OrangeBot.AI Digest — 2026-01-11

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As language models become increasingly capable, users expect them to provide not only accurate responses but also behaviors aligned with diverse human preferences across a variety of scenarios. To achieve this, Reinforcement learning (RL) pipelines have begun incorporating multiple rewards, each capturing a distinct preference, to guide models toward these desired behaviors. However, recent work has defaulted to apply Group Relative Policy Optimization (GRPO) under multi-reward setting without examining its suitability. In this paper, we demonstrate that directly applying GRPO to normalize distinct rollout reward combinations causes them to collapse into identical advantage values, reducing the resolution of the training signal and resulting in suboptimal convergence and, in some cases, early training failure. We then introduce Group reward-Decoupled Normalization Policy Optimization (GDPO), a new policy optimization method to resolve these issues by decoupling the normalization of individual rewards, more faithfully preserving their relative differences and enabling more accurate multi-reward optimization, along with substantially improved training stability. We compare GDPO with GRPO across three tasks: tool calling, math reasoning, and coding reasoning, evaluating both correctness metrics (accuracy, bug ratio) and constraint adherence metrics (format, length). Across all settings, GDPO consistently outperforms GRPO, demonstrating its effectiveness and generalizability for multi-reward reinforcement learning optimization.

Applying weight decay (WD) to matrix layers is standard practice in large-language-model pretraining. Prior work suggests that stochastic gradient noise induces a Brownian-like expansion of the weight matrices W, whose growth is counteracted by WD, leading to a WD-noise equilibrium with a certain weight norm ||W||. In this work, we view the equilibrium norm as a harmful artifact of the training procedure, and address it by introducing learnable multipliers to learn the optimal scale. First, we attach a learnable scalar multiplier to W and confirm that the WD-noise equilibrium norm is suboptimal: the learned scale adapts to data and improves performance. We then argue that individual row and column norms are similarly constrained, and free their scale by introducing learnable per-row and per-column multipliers. Our method can be viewed as a learnable, more expressive generalization of muP multipliers. It outperforms a well-tuned muP baseline, reduces the computational overhead of multiplier tuning, and surfaces practical questions such as forward-pass symmetries and the width-scaling of the learned multipliers. Finally, we validate learnable multipliers with both Adam and Muon optimizers, where it shows improvement in downstream evaluations matching the improvement of the switching from Adam to Muon.

Nighttime color constancy remains a challenging problem in computational photography due to low-light noise and complex illumination conditions. We present RL-AWB, a novel framework combining statistical methods with deep reinforcement learning for nighttime white balance. Our method begins with a statistical algorithm tailored for nighttime scenes, integrating salient gray pixel detection with novel illumination estimation. Building on this foundation, we develop the first deep reinforcement learning approach for color constancy that leverages the statistical algorithm as its core, mimicking professional AWB tuning experts by dynamically optimizing parameters for each image. To facilitate cross-sensor evaluation, we introduce the first multi-sensor nighttime dataset. Experiment results demonstrate that our method achieves superior generalization capability across low-light and well-illuminated images. Project page: https://ntuneillee.github.io/research/rl-awb/

Large language models (LLMs) exhibit strengths across diverse domains. However, achieving strong performance across these domains with a single general-purpose model typically requires scaling to sizes that are prohibitively expensive to train and deploy. On the other hand, while smaller domain-specialized models are much more efficient, they struggle to generalize beyond their training distributions. To address this dilemma, we propose FusionRoute, a robust and effective token-level multi-LLM collaboration framework in which a lightweight router simultaneously (i) selects the most suitable expert at each decoding step and (ii) contributes a complementary logit that refines or corrects the selected expert's next-token distribution via logit addition. Unlike existing token-level collaboration methods that rely solely on fixed expert outputs, we provide a theoretical analysis showing that pure expert-only routing is fundamentally limited: unless strong global coverage assumptions hold, it cannot in general realize the optimal decoding policy. By augmenting expert selection with a trainable complementary generator, FusionRoute expands the effective policy class and enables recovery of optimal value functions under mild conditions. Empirically, across both Llama-3 and Gemma-2 families and diverse benchmarks spanning mathematical reasoning, code generation, and instruction following, FusionRoute outperforms both sequence- and token-level collaboration, model merging, and direct fine-tuning, while remaining competitive with domain experts on their respective tasks.

Large Language Models (LLMs) for complex reasoning is often hindered by high computational costs and latency, while resource-efficient Small Language Models (SLMs) typically lack the necessary reasoning capacity. Existing collaborative approaches, such as cascading or routing, operate at a coarse granularity by offloading entire queries to LLMs, resulting in significant computational waste when the SLM is capable of handling the majority of reasoning steps. To address this, we propose RelayLLM, a novel framework for efficient reasoning via token-level collaborative decoding. Unlike routers, RelayLLM empowers the SLM to act as an active controller that dynamically invokes the LLM only for critical tokens via a special command, effectively "relaying" the generation process. We introduce a two-stage training framework, including warm-up and Group Relative Policy Optimization (GRPO) to teach the model to balance independence with strategic help-seeking. Empirical results across six benchmarks demonstrate that RelayLLM achieves an average accuracy of 49.52%, effectively bridging the performance gap between the two models. Notably, this is achieved by invoking the LLM for only 1.07% of the total generated tokens, offering a 98.2% cost reduction compared to performance-matched random routers.

Chain-of-thought (CoT) reasoning has emerged as a powerful tool for multimodal large language models on video understanding tasks. However, its necessity and advantages over direct answering remain underexplored. In this paper, we first demonstrate that for RL-trained video models, direct answering often matches or even surpasses CoT performance, despite CoT producing step-by-step analyses at a higher computational cost. Motivated by this, we propose VideoAuto-R1, a video understanding framework that adopts a reason-when-necessary strategy. During training, our approach follows a Thinking Once, Answering Twice paradigm: the model first generates an initial answer, then performs reasoning, and finally outputs a reviewed answer. Both answers are supervised via verifiable rewards. During inference, the model uses the confidence score of the initial answer to determine whether to proceed with reasoning. Across video QA and grounding benchmarks, VideoAuto-R1 achieves state-of-the-art accuracy with significantly improved efficiency, reducing the average response length by ~3.3x, e.g., from 149 to just 44 tokens. Moreover, we observe a low rate of thinking-mode activation on perception-oriented tasks, but a higher rate on reasoning-intensive tasks. This suggests that explicit language-based reasoning is generally beneficial but not always necessary.

LLM agents have emerged as powerful systems for tackling multi-turn tasks by interleaving internal reasoning and external tool interactions. Agentic Reinforcement Learning has recently drawn significant research attention as a critical post-training paradigm to further refine these capabilities. In this paper, we present AT^2PO (Agentic Turn-based Policy Optimization via Tree Search), a unified framework for multi-turn agentic RL that addresses three core challenges: limited exploration diversity, sparse credit assignment, and misaligned policy optimization. AT^2PO introduces a turn-level tree structure that jointly enables Entropy-Guided Tree Expansion for strategic exploration and Turn-wise Credit Assignment for fine-grained reward propagation from sparse outcomes. Complementing this, we propose Agentic Turn-based Policy Optimization, a turn-level learning objective that aligns policy updates with the natural decision granularity of agentic interactions. ATPO is orthogonal to tree search and can be readily integrated into any multi-turn RL pipeline. Experiments across seven benchmarks demonstrate consistent improvements over the state-of-the-art baseline by up to 1.84 percentage points in average, with ablation studies validating the effectiveness of each component. Our code is available at https://github.com/zzfoutofspace/ATPO.

The diversity, quantity, and quality of manipulation data are critical for training effective robot policies. However, due to hardware and physical setup constraints, collecting large-scale real-world manipulation data remains difficult to scale across diverse environments. Recent work uses text-prompt conditioned image diffusion models to augment manipulation data by altering the backgrounds and tabletop objects in the visual observations. However, these approaches often overlook the practical need for multi-view and temporally coherent observations required by state-of-the-art policy models. Further, text prompts alone cannot reliably specify the scene setup. To provide the diffusion model with explicit visual guidance, we introduce visual identity prompting, which supplies exemplar images as conditioning inputs to guide the generation of the desired scene setup. To this end, we also build a scalable pipeline to curate a visual identity pool from large robotics datasets. Using our augmented manipulation data to train downstream vision-language-action and visuomotor policy models yields consistent performance gains in both simulation and real-robot settings.

Vision-language models (VLMs) achieve remarkable performance but remain vulnerable to adversarial attacks. Entropy, a measure of model uncertainty, is strongly correlated with the reliability of VLM. Prior entropy-based attacks maximize uncertainty at all decoding steps, implicitly assuming that every token contributes equally to generation instability. We show instead that a small fraction (about 20%) of high-entropy tokens, i.e., critical decision points in autoregressive generation, disproportionately governs output trajectories. By concentrating adversarial perturbations on these positions, we achieve semantic degradation comparable to global methods while using substantially smaller budgets. More importantly, across multiple representative VLMs, such selective attacks convert 35-49% of benign outputs into harmful ones, exposing a more critical safety risk. Remarkably, these vulnerable high-entropy forks recur across architecturally diverse VLMs, enabling feasible transferability (17-26% harmful rates on unseen targets). Motivated by these findings, we propose Entropy-bank Guided Adversarial attacks (EGA), which achieves competitive attack success rates (93-95%) alongside high harmful conversion, thereby revealing new weaknesses in current VLM safety mechanisms.

Video world models aim to simulate dynamic, real-world environments, yet existing methods struggle to provide unified and precise control over camera and multi-object motion, as videos inherently operate dynamics in the projected 2D image plane. To bridge this gap, we introduce VerseCrafter, a 4D-aware video world model that enables explicit and coherent control over both camera and object dynamics within a unified 4D geometric world state. Our approach is centered on a novel 4D Geometric Control representation, which encodes the world state through a static background point cloud and per-object 3D Gaussian trajectories. This representation captures not only an object's path but also its probabilistic 3D occupancy over time, offering a flexible, category-agnostic alternative to rigid bounding boxes or parametric models. These 4D controls are rendered into conditioning signals for a pretrained video diffusion model, enabling the generation of high-fidelity, view-consistent videos that precisely adhere to the specified dynamics. Unfortunately, another major challenge lies in the scarcity of large-scale training data with explicit 4D annotations. We address this by developing an automatic data engine that extracts the required 4D controls from in-the-wild videos, allowing us to train our model on a massive and diverse dataset.

Mixture of Experts models are widely assumed to achieve domain specialization through sparse routing. In this work, we question this assumption by introducing COMMITTEEAUDIT, a post hoc framework that analyzes routing behavior at the level of expert groups rather than individual experts. Across three representative models and the MMLU benchmark, we uncover a domain-invariant Standing Committee. This is a compact coalition of routed experts that consistently captures the majority of routing mass across domains, layers, and routing budgets, even when architectures already include shared experts. Qualitative analysis further shows that Standing Committees anchor reasoning structure and syntax, while peripheral experts handle domain-specific knowledge. These findings reveal a strong structural bias toward centralized computation, suggesting that specialization in Mixture of Experts models is far less pervasive than commonly believed. This inherent bias also indicates that current training objectives, such as load-balancing losses that enforce uniform expert utilization, may be working against the model's natural optimization path, thereby limiting training efficiency and performance.

LLM-as-a-Judge has revolutionized AI evaluation by leveraging large language models for scalable assessments. However, as evaluands become increasingly complex, specialized, and multi-step, the reliability of LLM-as-a-Judge has become constrained by inherent biases, shallow single-pass reasoning, and the inability to verify assessments against real-world observations. This has catalyzed the transition to Agent-as-a-Judge, where agentic judges employ planning, tool-augmented verification, multi-agent collaboration, and persistent memory to enable more robust, verifiable, and nuanced evaluations. Despite the rapid proliferation of agentic evaluation systems, the field lacks a unified framework to navigate this shifting landscape. To bridge this gap, we present the first comprehensive survey tracing this evolution. Specifically, we identify key dimensions that characterize this paradigm shift and establish a developmental taxonomy. We organize core methodologies and survey applications across general and professional domains. Furthermore, we analyze frontier challenges and identify promising research directions, ultimately providing a clear roadmap for the next generation of agentic evaluation.

Embodied question answering (EQA) in 3D environments often requires collecting context that is distributed across multiple viewpoints and partially occluded. However, most recent vision--language models (VLMs) are constrained to a fixed and finite set of input views, which limits their ability to acquire question-relevant context at inference time and hinders complex spatial reasoning. We propose Chain-of-View (CoV) prompting, a training-free, test-time reasoning framework that transforms a VLM into an active viewpoint reasoner through a coarse-to-fine exploration process. CoV first employs a View Selection agent to filter redundant frames and identify question-aligned anchor views. It then performs fine-grained view adjustment by interleaving iterative reasoning with discrete camera actions, obtaining new observations from the underlying 3D scene representation until sufficient context is gathered or a step budget is reached. We evaluate CoV on OpenEQA across four mainstream VLMs and obtain an average +11.56\% improvement in LLM-Match, with a maximum gain of +13.62\% on Qwen3-VL-Flash. CoV further exhibits test-time scaling: increasing the minimum action budget yields an additional +2.51\% average improvement, peaking at +3.73\% on Gemini-2.5-Flash. On ScanQA and SQA3D, CoV delivers strong performance (e.g., 116 CIDEr / 31.9 EM@1 on ScanQA and 51.1 EM@1 on SQA3D). Overall, these results suggest that question-aligned view selection coupled with open-view search is an effective, model-agnostic strategy for improving spatial reasoning in 3D EQA without additional training.

Camera-controlled generative video re-rendering methods, such as ReCamMaster, have achieved remarkable progress. However, despite their success in single-view setting, these works often struggle to maintain consistency across multi-view scenarios. Ensuring spatio-temporal coherence in hallucinated regions remains challenging due to the inherent stochasticity of generative models. To address it, we introduce PlenopticDreamer, a framework that synchronizes generative hallucinations to maintain spatio-temporal memory. The core idea is to train a multi-in-single-out video-conditioned model in an autoregressive manner, aided by a camera-guided video retrieval strategy that adaptively selects salient videos from previous generations as conditional inputs. In addition, Our training incorporates progressive context-scaling to improve convergence, self-conditioning to enhance robustness against long-range visual degradation caused by error accumulation, and a long-video conditioning mechanism to support extended video generation. Extensive experiments on the Basic and Agibot benchmarks demonstrate that PlenopticDreamer achieves state-of-the-art video re-rendering, delivering superior view synchronization, high-fidelity visuals, accurate camera control, and diverse view transformations (e.g., third-person to third-person, and head-view to gripper-view in robotic manipulation). Project page: https://research.nvidia.com/labs/dir/plenopticdreamer/

Chain-of-Thought (CoT) reasoning improves multi-step mathematical problem solving in large language models but remains vulnerable to exposure bias and error accumulation, as early mistakes propagate irreversibly through autoregressive decoding. In this work, we propose DiffCoT, a diffusion-styled CoT framework that reformulates CoT reasoning as an iterative denoising process. DiffCoT integrates diffusion principles at the reasoning-step level via a sliding-window mechanism, enabling unified generation and retrospective correction of intermediate steps while preserving token-level autoregression. To maintain causal consistency, we further introduce a causal diffusion noise schedule that respects the temporal structure of reasoning chains. Extensive experiments on three multi-step CoT reasoning benchmarks across diverse model backbones demonstrate that DiffCoT consistently outperforms existing CoT preference optimization methods, yielding improved robustness and error-correction capability in CoT reasoning.

根据 Netblocks 以及 Cloudflare Rader 的监测，伊朗断网三天。自 2025 年 12 月起，伊朗发生了一系列抗议活动，起因是民众对通货膨胀飙升、食品价格上涨以及伊朗里亚尔大幅贬值感到不满。示威活动最初由店主和市场商贩发起，进入新年后，抗议规模日益扩大。维基百科报道称目前有至少 2000 人死亡，但该数字难以核实。

微生物学在 19 世纪末进入黄金时代，研究人员发现了结核病、霍乱、伤寒等十几种疾病的细菌病原体。抗生素在 20 世纪中期进入黄金时代。如今疫苗的研发看起来也进入到了它的黄金时代，问题是美国的现任卫生部长是反疫苗者。在 2020 年代的前五年，研究人员研发出了新冠、疟疾、呼吸道合胞病毒(RSV)和基孔肯雅（Chikungunya）四种不同疾病的有效疫苗。疫苗的加速研发受益于过去两个世纪打造的基础。Edward Jenner 在 1796 年研发出天花疫苗是一次意外，他本人并不理解原理。Louis Pasteur 在 90 年后弄清楚了疫苗的原理——减毒和灭活——然后将其应用于治疗其它疾病。一代又一代的科学家构建起了配套的基础设施：用于细菌培养的培养皿、体外维持动物细胞存活的技术、用于工业生产的生物反应器、灭菌设备和冷链物流。如今相关工具更加强大。冷冻电镜能逐个原子揭示病毒蛋白的结构，这项技术直接促成了 RSV 疫苗的研发。基因组测序的成本从 2001 年约 1 亿美元降至 2014 年的低于 1000 美元。Katalin Kariko、Drew Weissman 等人改进的 mRNA 平台让疫苗的重新设计能在几周而不是数年内完成。未来可能出现更多的疫苗突破。然而突破能否实现取决于持续的投入。

为减少对美国科技公司的依赖，欧盟将注意力转向了开源软件，它正在征询欧洲开源社区、商界、学界和公共机构对开源相关问题的意见，截止日期 2 月 3 日。欧盟表示在数字领域面临严重依赖非欧盟国家(aka 美国)的问题。对美国的依赖减少了用户选择，削弱了欧盟企业的竞争力，因为无法控制数字基础设施，可能会引发供应链安全问题。过去几年开源作为一种可自由使用、修改和再分发的公共产品的潜力受到了广泛认可，开源软件能成为私有解决方案的有效替代。开源能增强用户自主权，帮助重新掌控和提升数字基础设施的韧性。

负责评选诺贝尔和平奖的挪威诺贝尔委员会通过官网发表了一份简短声明，它收到了有关诺贝尔和平奖得主身份是否永久有效的置评请求。声明表示，事实明确且早有定论：一旦诺贝尔奖公布，即不可撤销、不可分享，也不可转让他人。该决定为最终决定，永久有效。

根据 Netblocks 的监测，伊朗全国范围内断网，至今已持续近 48 小时。自 2025 年 12 月起，伊朗发生了一系列抗议活动，起因是民众对通货膨胀飙升、食品价格上涨以及伊朗里亚尔大幅贬值感到不满。示威活动最初由店主和市场商贩发起，进入新年后，抗议规模日益扩大。报道称至少逾两百人死亡，逾两千人被捕。由于互联网被切断，以及 Starlink 被干扰而无法访问，当地具体情况未知。

人类何时开始利用沾了有毒物质的武器去猎杀猎物？根据发表在《Science Advances》期刊上的一项研究，瑞典斯德哥尔摩大学（Stockholm University）的考古学家在南非出土的距今有 6 万年历史的箭头上发现了毒药残留。此前最早的毒药使用痕迹上溯至 3.5 万年前。这项发现进一步证实早期智人的认知能力在老练上已接近现代人类。为了在箭矢上涂抹毒药，猎人需要了解当地植物及其效用，能制作出剂量正确的特制武器。研究人员发现，有 6 万年历史的石英箭头端非常小，表明可能是为了有效将毒药注入伤口而设计的，不是为了造成钝器伤。研究人员发现了两种有毒化合物：buphanidrine 和 epibuphanisine，很可能来自于当地植物 Boophone disticha。这种植物今天还是当地猎人常用的毒药来源，被用于减缓猎物速度，而不是通过致命一击将其杀死。

根据 xdaforums 用户在论坛上公布的文件，越南政府禁止已 root 的手机使用任何手机银行应用。越政府要求，如果检测到手机已 root，手机银行应用必须自动退出或停止运行，将原因和任何违规方细节通知警方。在很多地方，手机银行应用拒绝在已 root 手机上运行已是一种常态。

不是所有狗都生而平等的。绝大部分狗只能学会简单的指令如“坐”或“趴”。少数被称为 gifted word learner（GWL）的狗则展现出了惊人的学习能力，它们能记住特定玩具的名字，能从一堆玩具中挑选出特定玩具。根据发表在《科学》期刊上的研究，GWL 狗甚至能通过旁听主人谈论新玩具而记住玩具名字。研究人员认为，GWL 狗的社会认知能力与 18 个月的人类婴儿相当。在实验中，研究人员让 GWL 狗坐在一旁，它们的主人向另一个人展示新玩具，在谈话中将玩具名字巧妙融入进去。虽然没有针对狗，但 GWL 狗已经记住了新玩具的名字，能在主人要求下从一堆玩具中找到它。狗是在另一个房间内去寻找特定玩具的，主人无法给它任何提示。

根据发表在《Advances in Atmospheric Sciences》期刊上的一项研究，2025 年全球海洋上层 2000 米热含量再创历史新高，已连续 9 年刷新有观测记录以来的最高值；同时全球平均海表温度位居历史第三高位，全球变暖趋势十分明确。2025 年上层 2000 米海洋热含量比 2024 年增加了 23±8 ZJ（1 ZJ=1021焦耳），相当于全球人类约 37 年的一次能源消耗总量。报告显示，自 1990 年代以后，海洋的增暖速率明显增强。1960-2025 年间，海洋增暖速率约为每十年每平方米 0.14 瓦特；而在 2005-2025 年这二十年，这一速率上升至每十年每平方米 0.32 瓦特。全球海洋变暖呈现显著的区域差异。2025 年全球海洋有 57% 的面积热含量达到了局地的历史前五水平，主要集中在南大洋、北印度洋、热带和南大西洋以及地中海等关键海区。2025 年全球平均海表温度比 2024 年略有下降，位居历史第三高。这主要归因于拉尼娜气候事件，它像一个巨大的泵，将热量从海洋表层“压”向了 100-300 米的次表层。

瑞典研究人员分析了 2023 年 3500 万商业航班中的 2700 万个，发现航空业可通过取消头等舱和商务舱、确保接近满员，以及使用效率最高的客机，能在不减少乘客出行的情况下将温室气体排放减半。研究发现，头等舱和商务舱乘客的碳排放是经济舱乘客的三倍多，而在最宽敞的高档舱位，乘客的碳排放最高能达到经济舱乘客的 12 倍。2023 年全球航空公司的座位利用率平均接近八成，美国机场的排放量占航空业总排放量的四分之一，污染程度比全球平均水平高 14%。

因一名宇航员出现健康问题 NASA 周四宣布提前结束 Crew-11 任务。Crew-11 任务于 2025 年 8 月 1 日发射，原计划于 2026 年 2 月 20 日左右返回地面，执行该任务的四名宇航员——38 岁的指挥官 Zena Cardman、58 岁的飞行员 Mike Fincke、55 岁的日本宇航员 Kimiya Yui、39 岁的俄罗斯宇航员 Oleg Platonov——将在未来几天提前返回。NASA 没有披露哪位宇航员的健康出现了问题，只是强调其情况非常稳定，它是出于谨慎而决定提前接回宇航员。NASA 首席医疗官表示出于隐私考虑不会公布相关的医疗细节。宇航员是在周三下午与地面专家进行了一次私人会诊。

哥伦比亚大学和耶鲁大学经济学家发表了一篇论文《Ruling For the Rich》，分析了美国最高法院 1953 年至今所有涉及经济问题的案例，发现共和党任命的大法官比民主党任命的大法官更可能做出有利于富人的裁决。1950 年代两党任命的大法官在倾向于投有利于富人的裁决上比例相似，然而到了 2022 年两党大法官之间差距达到了 47 个百分点。保守派大法官如 Clarence Thomas 或 Samuel Alito 有时会以意识形态为其裁决辩护，但他们并没有以一致的方式使用法律制定时的意识形态，让富人更富有更能解释他们的裁决。美国最高法院保守派占绝对多数。

富士胶片宣布于 30 日发售即时相机“拍立得”系列的新款产品：搭载视频拍摄功能的“instax mini Evo Cinema”。该产品可打印带有二维码的静态照片递给他人，用智能手机读取二维码就能观看视频。视频最长为 15 秒，也可下载到手机上。用户通过相机背面的显示器预览视频，从中选出心仪画面生成静态照片。使用该功能需要通过专用 APP 保存到富士胶片方面的服务器上。该产品预计售价为 5.5 万日元（约人民币 2440 元）。

塑料颗粒已经无处不在。根据发表在《Science Advances》期刊上的一项研究，中科院地球环境研究所的研究人员分析了广州和西安两大城市大气中的微塑料和纳米塑料浓度。广州和西安属于两种不同类型的城市，前者是靠近海的空气湿润的大都市，而后者则是半干旱的内陆城市。结果显示，广州空气中微塑料和纳米塑料浓度分别达到每立方米 1.8 × 10⁵ 个和 5.0 × 10⁴ 个，而西安为每立方米 1.4 × 10⁵ 个和 3.0 × 10⁴ 个。研究显示，降雨是清除大气塑料颗粒的主要机制。

二战前世界绝大部分望远镜由对天文观测感兴趣的富豪资助建造，二战之后大部分天文观测设备的资金则由政府提供。如今情况可能出现逆转。前 Google CEO Eric Schmidt 及其妻子 Wendy Schmidt 宣布捐资建造四个望远镜项目。其中最引人注目的项目是名为 Lazuli 的太空望远镜，如果发射和部署成功，Lazuli 将可以接替哈勃望远镜，提供更先进更强大的天文观测能力。这些天文观测设备统称为 Schmidt Observatory System，Schmidt 夫妇没有透露资助金额，但至少应该有 5 亿美元。