OrangeBot.AI Digest — 2025-07-24

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Investment Research for Everyone, Everywhere.

Label Studio is a multi-type data labeling and annotation tool with standardized output format

[Support 0.49.x]（Reset Cursor AI MachineID & Bypass Higher Token Limit） Cursor Ai ，自动重置机器ID ， 免费升级使用Pro功能: You've reached your trial request limit. / Too many free trial accounts used on this machine. Please upgrade to pro. We have this limit in place to prevent abuse. Please let us know if you believe this is a mistake.

BillionMail gives you open-source MailServer, NewsLetter, Email Marketing — fully self-hosted, dev-friendly, and free from monthly fees. Join the discord: https://discord.gg/asfXzBUhZr

Open Source HR and Payroll Software

Qwen3 is the large language model series developed by Qwen team, Alibaba Cloud.

21 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/

MinIO is a high-performance, S3 compatible object store, open sourced under GNU AGPLv3 license.

Python SDK, Proxy Server (LLM Gateway) to call 100+ LLM APIs in OpenAI format - [Bedrock, Azure, OpenAI, VertexAI, Cohere, Anthropic, Sagemaker, HuggingFace, Replicate, Groq]

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

The first AI designed for therapy

Streamline payables with robots chasing invoices & receipts

Full company data on millions of profile

Lovable now thinks, plans, and acts on its own

Your AI pet with a mind (and adventures) of its own

Business Context Engineering Made Simple for Marketing Teams

Free cap table management platform for early-stage founders

AI platform for building + deploying full-stack apps

An AI Agent that tells you why your systems break

Fix your outdated GitHub Docs on autopilot

Always‑on personal AI wearable

The AI ClickHouse® expert you don't have to hire

Memory system for Cursor agents

AI users to test your app, catch bugs, and give feedback

A video chat doorbell for websites

Achieving human-like perception and reasoning in Multimodal Large Language Models (MLLMs) remains a central challenge in artificial intelligence. While recent research has primarily focused on enhancing reasoning capabilities in MLLMs, a fundamental question persists: Can Multimodal Large Language Models truly perceive the world as humans do? This paper shifts focus from reasoning to perception. Rather than constructing benchmarks specifically for reasoning, we introduce the Turing Eye Test (TET), a challenging perception-oriented benchmark comprising four diagnostic tasks that evaluate MLLMs' performance on synthetic images that humans process intuitively. Our findings reveal that state-of-the-art MLLMs exhibit catastrophic failures on our perceptual tasks trivial for humans. Both in-context learning and training on language backbone-effective for previous benchmarks-fail to improve performance on our tasks, while fine-tuning the vision tower enables rapid adaptation, suggesting that our benchmark poses challenges for vision tower generalization rather than for the knowledge and reasoning capabilities of the language backbone-a key gap between current MLLMs and human perception. We release a representative subset of TET tasks in this version, and will introduce more diverse tasks and methods to enhance visual generalization in future work.

Yume aims to use images, text, or videos to create an interactive, realistic, and dynamic world, which allows exploration and control using peripheral devices or neural signals. In this report, we present a preview version of \method, which creates a dynamic world from an input image and allows exploration of the world using keyboard actions. To achieve this high-fidelity and interactive video world generation, we introduce a well-designed framework, which consists of four main components, including camera motion quantization, video generation architecture, advanced sampler, and model acceleration. First, we quantize camera motions for stable training and user-friendly interaction using keyboard inputs. Then, we introduce the Masked Video Diffusion Transformer~(MVDT) with a memory module for infinite video generation in an autoregressive manner. After that, training-free Anti-Artifact Mechanism (AAM) and Time Travel Sampling based on Stochastic Differential Equations (TTS-SDE) are introduced to the sampler for better visual quality and more precise control. Moreover, we investigate model acceleration by synergistic optimization of adversarial distillation and caching mechanisms. We use the high-quality world exploration dataset \sekai to train \method, and it achieves remarkable results in diverse scenes and applications. All data, codebase, and model weights are available on https://github.com/stdstu12/YUME. Yume will update monthly to achieve its original goal. Project page: https://stdstu12.github.io/YUME-Project/.

Designing high-quality presentation slides can be challenging for non-experts due to the complexity involved in navigating various design choices. Numerous automated tools can suggest layouts and color schemes, yet often lack the ability to refine their own output, which is a key aspect in real-world workflows. We propose DesignLab, which separates the design process into two roles, the design reviewer, who identifies design-related issues, and the design contributor who corrects them. This decomposition enables an iterative loop where the reviewer continuously detects issues and the contributor corrects them, allowing a draft to be further polished with each iteration, reaching qualities that were unattainable. We fine-tune large language models for these roles and simulate intermediate drafts by introducing controlled perturbations, enabling the design reviewer learn design errors and the contributor learn how to fix them. Our experiments show that DesignLab outperforms existing design-generation methods, including a commercial tool, by embracing the iterative nature of designing which can result in polished, professional slides.

Agentic search, as a more autonomous and adaptive paradigm of retrieval augmentation, is driving the evolution of intelligent search systems. However, existing evaluation frameworks fail to align well with the goals of agentic search. First, the complex queries commonly used in current benchmarks often deviate from realistic user search scenarios. Second, prior approaches tend to introduce noise when extracting ground truth for end-to-end evaluations, leading to distorted assessments at a fine-grained level. Third, most current frameworks focus solely on the quality of final answers, neglecting the evaluation of the iterative process inherent to agentic search. To address these limitations, we propose RAVine -- a Reality-Aligned eValuation framework for agentic LLMs with search. RAVine targets multi-point queries and long-form answers that better reflect user intents, and introduces an attributable ground truth construction strategy to enhance the accuracy of fine-grained evaluation. Moreover, RAVine examines model's interaction with search tools throughout the iterative process, and accounts for factors of efficiency. We benchmark a series of models using RAVine and derive several insights, which we hope will contribute to advancing the development of agentic search systems. The code and datasets are available at https://github.com/SwordFaith/RAVine.

Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a powerful paradigm for enhancing the reasoning capabilities of LLMs. Existing research has predominantly concentrated on isolated reasoning domains such as mathematical problem-solving, coding tasks, or logical reasoning. However, real world reasoning scenarios inherently demand an integrated application of multiple cognitive skills. Despite this, the interplay among these reasoning skills under reinforcement learning remains poorly understood. To bridge this gap, we present a systematic investigation of multi-domain reasoning within the RLVR framework, explicitly focusing on three primary domains: mathematical reasoning, code generation, and logical puzzle solving. We conduct a comprehensive study comprising four key components: (1) Leveraging the GRPO algorithm and the Qwen-2.5-7B model family, our study thoroughly evaluates the models' in-domain improvements and cross-domain generalization capabilities when trained on single-domain datasets. (2) Additionally, we examine the intricate interactions including mutual enhancements and conflicts that emerge during combined cross-domain training. (3) To further understand the influence of SFT on RL, we also analyze and compare performance differences between base and instruct models under identical RL configurations. (4) Furthermore, we delve into critical RL training details, systematically exploring the impacts of curriculum learning strategies, variations in reward design, and language-specific factors. Through extensive experiments, our results offer significant insights into the dynamics governing domain interactions, revealing key factors influencing both specialized and generalizable reasoning performance. These findings provide valuable guidance for optimizing RL methodologies to foster comprehensive, multi-domain reasoning capabilities in LLMs.

Existing informal language-based (e.g., human language) Large Language Models (LLMs) trained with Reinforcement Learning (RL) face a significant challenge: their verification processes, which provide crucial training signals, are neither reliable nor scalable. In fact, the prevalent large proprietary models could hardly generate verifiable programs. A promising yet largely uncharted alternative is formal language-based reasoning. Grounding LLMs in rigorous formal systems where generative models operate in formal language spaces (e.g., Dafny) enables the automatic and mathematically provable verification of their reasoning processes and outcomes. This capability is pivotal for achieving large-scale, reliable formal software verification. It is a common practice to employ human-annotated chain-of-thought and other human priors to induce the reasoning and coding capabilities of LLMs. Unfortunately, it becomes unacceptably all-consuming to provide such priors for supervising complex programming tasks. In this work, we systematically explore ways to reduce human priors with the formal language, Dafny, as the main environment for our pilot study. Our pipeline mainly relies on introducing an automatic and scalable data curation pipeline, and careful RL designs integrated with feedback from the formal language verifier. We introduce DafnyComp, a benchmark of compositional formal programs with auto-formalized specifications for specification reasoning. Our supervised fine-tuning (SFT) stage enables even small models (e.g., 0.5B) to generate syntactically valid and verifiable Dafny code, surpassing proprietary models. RL with regularization further improves performance, achieving stronger generalization to out-of-domain tasks and outperforming all strong baselines on the challenging DafnyComp benchmark.

Recent advances in sparse voxel representations have significantly improved the quality of 3D content generation, enabling high-resolution modeling with fine-grained geometry. However, existing frameworks suffer from severe computational inefficiencies due to the quadratic complexity of attention mechanisms in their two-stage diffusion pipelines. In this work, we propose Ultra3D, an efficient 3D generation framework that significantly accelerates sparse voxel modeling without compromising quality. Our method leverages the compact VecSet representation to efficiently generate a coarse object layout in the first stage, reducing token count and accelerating voxel coordinate prediction. To refine per-voxel latent features in the second stage, we introduce Part Attention, a geometry-aware localized attention mechanism that restricts attention computation within semantically consistent part regions. This design preserves structural continuity while avoiding unnecessary global attention, achieving up to 6.7x speed-up in latent generation. To support this mechanism, we construct a scalable part annotation pipeline that converts raw meshes into part-labeled sparse voxels. Extensive experiments demonstrate that Ultra3D supports high-resolution 3D generation at 1024 resolution and achieves state-of-the-art performance in both visual fidelity and user preference.

High-quality 3D assets are essential for various applications in computer graphics and 3D vision but remain scarce due to significant acquisition costs. To address this shortage, we introduce Elevate3D, a novel framework that transforms readily accessible low-quality 3D assets into higher quality. At the core of Elevate3D is HFS-SDEdit, a specialized texture enhancement method that significantly improves texture quality while preserving the appearance and geometry while fixing its degradations. Furthermore, Elevate3D operates in a view-by-view manner, alternating between texture and geometry refinement. Unlike previous methods that have largely overlooked geometry refinement, our framework leverages geometric cues from images refined with HFS-SDEdit by employing state-of-the-art monocular geometry predictors. This approach ensures detailed and accurate geometry that aligns seamlessly with the enhanced texture. Elevate3D outperforms recent competitors by achieving state-of-the-art quality in 3D model refinement, effectively addressing the scarcity of high-quality open-source 3D assets.

Text-to-image diffusion models (DMs) have achieved remarkable success in image generation. However, concerns about data privacy and intellectual property remain due to their potential to inadvertently memorize and replicate training data. Recent mitigation efforts have focused on identifying and pruning weights responsible for triggering replication, based on the assumption that memorization can be localized. Our research assesses the robustness of these pruning-based approaches. We demonstrate that even after pruning, minor adjustments to text embeddings of input prompts are sufficient to re-trigger data replication, highlighting the fragility of these defenses. Furthermore, we challenge the fundamental assumption of memorization locality, by showing that replication can be triggered from diverse locations within the text embedding space, and follows different paths in the model. Our findings indicate that existing mitigation strategies are insufficient and underscore the need for methods that truly remove memorized content, rather than attempting to suppress its retrieval. As a first step in this direction, we introduce a novel adversarial fine-tuning method that iteratively searches for replication triggers and updates the model to increase robustness. Through our research, we provide fresh insights into the nature of memorization in text-to-image DMs and a foundation for building more trustworthy and compliant generative AI.

The rapid advancement of video diffusion models has been hindered by fundamental limitations in temporal modeling, particularly the rigid synchronization of frame evolution imposed by conventional scalar timestep variables. While task-specific adaptations and autoregressive models have sought to address these challenges, they remain constrained by computational inefficiency, catastrophic forgetting, or narrow applicability. In this work, we present Pusa, a groundbreaking paradigm that leverages vectorized timestep adaptation (VTA) to enable fine-grained temporal control within a unified video diffusion framework. Besides, VTA is a non-destructive adaptation, which means it fully preserves the capabilities of the base model. By finetuning the SOTA Wan2.1-T2V-14B model with VTA, we achieve unprecedented efficiency -- surpassing the performance of Wan-I2V-14B with leq 1/200 of the training cost (\500 vs. \geq 100,000) and leq 1/2500 of the dataset size (4K vs. geq 10M samples). Pusa not only sets a new standard for image-to-video (I2V) generation, achieving a VBench-I2V total score of 87.32\% (vs. 86.86\% of Wan-I2V-14B), but also unlocks many zero-shot multi-task capabilities such as start-end frames and video extension -- all without task-specific training. Meanwhile, Pusa can still perform text-to-video generation. Mechanistic analyses reveal that our approach preserves the foundation model's generative priors while surgically injecting temporal dynamics, avoiding the combinatorial explosion inherent to vectorized timesteps. This work establishes a scalable, efficient, and versatile paradigm for next-generation video synthesis, democratizing high-fidelity video generation for research and industry alike. Code is open-sourced at https://github.com/Yaofang-Liu/Pusa-VidGen

Large Language Models (LLMs) perform best with well-crafted prompts, yet prompt engineering remains manual, inconsistent, and inaccessible to non-experts. We introduce Promptomatix, an automatic prompt optimization framework that transforms natural language task descriptions into high-quality prompts without requiring manual tuning or domain expertise. Promptomatix supports both a lightweight meta-prompt-based optimizer and a DSPy-powered compiler, with modular design enabling future extension to more advanced frameworks. The system analyzes user intent, generates synthetic training data, selects prompting strategies, and refines prompts using cost-aware objectives. Evaluated across 5 task categories, Promptomatix achieves competitive or superior performance compared to existing libraries, while reducing prompt length and computational overhead making prompt optimization scalable and efficient.

FDA 几周前宣布使用名为 Elsa 的 AI 工具去加快药品和医疗设备的审批速度。内部人士称 Elsa 可用于生成会议记录和摘要或创建电子邮件和公报模板，但它也会捏造不存在的研究——也就是所谓的“幻觉”。FDA 内部人士称，幻觉让 Elsa 变得不可靠，无法用于重要工作。一位工作人员说，任何你没有时间仔细核查的东西都是不可靠的，AI 会很自信的产生幻觉。另一名工作人员说，AI 本应该帮助节省时间，但我浪费了很多额外时间去检查虚假或歪曲的研究。工作人员表示目前 Elsa 无法帮助加快药品和医疗设备的审批，仍然需要科学家进行评估，以确定药品和医疗设备是否安全有效。

连线报道，硅谷 AI 创业公司正在拥抱中国引发争议的 996 工作制。所谓 996 指的是从早上 9 点工作到晚上 9点，每周 6 天，一周工作 72 小时，近两倍于标准的 8 小时工作制(每周五天共 40 小时)。996 工作制引发了现代奴隶制的批评，但 AI 创业公司为了彼此之间展开竞争以及为了与中国公司竞争，纷纷拥抱 996。经营着一家人力资源和招聘公司的 Adrian Kinnersley 对这一现象如此普遍表示惊讶，他说有好几个客户在面试前筛选应聘者的前提条件之一就是他们是否愿意接受 996 工作制。AI 创业公司 Rilla 称该公司 80 名员工几乎所有人都遵守 996 工作制。它的招聘广告明确指出每周工作时间需超过 70 小时，如果对工作时间安排不满意就不要来应聘。该公司提供早餐、午餐和晚餐，周六也不例外。AI 物流创业公司 Sotira CEO Amrita Bhasin 认为对公司高层来说，996 工作制基本上是强制性的，但强加于普通员工是不公平的。

南太平洋岛国图瓦卢（Tuvalu）是全世界受气候威胁最严重的地区之一，由于海平面上升，科学家担心未来 80 年内图瓦卢将不再适合人类居住。它由九个环形珊瑚岛群组成，其中两个基本被海浪淹没。根据 2022 年的人口统计数据，图瓦卢总人口为 10643 人。它的邻国澳大利亚根据气候移民协议每年向其发放气候移民签证。澳大利亚透露寻求签证的人数高达 8750 人，占到该国总人口的 82%，但它今年只有 280 个签证名额。

索尼正在测试 PlayStation 5 的省电(Power Saver)模式，官方博客解释称，该选项将允许游戏以更低的功耗运行。该功能目前处于 Beta 测试阶段，正式上线之后玩家将可以选择“省电”选项。启用省电模式后，支持的 PS5 游戏将会降低性能，从而降低 PS5 的功耗，不支持的游戏则性能和功耗都不会降低。对于何谓降低性能，索尼称 VR 模式将不可用，部分游戏功能可能会受限。

亚马逊 AWS 证实它关闭了位于上海的 AI 实验室，称在彻底检查组织架构、优先事项和未来需要关注的重点之后，做出了一个艰难的商业决策。AWS 是在 2018 年 9 月宣布成立上海实验室，任命上海纽约大学（NYU Shanghai）计算机科学教授 Zhang Zheng 为主任。AWS 称有约 12 名员工受到实验室关闭的影响。曾在该 AI 实验室工作的科学家 Wang Minjie 将实验室成立时期形容为外资 AI 实验室在华的黄金时代。微软和 IBM 最近也都关闭了在华研究中心，微软还将部分在华研究人员转移到国外。

英国政府计划禁止公共部门和关键基础设施运营商在遭受勒索软件攻击后支付赎金。地方议会、学校以及 NHS（英国国家医疗服务系统）都需要遵守拟议中的新规定。勒索软件组织在利用漏洞或其它手段入侵一组织的计算机系统之后，通常会实施双重勒索，窃取数据然后加密数据，以恢复数据和不泄漏数据勒索受害者。如果受害者没有备份，那么除了支付赎金他们可能别无选择。要遏制勒索软件组织，拒绝支付赎金以及做好备份等安全措施至关重要。而支付赎金只会强化网络罪犯的犯罪动机。英国政府表示，禁令就是为了确保公众所依赖的关键服务不再成为勒索软件黑帮的攻击目标。

在 Valve 因支付公司 Mastercard 和 Visa 等的压力而下架部分成人游戏之后，著名独立游戏发行平台 Itch.io 也在相同的压力下采取了类似的措施：它在搜索结果中移除了成人游戏，这意味着用户将很难找到和发现成人游戏。用户在官方论坛指责 Itch.io 在没有任何通知的情况下限制成人游戏，是对信任的辜负。 更新：Itch.io 证实在支付公司压力下被迫对 NSFW 内容急速采取行动，否则平台的支付功能可能受到影响。

6 月和 7 月份席卷西欧的创纪录热浪引发了安装空调相关的政治斗争。欧洲因为地理位置通常不是太热，不需要安装空调。但气候变化让欧洲成为暖化最快的大陆，自 1980 年代以来欧洲的暖化速度是全球平均水平的两倍。在最新的热浪期间，法国逾 1000 所学校因缺乏空调而部分或全部关闭。右翼政党要求大规模安装空调，而政府官员则担心大规模安装空调会让城市升温，加剧热浪。

根据发表在 PNAS 期刊上的一项研究，气候变化正导致极端严重的森林火灾日益常见。2023 年和 2024 年是有记录以来最热的年份，全球逾 7800 万英亩森林被烧毁。火灾将浓烟和数十亿吨二氧化碳排放到大气中，数百万人因此面临恶劣的空气质量。对卫星图像的研究发现，2023 年和 2024 年因火灾损失的森林树冠（forest canopy）面积是过去近二十年年均损失面积的至少两倍。

美国国土安全部 (DHS)和美国公民及移民服务局 (USCIS)正在考虑重新评估 H-1B 签证的签发方式。美国政府考虑将 H-1B 签证的分配制度从目前的抽签制改为有利于符合特定标准——可能与技能相关——的申请人制度。H-1B 签证是美国签发给从事专业技术类工作的外籍人士的签证，必须由雇主出面为申请人申请，且申请人的雇用申请书需要美国公民及移民服务局的批准。根据美国公民及移民服务局的数据，截至 2019 年，美国约有 60 万名 H-1B 工人。H-1B 签证备受科技公司的青睐，但长期以来一直饱受批评，因为该签证被认为容易压低美国工人工资，限制移民工人的劳工权利，被外包公司滥用。

OpenAI 披露，ChatGPT 用户每天发送逾 25 亿提示词，其中 3.3 亿来自美国用户，免费版 ChatGPT 周活跃用户超过 5 亿。OpenAI 去年 12 月公布的数据是每天处理逾 10 亿次查询请求，这意味着 8 个月增长超过一倍。这些数据凸显了 ChatGPT 的普及度，它正在改变用户的信息搜索习惯。Google 没有披露它的每日搜索数据，它最近透露一年处理了 5 万亿次搜索请求，平均每天接近 140 亿次。Google 一开始也是免费服务，但最后它不得不依赖广告，它每天的搜索量如果下降则可能会影响广告收入。OpenAI 目前仍然处于烧钱阶段，其付费服务远不足以抵消支出，它最终如何盈利仍然有待观察。

Brave 浏览器对 Windows 11 用户默认屏蔽了受争议的 Microsoft Recall 功能。在这之前，Signal 桌面应用也宣布默认屏蔽 Recall。Microsoft Recall 会在用户使用电脑时每几秒钟截取一次屏幕截图，提取屏幕内容将其存储在一个可搜索的数据库中。Recall 引发了隐私和安全方面的争议，微软因此进行了多项调整。Brave 官方博客称，该浏览器的重心是默认最大化保护用户隐私，根据微软的说法，浏览器的隐私浏览窗口不会被截图，因此它通知操作系统 Brave 浏览器的所有浏览窗口都是“隐私窗口”，确保标签页内容不会被记录下来。

Annie Chen 是在 2017 年第一次注意到异常的呼吸急促，家庭医生告诉她不要担心，她的父亲有重烟瘾，71 岁时因肺癌去世，但她从未吸过烟。两年后医生给她做了 X 光检查，显示肺癌晚期。Chen 女士的病例代表了研究和治疗肺癌的医生所面临的一个日益困惑的现实。因吸烟率下降，过去几十年肺癌的发病率和死亡率也在下降，但非吸烟的肺癌患者的比例则出现了增长。研究人员估计，全球约有 10%-25% 的肺癌患者从未吸烟，在部分亚裔和亚裔美国女性人群中，比例可能高达 50% 或以上。对全世界 871 名非吸烟肺癌患者的研究发现，对于生活在空气污染严重地区的人群，某些 DNA 突变更为常见。污染不仅会直接损害 DNA，还会加速细胞分裂。非吸烟肺癌病人的癌症生物学特征与吸烟者不同，可能需要不同的预防和检测策略。非吸烟肺癌患者更有可能携带特定的致癌突变，而吸烟者则会随着时间推移积累更多突变。

Google 已经为其搜索结果页面引入了 AI 摘要功能，它宣称该功能不会抢走网站的流量。然而皮尤研究中心的一项研究给出了不同的答案：AI 摘要会显著降低搜索结果页的点击率。研究人员分析 2025 年 3 月收集的 Ipsos KnowledgePanel 900 名用户的数据，显示当页面包含 AI 摘要时，用户点击搜索结果的可能性要小得多。如果搜索结果页面不包含 AI 摘要，用户的点击率为 15%；如果包含 AI 答案，点击率降为 8%。对于 Google 在 AI 摘要中包含的链接，研究发现其点击率为 1%——链接的来源主要是维基百科、YouTube 和 Reddit。更令人担忧的是用户在看到 AI 摘要之后更可能关闭会话，也就是不再继续搜索，不去验证 AI 摘要是否正确——而幻觉是生成式 AI 的固有问题，幻觉指的是虚构的错误信息。研究表明，Google 对 AI 的使用正在改变收集信息与搜索结果互动的方式。

微软过去六个月从 Google AI 研究部门 DeepMind 至少挖走了 24 名 AI 工程师，硅谷巨头之间的 AI 人才战在火热持续中。本周二，Google Gemini 聊天机器人前工程主管 Amar Subramanya 在职业社交网络 LinkedIn 上发帖宣布自己担任微软企业 AI 副总裁，成为最新一名投奔微软的前 Google AI 工程师。他称赞新雇主的文化氛围耳目一新。其他已加入微软的 DeepMind AI 工程师包括了前工程主管 Sonal Gupta、软件工程师 Adam Sadovsky 和产品经理 Tim Frank。