OrangeBot.AI Digest — 2026-02-11

A Python library for extracting structured information from unstructured text using LLMs with precise source grounding and interactive visualization.

GitHub Agentic Workflows

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Chrome DevTools for coding agents

Official Claude Code compound engineering plugin

In-depth tutorials on LLMs, RAGs and real-world AI agent applications.

A list of free LLM inference resources accessible via API.

As high-quality public text approaches exhaustion, a phenomenon known as the Data Wall, pre-training is shifting from more tokens to better tokens. However, existing methods either rely on heuristic static filters that ignore training dynamics, or use dynamic yet optimizer-agnostic criteria based on raw gradients. We propose OPUS (Optimizer-induced Projected Utility Selection), a dynamic data selection framework that defines utility in the optimizer-induced update space. OPUS scores candidates by projecting their effective updates, shaped by modern optimizers, onto a target direction derived from a stable, in-distribution proxy. To ensure scalability, we employ Ghost technique with CountSketch for computational efficiency, and Boltzmann sampling for data diversity, incurring only 4.7\% additional compute overhead. OPUS achieves remarkable results across diverse corpora, quality tiers, optimizers, and model scales. In pre-training of GPT-2 Large/XL on FineWeb and FineWeb-Edu with 30B tokens, OPUS outperforms industrial-level baselines and even full 200B-token training. Moreover, when combined with industrial-level static filters, OPUS further improves pre-training efficiency, even with lower-quality data. Furthermore, in continued pre-training of Qwen3-8B-Base on SciencePedia, OPUS achieves superior performance using only 0.5B tokens compared to full training with 3B tokens, demonstrating significant data efficiency gains in specialized domains.

Autonomous GUI agents interact with environments by perceiving interfaces and executing actions. As a virtual sandbox, the GUI World model empowers agents with human-like foresight by enabling action-conditioned prediction. However, existing text- and pixel-based approaches struggle to simultaneously achieve high visual fidelity and fine-grained structural controllability. To this end, we propose Code2World, a vision-language coder that simulates the next visual state via renderable code generation. Specifically, to address the data scarcity problem, we construct AndroidCode by translating GUI trajectories into high-fidelity HTML and refining synthesized code through a visual-feedback revision mechanism, yielding a corpus of over 80K high-quality screen-action pairs. To adapt existing VLMs into code prediction, we first perform SFT as a cold start for format layout following, then further apply Render-Aware Reinforcement Learning which uses rendered outcome as the reward signal by enforcing visual semantic fidelity and action consistency. Extensive experiments demonstrate that Code2World-8B achieves the top-performing next UI prediction, rivaling the competitive GPT-5 and Gemini-3-Pro-Image. Notably, Code2World significantly enhances downstream navigation success rates in a flexible manner, boosting Gemini-2.5-Flash by +9.5% on AndroidWorld navigation. The code is available at https://github.com/AMAP-ML/Code2World.

Human problem-solving is never the repetition of a single mindset, by which we mean a distinct mode of cognitive processing. When tackling a specific task, we do not rely on a single mindset; instead, we integrate multiple mindsets within the single solution process. However, existing LLM reasoning methods fall into a common trap: they apply the same fixed mindset across all steps, overlooking that different stages of solving the same problem require fundamentally different mindsets. This single-minded assumption prevents models from reaching the next level of intelligence. To address this limitation, we propose Chain of Mindset (CoM), a training-free agentic framework that enables step-level adaptive mindset orchestration. CoM decomposes reasoning into four functionally heterogeneous mindsets: Spatial, Convergent, Divergent, and Algorithmic. A Meta-Agent dynamically selects the optimal mindset based on the evolving reasoning state, while a bidirectional Context Gate filters cross-module information flow to maintain effectiveness and efficiency. Experiments across six challenging benchmarks spanning mathematics, code generation, scientific QA, and spatial reasoning demonstrate that CoM achieves state-of-the-art performance, outperforming the strongest baseline by 4.96\% and 4.72\% in overall accuracy on Qwen3-VL-32B-Instruct and Gemini-2.0-Flash, while balancing reasoning efficiency. Our code is publicly available at https://github.com/QuantaAlpha/chain-of-mindset{https://github.com/QuantaAlpha/chain-of-mindset}.

The transition from symbolic manipulation to science-grade reasoning represents a pivotal frontier for Large Language Models (LLMs), with physics serving as the critical test anchor for binding abstract logic to physical reality. Physics demands that a model maintain physical consistency with the laws governing the universe, a task that fundamentally requires multimodal perception to ground abstract logic in reality. At the Olympiad level, diagrams are often constitutive rather than illustrative, containing essential constraints, such as boundary conditions and spatial symmetries, that are absent from the text. To bridge this visual-logical gap, we introduce P1-VL, a family of open-source vision-language models engineered for advanced scientific reasoning. Our method harmonizes Curriculum Reinforcement Learning, which employs progressive difficulty expansion to stabilize post-training, with Agentic Augmentation, enabling iterative self-verification at inference. Evaluated on HiPhO, a rigorous benchmark of 13 exams from 2024-2025, our flagship P1-VL-235B-A22B becomes the first open-source Vision-Language Model (VLM) to secure 12 gold medals and achieves the state-of-the-art performance in the open-source models. Our agent-augmented system achieves the No.2 overall rank globally, trailing only Gemini-3-Pro. Beyond physics, P1-VL demonstrates remarkable scientific reasoning capacity and generalizability, establishing significant leads over base models in STEM benchmarks. By open-sourcing P1-VL, we provide a foundational step toward general-purpose physical intelligence to better align visual perceptions with abstract physical laws for machine scientific discovery.

Recent advances in large language model (LLM) have empowered autonomous agents to perform complex tasks that require multi-turn interactions with tools and environments. However, scaling such agent training is limited by the lack of diverse and reliable environments. In this paper, we propose Agent World Model (AWM), a fully synthetic environment generation pipeline. Using this pipeline, we scale to 1,000 environments covering everyday scenarios, in which agents can interact with rich toolsets (35 tools per environment on average) and obtain high-quality observations. Notably, these environments are code-driven and backed by databases, providing more reliable and consistent state transitions than environments simulated by LLMs. Moreover, they enable more efficient agent interaction compared with collecting trajectories from realistic environments. To demonstrate the effectiveness of this resource, we perform large-scale reinforcement learning for multi-turn tool-use agents. Thanks to the fully executable environments and accessible database states, we can also design reliable reward functions. Experiments on three benchmarks show that training exclusively in synthetic environments, rather than benchmark-specific ones, yields strong out-of-distribution generalization. The code is available at https://github.com/Snowflake-Labs/agent-world-model.

Block-sparse attention is promising for accelerating long-context LLM pre-filling, yet identifying relevant blocks efficiently remains a bottleneck. Existing methods typically employ coarse-grained attention as a proxy for block importance estimation, but often resort to expensive token-level searching or scoring, resulting in significant selection overhead. In this work, we trace the inaccuracy of standard coarse-grained attention via mean pooling to a theoretical root cause: the interaction between mean pooling and Rotary Positional Embeddings (RoPE). We prove that mean pooling acts as a low-pass filter that induces destructive interference in high-frequency dimensions, effectively creating a "blind spot" for local positional information (e.g., slash patterns). To address this, we introduce Prism, a training-free spectral-aware approach that decomposes block selection into high-frequency and low-frequency branches. By applying energy-based temperature calibration, Prism restores the attenuated positional signals directly from pooled representations, enabling block importance estimation using purely block-level operations, thereby improving efficiency. Extensive evaluations confirm that Prism maintains accuracy parity with full attention while delivering up to 5.1times speedup.

Recently, Diffusion Large Language Models (dLLMs) have demonstrated unique efficiency advantages, enabled by their inherently parallel decoding mechanism and flexible generation paradigm. Meanwhile, despite the rapid advancement of Search Agents, their practical deployment is constrained by a fundamental limitation, termed as 1) Latency Challenge: the serial execution of multi-round reasoning, tool calling, and tool response waiting under the ReAct agent paradigm induces severe end-to-end latency. Intuitively, dLLMs can leverage their distinctive strengths to optimize the operational efficiency of agents under the ReAct agent paradigm. Practically, existing dLLM backbones face the 2) Agent Ability Challenge. That is, existing dLLMs exhibit remarkably weak reasoning and tool-calling capabilities, preventing these advantages from being effectively realized in practice. In this paper, we propose DLLM-Searcher, an optimization framework for dLLM-based Search Agents. To solve the Agent Ability Challenge, we design a two-stage post-training pipeline encompassing Agentic Supervised Fine-Tuning (Agentic SFT) and Agentic Variance-Reduced Preference Optimization Agentic VRPO, which enhances the backbone dLLM's information seeking and reasoning capabilities. To mitigate the Latency Challenge, we leverage the flexible generation mechanism of dLLMs and propose a novel agent paradigm termed Parallel-Reasoning and Acting P-ReAct. P-ReAct guides the model to prioritize decoding tool_call instructions, thereby allowing the model to keep thinking while waiting for the tool's return. Experimental results demonstrate that DLLM-Searcher achieves performance comparable to mainstream LLM-based search agents and P-ReAct delivers approximately 15% inference acceleration. Our code is available at https://anonymous.4open.science/r/DLLM-Searcher-553C

Scaling action-controllable world models is limited by the scarcity of action labels. While latent action learning promises to extract control interfaces from unlabeled video, learned latents often fail to transfer across contexts: they entangle scene-specific cues and lack a shared coordinate system. This occurs because standard objectives operate only within each clip, providing no mechanism to align action semantics across contexts. Our key insight is that although actions are unobserved, their semantic effects are observable and can serve as a shared reference. We introduce SeqΔ-REPA, a sequence-level control-effect alignment objective that anchors integrated latent action to temporal feature differences from a frozen, self-supervised video encoder. Building on this, we present Olaf-World, a pipeline that pretrains action-conditioned video world models from large-scale passive video. Extensive experiments demonstrate that our method learns a more structured latent action space, leading to stronger zero-shot action transfer and more data-efficient adaptation to new control interfaces than state-of-the-art baselines.

Auto-regressive video generation enables long video synthesis by iteratively conditioning each new batch of frames on previously generated content. However, recent work has shown that such pipelines suffer from severe temporal drift, where errors accumulate and amplify over long horizons. We hypothesize that this drift does not primarily stem from insufficient model capacity, but rather from inference-time error propagation. Specifically, we contend that drift arises from the uncontrolled reuse of corrupted latent conditioning tokens during auto-regressive inference. To correct this accumulation of errors, we propose a simple, inference-time method that mitigates temporal drift by identifying and removing unstable latent tokens before they are reused for conditioning. For this purpose, we define unstable tokens as latent tokens whose representations deviate significantly from those of the previously generated batch, indicating potential corruption or semantic drift. By explicitly removing corrupted latent tokens from the auto-regressive context, rather than modifying entire spatial regions or model parameters, our method prevents unreliable latent information from influencing future generation steps. As a result, it significantly improves long-horizon temporal consistency without modifying the model architecture, training procedure, or leaving latent space.

Recent studies have explored autoregressive models for image generation, with promising results, and have combined diffusion models with autoregressive frameworks to optimize image generation via diffusion losses. In this study, we present a theoretical analysis of diffusion and autoregressive models with diffusion loss, highlighting the latter's advantages. We present a theoretical comparison of conditional diffusion and autoregressive diffusion with diffusion loss, demonstrating that patch denoising optimization in autoregressive models effectively mitigates condition errors and leads to a stable condition distribution. Our analysis also reveals that autoregressive condition generation refines the condition, causing the condition error influence to decay exponentially. In addition, we introduce a novel condition refinement approach based on Optimal Transport (OT) theory to address ``condition inconsistency''. We theoretically demonstrate that formulating condition refinement as a Wasserstein Gradient Flow ensures convergence toward the ideal condition distribution, effectively mitigating condition inconsistency. Experiments demonstrate the superiority of our method over diffusion and autoregressive models with diffusion loss methods.

Vision-Language-Action (VLA) models have emerged as a promising paradigm for general-purpose robotic control, with test-time scaling (TTS) gaining attention to enhance robustness beyond training. However, existing TTS methods for VLAs require additional training, verifiers, and multiple forward passes, making them impractical for deployment. Moreover, they intervene only at action decoding while keeping visual representations fixed-insufficient under perceptual ambiguity, where reconsidering how to perceive is as important as deciding what to do. To address these limitations, we propose SCALE, a simple inference strategy that jointly modulates visual perception and action based on 'self-uncertainty', inspired by uncertainty-driven exploration in Active Inference theory-requiring no additional training, no verifier, and only a single forward pass. SCALE broadens exploration in both perception and action under high uncertainty, while focusing on exploitation when confident-enabling adaptive execution across varying conditions. Experiments on simulated and real-world benchmarks demonstrate that SCALE improves state-of-the-art VLAs and outperforms existing TTS methods while maintaining single-pass efficiency.

Transforming a large language model (LLM) into a Vision-Language Model (VLM) can be achieved by mapping the visual tokens from a vision encoder into the embedding space of an LLM. Intriguingly, this mapping can be as simple as a shallow MLP transformation. To understand why LLMs can so readily process visual tokens, we need interpretability methods that reveal what is encoded in the visual token representations at every layer of LLM processing. In this work, we introduce LatentLens, a novel approach for mapping latent representations to descriptions in natural language. LatentLens works by encoding a large text corpus and storing contextualized token representations for each token in that corpus. Visual token representations are then compared to their contextualized textual representations, with the top-k nearest neighbor representations providing descriptions of the visual token. We evaluate this method on 10 different VLMs, showing that commonly used methods, such as LogitLens, substantially underestimate the interpretability of visual tokens. With LatentLens instead, the majority of visual tokens are interpretable across all studied models and all layers. Qualitatively, we show that the descriptions produced by LatentLens are semantically meaningful and provide more fine-grained interpretations for humans compared to individual tokens. More broadly, our findings contribute new evidence on the alignment between vision and language representations, opening up new directions for analyzing latent representations.

Equipping embodied agents with the ability to reason about tasks, foresee physical outcomes, and generate precise actions is essential for general-purpose manipulation. While recent Vision-Language-Action (VLA) models have leveraged pre-trained foundation models, they typically focus on either linguistic planning or visual forecasting in isolation. These methods rarely integrate both capabilities simultaneously to guide action generation, leading to suboptimal performance in complex, long-horizon manipulation tasks. To bridge this gap, we propose BagelVLA, a unified model that integrates linguistic planning, visual forecasting, and action generation within a single framework. Initialized from a pretrained unified understanding and generative model, BagelVLA is trained to interleave textual reasoning and visual prediction directly into the action execution loop. To efficiently couple these modalities, we introduce Residual Flow Guidance (RFG), which initializes from current observation and leverages single-step denoising to extract predictive visual features, guiding action generation with minimal latency. Extensive experiments demonstrate that BagelVLA outperforms existing baselines by a significant margin on multiple simulated and real-world benchmarks, particularly in tasks requiring multi-stage reasoning.

Training generalist agents capable of adapting to diverse scenarios requires interactive environments for self-exploration. However, interactive environments remain critically scarce, and existing synthesis methods suffer from significant limitations regarding environmental diversity and scalability. To address these challenges, we introduce ScaleEnv, a framework that constructs fully interactive environments and verifiable tasks entirely from scratch. Specifically, ScaleEnv ensures environment reliability through procedural testing, and guarantees task completeness and solvability via tool dependency graph expansion and executable action verification. By enabling agents to learn through exploration within ScaleEnv, we demonstrate significant performance improvements on unseen, multi-turn tool-use benchmarks such as τ^2-Bench and VitaBench, highlighting strong generalization capabilities. Furthermore, we investigate the relationship between increasing number of domains and model generalization performance, providing empirical evidence that scaling environmental diversity is critical for robust agent learning.

Pretraining Vision-Language-Action (VLA) policies on internet-scale video is appealing, yet current latent-action objectives often learn the wrong thing: they remain anchored to pixel variation rather than action-relevant state transitions, making them vulnerable to appearance bias, nuisance motion, and information leakage. We introduce VLA-JEPA, a JEPA-style pretraining framework that sidesteps these pitfalls by design. The key idea is leakage-free state prediction: a target encoder produces latent representations from future frames, while the student pathway sees only the current observation -- future information is used solely as supervision targets, never as input. By predicting in latent space rather than pixel space, VLA-JEPA learns dynamics abstractions that are robust to camera motion and irrelevant background changes. This yields a simple two-stage recipe -- JEPA pretraining followed by action-head fine-tuning -- without the multi-stage complexity of prior latent-action pipelines. Experiments on LIBERO, LIBERO-Plus, SimplerEnv and real-world manipulation tasks show that VLA-JEPA achieves consistent gains in generalization and robustness over existing methods.

Moderna 周二披露 FDA 拒绝审核其实验性 mRNA 流感疫苗。FDA 由美国卫生部直接管辖，而现任卫生部长 Robert F. Kennedy Jr.是一位反疫苗者。在耗资数亿美元、招募近 41000 名受试者的试验中，Moderna 将mRNA-1010 疫苗的安全性和有效性与已批准流感疫苗进行对比。试验结果表明，mRNA-1010 疫苗优于对照疫苗。但今年 2 月 3 日 FDA 以试验不够充分控制不够好为由拒绝对疫苗的上市申请进行审核。Moderna 表示已请求与 FDA 会面以了解拒绝的理由。mRNA-1010 疫苗已获得欧盟、加拿大和澳大利亚的审核批准。

Google 释出了 Chrome 145，主要变化包括：重新加入对 JPEG-XL 图像的支持，text-justify CSS 属性、支持多列换行、设备绑定会话凭据、IndexedDB 的 SQLite 后端、默认情况下减少用户代理字符串、Upsert 等。Google Chrome 是在 2023 年 移除了对实验性的 JPEG-XL 图像格式的支持，此举引发了很多争议，因为 Chrome/Chromium 占据了九成市场份额，它是 Web 标准事实上的仲裁者。但到了 2025 年事情有了戏剧性转变。Google 改变了主意，开始恢复对 JPEG-XL 图像的支持，去年 12 月 Chrome/Chromium 代码库合并了 Rust 语言开发的 JPEG-XL 图像解码器 jxl-rs。

NetBSD 项目释出了 NetBSD 11.0 的首个 RC 版本。主要新特性包括：支持 64 位 RISC-V 平台；增强对 POSIX.1-2024 和 C23 编程接口标准的兼容性；增强对 compat_linux(8) 中 Linux 系统调用的支持；初步支持高通骁龙 X Elite 平台；改进 npf(7) 防火墙；新 MICROVM kernel for x86，专为实现极速虚拟机启动设计，在 2020 年时代 x86 CPU 上启动仅需 10 毫秒；新 virt68k，等等。

微软最近几年为其以精简著称的记事本应用引入了新功能，其中包括 AI 和 Markdown，新增功能也扩大了其攻击面，它刚刚爆出了一个远程代码执行漏洞 CVE-2026-20841，该漏洞与处理外链有关：当用户用记事本打开一个 Markdown 文件，攻击者可以引诱用户点击一个恶意链接，导致应用启动未经验证的协议去加载并执行远程文件。

字节跳动最近发布了 AI 视频生成工具 Seedance 2.0，它能同时处理多达四种类型的输入：图像、视频、音频和文本。用户能组合九张图像、三个视频和三个音频文件最多十二个文件。生成的视频时长为 4-15 秒（或 60 秒），能自动添加音效或音乐。但由于潜在的安全风险，字节跳动禁用了 Seedance 2.0 的人脸转语音功能。模型展现了能仅仅根据面部图像生成高度精确的个人语音的能力。根据脸部照片生成个人声音不是新研究，早在 2024 年的 USENIX 安全会议上，新加坡国立大学的研究人员就发表论文《Can I Hear Your Face? Pervasive Attack on Voice Authentication Systems with a Single Face Image》，介绍根据人脸生成语音攻击语音身份验证系统，因为人脸特征与语音特征之间存在高度关联。

自 1790 年普查人口以来，美国首次面临人口总数下降。美国人口普查局原本预测要到 2081 年才可能出现人口减少，但在特朗普政府的加速努力下，人口下降有望提前 50 年发生，最快今年就可能到来。根据上月底公布的最新人口普查数据，截至 2025 年 7 月的一年内美国人口增长率放缓至 0.5%，为疫情爆发以来的最低水平，净移民人数从前一年的 270 万降至 130 万。人口普查专家预计截至 2026 年 7 月的一年内净移民人数将降至 31.6 万，表示美国正朝着净移民负值的方向发展。美国企业研究所和布鲁金斯学会的研究人员估计，2026 年美国的净移民总数在 + 18.5 万和 -92.5万之间。而美国最新的出生人口减去死亡人口的差额是 51.9 万，这一差额预计到 2030 年将消失。移民外流加上净人口增长减少，将导致美国人口比预期的更快出现收缩。

Nala Ginrut 写道： 代表着日本技术荣耀以及号成“能养活一亿人”、“有丰田在就有日本在”的丰田最近发布了一款开源游戏引擎，结果是在北美发布的，IP也归属北美了。所以大部份人看到的日本，是否真的是日本呢？本文从另一个侧面来探讨一下，知己知彼，搞不好还能从日本顺走一些亚洲科技企业出海的经验。

今天美国市值最高的企业是英伟达，其市值比一代人前市值最高的企业高出数万亿美元。但英伟达雇佣的员工总数远少于以前的企业巨头。WSJ 的分析显示，美国经济的巨量资金流向了资本而不是劳工，两者之间的差距日益扩大。1980 年美国劳工的收入占 GDP 比重的 58%，2025 年第三季度劳工的收入占比降至了 51.4%，与此同时企业利润占 GDP 的比重从 7% 升至 11.7%。基于通货膨胀进行调整之后，英伟达的市值是 IBM 在 1985 年市值的 20 倍，而其员工总数只有 IBM 当年的十分之一。美国自 2019 年底以来，平均时薪上涨了 3%，企业利润却增长了 43%。耶鲁大学经济学家 Pascual Restrepo 预测，AI 会进一步缩小劳工收入的占比。

根据发表在《科学》期刊上的一项研究，倭黑猩猩具备靠想象力玩游戏的能力，此前想象力被认为是人类独有。在受控实验中，名叫 Kanzi 的倭黑猩猩持续与虚构的“果汁杯”和“装有葡萄的碗”互动。研究人员通过三次测试观察其行为：首次测试中，Kanzi能准确识别被倒入“假想果汁”的杯子；第二次测试中，面对真果汁与假想果汁，它始终选择真实饮品；第三次测试中，它再次成功定位假想中的“葡萄”。这表明 Kanzi 能理解假想物体的存在，同时区分其与实物的差异。总体来看，Kanzi 的表现并非完美无缺，但整体稳定可靠。研究人员认为，若想象力并非人类独有，将促使人类重新审视自身的独特性及其他生物的内心世界。

根据发表在《The Journal of World Prehistory》期刊上的一项研究，数学可能比文字更早出现。我们通常认为数学思维的起源与文字的出现紧密相连，它们诞生的时代大约是在五到六千年前。但最新研究挑战了这一观点。研究人员分析了美索不达米亚北部 Halafian 遗址出土的彩绘陶片上的花瓣图案，陶片的历史可追溯到 8000 年前。研究人员发现，陶片花瓣遵循几何序列：4、8、16、32 和 64，即 2 的几何级数，不是随意的选择，凸显了艺术家对几何序列、对称性和空间划分的理解。数学的出现可能是为了解决早期村落的资源分配问题。

Ivan 所在的油轮去年 11 月运载 75 万桶俄罗斯原油从远东前往中国，国际运输工人联盟(International Transport Workers' Federation，ITF) 在得知船员已经几个月没有拿到工资后于 12 月宣布油轮废弃。这艘油轮目前停留在国际水域，由于受到多方密切关注中国不允许其靠岸。ITF 已经帮助 Ivan 和其他船员拿到了 12 月的工资，运送了食物、饮用水和其它生活必需品。部分船员已经回国，包括 Ivan 在内的大部分船员仍然滞留在船上。根据 ITF 的统计，2016 年全球共有 20 艘船遗弃。但到 2025 年这一数字飙升至 410 艘，6223 名商船海员沦为受害者。这两个数字比 2024 年增长了近三分之一。油轮遗弃的主要原因是地缘政治不稳定。类似 Ivan 被困的油轮，大部分船东身份不明、船龄老旧、可能没有保险，在监管不严的国家如巴拿马、利比里亚和马绍尔群岛注册。冈比亚在 2023 年没有任何油轮，但到了 2025 年 3 月，该国注册的油轮达到了 35 艘。根据国际海事组织（IMO）的指导方针，如果海员至少两个月合同工资未支付，就构成了遗弃。2025 年因油轮遗弃印度籍海员受影响人数最多有 1125 人，占总数的 18%。菲律宾（539人）和叙利亚（309人）位列第二和第三。

根据发表在《The Lancet Planetary Health》期刊上的一项研究，电动汽车有助于改善空气质量。研究调查了纯电和插电混动汽车数量增加对加州空气污染的影响。加州是美国插电汽车保有量最大的州，数量已足以对空气质量产生积极影响。研究利用卫星数据，通过 NO2 吸收和反射太阳光追踪 NO2 水平，结果显示，2019-2023 年间每新增 200 辆纯电或插电混动汽车，NO2 水平下降 1.1%。NO2 会触发哮喘支气管炎，增加患心脏病和中风风险。研究还证实，汽油汽车增加的社区污染物排放量会上升。

生于素食家庭的婴儿在早期可能略微偏瘦，但到两岁时，体重就能赶上杂食家庭的同龄婴儿。以色列内盖夫本-古里安大学的研究人员分析了 2014-2023 年间从以色列国家家庭护理中心收集的 120 万名婴儿的数据，记录了每个婴儿从出生到 24 个月的身长、体重和头围。研究团队将生长数据与婴儿父母报告的饮食类型进行了比较。绝大多数家庭表示他们是杂食家庭，只有 1.2% 的家庭自称是素食者，0.3% 的家庭自称是纯素食者。素食家庭和纯素食家庭中仍然有大约 1.8 万名婴儿。研究人员按照饮食类型将婴儿分为三组。在出生后的 60 天内，三组婴儿的身长、头围以及生长发育受限的发生率都相似。来自无肉家庭（尤其是纯素食家庭）的婴儿体重偏轻的可能性较高。到 2 岁左右，这些差异基本消失，三组幼儿的生长指标趋于一致。研究人员指出，这项研究应该能让人放心，无肉饮食可以支持婴儿早期的健康成长，他同时指出，这些饮食情况是由父母自行报告的，这可能会影响结果的准确性。

Google 旗下的 YouTube Music 服务限制免费用户查看歌词。免费用户报告他们能查看的歌词次数有限制，他们收到了剩余多少次查看的警告。Google 尚未正式宣布 YouTube Music 歌词功能只提供给付费订阅用户。Google 发言人在回应中表示他们还在测试，尚未做出最终决定。YouTube Video 和 Music 的月费为 14 美元，YouTube Music 月费为 11 美元。音乐流媒体巨头 Spotify 也曾在 2024 年限制用户访问歌词，因用户反应强烈它取消了限制。

996 工作制在中国饱受争议，而美国 AI 初创公司正将这一工作制视为特色。纽约 AI 初创公司 Rilla 在招聘广告中警告应聘者每周工作时间可能长达 70 小时。只有 7 个人的初创公司 Browser-Use 开发浏览器与 AI 的交互工具，它将一个共享空间作为办公以及生活的场所，更进一步的模糊了工作和生活的界限。风投 Menlo Ventures 的合伙人 Deedy Das 指出，长时间工作并不意味着员工工作效率高或有更高的生产力，这么做会疏远有家庭的员工以及富有经验的年长员工。长时间工作还会导致职业倦怠。他认为公司创始人长时间工作无可厚非，因为攸关自身利益，如果公司成功他们会变得非常富有。密歇根州立大学的研究发现，一名每周工作 70 小时的员工的产出与一名每周工作 50 小时的员工几乎相同。