OrangeBot.AI Digest — 2025-07-13

Claude Code is an agentic coding tool that lives in your terminal, understands your codebase, and helps you code faster by executing routine tasks, explaining complex code, and handling git workflows - all through natural language commands.

Next.js Commerce

an open source, extensible AI agent that goes beyond code suggestions - install, execute, edit, and test with any LLM

A collection of inspiring lists, manuals, cheatsheets, blogs, hacks, one-liners, cli/web tools and more.

CUDA Templates for Linear Algebra Subroutines

A list of SaaS, PaaS and IaaS offerings that have free tiers of interest to devops and infradev

uBlock Origin - An efficient blocker for Chromium and Firefox. Fast and lean.

This repository contains a 90-day cybersecurity study plan, along with resources and materials for learning various cybersecurity concepts and technologies. The plan is organized into daily tasks, covering topics such as Network+, Security+, Linux, Python, Traffic Analysis, Git, ELK, AWS, Azure, and Hacking. The repository also includes a `LEARN.md

Qlib is an AI-oriented Quant investment platform that aims to use AI tech to empower Quant Research, from exploring ideas to implementing productions. Qlib supports diverse ML modeling paradigms, including supervised learning, market dynamics modeling, and RL, and is now equipped with https://github.com/microsoft/RD-Agent to automate R&D process.

The AI Browser Automation Framework

Python library for Agentic Document Extraction from LandingAI

Build Real-Time Knowledge Graphs for AI Agents

Go Interview Practice is a series of coding challenges to help you prepare for technical interviews in Go. Solve problems, submit your solutions, and receive instant feedback with automated testing. Track your progress with per-challenge scoreboards and improve your coding skills step by step.

Job interview simulator for UX/UI & product designers

Monitor 2200+ live trends with real search volumes

The 1T parameter open model for agentic intelligence

a simple planner where you can create & share plans with AI

Your notes, explained out loud by a live AI tutor

Chat with your UI design partner

A visual timer that turns your effort into a work of art

The AI co-pilot for tech scoping & budgeting.

We introduce a full-stack framework that scales up reasoning in vision-language models (VLMs) to long videos, leveraging reinforcement learning. We address the unique challenges of long video reasoning by integrating three critical components: (1) a large-scale dataset, LongVideo-Reason, comprising 52K long video QA pairs with high-quality reasoning annotations across diverse domains such as sports, games, and vlogs; (2) a two-stage training pipeline that extends VLMs with chain-of-thought supervised fine-tuning (CoT-SFT) and reinforcement learning (RL); and (3) a training infrastructure for long video RL, named Multi-modal Reinforcement Sequence Parallelism (MR-SP), which incorporates sequence parallelism and a vLLM-based engine tailored for long video, using cached video embeddings for efficient rollout and prefilling. In experiments, LongVILA-R1-7B achieves strong performance on long video QA benchmarks such as VideoMME. It also outperforms Video-R1-7B and even matches Gemini-1.5-Pro across temporal reasoning, goal and purpose reasoning, spatial reasoning, and plot reasoning on our LongVideo-Reason-eval benchmark. Notably, our MR-SP system achieves up to 2.1x speedup on long video RL training. LongVILA-R1 demonstrates consistent performance gains as the number of input video frames scales. LongVILA-R1 marks a firm step towards long video reasoning in VLMs. In addition, we release our training system for public availability that supports RL training on various modalities (video, text, and audio), various models (VILA and Qwen series), and even image and video generation models. On a single A100 node (8 GPUs), it supports RL training on hour-long videos (e.g., 3,600 frames / around 256k tokens).

While diffusion model fine-tuning offers a powerful approach for customizing pre-trained models to generate specific objects, it frequently suffers from overfitting when training samples are limited, compromising both generalization capability and output diversity. This paper tackles the challenging yet most impactful task of adapting a diffusion model using just a single concept image, as single-image customization holds the greatest practical potential. We introduce T-LoRA, a Timestep-Dependent Low-Rank Adaptation framework specifically designed for diffusion model personalization. In our work we show that higher diffusion timesteps are more prone to overfitting than lower ones, necessitating a timestep-sensitive fine-tuning strategy. T-LoRA incorporates two key innovations: (1) a dynamic fine-tuning strategy that adjusts rank-constrained updates based on diffusion timesteps, and (2) a weight parametrization technique that ensures independence between adapter components through orthogonal initialization. Extensive experiments show that T-LoRA and its individual components outperform standard LoRA and other diffusion model personalization techniques. They achieve a superior balance between concept fidelity and text alignment, highlighting the potential of T-LoRA in data-limited and resource-constrained scenarios. Code is available at https://github.com/ControlGenAI/T-LoRA.

Models like OpenAI-o3 pioneer visual grounded reasoning by dynamically referencing visual regions, just like human "thinking with images". However, no benchmark exists to evaluate these capabilities holistically. To bridge this gap, we propose TreeBench (Traceable Evidence Evaluation Benchmark), a diagnostic benchmark built on three principles: (1) focused visual perception of subtle targets in complex scenes, (2) traceable evidence via bounding box evaluation, and (3) second-order reasoning to test object interactions and spatial hierarchies beyond simple object localization. Prioritizing images with dense objects, we initially sample 1K high-quality images from SA-1B, and incorporate eight LMM experts to manually annotate questions, candidate options, and answers for each image. After three stages of quality control, TreeBench consists of 405 challenging visual question-answering pairs, even the most advanced models struggle with this benchmark, where none of them reach 60% accuracy, e.g., OpenAI-o3 scores only 54.87. Furthermore, we introduce TreeVGR (Traceable Evidence Enhanced Visual Grounded Reasoning), a training paradigm to supervise localization and reasoning jointly with reinforcement learning, enabling accurate localizations and explainable reasoning pathways. Initialized from Qwen2.5-VL-7B, it improves V* Bench (+16.8), MME-RealWorld (+12.6), and TreeBench (+13.4), proving traceability is key to advancing vision-grounded reasoning. The code is available at https://github.com/Haochen-Wang409/TreeVGR.

Recent advances in multimodal large language models (MLLMs) have shown remarkable capabilities in integrating vision and language for complex reasoning. While most existing benchmarks evaluate models under offline settings with a fixed set of pre-recorded inputs, we introduce OST-Bench, a benchmark designed to evaluate Online Spatio-Temporal understanding from the perspective of an agent actively exploring a scene. The Online aspect emphasizes the need to process and reason over incrementally acquired observations, while the Spatio-Temporal component requires integrating current visual inputs with historical memory to support dynamic spatial reasoning. OST-Bench better reflects the challenges of real-world embodied perception. Built on an efficient data collection pipeline, OST-Bench consists of 1.4k scenes and 10k question-answer pairs collected from ScanNet, Matterport3D, and ARKitScenes. We evaluate several leading MLLMs on OST-Bench and observe that they fall short on tasks requiring complex spatio-temporal reasoning. Under the online setting, their accuracy declines as the exploration horizon extends and the memory grows. Through further experimental analysis, we identify common error patterns across models and find that both complex clue-based spatial reasoning demands and long-term memory retrieval requirements significantly drop model performance along two separate axes, highlighting the core challenges that must be addressed to improve online embodied reasoning. To foster further research and development in the field, our codes, dataset, and benchmark are available. Our project page is: https://rbler1234.github.io/OSTBench.github.io/

Video large language models (LLMs) achieve strong video understanding by leveraging a large number of spatio-temporal tokens, but suffer from quadratic computational scaling with token count. To address this, we propose a training-free spatio-temporal token merging method, named STTM. Our key insight is to exploit local spatial and temporal redundancy in video data which has been overlooked in prior work. STTM first transforms each frame into multi-granular spatial tokens using a coarse-to-fine search over a quadtree structure, then performs directed pairwise merging across the temporal dimension. This decomposed merging approach outperforms existing token reduction methods across six video QA benchmarks. Notably, STTM achieves a 2times speed-up with only a 0.5% accuracy drop under a 50% token budget, and a 3times speed-up with just a 2% drop under a 30% budget. Moreover, STTM is query-agnostic, allowing KV cache reuse across different questions for the same video. The project page is available at https://www.jshyun.me/projects/sttm.

Videos inherently represent 2D projections of a dynamic 3D world. However, our analysis suggests that video diffusion models trained solely on raw video data often fail to capture meaningful geometric-aware structure in their learned representations. To bridge this gap between video diffusion models and the underlying 3D nature of the physical world, we propose Geometry Forcing, a simple yet effective method that encourages video diffusion models to internalize latent 3D representations. Our key insight is to guide the model's intermediate representations toward geometry-aware structure by aligning them with features from a pretrained geometric foundation model. To this end, we introduce two complementary alignment objectives: Angular Alignment, which enforces directional consistency via cosine similarity, and Scale Alignment, which preserves scale-related information by regressing unnormalized geometric features from normalized diffusion representation. We evaluate Geometry Forcing on both camera view-conditioned and action-conditioned video generation tasks. Experimental results demonstrate that our method substantially improves visual quality and 3D consistency over the baseline methods. Project page: https://GeometryForcing.github.io.

LLMs are increasingly deployed as agents, systems capable of planning, reasoning, and dynamically calling external tools. However, in visual reasoning, prior approaches largely remain limited by predefined workflows and static toolsets. In this report, we present PyVision, an interactive, multi-turn framework that enables MLLMs to autonomously generate, execute, and refine Python-based tools tailored to the task at hand, unlocking flexible and interpretable problem-solving. We develop a taxonomy of the tools created by PyVision and analyze their usage across a diverse set of benchmarks. Quantitatively, PyVision achieves consistent performance gains, boosting GPT-4.1 by +7.8% on V* and Claude-4.0-Sonnet by +31.1% on VLMsAreBlind-mini. These results point to a broader shift: dynamic tooling allows models not just to use tools, but to invent them, advancing toward more agentic visual reasoning.

In this paper, we introduce LangSplatV2, which achieves high-dimensional feature splatting at 476.2 FPS and 3D open-vocabulary text querying at 384.6 FPS for high-resolution images, providing a 42 times speedup and a 47 times boost over LangSplat respectively, along with improved query accuracy. LangSplat employs Gaussian Splatting to embed 2D CLIP language features into 3D, significantly enhancing speed and learning a precise 3D language field with SAM semantics. Such advancements in 3D language fields are crucial for applications that require language interaction within complex scenes. However, LangSplat does not yet achieve real-time inference performance (8.2 FPS), even with advanced A100 GPUs, severely limiting its broader application. In this paper, we first conduct a detailed time analysis of LangSplat, identifying the heavyweight decoder as the primary speed bottleneck. Our solution, LangSplatV2 assumes that each Gaussian acts as a sparse code within a global dictionary, leading to the learning of a 3D sparse coefficient field that entirely eliminates the need for a heavyweight decoder. By leveraging this sparsity, we further propose an efficient sparse coefficient splatting method with CUDA optimization, rendering high-dimensional feature maps at high quality while incurring only the time cost of splatting an ultra-low-dimensional feature. Our experimental results demonstrate that LangSplatV2 not only achieves better or competitive query accuracy but is also significantly faster. Codes and demos are available at our project page: https://langsplat-v2.github.io.

Can a pretrained neural network adapt its architecture to different inputs without any finetuning? Do we need all layers for simple tasks, and are they adequate for challenging tasks? We found that the layers of a pretrained large language model (LLM) can be manipulated as separate modules to build a better and even shallower model customized for each test sample. In particular, each layer from the pretrained model can be skipped/pruned or repeated multiple times as recurrent neural networks (RNN), and stacked with others in arbitrary orders, yielding a chain-of-layers (CoLa) per sample. This compositional space greatly expands the scope of existing works on looped/recurrent pretrained modules, layer pruning, or early-exit networks. We develop a Monte Carlo Tree Search (MCTS) protocol to explore and identify the optimal CoLa for each sample from math and commonsense reasoning benchmarks. Compared to a static model of a fixed depth, CoLa allows shortcut paths (fast thinking), recurrence of the same layer(s) (slow thinking), and combining both, offering more flexible, dynamic architectures for different inputs. We conduct an extensive analysis of the MCTS-optimized CoLa, which leads to two key findings: (1) For >75% of samples with correct predictions by the original LLM, we can find shorter CoLa, suggesting a large space for improving inference efficiency; (2) For >60% of samples with originally incorrect predictions, we can identify CoLa achieving correct predictions, suggesting a large space of performance enhancement. Our results highlight the shortcomings of using a fixed architecture of pre-trained LLMs for inference on different samples and pave the way to unlock the generalization power of test-time depth adaptation.

Despite the significant progress that has been made in video generative models, existing state-of-the-art methods can only produce videos lasting 5-16 seconds, often labeled "long-form videos". Furthermore, videos exceeding 16 seconds struggle to maintain consistent character appearances and scene layouts throughout the narrative. In particular, multi-subject long videos still fail to preserve character consistency and motion coherence. While some methods can generate videos up to 150 seconds long, they often suffer from frame redundancy and low temporal diversity. Recent work has attempted to produce long-form videos featuring multiple characters, narrative coherence, and high-fidelity detail. We comprehensively studied 32 papers on video generation to identify key architectural components and training strategies that consistently yield these qualities. We also construct a comprehensive novel taxonomy of existing methods and present comparative tables that categorize papers by their architectural designs and performance characteristics.

Deriving compact and temporally aware visual representations from dynamic scenes is essential for successful execution of sequential scene understanding tasks such as visual tracking and robotic manipulation. In this paper, we introduce Token Bottleneck (ToBo), a simple yet intuitive self-supervised learning pipeline that squeezes a scene into a bottleneck token and predicts the subsequent scene using minimal patches as hints. The ToBo pipeline facilitates the learning of sequential scene representations by conservatively encoding the reference scene into a compact bottleneck token during the squeeze step. In the expansion step, we guide the model to capture temporal dynamics by predicting the target scene using the bottleneck token along with few target patches as hints. This design encourages the vision backbone to embed temporal dependencies, thereby enabling understanding of dynamic transitions across scenes. Extensive experiments in diverse sequential tasks, including video label propagation and robot manipulation in simulated environments demonstrate the superiority of ToBo over baselines. Moreover, deploying our pre-trained model on physical robots confirms its robustness and effectiveness in real-world environments. We further validate the scalability of ToBo across different model scales.

Despite incredible progress in language models (LMs) in recent years, largely resulting from moving away from specialized models designed for specific tasks to general models based on powerful architectures (e.g. the Transformer) that learn everything from raw data, pre-processing steps such as tokenization remain a barrier to true end-to-end foundation models. We introduce a collection of new techniques that enable a dynamic chunking mechanism which automatically learns content -- and context -- dependent segmentation strategies learned jointly with the rest of the model. Incorporating this into an explicit hierarchical network (H-Net) allows replacing the (implicitly hierarchical) tokenization-LM-detokenization pipeline with a single model learned fully end-to-end. When compute- and data- matched, an H-Net with one stage of hierarchy operating at the byte level outperforms a strong Transformer language model operating over BPE tokens. Iterating the hierarchy to multiple stages further increases its performance by modeling multiple levels of abstraction, demonstrating significantly better scaling with data and matching a token-based Transformer of twice its size. H-Nets pretrained on English show significantly increased character-level robustness, and qualitatively learn meaningful data-dependent chunking strategies without any heuristics or explicit supervision. Finally, the H-Net's improvement over tokenized pipelines is further increased in languages and modalities with weaker tokenization heuristics, such as Chinese and code, or DNA sequences (nearly 4x improvement in data efficiency over baselines), showing the potential of true end-to-end models that learn and scale better from unprocessed data.

Bullshit, as conceptualized by philosopher Harry Frankfurt, refers to statements made without regard to their truth value. While previous work has explored large language model (LLM) hallucination and sycophancy, we propose machine bullshit as an overarching conceptual framework that can allow researchers to characterize the broader phenomenon of emergent loss of truthfulness in LLMs and shed light on its underlying mechanisms. We introduce the Bullshit Index, a novel metric quantifying LLMs' indifference to truth, and propose a complementary taxonomy analyzing four qualitative forms of bullshit: empty rhetoric, paltering, weasel words, and unverified claims. We conduct empirical evaluations on the Marketplace dataset, the Political Neutrality dataset, and our new BullshitEval benchmark (2,400 scenarios spanning 100 AI assistants) explicitly designed to evaluate machine bullshit. Our results demonstrate that model fine-tuning with reinforcement learning from human feedback (RLHF) significantly exacerbates bullshit and inference-time chain-of-thought (CoT) prompting notably amplify specific bullshit forms, particularly empty rhetoric and paltering. We also observe prevalent machine bullshit in political contexts, with weasel words as the dominant strategy. Our findings highlight systematic challenges in AI alignment and provide new insights toward more truthful LLM behavior.

Most state-of-the-art Visual-Language Models (VLMs) are seemingly limited by the linear separabilty of their visual embeddings on abstract reasoning tasks. This work investigates this "linear reasoning bottleneck" by introducing the Linear Separability Ceiling (LSC), the performance of a simple linear classifier on a VLM's visual embeddings. We find this bottleneck is widespread and stems not from poor perception, but from failures in the language model's reasoning pathways. We demonstrate this is a solvable alignment issue. The required intervention, however, is task-dependent: activating existing pathways suffices for semantic concepts, while complex relational reasoning requires adapting core model weights. Using postfix tuning as a methodological control, we find strong evidence for powerful, dormant reasoning pathways within VLMs. However, for complex relational tasks requiring deeper adaptation, explicitly improving representation quality causes the model to fail on new prompt formats despite its embeddings remaining well separated. Ultimately, this work provides a new lens for VLM analysis, showing that robust reasoning is a matter of targeted alignment, not simply improved representation learning.

The rapid advancements of AI agents have ignited the long-held ambition of leveraging them to accelerate scientific discovery. Achieving this goal requires a deep understanding of the frontiers of human knowledge. As such, Humanity's Last Exam (HLE) provides an exceptionally challenging touchstone for evaluating scientific AI agents. In this work, we aim to construct the foundational architecture for general-purpose agents and validate the capabilities through leading performance on HLE. To achieve this, we introduce X-Master, a tool-augmented reasoning agent designed to emulate human researchers by interacting flexibly with external tools during its reasoning process. This agent, guided by the conceptualization of code as an interaction language, can flexibly leverage built-in Python libraries and our customized tools to augment the reasoning. We further scale its capabilities through X-Masters, a scattered-and-stacked agentic workflow that systematically enhances breadth and depth of reasoning. Our open-source solution, X-Masters, sets a new state-of-the-art record on HLE with a score of 32.1%, surpassing OpenAI's and Google's Deep Research (26.6% and 26.9%) and becoming the first to exceed the 30% threshold. This work allows us to gain a deeper understanding of complex task-solving and accumulates valuable experience that can inform future advancements, guiding subsequent model training.

微软开始向 Canary 版本的 Windows Insider 测试者释出了新 Windows 11 版本，该版本提供了新的故障恢复模式 quick machine recovery(QMR 或快速机器恢复)。Windows 有 4 种测试版本，Canary、Dev、Beta 和 Release Preview，其中 Canary 是最不稳定的，实验性最强，在经过四轮测试之后，新功能会提供给正式版用户。QMR 会让 Windows 11 PC 启动到 Windows Recovery Environment（WinRE），连接互联网，下载微软为“普遍存在的启动问题”提供的修复程序——这些启动问题可能导致 PC 无法正常启动。QMR 是微软针对去年发生的 CrowdStrike 事件采取的措施之一。有问题的 CrowdStrike 安全补丁导致数百万台 Windows PC 蓝屏死机，大量服务中断。

几乎所有高收入国家的生育率都降至历史最低水平，经济学家认为原因并非是经济因素，而是成年人生活中的优先事项发生了根本性改变。几乎所有经合组织国家的总和生育率都降至更替水平以下，很多国家的生育率长时间低于 1.5，新加坡以及韩国和中国等东亚国家的生育率处于或低于每名妇女生育一个孩子。经济学家表示，经济因素如短期收入变化无法解决这种普遍的下降趋势，而有证据表明，生活优先事项的转变如不断发展的经济机会以及更广泛的社会文化力量，这些因素削弱了为人父母在成年人生活中的角色。

天文学家本月早些时候宣布可能发现了已知第三个星际天体、第二个星际彗星 3I/ATLAS（第一个是 Oumuamua，第二个是星际彗星 2I/Borisov）。天文学家现在报告 3I/ATLAS 可能比太阳系还要古老 30 亿年，很可能是人类目前见过最古老的彗星。与之前发现的星际天体 1I/ ʻOumuamua 和 2I/Borisov 不同，3I/ATLAS 似乎不是沿着平坦的银河平面移动，而是以一条更陡峭的路径穿越银河，科学家推测它可能来自银河系的「厚盘」—那是一个分布着许多年老恒星的区域，位置在我们熟悉的银河盘面上下。研究推估，这颗彗星若是从厚盘区域的某颗古老恒星系统中诞生，那么它应该富含水冰成分。随着它逐渐靠近太阳，阳光将会加热并激发彗星表面产生活动，释放出气体与尘埃，进而形成明亮的彗发和彗尾。根据初步观测，3I/ATLAS 已经显现出活跃的征兆，甚至可能比之前那两颗星际天体的活跃程度更高。如果这些特征获得确认，将有助于我们推估未来还能从望远镜观测到类似星际天体的数量。

本周二是今年至今最短的一天。根据 U.S. Naval Observatory and the International Earth Rotation and Reference Systems Service 的数据，周二的自转时间比标准的 24 小时短 1.34 毫秒。地球自转受到了地核运动、大气变化和月球位置等因素的影响。地球最近几年的自转都比通常更快，自转一周的时间经常短于 24 小时。未来几周或几个月可能会出现更多类似的情况。但从千万年的时间跨度上看，地球一天的时间长度并没有变短，而是在变长，比如霸王龙生活的 7000 万年前，一天的时间长度只有 23.5 小时。

神州租车宣布与百度合作推出自主驾驶汽车租车服务。新闻稿称自动驾驶租车有运营范围，但没有具体说明，也没有提及是否地理围栏。新闻稿称，自动驾驶汽车支持三人乘坐，其定价和标准的短租相同，预约时间从 4 小时到 7 天不等。

中国继续以创纪录的速度增加太阳能和风能。根据旧金山 Global Energy Monitor 的数据，中国在建太阳能和风电装机容量占全球四分之三：中国在建装机容量为 510 GW，而全球在建总装机容量为 689GW。每 GW 发电量能为约 100 万户家庭供电。

瑞典安保部门成员在健身应用 Strava 上分享了跑步和骑行路线，被指泄漏了首相的行踪。首相的保镖至少 35 次将锻炼记录上传到 Strava 应用，泄露了首相 Ulf Kristersson 本人的活动路线，包括跑步地点、国外过夜旅行细节，以及本应保密的私人住宅位置。

Google 改变了向 Android 开发者提供早期功能的方式，它正用滚动更新的 Canary 版本（Canary channel）取代开发者预览版本，此举旨在让开发者能更早更持续的接触到实验性工具和 API。以前开发者预览版本需要手动刷入设备，只在每个发布周期的早期阶段提供，进入 Beta 阶段后就停止了，这可能会影响到尚未为 Beta 版本准备好的功能，开发者也没办法收集反馈。Canary 版本能与 Beta 版本并行运行，能自动更新，解决了上述问题。

性别决定是最基本的生物发育过程之一，也是科学界探索的热点。在发育过程中表达的基因对多种器官的形成起着至关重要的作用。位于 Y 染色体上的哺乳动物性别决定基因 Sry 是睾丸形成所必需的，该基因仅在胚胎发生过程中的很短时间内被激活。然而 Sry 激活背后的具体机制仍不清楚。大阪大学领导的研究团队发现铁在雄性哺乳动物的性别发育中发挥关键作用。无论是饮食还是药物干预引起的母体缺铁，都可能导致 Sry 基因的组蛋白甲基化发生改变。缺铁的雌鼠产下的一些 XY 后代被证实会发生性别逆转，这可能是与 Sry 基因激活受损有关。

2022 年 9 月 26 日，NASA 执行双小行星重定向测试(DART)任务的飞船撞击了名为 Dimorphos 的小行星。这是世界首次行星防御技术演示，撞击成功偏转了小行星轨道，但同时也释放了大量石头，而这些巨石可能会使得未来的偏转小行星的工作更加复杂。撞击产生了 104 块半径从 0.2 米到 3.6 米不等的巨石。研究团队发现，这些喷出的巨石们的动量高达 DART 撞击器的三倍。这些巨石不仅产生几乎与 DART 撞击相同的冲击力，四散的方向也明显分为两群。研究团队认为，这些被抛出的巨石可能来自特定来源，或许是小行星上的一些较大巨石，它们在 DART 探测器主体撞击地表之前就被太阳能板击碎而飞溅出来。由于 DART 撞击产生的巨石的动量主要垂直于太空船的轨道，这意味着它可能使轨道面倾斜一度以上，并导致小行星在太空中不规则地翻滚。这项研究结果说明了未来若欲再度以撞击方式偏转小行星的轨道，其表面地形与岩石分布状态，将可能对偏转成果产生不可预期的变数。

纳米大小的塑料颗粒已经无处不在，在海洋中其质量数以千万吨。根据发表在《自然》期刊上的一项研究，研究人员从北大西洋代表不同环境的三个深度采集水样。他们从中发现了三种类型的纳米塑料：聚对苯二甲酸乙二醇酯(PET)、聚苯乙烯(PS)和聚氯乙烯(PVC)。纳米塑料的平均浓度为每立方米 18 毫克，相当于有 2700 万吨纳米塑料分布在温带至亚热带北大西洋的表层。纳米塑料不同于体积更大的塑料颗粒，它们会悬浮在水中，不会沉降到海底，能够进入食物链。