release the code at https://github.com/AstroPilot-AI/Denario. A Denario demo can also be run directly on the web at https://huggingface.co/spaces/astropilot-ai/Denario, and the full app will be deployed on the cloud.

晚上没睡好，第二天总是很难集中注意力，这可能是因为你的大脑正试图自我刷新，导致短暂的注意力缺失。 睡眠期间，大脑会进行一个冲洗循环——脑脊液被反复冲入大脑，再从大脑底部流出。这一过程能够清除白天积累的代谢废物，否则会损害脑细胞。MIT 的科学家想知道通常在睡眠不足时发生的注意力涣散，是否可能是大脑在清醒时试图弥补“自我冲洗”的结果。为了研究这个问题，科学家将试验分为两个阶段。第一阶段让26名19岁到40岁的参与者睡个好觉，得到充分的休息。第二阶段则是两周后，让他们在实验室里彻夜不眠。结果显示缺乏睡眠让参与者更难集中注意力。当研究人员分析大脑扫描结果时，发现参与者在脑脊液从大脑底部流出前约两秒就失去了注意力。更重要的是，在注意力恢复后约1秒，脑脊液被冲入大脑。研究结果表明，当大脑无法在睡眠中自我清洁时，它就会在你醒着时进行清洁，但这会影响注意力。

OpenAI 尚未盈利，其年收入仅为亚马逊的 2%。它的企业重组基本完成，未来有望上市，可能成为第一家 1 万亿美元 IPO 的公司。它与科技行业知名的企业如英伟达和甲骨文达成了复杂的交易，承诺投资和购买高达万亿美元的算力，通过一系列金额巨大的交易，OpenAI 似乎达到了“大到不能倒”的程度，如果真的倒下可能会对整个经济造成系统性风险。在部分人眼里，OpenAI 集苹果、Facebook、Google 和特斯拉于一身，像一家有无限潜力的公司，能颠覆智能手机市场，创造自己的社媒网络，取代搜索引擎，引领机器人时代的到来，重塑所有商业和行业。但在另一部分人的眼里，OpenAI 像荷兰的“郁金香热”（Tulip Mania），是大萧条的先兆，下一个互联网泡沫（dot-com），他们认为 OpenAI 是想要制造弗兰肯斯坦的疯狂科学家，是导致失业率上升的杀手。

世界主要社交媒体平台同意遵守澳大利亚对 16 岁以下青少年的社媒禁令。Meta、Snap 和 TikTok 对澳大利亚议会确认，将在 12 月 10 日该法律生效后开始删除和停用逾百万未成年人账户。未能屏蔽未成年人用户的公司将面临最高 3250 万美元的罚款。在账户停用前青少年可以选择下载其数据，而部门社媒平台还将允许保留数据直至他们年满 17 周岁。年龄验证预计一开始不会太完美，未成年用户可能不能正确识别，而成年用户可能会被错误识别为未成年人。

从本月开始，韩国所有有 80 个以上停车位的停车场将被强制安装太阳能顶棚和停车棚。新法律不仅适用于新建停车场，现有停车场也需要遵守。韩国产业通商部 8 月宣布准备对《新能源和可再生能源开发、利用和推广促进法》实施细则进行修订，规定韩国所有拥有 80 个以上停车位的公共和私人停车场都必须加装太阳能电池板。此举旨在积极扩大可再生能源，创造更多太阳能和建筑工作。此外太阳能车棚还可在暴雨、暴雪和炎炎夏日的气候下保护汽车，保持车内凉爽，延长塑料和座椅面料的使用寿命，甚至可通过降低电动汽车和插电混动汽车的空调负荷延长其续航里程。

工程师 Harishanka 监控了其拥有的 iLife A11 智能吸尘器的进出流量，发现吸尘器一直在向厂商（深圳智意）发送日志和遥测数据——这些行为他并没有授权。他决定屏蔽厂商遥测服务器的 IP 地址，同时继续开放固件和 OTA 服务器的访问。结果他的吸尘器很快就连开机都无法开机了。他送去维修，但都没有查出任何问题。吸尘器每次都能正常工作几天，然后停止工作。他决定拆开吸尘器查找问题根源。吸尘器使用了全志的 A33 SoC，运行 TinaLinux 操作系统，使用微控制器 GD32F103 管理传感器，测试发现硬件本身没有问题，因此他将注意力转向操作系统和软件。他在日志里发现了一个指令，其时间戳与设备停止工作时间完全吻合，这显然是一条终止指令，在他撤销该指令并重启设备后，设备恢复了正常工作。他建议不要将家里的主要 WiFi 网络连接物联网设备，将这些智能设备视为家里的陌生人。

安全公司趋势科技在今年 3 月报告了一个自 2017 年以来就被多达 11 个 APT 组织利用的 0day 漏洞 CVE-2025-9491，该漏洞源自 Windows Shortcut 二进制格式中的一个 bug。七个月之后微软仍然未能修复该漏洞。安全公司 Arctic Wolf 上周四报告 APT 组织 UNC-6384 正利用该漏洞攻击多个欧洲国家。由于目前仍然没有补丁，抵御攻击的选择相当有限。最有效的反制是限制使用来自不受信任来源的 .lnk 文件。安全公司还报告了另一个微软已经释出补丁但被认为不完整的漏洞 CVE-2025-59287 正被活跃利用，该漏洞存在于 Windows Server Update Services（WSUS）中，可能会导致远程代码执行，其威胁等级 9.8/10。

也许还有人记得特斯拉 CEO 马斯克（Elon Musk）在 2017 年宣布的 Roadster 2.0 超级跑车，售价 20 万美元，续航距离 1000 公里，配备了低温气体推进器，给很多人留下了深刻印象。然而八年过去了，Roadster 2.0 仍然遥遥无期。许多提前下了 5 万美元定金的客户也开始对这俩跑车的面世不再抱有期望，他们希望拿回定金，结果发现退款流程几乎不存在。其中之一就是马斯克如今的竞争对手、OpenAI CEO Sam Altman。Altman 在 2018 年 7 月 11 日订购了一辆 Roadster 跑车，支付了 45,000 美元定金（相当于今天的 58,206 美元）。在向特斯拉发出要求退款的邮件之后，他发现与预订相关联的邮箱地址已被删除。Altman 可能忘了询问 ChatGPT 相关的退款流程。如果 ChatGPT 使用互联网上抓取的数据进行训练，特斯拉的客户过去几年已经留下了大量如何退款的讨论，也早就报告特斯拉的支持网站几乎没有提供任何退款的选项，无论是 reservations@tesla.com 和 support@tesla.com 邮箱地址都没有任何回应。

Debian 开发者 Julian Andres Klode 在万圣节宣布，他计划最早从 2026 年 5 月起 APT 打包工具要求使用 Rust 编译器。理由是 APT 代码库的部分会受益于内存安全编程语言 Rust，有必要在 Debian 中强制要求使用 Rust，因此最早从明年 5 月起将在 APT 中引入 Rust 硬依赖和 Rust 代码。首批是 Rust 编译器和标准库，以及 Sequoia 生态系统。缺乏 Rust 支持的 m68k、Hewlett Packard Precision Architecture (HPPA)、SuperH/SH4 和 Alpha 的 Debian 版本面临被弃用。

可能是想要在特朗普任期内实现载人登月，NASA 要求参与 Artemis III 载人登月计划的承包商递交加快进度的计划，如果不满意 NASA 可能会寻求替代方案。Artemis 月球着陆器承包商 SpaceX 和 Blue Origin 都回应了 NASA 的要求。NASA 预计将在美国政府停摆结束向各大公司发出替代提案邀请。在主要航空航天公司中，能提出替代方案的公司可能是洛马（Lockheed Martin）。洛马制造了 Artemis 任务使用的 Orion 飞船。它有可能利用 Orion 飞船的备用零部件组装出一艘两级月球着陆器。

Valve 公布的 2025 年 10 月 Steam 硬件和软件调查显示，玩家运行的操作系统中 Linux 比例突破 3% 达到 3.05%（增加 0.41%），Windows 多年来首次跌至 95% 以内占 94.84%，OSX 占 2.11%。上一次 Linux 用户比例接近 3% 还是十年前，Linux 使用增长的趋势主要受到掌机 Steam Deck 的推动。在所有 Linux 操作系统中，Steam Deck 运行的 SteamOS 占 27%，AMD CPU 占 67.1%，英特尔占 32.89%。而在 Windows 平台，英特尔占 57.8%，AMD 占 42%。对于用户使用的语言，简体中文占 24.01%，英文占 37.96%。

arXiv 平台宣布修改计算机科学类的论文预印本接受政策，原因是由于大模型加快了论文撰写速度，导致计算机科学类的论文投稿数量大幅增长，其中很多价值不高，因此为确保论文质量，它现在要求计算机科学类的综述论文和立场论文需要先被期刊或会议接受，通过同行评审，它才会考虑接受。在递交计算机科学综述论文和立场论文时，作者需要提供通过同行评审的证明，不提供证明可能会被拒收。arXiv 称它以前只收到少量综述论文和立场论文，但如今每月都会收到数百篇综述，其中很多只是带注释的参考文献，没有对开放性研究问题进行实质性讨论。

奥地利经济部朝数字主权迈出了决定性一步：将该部的 1200 名员工从私有的外国云平台迁移到基于 Nextcloud 的云协作平台，该开源平台托管在奥地利境内的基础设施上。越来越多的欧洲政府组织正致力于摆脱对美国科技供应商的控制，掌控自己的敏感数据。欧洲企业也在推动这一趋势，它们组建了非盈利基金会 EuroStack Initiative，呼吁欧洲政府购买本土产品和资助本土项目。其它欧洲政府也在采取类似的行动：德国 Schleswig-Holstein 州抛弃了微软的 Exchange 和 Outlook，改用开源替代；奥地利军方、丹麦政府机构和法国里昂市也都抛弃了微软的软件。

新冠疫情爆发后，各国采取了封锁、社交距离和旅行限制等公共卫生措施，旨在通过避免密切接触减少病毒传播。根据发表在《科学》期刊上的一项研究，人类并非是唯一一种改变空间环境以降低疫情风险的动物。黑花园蚁（Lasius niger）会改变巢穴结构减缓疫情爆发——相当于昆虫版的社交隔离。研究人员首先观察了两组蚂蚁挖掘巢穴，然后让其中一组暴露在绿僵菌（Metarhizium brunneum）孢子中，利用 micro-CT 扫描巢穴在之后数天内的结构变化。研究团队发现，暴露在绿僵菌孢子的蚂蚁巢穴入口平均距离增加了约 6 毫米，在偏僻地方建造了更长更曲折的巢室，蚂蚁甚至挖掘了多条隧道，可能是为了避免接触而设置的替代运输路线。

黑猩猩是自然科学家。人类能面对矛盾证据时更新自身信念，这是所有科学研究的基础。黑猩猩也有类似的能力，能根据新证据的数量和质量更新自身信念。研究人员通过五项实验测试了生活在乌干达 Ngamba 岛黑猩猩自然保护区的 15-23 只黑猩猩，调查了它们根据不同证据修正决策的能力。研究发现，黑猩猩能综合考虑所有证据的相对强度进行决策。研究还发现，黑猩猩不仅对证据的类型敏感，而且对支持其选择的证据数量也很敏感。实验中黑猩猩在所有情况下做出理性选择的频率是做出非理性选择的两到三倍。这些发现表明，人类并非是唯一一种理性动物。