OrangeBot.AI Digest — 2025-09-25

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.

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

A feature-rich command-line audio/video downloader

All Algorithms implemented in Python

人人都能用英语

Video-based AI memory library. Store millions of text chunks in MP4 files with lightning-fast semantic search. No database needed.

🤖 Fully typesafe Router for React (and friends) w/ built-in caching, 1st class search-param APIs, client-side cache integration and isomorphic rendering.

Truly independent web browser

A payments protocol for the internet. Built on HTTP.

30 days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than100 days, follow your own pace. These videos may help too: https://www.youtube.com/channel/UC7PNRuno1rzYPb1xLa4yktw

Free and Open Source, Distributed, RESTful Search Engine

OpenZeppelin Contracts is a library for secure smart contract development.

The LLM Evaluation Framework

JavaScript/TypeScript-native, low-boilerplate, object-capability RPC system

node of the decentralized oracle network, bridging on and off-chain computation

The remarkable zero-shot capabilities of Large Language Models (LLMs) have propelled natural language processing from task-specific models to unified, generalist foundation models. This transformation emerged from simple primitives: large, generative models trained on web-scale data. Curiously, the same primitives apply to today's generative video models. Could video models be on a trajectory towards general-purpose vision understanding, much like LLMs developed general-purpose language understanding? We demonstrate that Veo 3 can solve a broad variety of tasks it wasn't explicitly trained for: segmenting objects, detecting edges, editing images, understanding physical properties, recognizing object affordances, simulating tool use, and more. These abilities to perceive, model, and manipulate the visual world enable early forms of visual reasoning like maze and symmetry solving. Veo's emergent zero-shot capabilities indicate that video models are on a path to becoming unified, generalist vision foundation models.

Implicit Chain-of-Thought (CoT) methods present a promising, token-efficient alternative to explicit CoT reasoning in Large Language Models (LLMs), but a persistent performance gap has limited the application of implicit CoT. We identify a core latent instability issue by scaling the computational budget of implicit CoT approaches: as we increase the number of implicit reasoning tokens to enhance performance, the training process often becomes unstable and collapses. Our analysis reveals that this instability arises from the latent representations becoming homogeneous and losing their semantic diversity, a failure caused by insufficient step-level supervision in existing implicit CoT approaches. To address this issue, we propose SIM-CoT, a plug-and-play training module that introduces step-level supervision to stabilize and enrich the latent reasoning space. Specifically, SIM-CoT employs an auxiliary decoder during training to align each implicit token with its corresponding explicit reasoning step, ensuring that latent states capture distinct and meaningful information. The proposed auxiliary decoder is removed during inference, preserving the computational efficiency of implicit CoT methods with no added overhead. In addition, the auxiliary decoder affords interpretability of implicit reasoning by projecting each latent token onto an explicit reasoning vocabulary, enabling per-step visualization of semantic roles and diagnosis. SIM-CoT significantly enhances both the in-domain accuracy and out-of-domain stability of various implicit CoT methods, boosting baselines like Coconut by +8.2% on GPT-2 and CODI by +3.0% on LLaMA-3.1 8B. Demonstrating strong scalability, SIM-CoT also surpasses the explicit CoT baseline on GPT-2 by 2.1% with 2.3\times greater token efficiency, while substantially closing the performance gap on larger models like LLaMA-3.1 8B.

We introduce EmbeddingGemma, a new lightweight, open text embedding model based on the Gemma 3 language model family. Our innovative training recipe strategically captures knowledge from larger models via encoder-decoder initialization and geometric embedding distillation. We improve model robustness and expressiveness with a spread-out regularizer, and ensure generalizability by merging checkpoints from varied, optimized mixtures. Evaluated on the Massive Text Embedding Benchmark (MTEB) across multilingual, English, and code domains, EmbeddingGemma (300M) achieves state-of-the-art results. Notably, it outperforms prior top models, both proprietary and open, with fewer than 500M parameters, and provides performance comparable to models double its size, offering an exceptional performance-to-cost ratio. Remarkably, this lead persists when quantizing model weights or truncating embedding outputs. This makes EmbeddingGemma particularly well-suited for low-latency and high-throughput use cases such as on-device applications. We provide ablation studies exploring our key design choices. We release EmbeddingGemma to the community to promote further research.

In this paper, we introduce a Group Relative Policy Optimization (GRPO)-based method for training Speech-Aware Large Language Models (SALLMs) on open-format speech understanding tasks, such as Spoken Question Answering and Automatic Speech Translation. SALLMs have proven highly effective for speech understanding tasks. GRPO has recently gained traction for its efficiency in training LLMs, and prior work has explored its application to SALLMs, primarily in multiple-choice tasks. Building on this, we focus on open-format tasks that better reflect the generative abilities of the models. Our approach leverages GRPO with BLEU as the reward signal to optimize SALLMs, and we demonstrate empirically that it surpasses standard SFT across several key metrics. Finally, we explore the potential of incorporating off-policy samples within GRPO for these tasks, highlighting avenues for further improvement and further research.

Recent advances in foundation models highlight a clear trend toward unification and scaling, showing emergent capabilities across diverse domains. While image generation and editing have rapidly transitioned from task-specific to unified frameworks, video generation and editing remain fragmented due to architectural limitations and data scarcity. In this work, we introduce EditVerse, a unified framework for image and video generation and editing within a single model. By representing all modalities, i.e., text, image, and video, as a unified token sequence, EditVerse leverages self-attention to achieve robust in-context learning, natural cross-modal knowledge transfer, and flexible handling of inputs and outputs with arbitrary resolutions and durations. To address the lack of video editing training data, we design a scalable data pipeline that curates 232K video editing samples and combines them with large-scale image and video datasets for joint training. Furthermore, we present EditVerseBench, the first benchmark for instruction-based video editing covering diverse tasks and resolutions. Extensive experiments and user studies demonstrate that EditVerse achieves state-of-the-art performance, surpassing existing open-source and commercial models, while exhibiting emergent editing and generation abilities across modalities.

Cutting-edge Artificial Intelligence (AI) techniques keep reshaping our view of the world. For example, Large Language Models (LLMs) based applications such as ChatGPT have shown the capability of generating human-like conversation on extensive topics. Due to the impressive performance on a variety of language-related tasks (e.g., open-domain question answering, translation, and document summarization), one can envision the far-reaching impacts that can be brought by the LLMs with broader real-world applications (e.g., customer service, education and accessibility, and scientific discovery). Inspired by their success, this paper will offer an overview of state-of-the-art LLMs and their integration into a wide range of academic disciplines, including: (1) arts, letters, and law (e.g., history, philosophy, political science, arts and architecture, law), (2) economics and business (e.g., finance, economics, accounting, marketing), and (3) science and engineering (e.g., mathematics, physics and mechanical engineering, chemistry and chemical engineering, life sciences and bioengineering, earth sciences and civil engineering, computer science and electrical engineering). Integrating humanity and technology, in this paper, we will explore how LLMs are shaping research and practice in these fields, while also discussing key limitations, open challenges, and future directions in the era of generative AI. The review of how LLMs are engaged across disciplines-along with key observations and insights-can help researchers and practitioners interested in exploiting LLMs to advance their works in diverse real-world applications.

We propose Lavida-O, a unified Masked Diffusion Model (MDM) for multimodal understanding and generation. Unlike existing multimodal MDMs such as MMaDa and Muddit which only support simple image-level understanding tasks and low-resolution image generation, Lavida-O presents a single framework that enables image-level understanding, object grounding, image editing, and high-resolution (1024px) text-to-image synthesis. Lavida-O incorporates a novel Elastic Mixture-of-Transformers (Elastic-MoT) architecture that couples a lightweight generation branch with a larger understanding branch, supported by token compression, universal text conditioning and stratified sampling for efficient and high-quality generation. Lavida-O further incorporates planning and iterative self-reflection in image generation and editing tasks, seamlessly boosting generation quality with its understanding capabilities. Lavida-O achieves state-of-the-art performance on a wide range of benchmarks including RefCOCO object grounding, GenEval text-to-image generation, and ImgEdit image editing, outperforming existing autoregressive models and continuous diffusion models such as Qwen2.5-VL and FluxKontext-dev, while offering considerable speedup at inference. These advances establish Lavida-O as a new paradigm for scalable multimodal reasoning and generation.

Existing video generation models excel at producing photo-realistic videos from text or images, but often lack physical plausibility and 3D controllability. To overcome these limitations, we introduce PhysCtrl, a novel framework for physics-grounded image-to-video generation with physical parameters and force control. At its core is a generative physics network that learns the distribution of physical dynamics across four materials (elastic, sand, plasticine, and rigid) via a diffusion model conditioned on physics parameters and applied forces. We represent physical dynamics as 3D point trajectories and train on a large-scale synthetic dataset of 550K animations generated by physics simulators. We enhance the diffusion model with a novel spatiotemporal attention block that emulates particle interactions and incorporates physics-based constraints during training to enforce physical plausibility. Experiments show that PhysCtrl generates realistic, physics-grounded motion trajectories which, when used to drive image-to-video models, yield high-fidelity, controllable videos that outperform existing methods in both visual quality and physical plausibility. Project Page: https://cwchenwang.github.io/physctrl

Recent advances in Large Vision-Language models (LVLM) have spurred significant progress in document parsing task. Compared to traditional pipeline-based methods, end-to-end paradigms have shown their excellence in converting PDF images into structured outputs through integrated Optical Character Recognition (OCR), table recognition, mathematical formula recognition and so on. However, the absence of explicit analytical stages for document layouts and reading orders limits the LVLM's capability in handling complex document types such as multi-column newspapers or posters. To address this limitation, we propose in this report Logics-Parsing: an end-to-end LVLM-based model augmented with reinforcement learning. Our model incorporates meticulously designed reward mechanisms to optimize complex layout analysis and reading order inference. In addition, we expand the model's versatility by incorporating diverse data types such as chemical formulas and handwritten Chinese characters into supervised fine-tuning. Finally, to enable rigorous evaluation of our approach, we introduce LogicsParsingBench, a curated set of 1,078 page-level PDF images spanning nine major categories and over twenty sub-categories, which will be released later. Comprehensive experiments conducted on LogicsParsingBench have validated the efficacy and State-of-the-art (SOTA) performance of our proposed model across diverse document analysis scenarios. Project Page: https://github.com/alibaba/Logics-Parsing

Protein folding models have achieved groundbreaking results typically via a combination of integrating domain knowledge into the architectural blocks and training pipelines. Nonetheless, given the success of generative models across different but related problems, it is natural to question whether these architectural designs are a necessary condition to build performant models. In this paper, we introduce SimpleFold, the first flow-matching based protein folding model that solely uses general purpose transformer blocks. Protein folding models typically employ computationally expensive modules involving triangular updates, explicit pair representations or multiple training objectives curated for this specific domain. Instead, SimpleFold employs standard transformer blocks with adaptive layers and is trained via a generative flow-matching objective with an additional structural term. We scale SimpleFold to 3B parameters and train it on approximately 9M distilled protein structures together with experimental PDB data. On standard folding benchmarks, SimpleFold-3B achieves competitive performance compared to state-of-the-art baselines, in addition SimpleFold demonstrates strong performance in ensemble prediction which is typically difficult for models trained via deterministic reconstruction objectives. Due to its general-purpose architecture, SimpleFold shows efficiency in deployment and inference on consumer-level hardware. SimpleFold challenges the reliance on complex domain-specific architectures designs in protein folding, opening up an alternative design space for future progress.

Accurate classification of products under the Harmonized Tariff Schedule (HTS) is a critical bottleneck in global trade, yet it has received little attention from the machine learning community. Misclassification can halt shipments entirely, with major postal operators suspending deliveries to the U.S. due to incomplete customs documentation. We introduce the first benchmark for HTS code classification, derived from the U.S. Customs Rulings Online Search System (CROSS). Evaluating leading LLMs, we find that our fine-tuned Atlas model (LLaMA-3.3-70B) achieves 40 percent fully correct 10-digit classifications and 57.5 percent correct 6-digit classifications, improvements of 15 points over GPT-5-Thinking and 27.5 points over Gemini-2.5-Pro-Thinking. Beyond accuracy, Atlas is roughly five times cheaper than GPT-5-Thinking and eight times cheaper than Gemini-2.5-Pro-Thinking, and can be self-hosted to guarantee data privacy in high-stakes trade and compliance workflows. While Atlas sets a strong baseline, the benchmark remains highly challenging, with only 40 percent 10-digit accuracy. By releasing both dataset and model, we aim to position HTS classification as a new community benchmark task and invite future work in retrieval, reasoning, and alignment.

Large language models (LLMs) are increasingly being integrated into software development processes. The ability to generate code and submit pull requests with minimal human intervention, through the use of autonomous AI agents, is poised to become a standard practice. However, little is known about the practical usefulness of these pull requests and the extent to which their contributions are accepted in real-world projects. In this paper, we empirically study 567 GitHub pull requests (PRs) generated using Claude Code, an agentic coding tool, across 157 diverse open-source projects. Our analysis reveals that developers tend to rely on agents for tasks such as refactoring, documentation, and testing. The results indicate that 83.8% of these agent-assisted PRs are eventually accepted and merged by project maintainers, with 54.9% of the merged PRs are integrated without further modification. The remaining 45.1% require additional changes benefit from human revisions, especially for bug fixes, documentation, and adherence to project-specific standards. These findings suggest that while agent-assisted PRs are largely acceptable, they still benefit from human oversight and refinement.

We present an open-source Python library for simulating two-dimensional incompressible Kelvin-Helmholtz instabilities in stratified shear flows. The solver employs a fractional-step projection method with spectral Poisson solution via Fast Sine Transform, achieving second-order spatial accuracy. Implementation leverages NumPy, SciPy, and Numba JIT compilation for efficient computation. Four canonical test cases explore Reynolds numbers 1000--5000 and Richardson numbers 0.1--0.3: classical shear layer, double shear configuration, rotating flow, and forced turbulence. Statistical analysis using Shannon entropy and complexity indices reveals that double shear layers achieve 2.8times higher mixing rates than forced turbulence despite lower Reynolds numbers. The solver runs efficiently on standard desktop hardware, with 384times192 grid simulations completing in approximately 31 minutes. Results demonstrate that mixing efficiency depends on instability generation pathways rather than intensity measures alone, challenging Richardson number-based parameterizations and suggesting refinements for subgrid-scale representation in climate models.

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。

NASA 宣布计划最早于明年 2 月启动阿波罗任务之后的首次载人月球飞行任务。这一时间表让很多人感到意外。NASA 是在 2022 年执行了 Artemis 登月计划的首次飞行任务，发射代号为 Artemis I，为无人飞行，使用登月火箭 Space Launch System（SLS）把 Orion 飞船送往月球并返回地球，为期六周，这次任务主要是测试系统。第二次任务代号 Artemis II，将把四名宇航员送往月球轨道然后返回，为期十天。这次任务最早的发射窗口是明年的 2 月 5 日。四名宇航员包括 3 名 NASA 宇航员 Reid Wiseman、Victor Glover 和 Christina Koch，以及加拿大宇航员 Jeremy Hansen。

Meta CEO 马克·扎克伯格(Mark Zuckerberg)通过其 Instagram 账号宣布该平台的月活用户数突破 30 亿。Meta /Facebook 于 2012 年以 10 亿美元收购了照片分享应用 Instagram，当时它有 3000 万用户，2018 年它的月活跃用户数突破了 10 亿，2022 年 10 月扎克伯格披露 Instagram 的月活用户数突破 20 亿。月活用户数从 20 亿增长到 30 亿要比 10 亿到 20 亿更快。

日本爱知县(Aichi)丰明（Toyoake）市议会通过了《促进正确使用智能手机》法令，象征性的将智能手机的娱乐性使用时间限制为每天两小时，此举旨在让市民尤其是学生获得充足的睡眠。违反两小时限制的人并不会受到惩罚。法令称，过度使用手机的人及其家人在日常和社交生活中会面临困难，限制每天两小时可能会有所帮助。丰明市有 6.9 万居民，该法案只是建议性的，并不具有约束力。

Open Source Security Foundation(OpenSSF)警告开源基础设施不能运行在祈祷之上，称开源基础设施并非免费，而现代软件开发背后的重要机制正面临崩溃。Eclipse 基金会、Rust 基金会、Sonatype 和 Python 软件基金会等八个组织在一封公开信中声明，包管理器如 Maven Central、PyPI、crates.io、npm 和 Packagist 每月处理数十亿次下载，但运营的组织经常只能依靠捐赠、拨款和少数赞助商的善意勉强维持。开源基础设施使用的带宽、存储、人员和合规性成本正加速增长。如果没有商业规模的支持，商业规模的使用不可持续。

一项公布在预印本平台 medRxiv 的研究对文献数据库分析后发现，包括 ChatGPT 和 Gemini 在内的文本生成 AI 工具被用来改写科学论文并生成抄袭版本，充作新的研究成果。该研究指出，在过去 4.5 年间，有 400 多篇此类论文发表于 112 种期刊，而且 AI 生成的生物医学研究论文能够避开出版商的查重。研究警告称，一些个人和论文工厂可能正基于公开可用的健康数据集，利用大型语言模型（LLM）批量生产缺乏科学价值的劣质论文。这就像打开了潘多拉魔盒，有价值的文献会被大量合成论文淹没。研究人员将搜索重点放在重复研究上，即这些研究的变量与健康结果与已有研究相同，但分析的是略有不同的数据子集，比如不同调查年份的结果或者不同年龄、性别的参与者。

根据发表在《Journal of Ethology》期刊上的一项研究，科学家通过镜头捕捉到类似人类三人行的罕见鲨鱼交配情景。作为研究项目的一部分，澳大利亚阳光海岸大学博士后 Hugo Lassauce 观察了鲨鱼一年时间，在一次观察中他发现海底沙滩上两只雄性豹纹鲨抓住了一只雌性的胸鳍，这是典型的交配前求偶行为。Lassauce 在水中待了一个小时，几乎冻僵，终于记录到了两雄一雌的交配过程：一只持续了 63 秒，另一只持续了 47 秒。然后两只雄性筋疲力尽的躺在水底不动，而雌性则精神抖擞的离开了。Lassauce 使用了两台 GoPro Hero 5 相机，他记录到交配前三只鲨鱼几乎一动不动的持续了近一小时，之后才开始交配。

BetterUp Labs 和 Stanford Social Media Lab 调查了 1,150 名全职员工，发现过去一个月四成员工收到了工作相关的 AI 垃圾（workslop）——即 AI 生成的看似精雕细琢但缺乏实质性内容的文本。收到之后员工们平均每条需要花 1 小时 56 分钟处理，给企业造成约 186 美元的损失。对一家有 10,000 名员工的公司而言，每年造成的生产力损失逾 900 万美元。员工报告，收到的内容有 15.4% 属于工作垃圾。其中同事之间传递的工作垃圾占到了 40%，有 18% 的内容从直接下属传递给经理，还有 16% 的内容则是自上而下传递。除了财务成本之外，工作垃圾还会损害职场关系——半数接收者认为发送者缺乏创造力、能力和可靠性，42% 的人则不太信任他们。