OrangeBot.AI Digest — 2025-09-26

人人都能用英语

"RAG-Anything: All-in-One RAG Framework"

The best way to get AI to solve hard problems in complex codebases.

Gin is a high-performance HTTP web framework written in Go. It provides a Martini-like API but with significantly better performance—up to 40 times faster—thanks to httprouter. Gin is designed for building REST APIs, web applications, and microservices.

Opinionated Arch/Hyprland Setup

Open Source AI Platform - AI Chat with advanced features that works with every LLM

Stay on top of trending topics on social media and the web with AI

The fastest path to AI-powered full stack observability, even for lean teams.

The Free Software Media System - Server Backend & API

The official repo for “Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting”, ACL, 2025.

An open-source AI agent that brings the power of Gemini directly into your terminal.

Run your own AI cluster at home with everyday devices 📱💻 🖥️⌚

Grab your own sweet-looking '.is-a.dev' subdomain.

Examples and guides for using the Gemini API

Ultralytics YOLO 🚀

Policy-based reinforcement learning currently plays an important role in improving LLMs on mathematical reasoning tasks. However, existing rollout-based reinforcement learning methods (GRPO, DAPO, GSPO, etc.) fail to explicitly consider LLMs' learning ability for samples of different difficulty levels, which is contrary to the human cognitive process of mathematical reasoning tasks from easy to difficult. Intuitively, we find that the variance of the rollout group's reward in RLVR partly reflects the difficulty of the current sample for LLMs. Samples that are too easy or too difficult have a lower variance, while samples with moderate difficulty have a higher variance. Based on this, we propose VCRL, a curriculum reinforcement learning framework that dynamically controls the difficulty of training samples based on the variance of group rewards. Experiments on five mathematical benchmarks and two models reveal the advantages of VCRL over the current LLM RL baselines.

We present a scientific reasoning foundation model that aligns natural language with heterogeneous scientific representations. The model is pretrained on a 206B-token corpus spanning scientific text, pure sequences, and sequence-text pairs, then aligned via SFT on 40M instructions, annealed cold-start bootstrapping to elicit long-form chain-of-thought, and reinforcement learning with task-specific reward shaping, which instills deliberate scientific reasoning. It supports four capability families, covering up to 103 tasks across workflows: (i) faithful translation between text and scientific formats, (ii) text/knowledge extraction, (iii) property prediction, (iv) property classification, (v) unconditional and conditional sequence generation and design. Compared with specialist systems, our approach broadens instruction coverage, improves cross-domain generalization, and enhances fidelity. We detail data curation and training and show that cross-discipline learning strengthens transfer and downstream reliability. The model, instruct tuning datasets and the evaluation code are open-sourced at https://huggingface.co/SciReason and https://github.com/open-sciencelab/SciReason.

Large multimodal reasoning models have achieved rapid progress, but their advancement is constrained by two major limitations: the absence of open, large-scale, high-quality long chain-of-thought (CoT) data, and the instability of reinforcement learning (RL) algorithms in post-training. Group Relative Policy Optimization (GRPO), the standard framework for RL fine-tuning, is prone to gradient vanishing when reward variance is low, which weakens optimization signals and impairs convergence. This work makes three contributions: (1) We propose Variance-Aware Sampling (VAS), a data selection strategy guided by Variance Promotion Score (VPS) that combines outcome variance and trajectory diversity to promote reward variance and stabilize policy optimization. (2) We release large-scale, carefully curated resources containing ~1.6M long CoT cold-start data and ~15k RL QA pairs, designed to ensure quality, difficulty, and diversity, along with a fully reproducible end-to-end training codebase. (3) We open-source a family of multimodal reasoning models in multiple scales, establishing standardized baselines for the community. Experiments across mathematical reasoning benchmarks demonstrate the effectiveness of both the curated data and the proposed VAS. Comprehensive ablation studies and analyses provide further insight into the contributions of each component. In addition, we theoretically establish that reward variance lower-bounds the expected policy gradient magnitude, with VAS serving as a practical mechanism to realize this guarantee. Our code, data, and checkpoints are available at https://github.com/LengSicong/MMR1.

Recent advances in reinforcement learning (RL) have significantly enhanced the agentic capabilities of large language models (LLMs). In long-term and multi-turn agent tasks, existing approaches driven solely by outcome rewards often suffer from the problem of sparse supervision. To address the challenge, we propose Tree-based Group Relative Policy Optimization (Tree-GRPO), a grouped agent RL method based on tree search, where each tree node represents the complete agent interaction step. By sharing common prefixes, the tree search sampling increases the number of rollouts achievable within a fixed budget of tokens or tool calls. Moreover, we find that the tree-structured trajectory naturally allows the construction of step-wise process supervised signals even using only the outcome reward. Based on this, Tree-GRPO estimates the grouped relative advantages both on intra-tree and inter-tree levels. Through theoretical analysis, we demonstrate that the objective of intra-tree level group relative policy optimization is equivalent to that of step-level direct preference learning. Experiments across 11 datasets and 3 types of QA tasks demonstrate the superiority of the proposed tree-based RL over the chain-based RL method.

We introduce Seedream 4.0, an efficient and high-performance multimodal image generation system that unifies text-to-image (T2I) synthesis, image editing, and multi-image composition within a single framework. We develop a highly efficient diffusion transformer with a powerful VAE which also can reduce the number of image tokens considerably. This allows for efficient training of our model, and enables it to fast generate native high-resolution images (e.g., 1K-4K). Seedream 4.0 is pretrained on billions of text-image pairs spanning diverse taxonomies and knowledge-centric concepts. Comprehensive data collection across hundreds of vertical scenarios, coupled with optimized strategies, ensures stable and large-scale training, with strong generalization. By incorporating a carefully fine-tuned VLM model, we perform multi-modal post-training for training both T2I and image editing tasks jointly. For inference acceleration, we integrate adversarial distillation, distribution matching, and quantization, as well as speculative decoding. It achieves an inference time of up to 1.8 seconds for generating a 2K image (without a LLM/VLM as PE model). Comprehensive evaluations reveal that Seedream 4.0 can achieve state-of-the-art results on both T2I and multimodal image editing. In particular, it demonstrates exceptional multimodal capabilities in complex tasks, including precise image editing and in-context reasoning, and also allows for multi-image reference, and can generate multiple output images. This extends traditional T2I systems into an more interactive and multidimensional creative tool, pushing the boundary of generative AI for both creativity and professional applications. Seedream 4.0 is now accessible on https://www.volcengine.com/experience/ark?launch=seedream.

Recent advances in 3D-native generative models have accelerated asset creation for games, film, and design. However, most methods still rely primarily on image or text conditioning and lack fine-grained, cross-modal controls, which limits controllability and practical adoption. To address this gap, we present Hunyuan3D-Omni, a unified framework for fine-grained, controllable 3D asset generation built on Hunyuan3D 2.1. In addition to images, Hunyuan3D-Omni accepts point clouds, voxels, bounding boxes, and skeletal pose priors as conditioning signals, enabling precise control over geometry, topology, and pose. Instead of separate heads for each modality, our model unifies all signals in a single cross-modal architecture. We train with a progressive, difficulty-aware sampling strategy that selects one control modality per example and biases sampling toward harder signals (e.g., skeletal pose) while downweighting easier ones (e.g., point clouds), encouraging robust multi-modal fusion and graceful handling of missing inputs. Experiments show that these additional controls improve generation accuracy, enable geometry-aware transformations, and increase robustness for production workflows.

AutoIntent is an automated machine learning tool for text classification tasks. Unlike existing solutions, AutoIntent offers end-to-end automation with embedding model selection, classifier optimization, and decision threshold tuning, all within a modular, sklearn-like interface. The framework is designed to support multi-label classification and out-of-scope detection. AutoIntent demonstrates superior performance compared to existing AutoML tools on standard intent classification datasets and enables users to balance effectiveness and resource consumption.

The adoption of Large Language Models (LLMs) as automated evaluators (LLM-as-a-judge) has revealed critical inconsistencies in current evaluation frameworks. We identify two fundamental types of inconsistencies: (1) Score-Comparison Inconsistency, where lower-rated responses outperform higher-scored ones in pairwise comparisons, and (2) Pairwise Transitivity Inconsistency, manifested through circular preference chains (A>B>C>A) and equivalence contradictions (A=B=C\neq A). We argue that these issues come from information loss in discrete rating systems and ambiguous tie judgments during pairwise evaluation. We propose TrustJudge, a probabilistic framework that addresses these limitations through two key innovations: 1) distribution-sensitive scoring that computes continuous expectations from discrete rating probabilities, preserving information entropy for more precise scoring, and 2) likelihood-aware aggregation that resolves transitivity violations using bidirectional preference probabilities or perplexity. We also formalize the theoretical limitations of current LLM-as-a-judge frameworks and demonstrate how TrustJudge's components overcome them. When evaluated with Llama-3.1-70B-Instruct as judge using our dataset, TrustJudge reduces Score-Comparison inconsistency by 8.43% (from 23.32% to 14.89%) and Pairwise Transitivity inconsistency by 10.82% (from 15.22% to 4.40%), while maintaining higher evaluation accuracy. Our work provides the first systematic analysis of evaluation framework inconsistencies in LLM-as-a-judge paradigms, offering both theoretical insights and practical solutions for reliable automated assessment. The framework demonstrates consistent improvements across various model architectures and scales, enabling more trustworthy LLM evaluation without requiring additional training or human annotations. The codes can be found at https://github.com/TrustJudge/TrustJudge.

Reinforcement learning (RL) has become a powerful paradigm for optimizing large language models (LLMs) to handle complex reasoning tasks. A core challenge in this process lies in managing policy entropy, which reflects the balance between exploration and exploitation during training. Existing methods, such as proximal policy optimization (PPO) and its variants, discard valuable gradient signals from low-probability tokens due to the clipping mechanism. We systematically analyze the entropy dynamics and reveal that these clipped tokens play a critical yet overlooked role in regulating entropy evolution. We propose Controlling Entropy via Gradient-Preserving Policy Optimization (CE-GPPO), a novel algorithm that reintroduces gradients from clipped tokens in native PPO in a gentle and bounded manner. By controlling the magnitude of gradients from tokens outside the clipping interval, CE-GPPO is able to achieve an exploration-exploitation trade-off. We provide theoretical justification and empirical evidence showing that CE-GPPO effectively mitigates entropy instability. Extensive experiments on mathematical reasoning benchmarks show that CE-GPPO consistently outperforms strong baselines across different model scales.

We present CHARM, a novel parametric representation and generative framework for anime hairstyle modeling. While traditional hair modeling methods focus on realistic hair using strand-based or volumetric representations, anime hairstyle exhibits highly stylized, piecewise-structured geometry that challenges existing techniques. Existing works often rely on dense mesh modeling or hand-crafted spline curves, making them inefficient for editing and unsuitable for scalable learning. CHARM introduces a compact, invertible control-point-based parameterization, where a sequence of control points represents each hair card, and each point is encoded with only five geometric parameters. This efficient and accurate representation supports both artist-friendly design and learning-based generation. Built upon this representation, CHARM introduces an autoregressive generative framework that effectively generates anime hairstyles from input images or point clouds. By interpreting anime hairstyles as a sequential "hair language", our autoregressive transformer captures both local geometry and global hairstyle topology, resulting in high-fidelity anime hairstyle creation. To facilitate both training and evaluation of anime hairstyle generation, we construct AnimeHair, a large-scale dataset of 37K high-quality anime hairstyles with separated hair cards and processed mesh data. Extensive experiments demonstrate state-of-the-art performance of CHARM in both reconstruction accuracy and generation quality, offering an expressive and scalable solution for anime hairstyle modeling. Project page: https://hyzcluster.github.io/charm/

Recent years, multimodal models have made remarkable strides and pave the way for intelligent browser use agents. However, when solving tasks on real world webpages in multi-turn, long-horizon trajectories, current agents still suffer from disordered action sequencing and excessive trial and error during execution. This paper introduces Recon-Act, a self-evolving multi-agent framework grounded in Reconnaissance-Action behavioral paradigm. The system comprises a Reconnaissance Team and an Action Team: the former conducts comparative analysis and tool generation, while the latter handles intent decomposition, tool orchestration, and execution. By contrasting the erroneous trajectories with successful ones, the Reconnaissance Team infers remedies, and abstracts them into a unified notion of generalized tools, either expressed as hints or as rule-based codes, and register to the tool archive in real time. The Action Team reinference the process empowered with these targeting tools, thus establishing a closed-loop training pipeline of data-tools-action-feedback. Following the 6 level implementation roadmap proposed in this work, we have currently reached Level 3 (with limited human-in-the-loop intervention). Leveraging generalized tools obtained through reconnaissance, Recon-Act substantially improves adaptability to unseen websites and solvability on long-horizon tasks, and achieves state-of-the-art performance on the challenging VisualWebArena dataset.

Image composition aims to seamlessly insert a user-specified object into a new scene, but existing models struggle with complex lighting (e.g., accurate shadows, water reflections) and diverse, high-resolution inputs. Modern text-to-image diffusion models (e.g., SD3.5, FLUX) already encode essential physical and resolution priors, yet lack a framework to unleash them without resorting to latent inversion, which often locks object poses into contextually inappropriate orientations, or brittle attention surgery. We propose SHINE, a training-free framework for Seamless, High-fidelity Insertion with Neutralized Errors. SHINE introduces manifold-steered anchor loss, leveraging pretrained customization adapters (e.g., IP-Adapter) to guide latents for faithful subject representation while preserving background integrity. Degradation-suppression guidance and adaptive background blending are proposed to further eliminate low-quality outputs and visible seams. To address the lack of rigorous benchmarks, we introduce ComplexCompo, featuring diverse resolutions and challenging conditions such as low lighting, strong illumination, intricate shadows, and reflective surfaces. Experiments on ComplexCompo and DreamEditBench show state-of-the-art performance on standard metrics (e.g., DINOv2) and human-aligned scores (e.g., DreamSim, ImageReward, VisionReward). Code and benchmark will be publicly available upon publication.

This paper introduces a simple and scalable approach to improve the data efficiency of large language model (LLM) training by augmenting existing text data with thinking trajectories. The compute for pre-training LLMs has been growing at an unprecedented rate, while the availability of high-quality data remains limited. Consequently, maximizing the utility of available data constitutes a significant research challenge. A primary impediment is that certain high-quality tokens are difficult to learn given a fixed model capacity, as the underlying rationale for a single token can be exceptionally complex and deep. To address this issue, we propose Thinking augmented Pre-Training (TPT), a universal methodology that augments text with automatically generated thinking trajectories. Such augmentation effectively increases the volume of the training data and makes high-quality tokens more learnable through step-by-step reasoning and decomposition. We apply TPT across diverse training configurations up to 100B tokens, encompassing pre-training with both constrained and abundant data, as well as mid-training from strong open-source checkpoints. Experimental results indicate that our method substantially improves the performance of LLMs across various model sizes and families. Notably, TPT enhances the data efficiency of LLM pre-training by a factor of 3. For a 3B parameter model, it improves the post-training performance by over 10% on several challenging reasoning benchmarks.

Code large language models have demonstrated remarkable capabilities in programming tasks, yet current benchmarks primarily focus on single modality rather than visual game development. Most existing code-related benchmarks evaluate syntax correctness and execution accuracy, overlooking critical game-specific metrics such as playability, visual aesthetics, and user engagement that are essential for real-world deployment. To address the gap between current LLM capabilities in algorithmic problem-solving and competitive programming versus the comprehensive requirements of practical game development, we present V-GameGym, a comprehensive benchmark comprising 2,219 high-quality samples across 100 thematic clusters derived from real-world repositories, adopting a novel clustering-based curation methodology to ensure both diversity and structural completeness. Further, we introduce a multimodal evaluation framework with an automated LLM-driven pipeline for visual code synthesis using complete UI sandbox environments. Our extensive analysis reveals that V-GameGym effectively bridges the gap between code generation accuracy and practical game development workflows, providing quantifiable quality metrics for visual programming and interactive element generation.

Large Reasoning Models (LRMs) have shown impressive capabilities in complex problem-solving, often benefiting from training on difficult mathematical problems that stimulate intricate reasoning. Recent efforts have explored automated synthesis of mathematical problems by prompting proprietary models or large-scale open-source models from seed data or inherent mathematical concepts. However, scaling up these methods remains challenging due to their high computational/API cost, complexity of prompting, and limited difficulty level of the generated problems. To overcome these limitations, we propose ScaleDiff, a simple yet effective pipeline designed to scale the creation of difficult problems. We efficiently identify difficult problems from existing datasets with only a single forward pass using an adaptive thinking model, which can perceive problem difficulty and automatically switch between "Thinking" and "NoThinking" modes. We then train a specialized difficult problem generator (DiffGen-8B) on this filtered difficult data, which can produce new difficult problems in large scale, eliminating the need for complex, per-instance prompting and its associated high API costs. Fine-tuning Qwen2.5-Math-7B-Instruct on the ScaleDiff-Math dataset yields a substantial performance increase of 11.3% compared to the original dataset and achieves a 65.9% average accuracy on AIME'24, AIME'25, HMMT-Feb'25, BRUMO'25, and MATH500, outperforming recent strong LRMs like OpenThinker3. Notably, this performance is achieved using the cost-efficient Qwen3-8B model as a teacher, demonstrating that our pipeline can effectively transfer advanced reasoning capabilities without relying on larger, more expensive teacher models. Furthermore, we observe a clear scaling phenomenon in model performance on difficult benchmarks as the quantity of difficult problems increases. Code: https://github.com/QizhiPei/ScaleDiff.

天文学家长期以来一直依赖超级计算机去模拟宇宙的宏大结构，但一款名为 Effort.jl 的新开源工具正改变这一现状。意大利和加拿大研究团队开发了模拟器 Effort.jl，模拟复杂宇宙模型如 EFTofLSS 如何响应。他们的测试显示，Effort.jl 只需几分钟即可在标准笔记本电脑上提供具有相同精度的结果。这项突破性技术将神经网络与物理知识的巧妙运用相结合，在保持可靠性的同时，大幅缩短了计算时间。Effort.jl 源代码发布在 GitHub 上，采用 MIT 许可证。

微软和华硕宣布开放预购将于 10 月 16 日上市的 ROG Xbox Ally 系列掌机，正式公布了掌机价格。ROG Xbox Ally 系列掌机运行 Windows 11，操作系统为掌机进行了优化，支持包括 Valve Steam 和 Epic Games Store 在内的游戏商店，虽然使用 Xbox 品牌，但并不支持 Xbox 游戏，而是 Windows PC 游戏。ROG Xbox Ally 系列共两个型号，均采用 7 英寸 1080p IPS 显示屏，刷新率 120 Hz，支持 Wi-Fi 6E 和蓝牙 5.4，但内部配置有显著差异。低端的 Xbox Ally 搭载 AMD Ryzen Z2 A 芯片，其配置与 Valve 三年前的 Steam Deck 掌机几乎完全相同，售价 599.99 美元。高端的 Xbox Ally X 搭载了 Ryzen AI Z2 Extreme 处理器，配备 8 核 Zen 5 CPU、16 核 RDNA3.5 GPU、1TB 存储空间、24GB LPDDR5X-8000 内存以及 NPU，售价 999.99 美元。

OpenAI 准备建造的数据中心耗电量相当于纽约和圣迭戈两大城市之和。芝加哥大学计算机科学教授 Andrew Chien 表示，作为从业 40 年的计算机科学家，大部分时间里，计算的耗电量通常是经济用电量中非常小的一部分，而如今计算的耗电量正占到经济用电量的很大一部分。他对此既感到兴奋又感到担忧，认为现有的电力基础设施以及在建设施发电量都满足不了 AI 公司日益膨胀的耗电需求。他说，到 2030 年计算可能占到全球总电力的 10% 或 12%。我们即将迎来思考 AI 及其社会影响的重要时刻。OpenAI 准备建造的数据中心耗电量达到惊人的 10-17GW，而美国在 2030 年之前可并网的核电容量不到 1 GW，如此巨大的鸿沟如何填补？

在发现证据表明以色列国防部使用微软云服务 Azure 监视加沙居民后，微软禁止以国防部访问某些服务和订阅。《卫报》今年八月报告以色列使用 Azure 存储加沙居民数据并对其进行监视，微软随后启动了一项内部调查，调查仍然在进行之中。微软总裁 Brad Smith 通过官方博客宣称，微软长期以来将隐私作为一项基本权利加以保护，作为员工他们都对隐私保护有着共同的兴趣，因为隐私保护通过确保客户能完全信任微软的服务而创造商业价值。

Maria Branyas 能活到 117 岁的原因之一是她拥有异常年轻的基因组。Branyas 于 2024 年去世，当时她是世界最长寿的人，她去世前提供了自己的血液、唾液、尿液和粪便样本供科学家研究。科学家分析后发现，她体内细胞的生物年龄看起来比她的实际年龄更年轻。她晚年总体健康状况良好，心血管健康状况极佳，炎症水平极低。她的免疫系统和肠道菌群指标与更年轻的人​​群相当，她的“坏”胆固醇和甘油三酯水平极低，“好”胆固醇水平极高。所有这些因素都有助于解释她健康状况如此出色的和寿命如此高。科学家还发现，她的染色体末端端粒异常短。非常短的端粒可能为她带来了优势。科学家认为她体内细胞的短寿可能阻止了癌症的增殖。

研究人员使用来自《Science》及其姊妹期刊《Science Advances》内部数据，统计了超过 11 万份论文投稿，其中《Science》投稿接近7万份。结果显示《Science》对所有论文的平均接受率为6.1%。然而在国家层面上，差距异常明显：中国学者的论文接受率只有 2.3%，而美国与加拿大学者则达到 8.3%。此外机构知名程度、共同作者的数量，乃至学科主题，都在无形中塑造着论文不同的命运。调查发现，知名机构的论文更受《Science》青睐。知名程度处于“第一或第二梯队”的机构，论文接受率为 11.6%；而“最末梯队”的机构，接受率仅为 3.4%。共同作者越多，论文接受率越高。有 10 位或更多作者的论文接受率为 9.9%，有 1~5 位作者的论文接收率仅为 3.3%。调查人员将论文主题分为十大类，某类论文的接受率为9.6%，而另一类的接受率仅为 1.7%。调查还揭示了另一个重要事实：论文的命运往往首先掌握在编辑手中。许多稿件甚至还没进入同行评审，就已经被挡在“第一道门”之外。统计分析显示，编辑的初筛比审稿人给出的建议更能影响稿件是否最终被录用。

根据发表在《Autonomic Neuroscience》期刊上的一项研究，定期锻炼不仅能增强心脏功能，还能重塑控制心脏的神经。研究首次表明，适度的有氧训练有助于重塑控制心脏的神经，且对心脏两侧的神经产生不同的影响。研究使用了名叫体视学（stereology）的 3D 定量成像分析方法。研究结果表明，经过 10 周训练的大鼠，其身体右侧心血管丛的神经元数量约为左侧的四倍，而未训练的大鼠则相反，左侧神经元的大小几乎增加了一倍，而右侧神经元的大小则略有缩小。研究人员认为，最新发现能被用于更有效的治疗一系列疾病，包括心律不齐、胸痛、心绞痛和“心碎”综合征。

在与英伟达、美国政府和软银集团达成了数十亿美元的协议之后，英特尔已接洽苹果讨论投资和更紧密的合作。双方的磋商处于早期阶段，可能不会达成协议。达成利润丰厚的合作伙伴关系并说服外部客户使用其工厂代工芯片是芯片巨人未来发展的关键。如果能说服苹果投资英特尔，这将是对英特尔的又一次信任投票。苹果在 2020 年之后转向设计自己的笔记本用 ARM 芯片，在这之前它一直是英特尔的长期客户。对苹果而言，它高度依赖于英特尔的竞争对手台积电代工芯片，如果能利用英特尔的芯片工厂，将有助于实现芯片制造供应商多元化。

微软 AI 部门 CEO Mustafa Suleyman 表示该公司计划将 Edge 改造成一款“智能体浏览器（agentic browser）”，在用户注视下浏览器集成的 AI 助手 Copilot 将控制标签页、浏览网站，完成不同任务。Suleyman 描述了 Copilot 打开标签页、同时阅读多个网页，实时透明的执行搜索。AI 助手能直接访问网站，保留了内容出版商的流量。Copilot 目前的功能包括标签页导航、页面滚动和内容高亮显示等。Suleyman 预测，AI 助手将在数年内负责大多数浏览任务，而用户则提供监督和反馈。

一颗俄罗斯生物实验卫星在轨道飞行 30 天后返回地面。Bion-M 2 号卫星于 8 月 20 日携带 75 只小鼠和 1000 只果蝇搭载联盟号火箭发射升空，这些动物被用于研究太空飞行过程中暴露在高水平宇宙辐射下的影响。9 月 19 日，装有 75 只小鼠和 1500 只果蝇，以及细胞培养物、微生物、植物种子等的着陆舱着陆在 Orenburg 地区的草原上。

每年有超过 4 亿吨塑料污染了海滩、河流，甚至海洋最深处——深度可达1.1万米。除了污染环境，塑料还加剧了气候变化。据估计，塑料生产每年约产生 18 亿吨温室气体。科学证据还表明，在日常生活中使用塑料材料已经影响到人类健康。大量塑料颗粒从窗帘、家具、衣物和其他塑料制品上脱落。这些颗粒悬浮在空气中，溶解在饮用水中，附着在食物上，可以被吸入、摄入或与皮肤接触。现在，科学家已在人类血液、大脑、胎盘、母乳甚至骨骼中发现了微塑料。《国际骨质疏松症》发表的一项研究回顾了 62 篇科学论文，发现微塑料以各种方式损害骨骼健康。一个典型例子是，它们通过促进破骨细胞的形成损害骨髓干细胞的功能。破骨细胞是一种多核细胞，通过骨吸收的过程降解组织。根据国际骨质疏松症基金会的数据，由于人口老龄化，全球骨质疏松症相关骨折的发病率正在上升。预计到 2050 年，骨质疏松症相关骨折将增加 32%。

国防科技大学团队受不倒翁结构的启发，提出了一款圆柱对称结构的筒状气动阻尼器，在垂直方向可以形成各向同性阻尼效应，将其布置于飞行器上方形成一款不倒翁微飞行器，可大大提升扑翼微飞行器垂直方向的自稳定性能。基于 X 型直驱式扑翼架构进行结构优化，研制出质量 204 mg、翼展 68 mm 的微型飞行器。通过改进升力生成机制，在保持驱动条件不变的情况下在最大升力可达 7.6mN，实现升力性能 41.5% 的提升，并将结构不对称导致的运动误差降低 5%。这一进展为后续集成被动稳定系统奠定了重要基础。研究报告发表在《Research》期刊上。

安理会 24 日召开关于 AI 和平利用与风险的讨论会议。联合国秘书长古特雷斯主张要到 2026 年针对不基于人类判断、利用 AI 实施攻击的武器建立国际监管。主持会议的安理会本月轮值主席国韩国总统李在明也表达了相同的观点。美国代表提出反对，称 AI 的开发和利用是关乎“国家独立和主权的问题。拒绝国际管理。”提出美国优先的特朗普政府在 AI 领域也明显展现出轻视多边协作的态度。古特雷斯强调，运用AI将在粮食短缺、难民出现等预测和早期应对方面带来优势。但他指出，如果完全没有国际规则的状态持续，在武器上的使用或将加快。古特雷斯指出尤其是核武器的使用必须由人类而非 AI 做出判断。中国等表态支持。

人眼主要对可见光范围内三个波谱波段——红、绿和蓝——敏感。相比下智能手机摄像头传感器具有高光谱特性，能对更多光谱波段敏感。现在科学家找到了一种简单方法，让任何智能手机摄像头都能变成高光谱传感器——只要在其视野内放置一张图表卡片。研究人员正在申请专利，认为新技术可应用于国防、安全、医学、法医、农业、环境监测、工业质量控制以及食品和饮料质量分析。科学领域使用的高光谱传感器对颜色有极高灵敏度，可根据光谱特征识别化学物质。将智能手机摄像头传感器变成高光谱传感器的现有方法存在诸多缺陷，通用性不高。在最新研究中，科学家设计出一张可打印在卡片上的色彩图表，并研发出一种算法去分析用这张卡片拍摄的手机相片，能以科学级高光谱传感器相当的灵敏度提取出高光谱数据。研究人员表示，这相当于将普通智能手机变成袖珍光谱仪。

Windows 10 即将于 10 月 14 日结束支持，此后微软将不再提供免费的安全更新，但 Windows 10 仍然有大量用户使用，用户担心他们可能需要购买新电脑才能保护自己免遭网络风险。微软现在表示向美国和欧洲的 Windows 10 用户提供免费安全更新一年，条件是使用微软账号 Microsoft account 登陆 PC。