OrangeBot.AI Digest — 2025-12-02

🎯 告别信息过载，AI 助你看懂新闻资讯热点，简单的舆情监控分析 - 多平台热点聚合+基于 MCP 的AI分析工具。监控35个平台（抖音、知乎、B站、华尔街见闻、财联社等），智能筛选+自动推送+AI对话分析（用自然语言深度挖掘新闻：趋势追踪、情感分析、相似检索等13种工具）。支持企业微信/个人微信/飞书/钉钉/Telegram/邮件/ntfy/bark/slack 推送，30秒网页部署，1分钟手机通知，无需编程。支持Docker部署⭐ 让算法为你服务，用AI理解热点

An open-source, code-first Go toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control.

所有小初高、大学PDF教材。

[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.

Node Version Manager - POSIX-compliant bash script to manage multiple active node.js versions

The Cloud Native Application Proxy

[EMNLP2025] "LightRAG: Simple and Fast Retrieval-Augmented Generation"

A list of open source games.

verl: Volcano Engine Reinforcement Learning for LLMs

Open-Source Memory Engine for LLMs, AI Agents & Multi-Agent Systems

Curated coding interview preparation materials for busy software engineers

Send a phone call from AI agent, in an API call. Or, directly call the bot from the configured phone number!

Run Windows Subsystem For Android on your Windows 10 and Windows 11 PC using prebuilt binaries with Google Play Store (MindTheGapps) and/or Magisk or KernelSU (root solutions) built in.

Powerful web graphics runtime built on WebGL, WebGPU, WebXR and glTF

Collection of publicly available IPTV channels from all over the world

Large language models (LLMs) have fundamentally transformed automated software development by enabling direct translation of natural language descriptions into functional code, driving commercial adoption through tools like Github Copilot (Microsoft), Cursor (Anysphere), Trae (ByteDance), and Claude Code (Anthropic). While the field has evolved dramatically from rule-based systems to Transformer-based architectures, achieving performance improvements from single-digit to over 95\% success rates on benchmarks like HumanEval. In this work, we provide a comprehensive synthesis and practical guide (a series of analytic and probing experiments) about code LLMs, systematically examining the complete model life cycle from data curation to post-training through advanced prompting paradigms, code pre-training, supervised fine-tuning, reinforcement learning, and autonomous coding agents. We analyze the code capability of the general LLMs (GPT-4, Claude, LLaMA) and code-specialized LLMs (StarCoder, Code LLaMA, DeepSeek-Coder, and QwenCoder), critically examining the techniques, design decisions, and trade-offs. Further, we articulate the research-practice gap between academic research (e.g., benchmarks and tasks) and real-world deployment (e.g., software-related code tasks), including code correctness, security, contextual awareness of large codebases, and integration with development workflows, and map promising research directions to practical needs. Last, we conduct a series of experiments to provide a comprehensive analysis of code pre-training, supervised fine-tuning, and reinforcement learning, covering scaling law, framework selection, hyperparameter sensitivity, model architectures, and dataset comparisons.

Large multimodal models (LMMs) have shown great potential for video reasoning with textual Chain-of-Thought. However, they remain vulnerable to hallucinations, especially when processing long-form videos where evidence is sparse and temporally dispersed. Inspired by how humans comprehend long videos - by first skimming globally and then examining relevant clips for details - we introduce LongVT, an end-to-end agentic framework that enables "Thinking with Long Videos" via interleaved Multimodal Chain-of-Tool-Thought. Specifically, we exploit LMMs' inherent temporal grounding ability as a native video cropping tool to zoom in on a specific video clip and resample finer-grained video frames. This global-to-local reasoning loop continues until answers are grounded in retrieved visual evidence. Given the scarcity of fine-grained question-answering (QA) data for the long video reasoning task, we curate and will release a data suite named VideoSIAH to facilitate both training and evaluation. Specifically, our training dataset consists of 247.9K samples for tool-integrated cold-start supervised fine-tuning, 1.6K samples for agentic reinforcement learning, and 15.4K samples for agentic reinforcement fine-tuning, respectively. Our evaluation benchmark consists of 1,280 QA pairs that are carefully curated through a semi-automatic data pipeline with human-in-the-loop validation. With a meticulously designed three-stage training strategy and extensive empirical validation, LongVT consistently outperforms existing strong baselines across four challenging long-video understanding and reasoning benchmarks. Our codes, data, and model checkpoints are publicly available at https://github.com/EvolvingLMMs-Lab/LongVT .

Current multimodal models aim to transcend the limitations of single-modality representations by unifying understanding and generation, often using text-to-image (T2I) tasks to calibrate semantic consistency. However, their reliance on static, single-image generation in training and evaluation leads to overfitting to static pattern matching and semantic fusion, while fundamentally hindering their ability to model dynamic processes that unfold over time. To address these constraints, we propose Envision-a causal event progression benchmark for chained text-to-multi-image generation. Grounded in world knowledge and structured by spatiotemporal causality, it reorganizes existing evaluation dimensions and includes 1,000 four-stage prompts spanning six scientific and humanities domains. To transition evaluation from single images to sequential frames and assess whether models truly internalize world knowledge while adhering to causal-temporal constraints, we introduce Envision-Score, a holistic metric integrating multi-dimensional consistency, physicality, and aesthetics. Comprehensive evaluation of 15 models (10 specialized T2I models, 5 unified models) uncovers: specialized T2I models demonstrate proficiency in aesthetic rendering yet lack intrinsic world knowledge. Unified multimodal models bridge this gap, consistently outperforming specialized counterparts in causal narrative coherence. However, even these unified architectures remain subordinate to closed-source models and struggle to overcome the core challenge of spatiotemporal consistency. This demonstrates that a focus on causally-isolated single images impedes multi-frame reasoning and generation, promoting static pattern matching over dynamic world modeling-ultimately limiting world knowledge internalization, generation.

This paper proposes a novel formulation for reinforcement learning (RL) with large language models, explaining why and under what conditions the true sequence-level reward can be optimized via a surrogate token-level objective in policy gradient methods such as REINFORCE. Specifically, through a first-order approximation, we show that this surrogate becomes increasingly valid only when both the training-inference discrepancy and policy staleness are minimized. This insight provides a principled explanation for the crucial role of several widely adopted techniques in stabilizing RL training, including importance sampling correction, clipping, and particularly Routing Replay for Mixture-of-Experts (MoE) models. Through extensive experiments with a 30B MoE model totaling hundreds of thousands of GPU hours, we show that for on-policy training, the basic policy gradient algorithm with importance sampling correction achieves the highest training stability. When off-policy updates are introduced to accelerate convergence, combining clipping and Routing Replay becomes essential to mitigate the instability caused by policy staleness. Notably, once training is stabilized, prolonged optimization consistently yields comparable final performance regardless of cold-start initialization. We hope that the shared insights and the developed recipes for stable RL training will facilitate future research.

Deep Research Agents (DRAs) aim to automatically produce analyst-level reports through iterative information retrieval and synthesis. However, most existing DRAs were validated on question-answering benchmarks, while research on generating comprehensive reports remains overlooked. Worse, current benchmarks for report synthesis suffer from task complexity and subjective metrics -- this fails to reflect user demands and limits the practical utility of generated reports. To address these gaps, we present Fine-grained DEepResearch bench (FINDER), an enhanced benchmark consisting of 100 human-curated research tasks with 419 structured checklist items that standardize report structure, analytical depth, and factual grounding. Based on approximately 1,000 reports produced by mainstream DRAs, we further propose Deep rEsearch Failure Taxonomy (DEFT), the first failure taxonomy for deep research agents. DEFT contains 14 fine-grained failure modes across reasoning, retrieval, and generation, and is built upon grounded theory with human-LLM co-annotating and inter-annotator reliability validation. Our experimental findings reveal that current DRAs struggle not with task comprehension but with evidence integration, verification, and reasoning-resilient planning.

Recent video diffusion models can synthesize visually compelling clips, yet often violate basic physical laws-objects float, accelerations drift, and collisions behave inconsistently-revealing a persistent gap between visual realism and physical realism. We propose NewtonRewards, the first physics-grounded post-training framework for video generation based on verifiable rewards. Instead of relying on human or VLM feedback, NewtonRewards extracts measurable proxies from generated videos using frozen utility models: optical flow serves as a proxy for velocity, while high-level appearance features serve as a proxy for mass. These proxies enable explicit enforcement of Newtonian structure through two complementary rewards: a Newtonian kinematic constraint enforcing constant-acceleration dynamics, and a mass conservation reward preventing trivial, degenerate solutions. We evaluate NewtonRewards on five Newtonian Motion Primitives (free fall, horizontal/parabolic throw, and ramp sliding down/up) using our newly constructed large-scale benchmark, NewtonBench-60K. Across all primitives in visual and physics metrics, NewtonRewards consistently improves physical plausibility, motion smoothness, and temporal coherence over prior post-training methods. It further maintains strong performance under out-of-distribution shifts in height, speed, and friction. Our results show that physics-grounded verifiable rewards offer a scalable path toward physics-aware video generation.

Previous works have explored various customized generation tasks given a reference image, but they still face limitations in generating consistent fine-grained details. In this paper, our aim is to solve the inconsistency problem of generated images by applying a reference-guided post-editing approach and present our ImageCritic. We first construct a dataset of reference-degraded-target triplets obtained via VLM-based selection and explicit degradation, which effectively simulates the common inaccuracies or inconsistencies observed in existing generation models. Furthermore, building on a thorough examination of the model's attention mechanisms and intrinsic representations, we accordingly devise an attention alignment loss and a detail encoder to precisely rectify inconsistencies. ImageCritic can be integrated into an agent framework to automatically detect inconsistencies and correct them with multi-round and local editing in complex scenarios. Extensive experiments demonstrate that ImageCritic can effectively resolve detail-related issues in various customized generation scenarios, providing significant improvements over existing methods.

Current autoregressive video diffusion models are constrained by three core bottlenecks: (i) the finite temporal horizon imposed by the base model's 3D Rotary Positional Embedding (3D-RoPE), (ii) slow prompt responsiveness in maintaining fine-grained action control during long-form rollouts, and (iii) the inability to realize discontinuous cinematic transitions within a single generation stream. We introduce infty-RoPE, a unified inference-time framework that addresses all three limitations through three interconnected components: Block-Relativistic RoPE, KV Flush, and RoPE Cut. Block-Relativistic RoPE reformulates temporal encoding as a moving local reference frame, where each newly generated latent block is rotated relative to the base model's maximum frame horizon while earlier blocks are rotated backward to preserve relative temporal geometry. This relativistic formulation eliminates fixed temporal positions, enabling continuous video generation far beyond the base positional limits. To obtain fine-grained action control without re-encoding, KV Flush renews the KV cache by retaining only two latent frames, the global sink and the last generated latent frame, thereby ensuring immediate prompt responsiveness. Finally, RoPE Cut introduces controlled discontinuities in temporal RoPE coordinates, enabling multi-cut scene transitions within a single continuous rollout. Together, these components establish infty-RoPE as a training-free foundation for infinite-horizon, controllable, and cinematic video diffusion. Comprehensive experiments show that infty-RoPE consistently surpasses previous autoregressive models in overall VBench scores.

Unified multimodal models (UMMs) aim to jointly perform multimodal understanding and generation within a single framework. We present TUNA, a native UMM that builds a unified continuous visual representation by cascading a VAE encoder with a representation encoder. This unified representation space allows end-to-end processing of images and videos for both understanding and generation tasks. Compared to prior UMMs with decoupled representations, TUNA's unified visual space avoids representation format mismatches introduced by separate encoders, outperforming decoupled alternatives in both understanding and generation. Moreover, we observe that stronger pretrained representation encoders consistently yield better performance across all multimodal tasks, highlighting the importance of the representation encoder. Finally, in this unified setting, jointly training on both understanding and generation data allows the two tasks to benefit from each other rather than interfere. Our extensive experiments on multimodal understanding and generation benchmarks show that TUNA achieves state-of-the-art results in image and video understanding, image and video generation, and image editing, demonstrating the effectiveness and scalability of its unified representation design.

We present LFM2, a family of Liquid Foundation Models designed for efficient on-device deployment and strong task capabilities. Using hardware-in-the-loop architecture search under edge latency and memory constraints, we obtain a compact hybrid backbone that combines gated short convolutions with a small number of grouped query attention blocks, delivering up to 2x faster prefill and decode on CPUs compared to similarly sized models. The LFM2 family covers 350M-8.3B parameters, including dense models (350M, 700M, 1.2B, 2.6B) and a mixture-of-experts variant (8.3B total, 1.5B active), all with 32K context length. LFM2's training pipeline includes a tempered, decoupled Top-K knowledge distillation objective that avoids support mismatch; curriculum learning with difficulty-ordered data; and a three-stage post-training recipe of supervised fine-tuning, length-normalized preference optimization, and model merging. Pre-trained on 10-12T tokens, LFM2 models achieve strong results across diverse benchmarks; for example, LFM2-2.6B reaches 79.56% on IFEval and 82.41% on GSM8K. We further build multimodal and retrieval variants: LFM2-VL for vision-language tasks, LFM2-Audio for speech, and LFM2-ColBERT for retrieval. LFM2-VL supports tunable accuracy-latency tradeoffs via token-efficient visual processing, while LFM2-Audio separates audio input and output pathways to enable real-time speech-to-speech interaction competitive with models 3x larger. LFM2-ColBERT provides a low-latency encoder for queries and documents, enabling high-performance retrieval across multiple languages. All models are released with open weights and deployment packages for ExecuTorch, llama.cpp, and vLLM, making LFM2 a practical base for edge applications that need fast, memory-efficient inference and strong task capabilities.

Knowledge graphs (KGs) provide structured, verifiable grounding for large language models (LLMs), but current LLM-based systems commonly use KGs as auxiliary structures for text retrieval, leaving their intrinsic quality underexplored. In this work, we propose Wikontic, a multi-stage pipeline that constructs KGs from open-domain text by extracting candidate triplets with qualifiers, enforcing Wikidata-based type and relation constraints, and normalizing entities to reduce duplication. The resulting KGs are compact, ontology-consistent, and well-connected; on MuSiQue, the correct answer entity appears in 96% of generated triplets. On HotpotQA, our triplets-only setup achieves 76.0 F1, and on MuSiQue 59.8 F1, matching or surpassing several retrieval-augmented generation baselines that still require textual context. In addition, Wikontic attains state-of-the-art information-retention performance on the MINE-1 benchmark (86%), outperforming prior KG construction methods. Wikontic is also efficient at build time: KG construction uses less than 1,000 output tokens, about 3times fewer than AriGraph and <1/20 of GraphRAG. The proposed pipeline enhances the quality of the generated KG and offers a scalable solution for leveraging structured knowledge in LLMs.

Recent advancements in large language models (LLMs) have been driven by their emergent reasoning capabilities, particularly through long chain-of-thought (CoT) prompting, which enables thorough exploration and deliberation. Despite these advances, long-CoT LLMs often exhibit suboptimal reasoning behaviors, such as overthinking and excessively protracted reasoning chains, which can impair performance. In this paper, we analyze reasoning processes through an optimization lens, framing CoT as a gradient descent procedure where each reasoning step constitutes an update toward problem resolution. Building on this perspective, we introduce RePro (Rectifying Process-level Reward), a novel approach to refine LLM reasoning during post-training. RePro defines a surrogate objective function to assess the optimization process underlying CoT, utilizing a dual scoring mechanism to quantify its intensity and stability. These scores are aggregated into a composite process-level reward, seamlessly integrated into reinforcement learning with verifiable rewards (RLVR) pipelines to optimize LLMs. Extensive experiments across multiple reinforcement learning algorithms and diverse LLMs, evaluated on benchmarks spanning mathematics, science, and coding, demonstrate that RePro consistently enhances reasoning performance and mitigates suboptimal reasoning behaviors.

Diffusion Models have emerged as a leading class of generative models, yet their iterative sampling process remains computationally expensive. Timestep distillation is a promising technique to accelerate generation, but it often requires extensive training and leads to image quality degradation. Furthermore, fine-tuning these distilled models for specific objectives, such as aesthetic appeal or user preference, using Reinforcement Learning (RL) is notoriously unstable and easily falls into reward hacking. In this work, we introduce Flash-DMD, a novel framework that enables fast convergence with distillation and joint RL-based refinement. Specifically, we first propose an efficient timestep-aware distillation strategy that significantly reduces training cost with enhanced realism, outperforming DMD2 with only 2.1% its training cost. Second, we introduce a joint training scheme where the model is fine-tuned with an RL objective while the timestep distillation training continues simultaneously. We demonstrate that the stable, well-defined loss from the ongoing distillation acts as a powerful regularizer, effectively stabilizing the RL training process and preventing policy collapse. Extensive experiments on score-based and flow matching models show that our proposed Flash-DMD not only converges significantly faster but also achieves state-of-the-art generation quality in the few-step sampling regime, outperforming existing methods in visual quality, human preference, and text-image alignment metrics. Our work presents an effective paradigm for training efficient, high-fidelity, and stable generative models. Codes are coming soon.

Vision-Language-Action models (VLAs) are becoming increasingly capable across diverse robotic tasks. However, their real-world deployment remains slow and inefficient: demonstration videos are often sped up by 5-10x to appear smooth, with noticeable action stalls and delayed reactions to environmental changes. Asynchronous inference offers a promising solution to achieve continuous and low-latency control by enabling robots to execute actions and perform inference simultaneously. However, because the robot and environment continue to evolve during inference, a temporal misalignment arises between the prediction and execution intervals. This leads to significant action instability, while existing methods either degrade accuracy or introduce runtime overhead to mitigate it. We propose VLASH, a general asynchronous inference framework for VLAs that delivers smooth, accurate, and fast reaction control without additional overhead or architectural changes. VLASH estimates the future execution-time state by rolling the robot state forward with the previously generated action chunk, thereby bridging the gap between prediction and execution. Experiments show that VLASH achieves up to 2.03x speedup and reduces reaction latency by up to 17.4x compared to synchronous inference while fully preserving the original accuracy. Moreover, it empowers VLAs to handle fast-reaction, high-precision tasks such as playing ping-pong and playing whack-a-mole, where traditional synchronous inference fails. Code is available at https://github.com/mit-han-lab/vlash

We present GR-RL, a robotic learning framework that turns a generalist vision-language-action (VLA) policy into a highly capable specialist for long-horizon dexterous manipulation. Assuming the optimality of human demonstrations is core to existing VLA policies. However, we claim that in highly dexterous and precise manipulation tasks, human demonstrations are noisy and suboptimal. GR-RL proposes a multi-stage training pipeline that filters, augments, and reinforces the demonstrations by reinforcement learning. First, GR-RL learns a vision-language-conditioned task progress, filters the demonstration trajectories, and only keeps the transitions that contribute positively to the progress. Specifically, we show that by directly applying offline RL with sparse reward, the resulting Q-values can be treated as a robust progress function. Next, we introduce morphological symmetry augmentation that greatly improves the generalization and performance of GR-RL. Lastly, to better align the VLA policy with its deployment behaviors for high-precision control, we perform online RL by learning a latent space noise predictor. With this pipeline, GR-RL is, to our knowledge, the first learning-based policy that can autonomously lace up a shoe by threading shoelaces through multiple eyelets with an 83.3% success rate, a task requiring long-horizon reasoning, millimeter-level precision, and compliant soft-body interaction. We hope GR-RL provides a step toward enabling generalist robot foundations models to specialize into reliable real-world experts.

牛津大学出版社的 2025 年年度单词是 Rage bait。Rage bait 意思是愤怒诱饵，它和去年的年度单词 brain rot（脑腐）一起提醒我们，在算法时代，情绪已成为最被操弄的资源。Rage bait 专指那些刻意让人愤怒、挫折或感到冒犯，以拉高点击率或社群互动的网络内容，例如在网络上故意激怒你，只为了让你多按个怒的表情符号、骂两句、再分享出去，让算法把愤怒推得更远。根据牛津语料库资料，rage bait 在过去 12 个月内的使用频率增加三倍，成为媒体与社群平台经常提到的词。

华为的 GPU 相关专利申请量正在增加。截至 2023 年的 5 年里，申请数量增加到了原来的 10 倍，超过了美国英伟达和英特尔的申请量。这反映出华为正在大力开发 AI 相关技术。从包含 GPU 这一关键词的专利来看，最近几年三星电子与华为的申请量激增。2023 年华为的申请量为 3091 项，增加到了 2018 年的约 10 倍。2023 年的申请量相当于英特尔的 3 倍、英伟达的 5 倍。

新加坡教育部宣布，明年 1 月起，所有中学生在学校时，包括上课、课间休息，以及课后进行课程辅助活动、增益课或补课等，都不得使用智能手机和手表。学生在校时须把智能手机和手表放入储物柜或书包等指定存放空间；若有必要，学校会允许学生使用智能手机。新教育部称，此举旨在鼓励学生培养良好数码习惯，下课后与同学进行有意义的互动交流，以及培养健康生活方式。新加坡之前已经不允许小学生在校使用智能手机或手表，他们在学校必须把这些配备放在书包或指定存放空间，包括课间休息，及下课后进行学习项目的时候。

掌机 Steam Deck 的流行以及基于 Arch Linux 的发行版 SteamOS 的成功推动 Linux 用户比例达到 3.20%。根据 Valve 公布的 2025 年 11 月 Steam 硬件和软件调查，玩家运行的操作系统中 Linux 比例达到 3.20%（+0.15%），Windows 占 94.79%（-0.05%），OSX 占 2.02%（-0.09%），其中在 Windows 10 停止支持后 Windows 11 比例达到了 65.59%（+2.02%），Windows 10 降至三成以内占 29.06%（-2.08%）。在 Windows 平台，英特尔 CPU 占 57.30%（-0.52%），AMD 占 42.61%（+0.52%）。对于用户使用的语言，简体中文占 24.93%（+0.92%），英文占 37.37%（-0.59%）。

Let’s Encrypt 宣布到 2028 年将证书有效期从现在的 90 天缩短至 45 天。此举是为了遵守今年早些时候 Certification Authority Browser Forum (CA/Browser Forum)通过的缩短证书有效期决议。决议要求到 2026 年 3 月 15 日 TLS 证书最长有效期将缩短至 200 天；到 2027 年 3 月 15 日 TLS 证书最长有效期将缩短至 100 天；2029 年 3 月 15 日 TLS 证书最长有效期将缩短至 47 天。Let’s Encrypt 还将缩短验证域名控制权后允许为该域名签发证书的时间间隔，从目前的 30 天缩短至 7 小时。为减少对用户的影响，新的变更将分阶段实施：2026 年 5 月 13 日可选配置有效期 45 天，2027 年 2 月 10 日默认配置有效期 64 天，2028 年 2 月 16 日有效期进一步缩短为 45 天。

印度电信部要求智能手机制造商在手机上预装用户无法禁用的政府网络安全应用 Sanchar Saathi。该命令是于 11 月 28 日下达给手机制造商的，要求在 90 天内完成。该命令没有公开发布，而是通过私下下达给智能手机制造商。苹果、三星、vivo、OPPO 和小米等都受到新命令的约束。印度电信部还要求对于现有手机，制造商需要通过软件更新将该应用推送给用户。印度是全球最大的手机市场之一，有逾 12 亿用户。Sanchar Saathi 于 1 月推出，政府声称该应用帮助找回了逾 70 万部丢失的手机。印度此举可能会激怒苹果公司和隐私倡导者。苹果公司通常会拒绝此类请求，Counterpoint Research 研究总监 Tarun Pathak 称，苹果可能会寻求折衷方案：与其强制预装，不如协商要求提供一个选项，引导用户安装该应用。

2025 年 12 月 10 日，澳大利亚禁止 16 岁以下青少年使用社媒的禁令将正式生效，从这一天开始，社媒平台如 Snapchat、Facebook、Instagram、Kick、Reddit、Threads、TikTok、Twitch、X 和 YouTube 都必须采取措施移除未成年人账户，禁止未成年人注册，违反者将面临最高 4950 万澳元的罚款。Meta 表示将从 12 月 4 日起 Facebook、Instagram 和 Threads 关闭旧账户屏蔽新注册账户。Meta 鼓励 16 岁以下用户下载自己的内容。Snap 表示用户可停用账户最多三年，或者直至年满 16 岁。世界各国正在关注澳大利亚的这一社会试验，丹麦和马来西亚等国都宣布了类似计划。根据 CNN 的报告，很多澳大利亚学生并不知道即将生效的禁令，他们想知道 16 岁之后账户是否能恢复，或者谎报年龄会发生什么。

新西兰奥克兰海洋研究人员发现微塑料最大单一来源是汽车轮胎。研究人员称，汽车轮胎磨损之后产生的碎片会散落在路面上，其中半数留在那里，还有半数进入到环境，最终随着雨水进入到海洋。微塑料最小的仅纳米大小，肉眼无法看到，较大的有一到两毫米。现有的雨水处理设备基本上不会捕捉到微塑料。研究人员在毗邻公路的区域采集样本，在路边和沿海沉积物的每一个角落都发现了轮胎颗粒。研究人员称微塑料是一种全球性问题。

草甘膦是孟山都公司研究人员在 1970 年发现的，被广泛用于除草。2000 年包括 Gary M. Williams 在内的三名科学家在《Regulatory Toxicology and Pharmacology》期刊上发表了论文《Safety Evaluation and Risk Assessment of the Herbicide Roundup and Its Active Ingredient, Glyphosate, for Humans》，宣称使用草甘膦是安全的。这篇论文被引用了数百次，被广泛用于证明草甘膦的安全性，包括维基百科相关条目在内的文章都援引了这篇论文。然而 2017 年的诉讼披露孟山都公司的员工参与了论文的撰写， 三名科学家很可能只是该公司雇佣的枪手。论文存在利益冲突、结果有效性等问题。期刊终于在长期受到质疑之后采取行动联系了唯一在世的科学家 Williams 要求予以澄清，但没有收到任何回应。期刊宣布撤回这篇论文。

树莓派宣布因为近期内存价格飙升而对部分型号的 Raspberry Pi 4 和 5 产品涨价，同时宣布推出一款 1GB 版本的 Raspberry Pi 5，售价 45 美元。Raspberry Pi 4 和 5 价格上涨最高 25 美元，最低 5 美元。4GB 版本的 Raspberry Pi 4 从 55 美元涨到 60 美元，16GB 版本的 Raspberry Pi 5 从 120 美元涨到 145 美元。树莓派表示近期的内存价格飙升是 AI 热推动的，一旦情况缓解它将会调低价格。

吸血章鱼是一种居住在深海的头足类，是八腕总目的一种，其祖先在侏罗纪时期为了躲避蛇颈龙目的猎食而移居深海，亿年来其形态不曾改变，被称为是活化石。日本研究团队在骏河湾意外捕捉到一只吸血章鱼，对其进行测序后发现其碱基对超过 110 亿，是已知章鱼类动物最大基因组的两倍多。吸血章鱼虽然名字中有章鱼，但它既不是章鱼也不是鱿鱼，更不是吸血鬼，它是一种古老谱系中最后也是唯一的幸存者，该谱系中其它成员都消失了。它的历史可追溯到 1.83 亿年前，保留了祖先的诸多特征，同时演化出适应深海黑暗环境、以腐肉为生的生存方式。其基因组规模比鱿鱼和章鱼都大得多，其中 62% 由重复序列组成。吸血章鱼属于八腕总目，但保留了十腕总目的部分染色体结构。研究人员表示吸血章鱼让我们能直接观察头足类动物演化的最早阶段。

韩国电商巨头酷澎发生了 3000 余万个用户账号信息遭泄事件。遭泄的个人信息包含用户姓名、电子邮箱、电话号码、地址，甚至包含部分订购记录。根据韩国 《个人信息保护法》，若企业违反相关法律，可以被处以最多相当于销售额 3% 的罚款。酷澎今年前三季度累计销售额为 36.3 万亿韩元。若从中减去与个人信息泄露案关联度不高的业务部门业绩等，销售额为 31 万亿韩元。若再将其折算为年销售额，罚款或达 1.2 万亿韩元。根据酷澎递交给警方的报告，用户信息泄露非因遭黑客攻击，而由公司中国籍员工外泄所致。该员工早已离职并离境。

SmartTube 开发者上周宣布数字签名泄漏，他发布了使用新签名的新版本应用，督促用户切换到新版本。SmartTube 是 Android TV 和 Fire TV 设备上 YouTube 应用的流行替代。开发者透露，他用于构建官方 APK 文件的计算机遭到入侵，导致部分 APK 版本植入了恶意程序。暂时不清楚哪个版本的 APK 最早包含了恶意程序。APKMirror 上的 SmartTube v30.43 和 30.47 都被标记为感染恶意程序。开发者表示，所有旧版本 SmartTube 都已经从项目的 GitHub 库中移除，感染恶意程序的计算机也进行了处理，旧数字签名被弃用。SmartTube v30.56 是使用新签名在干净计算机上构建的首个版本。

日本共同社、每日新闻社与产经新闻社周一向 AI 搜索公司 Perplexity 发送抗议书，以该公司擅自使用新闻机构发布的新闻稿、侵犯著作权为由，要求立即停止使用。在这之前，读卖新闻、朝日新闻社和日本经济新闻社也都提出了类似的要求和诉讼。共同社在抗议书中指出，确认到自 2024 年 8 月起的约 1 年里，Perplexity 合计数十万次访问刊登共同社与加盟报社稿件的新闻网站“47NEWS”。抗议书强调，Perplexity 未获许可即收集和复制新闻内容，并用于生成回答，侵犯了著作权。抗议书还指出，Perplexity 回答所显示的参考来源是共同社新闻稿，但给出的回答却是与稿件内容不同的虚假信息，损害了共同社新闻产品的信誉和品牌价值。

11 月 27 日俄罗斯在拜科努尔航天发射场成功发射了联盟号 MS-28 载人飞船。但 31/6 发射台下的移动维护舱却因为火箭尾焰从高空倒扣坠落而严重受损，在修复前发射台将无法使用，至于修复时间专家估计从几个月到三年。拜科努尔航天发射场是目前俄罗斯唯一能向国际空间站发射联盟号载人飞船和进步号无人货船的发射场地，而无人货船 MS-33 原计划于 12 月 21 日发射。俄罗斯还有其它发射场，但它们或者位于不适合的维度如 Plesetsk 发射场，或者没有获得载人飞行认证如东方发射场，或者已经退役移交给博物馆如拜科努尔的加加林发射台。