OrangeBot.AI Digest — 2026-01-01

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Introduction to Machine Learning Systems

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Official code repo for the O'Reilly Book - "Hands-On Large Language Models"

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A next.js web application that integrates AI capabilities with draw.io diagrams. This app allows you to create, modify, and enhance diagrams through natural language commands and AI-assisted visualization.

Recently, studies exemplified by Hyper-Connections (HC) have extended the ubiquitous residual connection paradigm established over the past decade by expanding the residual stream width and diversifying connectivity patterns. While yielding substantial performance gains, this diversification fundamentally compromises the identity mapping property intrinsic to the residual connection, which causes severe training instability and restricted scalability, and additionally incurs notable memory access overhead. To address these challenges, we propose Manifold-Constrained Hyper-Connections (mHC), a general framework that projects the residual connection space of HC onto a specific manifold to restore the identity mapping property, while incorporating rigorous infrastructure optimization to ensure efficiency. Empirical experiments demonstrate that mHC is effective for training at scale, offering tangible performance improvements and superior scalability. We anticipate that mHC, as a flexible and practical extension of HC, will contribute to a deeper understanding of topological architecture design and suggest promising directions for the evolution of foundational models.

We introduce Youtu-LLM, a lightweight yet powerful language model that harmonizes high computational efficiency with native agentic intelligence. Unlike typical small models that rely on distillation, Youtu-LLM (1.96B) is pre-trained from scratch to systematically cultivate reasoning and planning capabilities. The key technical advancements are as follows: (1) Compact Architecture with Long-Context Support: Built on a dense Multi-Latent Attention (MLA) architecture with a novel STEM-oriented vocabulary, Youtu-LLM supports a 128k context window. This design enables robust long-context reasoning and state tracking within a minimal memory footprint, making it ideal for long-horizon agent and reasoning tasks. (2) Principled "Commonsense-STEM-Agent" Curriculum: We curated a massive corpus of approximately 11T tokens and implemented a multi-stage training strategy. By progressively shifting the pre-training data distribution from general commonsense to complex STEM and agentic tasks, we ensure the model acquires deep cognitive abilities rather than superficial alignment. (3) Scalable Agentic Mid-training: Specifically for the agentic mid-training, we employ diverse data construction schemes to synthesize rich and varied trajectories across math, coding, and tool-use domains. This high-quality data enables the model to internalize planning and reflection behaviors effectively. Extensive evaluations show that Youtu-LLM sets a new state-of-the-art for sub-2B LLMs. On general benchmarks, it achieves competitive performance against larger models, while on agent-specific tasks, it significantly surpasses existing SOTA baselines, demonstrating that lightweight models can possess strong intrinsic agentic capabilities.

Agentic crafting requires LLMs to operate in real-world environments over multiple turns by taking actions, observing outcomes, and iteratively refining artifacts. Despite its importance, the open-source community lacks a principled, end-to-end ecosystem to streamline agent development. We introduce the Agentic Learning Ecosystem (ALE), a foundational infrastructure that optimizes the production pipeline for agent LLMs. ALE consists of three components: ROLL, a post-training framework for weight optimization; ROCK, a sandbox environment manager for trajectory generation; and iFlow CLI, an agent framework for efficient context engineering. We release ROME (ROME is Obviously an Agentic Model), an open-source agent grounded by ALE and trained on over one million trajectories. Our approach includes data composition protocols for synthesizing complex behaviors and a novel policy optimization algorithm, Interaction-based Policy Alignment (IPA), which assigns credit over semantic interaction chunks rather than individual tokens to improve long-horizon training stability. Empirically, we evaluate ROME within a structured setting and introduce Terminal Bench Pro, a benchmark with improved scale and contamination control. ROME demonstrates strong performance across benchmarks like SWE-bench Verified and Terminal Bench, proving the effectiveness of the ALE infrastructure.

Recent advances in 3D reconstruction have achieved remarkable progress in high-quality scene capture from dense multi-view imagery, yet struggle when input views are limited. Various approaches, including regularization techniques, semantic priors, and geometric constraints, have been implemented to address this challenge. Latest diffusion-based methods have demonstrated substantial improvements by generating novel views from new camera poses to augment training data, surpassing earlier regularization and prior-based techniques. Despite this progress, we identify three critical limitations in these state-of-the-art approaches: inadequate coverage beyond known view peripheries, geometric inconsistencies across generated views, and computationally expensive pipelines. We introduce GaMO (Geometry-aware Multi-view Outpainter), a framework that reformulates sparse-view reconstruction through multi-view outpainting. Instead of generating new viewpoints, GaMO expands the field of view from existing camera poses, which inherently preserves geometric consistency while providing broader scene coverage. Our approach employs multi-view conditioning and geometry-aware denoising strategies in a zero-shot manner without training. Extensive experiments on Replica and ScanNet++ demonstrate state-of-the-art reconstruction quality across 3, 6, and 9 input views, outperforming prior methods in PSNR and LPIPS, while achieving a 25times speedup over SOTA diffusion-based methods with processing time under 10 minutes. Project page: https://yichuanh.github.io/GaMO/

Log anomaly detection is crucial for preserving the security of operating systems. Depending on the source of log data collection, various information is recorded in logs that can be considered log modalities. In light of this intuition, unimodal methods often struggle by ignoring the different modalities of log data. Meanwhile, multimodal methods fail to handle the interactions between these modalities. Applying multimodal sentiment analysis to log anomaly detection, we propose CoLog, a framework that collaboratively encodes logs utilizing various modalities. CoLog utilizes collaborative transformers and multi-head impressed attention to learn interactions among several modalities, ensuring comprehensive anomaly detection. To handle the heterogeneity caused by these interactions, CoLog incorporates a modality adaptation layer, which adapts the representations from different log modalities. This methodology enables CoLog to learn nuanced patterns and dependencies within the data, enhancing its anomaly detection capabilities. Extensive experiments demonstrate CoLog's superiority over existing state-of-the-art methods. Furthermore, in detecting both point and collective anomalies, CoLog achieves a mean precision of 99.63%, a mean recall of 99.59%, and a mean F1 score of 99.61% across seven benchmark datasets for log-based anomaly detection. The comprehensive detection capabilities of CoLog make it highly suitable for cybersecurity, system monitoring, and operational efficiency. CoLog represents a significant advancement in log anomaly detection, providing a sophisticated and effective solution to point and collective anomaly detection through a unified framework and a solution to the complex challenges automatic log data analysis poses. We also provide the implementation of CoLog at https://github.com/NasirzadehMoh/CoLog.

High-stakes decision making involves reasoning under uncertainty about the future. In this work, we train language models to make predictions on open-ended forecasting questions. To scale up training data, we synthesize novel forecasting questions from global events reported in daily news, using a fully automated, careful curation recipe. We train the Qwen3 thinking models on our dataset, OpenForesight. To prevent leakage of future information during training and evaluation, we use an offline news corpus, both for data generation and retrieval in our forecasting system. Guided by a small validation set, we show the benefits of retrieval, and an improved reward function for reinforcement learning (RL). Once we obtain our final forecasting system, we perform held-out testing between May to August 2025. Our specialized model, OpenForecaster 8B, matches much larger proprietary models, with our training improving the accuracy, calibration, and consistency of predictions. We find calibration improvements from forecasting training generalize across popular benchmarks. We open-source all our models, code, and data to make research on language model forecasting broadly accessible.

Recent advances in text-to-video (T2V) generation have achieved good visual quality, yet synthesizing videos that faithfully follow physical laws remains an open challenge. Existing methods mainly based on graphics or prompt extension struggle to generalize beyond simple simulated environments or learn implicit physical reasoning. The scarcity of training data with rich physics interactions and phenomena is also a problem. In this paper, we first introduce a Physics-Augmented video data construction Pipeline, PhyAugPipe, that leverages a vision-language model (VLM) with chain-of-thought reasoning to collect a large-scale training dataset, PhyVidGen-135K. Then we formulate a principled Physics-aware Groupwise Direct Preference Optimization, PhyGDPO, framework that builds upon the groupwise Plackett-Luce probabilistic model to capture holistic preferences beyond pairwise comparisons. In PhyGDPO, we design a Physics-Guided Rewarding (PGR) scheme that embeds VLM-based physics rewards to steer optimization toward physical consistency. We also propose a LoRA-Switch Reference (LoRA-SR) scheme that eliminates memory-heavy reference duplication for efficient training. Experiments show that our method significantly outperforms state-of-the-art open-source methods on PhyGenBench and VideoPhy2. Please check our project page at https://caiyuanhao1998.github.io/project/PhyGDPO for more video results. Our code, models, and data will be released at https://github.com/caiyuanhao1998/Open-PhyGDPO

Memory serves as the pivotal nexus bridging past and future, providing both humans and AI systems with invaluable concepts and experience to navigate complex tasks. Recent research on autonomous agents has increasingly focused on designing efficient memory workflows by drawing on cognitive neuroscience. However, constrained by interdisciplinary barriers, existing works struggle to assimilate the essence of human memory mechanisms. To bridge this gap, we systematically synthesizes interdisciplinary knowledge of memory, connecting insights from cognitive neuroscience with LLM-driven agents. Specifically, we first elucidate the definition and function of memory along a progressive trajectory from cognitive neuroscience through LLMs to agents. We then provide a comparative analysis of memory taxonomy, storage mechanisms, and the complete management lifecycle from both biological and artificial perspectives. Subsequently, we review the mainstream benchmarks for evaluating agent memory. Additionally, we explore memory security from dual perspectives of attack and defense. Finally, we envision future research directions, with a focus on multimodal memory systems and skill acquisition.

Vision-language-action (VLA) models have enabled language-conditioned, long-horizon robot manipulation, but most existing systems are limited to grippers. Scaling VLA policies to bimanual robots with high degree-of-freedom (DoF) dexterous hands remains challenging due to the expanded action space, frequent hand-object occlusions, and the cost of collecting real-robot data. We present GR-Dexter, a holistic hardware-model-data framework for VLA-based generalist manipulation on a bimanual dexterous-hand robot. Our approach combines the design of a compact 21-DoF robotic hand, an intuitive bimanual teleoperation system for real-robot data collection, and a training recipe that leverages teleoperated robot trajectories together with large-scale vision-language and carefully curated cross-embodiment datasets. Across real-world evaluations spanning long-horizon everyday manipulation and generalizable pick-and-place, GR-Dexter achieves strong in-domain performance and improved robustness to unseen objects and unseen instructions. We hope GR-Dexter serves as a practical step toward generalist dexterous-hand robotic manipulation.

Despite the growing reasoning capabilities of recent large language models (LLMs), their internal mechanisms during the reasoning process remain underexplored. Prior approaches often rely on human-defined concepts (e.g., overthinking, reflection) at the word level to analyze reasoning in a supervised manner. However, such methods are limited, as it is infeasible to capture the full spectrum of potential reasoning behaviors, many of which are difficult to define in token space. In this work, we propose an unsupervised framework (namely, RISE: Reasoning behavior Interpretability via Sparse auto-Encoder) for discovering reasoning vectors, which we define as directions in the activation space that encode distinct reasoning behaviors. By segmenting chain-of-thought traces into sentence-level 'steps' and training sparse auto-encoders (SAEs) on step-level activations, we uncover disentangled features corresponding to interpretable behaviors such as reflection and backtracking. Visualization and clustering analyses show that these behaviors occupy separable regions in the decoder column space. Moreover, targeted interventions on SAE-derived vectors can controllably amplify or suppress specific reasoning behaviors, altering inference trajectories without retraining. Beyond behavior-specific disentanglement, SAEs capture structural properties such as response length, revealing clusters of long versus short reasoning traces. More interestingly, SAEs enable the discovery of novel behaviors beyond human supervision. We demonstrate the ability to control response confidence by identifying confidence-related vectors in the SAE decoder space. These findings underscore the potential of unsupervised latent discovery for both interpreting and controllably steering reasoning in LLMs.

Respiratory sound classification is hindered by the limited size, high noise levels, and severe class imbalance of benchmark datasets like ICBHI 2017. While Transformer-based models offer powerful feature extraction capabilities, they are prone to overfitting and often converge to sharp minima in the loss landscape when trained on such constrained medical data. To address this, we introduce a framework that enhances the Audio Spectrogram Transformer (AST) using Sharpness-Aware Minimization (SAM). Instead of merely minimizing the training loss, our approach optimizes the geometry of the loss surface, guiding the model toward flatter minima that generalize better to unseen patients. We also implement a weighted sampling strategy to handle class imbalance effectively. Our method achieves a state-of-the-art score of 68.10% on the ICBHI 2017 dataset, outperforming existing CNN and hybrid baselines. More importantly, it reaches a sensitivity of 68.31%, a crucial improvement for reliable clinical screening. Further analysis using t-SNE and attention maps confirms that the model learns robust, discriminative features rather than memorizing background noise.

We present SpaceTimePilot, a video diffusion model that disentangles space and time for controllable generative rendering. Given a monocular video, SpaceTimePilot can independently alter the camera viewpoint and the motion sequence within the generative process, re-rendering the scene for continuous and arbitrary exploration across space and time. To achieve this, we introduce an effective animation time-embedding mechanism in the diffusion process, allowing explicit control of the output video's motion sequence with respect to that of the source video. As no datasets provide paired videos of the same dynamic scene with continuous temporal variations, we propose a simple yet effective temporal-warping training scheme that repurposes existing multi-view datasets to mimic temporal differences. This strategy effectively supervises the model to learn temporal control and achieve robust space-time disentanglement. To further enhance the precision of dual control, we introduce two additional components: an improved camera-conditioning mechanism that allows altering the camera from the first frame, and CamxTime, the first synthetic space-and-time full-coverage rendering dataset that provides fully free space-time video trajectories within a scene. Joint training on the temporal-warping scheme and the CamxTime dataset yields more precise temporal control. We evaluate SpaceTimePilot on both real-world and synthetic data, demonstrating clear space-time disentanglement and strong results compared to prior work. Project page: https://zheninghuang.github.io/Space-Time-Pilot/ Code: https://github.com/ZheningHuang/spacetimepilot

We present PFP, a neural network structure to compress long videos into short contexts, with an explicit pretraining objective to preserve the high-frequency details of single frames at arbitrary temporal positions. The baseline model can compress a 20-second video into a context at about 5k length, where random frames can be retrieved with perceptually preserved appearances. Such pretrained models can be directly fine-tuned as memory encoders for autoregressive video models, enabling long history memory with low context cost and relatively low fidelity loss. We evaluate the framework with ablative settings and discuss the trade-offs of possible neural architecture designs.

The diffusion model presents a powerful ability to capture the entire (conditional) data distribution. However, due to the lack of sufficient training and data to learn to cover low-probability areas, the model will be penalized for failing to generate high-quality images corresponding to these areas. To achieve better generation quality, guidance strategies such as classifier free guidance (CFG) can guide the samples to the high-probability areas during the sampling stage. However, the standard CFG often leads to over-simplified or distorted samples. On the other hand, the alternative line of guiding diffusion model with its bad version is limited by carefully designed degradation strategies, extra training and additional sampling steps. In this paper, we proposed a simple yet effective strategy Internal Guidance (IG), which introduces an auxiliary supervision on the intermediate layer during training process and extrapolates the intermediate and deep layer's outputs to obtain generative results during sampling process. This simple strategy yields significant improvements in both training efficiency and generation quality on various baselines. On ImageNet 256x256, SiT-XL/2+IG achieves FID=5.31 and FID=1.75 at 80 and 800 epochs. More impressively, LightningDiT-XL/1+IG achieves FID=1.34 which achieves a large margin between all of these methods. Combined with CFG, LightningDiT-XL/1+IG achieves the current state-of-the-art FID of 1.19.

Strategic dialogue requires agents to execute distinct dialogue acts, for which belief estimation is essential. While prior work often estimates beliefs accurately, it lacks a principled mechanism to use those beliefs during generation. We bridge this gap by first formalizing two core acts Adversarial and Alignment, and by operationalizing them via probabilistic constraints on what an agent may generate. We instantiate this idea in BEDA, a framework that consists of the world set, the belief estimator for belief estimation, and the conditional generator that selects acts and realizes utterances consistent with the inferred beliefs. Across three settings, Conditional Keeper Burglar (CKBG, adversarial), Mutual Friends (MF, cooperative), and CaSiNo (negotiation), BEDA consistently outperforms strong baselines: on CKBG it improves success rate by at least 5.0 points across backbones and by 20.6 points with GPT-4.1-nano; on Mutual Friends it achieves an average improvement of 9.3 points; and on CaSiNo it achieves the optimal deal relative to all baselines. These results indicate that casting belief estimation as constraints provides a simple, general mechanism for reliable strategic dialogue.

随着特朗普政府日益强化入境审查，甚至要倒查五年的社交媒体历史记录，外国科技从业者、研究人员和会议主讲越来越多的避开美国。行业会议和活动逐渐转移到更友好的欧洲、加拿大和亚洲地区。专栏作家 Steven Vaughan-Nichols 今年参加了 13 场科技会议，大部分是在美国之外举行的。在这些会议上，非美国人最关心的非科技话题都围绕着特朗普第二个任期给美国带来的巨大变化，与会者多表示他们不会去美国找工作，也不会去美国参加会议。展会主办方也开始取消原计划在美国举办的展会，转移到欧洲、加拿大和亚洲。曾经精英们为去美国愿意放弃一切，但如今情势出现了逆转。

以色列部署了无人机激光防御系统 Iron Beam。Iron Beam 功率达到 10 万瓦，能以极低的单次发射成本击落无人机、火箭弹和迫击炮弹。它由以色列拉斐尔先进防御系统（Rafael Advanced Defense Systems）研发，其核心是先进激光源和独一无二的光电瞄准系统，能在更远的作战距离上以高精度和高效率拦截各种目标。以色列没有透露太多细节，只是强调 Iron Beam 已成功拦截火箭弹、迫击炮弹和无人机，其投入使用标志着高能激光防御时代的开始。

根据印度政府的年终经济评估，该国 GDP 超过日本成为全球第四大经济体。按照目前的增长速度，印度 GDP 预计会在三年内超过德国成为全球第三大经济体，仅次于美国和中国。印度 GDP 今年估计为 4.18 万亿美元，预计 2030 年达到 7.3 万亿美元。印度 2025 年 GDP 正式数据要到 2026 年公布。国际货币基金组织的预测是印度 GDP 将在明年超过日本。

冰岛经历了有记录以来最暖和的平安夜，最高气温达到了 19.8C。冰岛气象局称，东部小镇 Seyðisfjörður 气温高达 19.8C，东部 Borgarfjörður 的 Bakkagerði 气温达到了 19.7C。12 月冰岛平均气温在 -1C 到 4C 之间。气象学家表示原因是热带暖气流笼罩了冰岛。今年以来冰岛各地遭遇了破纪录的热浪，首次在野外发现蚊子。此前冰岛是南极洲之外唯一一个没有野蚊子的地方。

2024 年中国企业开展的癌症治疗药物临床试验数量连续两年超过美国，居世界首位。统计数据显示，总部设在中国的企业开展的癌症临床试验数量在 2024 年为 896 项，约占全球整体的 39%。超过约为 32% 的美国，居世界首位。远超过欧洲（约 20%）和日本（约 4%）。 2023 年中国（约35%）以微弱优势首次超过美国（约34%），2024 年优势进一步拉大。而 2009 年中国企业在癌症领域的临床试验数量仅占全球的 2% 左右。百济神州董事长兼首席执行官欧雷强（John V. Oyler）指出，“全球 25% 的新增癌症患者在中国就诊。在推进癌症领域的业务和研究方面，中国的存在是不可缺少的”。

自由软件 Android 应用商店 F-Droid 宣布升级了其核心服务器硬件，显著加快了构建和发布更新的速度。F-Droid 表示它的服务器不是托管在某个不知位置不知道员工身份的数据中心，而是交给一位长期贡献者管理，他们知道位置，能远程控制，也知道维护者身份。此前使用的服务器硬件有 12 年历史运行了 5 年，升级到新服务器之后，发布更新的速度从 1-9 月每 3-4 天发布一次更新加快到 10 月每 2 天更新一次，11 月每天一次，12 月每天更新两次。

根据发表在《Archives of Sexual Behavior》期刊上的一项研究，俄罗斯入侵乌克兰改变了乌克兰人的成人内容消费习惯。调查显示，2022 年 3 月初乌克兰人的网络行为发生显著改变。这一时间段与俄罗斯入侵的时间点高度吻合，俄罗斯入侵始于 2 月 24 日。数据显示，期间乌克兰民众对色情内容的搜索量显著增加，对战争地图和保持社交距离信息的搜索量也大幅上升。战争严重程度与成人网站访问量之间存在显著的统计相关性。日益加剧的孤立感和危险感似乎推动了对色情内容兴趣的增长。研究人员称，在集体威胁和社会动荡的紧张时期，人们可能会转向独处的性行为如观看色情作为一种应对或自我调节的策略。

MongoDB 服务器软件在圣诞节前曝出了一个高危漏洞，影响自 2017 年以来发布的所有版本。该漏洞被称为 MongoBleed aka CVE-2025-14847。MongoDB 在 12 月 24 日释出了补丁，声称没有证据表明有人利用该漏洞。12 月 27 日，育碧热门游戏《彩虹六号：围攻X》的服务器遭到入侵，黑客向全服赠送了 21 亿游戏虚拟货币以及极其罕见的皮肤，其中包括开发者专属皮肤。此次攻击迫使育碧将整个游戏服务器下线，直到 12 月 29 日才重新上线，回滚黑客送出的虚拟币和皮肤。有报道称，黑客就是利用刚刚曝出但育碧没有及时修复的 MongoDB 漏洞入侵和接管游戏系统，同时还窃取了育碧几乎所有游戏的源代码——但这一消息尚未得到育碧官方证实。

美国海洋勘探公司 Ocean Infinity 准备对失踪 12 年的 MH370 展开新一轮搜寻。从吉隆坡飞往北京的 MH370 航班于 2014 年 3 月 8 日失踪，当时机上有 239 人。Ocean Infinity 与马来西亚政府签署了一份“无发现不收费”的协议，Ocean Infinity 只有在找到飞机残骸后才能获得 1.102 亿美元的报酬。它在 2018 年曾达成类似协议，但三个月搜寻一无所获。最新的搜寻行动预计将持续 55 天，针对的是南印度洋的一个偏远区域，将使用改进的声纳和分析技术。

Meta 以大约 20 亿美元的价格收购了中资背景的 AI 智能体初创公司 Manus。这个价格是 Manus 下一轮融资寻求的估值。Manus 母公司是总部位于新加坡的蝴蝶效应（Butterfly Effect），它在今年 3 月发布了一鸣惊人的智能体演示视频而引发广泛关注。它的投资者包括了 Benchmark、腾讯、真格基金和红杉资本等。Manus 对其 AI 模型的访问收取了 39 美元或 199 美元的高价，它最近声称有数百万用户，年度经常性收入突破了 1 亿美元。Meta 就是在此时与 Manus 展开了收购谈判。

KDE 开发者总结了桌面环境 Plasma 在 2025 年的重要进展：切换到 Wayland 显示服务器的工作基本完成，2027 年初发布的 Plasma 将停止支持 X11 会话；Plasma 持续改进和成熟，成为众多面向游戏发行版的默认桌面环境，这些发行版包括了 Bazzite、CachyOS、Garuda、Nobara，以及 Valve 掌机/主机运行的 SteamOS。Fedora 发行版也将其 Plasma 桌面版本与 GNOME 桌面版本放在同等位置，唯一能在苹果新 Mac 设备上运行的发行版 Asahi Linux 使用的也是 KDE Plasma 桌面。Parrot Linux 最近也开始默认使用 Plasma。EndeavourOS、Manjaro、NixOS、OpenMandriva、Slackware 和 TuxedoOS 等老牌发行版的默认桌面环境都是 Plasma。

加拿大麦吉尔大学与美国德雷塞尔大学团队联合开发出一种颇具创意的高分辨率3D打印新技术。他们将雌性蚊子的口器（吸血管）转化成了高分辨率的3D打印喷嘴。这种技术不仅能打印出精度达 20 微米的极细线条，还为解决昂贵、高能耗的微纳制造难题提供了可持续的生物学方案。高分辨率 3D 打印对喷嘴精度要求极高。目前市售的超细喷嘴多由特种金属或玻璃制成，制造工艺复杂，成本高昂。研究团队指出，传统喷嘴在生产和使用过程中不仅产生大量环境废弃物，还可能因工艺局限带来健康风险。为了寻找替代方案，研究团队将目光投向自然界中高度进化的微结构——蚊子口器。经过数百万年进化，蚊子口器形成了一种直径仅为人类发丝直径一半左右的天然微针结构，兼具特殊几何形态和力学韧性。研究团队在显微镜下分离出蚊子吸血管，并利用特种树脂将其固定在标准塑料分配器尖端。结果发现，这种生物喷嘴能承受极大的压力，打印出的复杂结构精细程度大约是目前商业打印喷嘴的 2 倍。

网信办发布了《人工智能拟人化互动服务管理暂行办法（征求意见稿）》，意见截止日期 1 月 25 日。该《暂行办法》包含了被认为全球最严厉的政策，要求服务商采取措施阻止 AI 帮助用户自杀或自残。《暂行办法》包括： 第八条 提供者应当落实拟人化互动服务安全主体责任，建立健全算法机制机理审核、科技伦理审查、信息发布审核、网络安全、数据安全、个人信息保护、反电信网络诈骗、重大风险预案、应急处置等管理制度，具有安全可控的技术保障措施，配备与产品规模、业务方向和用户群体相适应的内容管理技术和人员。 第九条 提供者应当在拟人化互动服务全生命周期履行安全责任，明确设计、运行、升级、终止服务等各阶段安全要求，保证安全措施与服务功能同步设计、同步使用，提升内生安全水平，加强运行阶段安全监测和风险评估，及时发现纠正系统偏差、处置安全问题，依法留存网络日志。提供者应当具备心理健康保护、情感边界引导、依赖风险预警等安全能力，不得将替代社会交往、控制用户心理、诱导沉迷依赖等作为设计目标。 第十一条 提供者应当具备用户状态识别能力，在保护用户个人隐私前提下，评估用户情绪及对产品和服务的依赖程度，发现用户存在极端情绪和沉迷的，采取必要措施予以干预。提供者应当预设回复模板，发现涉及威胁用户生命健康和财产安全的高风险倾向的，及时输出安抚和鼓励寻求帮助等内容，并提供专业援助方式。提供者应当建立应急响应机制，发现用户明确提出实施自杀、自残等极端情境时，由人工接管对话，并及时采取措施联络用户监护人、紧急联系人。针对未成年人、老年人用户，提供者应当在注册环节要求填写用户监护人、紧急联系人等信息。 第十七条 用户连续使用拟人化互动服务超过2个小时的，提供者应当以弹窗等方式动态提醒用户暂停使用服务。

中国车企的全球销量在 2025 年超过日本，首次跃居首位。根据2025 年 1～11 月各企业发布的资料和标普全球汽车（S&P Global Mobility）的数据，中国汽车的全球销量预计同比增长 17%，增至约 2700 万辆。中国在 2023 年首次位居汽车出口首位。整体销量也将在 2025 年跃居首位。日本车企合计销量约为 2500 万辆，与上年持平。过去世界汽车销售由美国和日本展开竞争。在顶峰时期的 2018 年日本销量近 3000 万辆。另一方面，中国国内的供应过剩迹象增强，最大车企比亚迪开始降价，价格竞争日趋激烈。中国汽车制造商正在转向出口寻找出路。

对尼尔森最新数据的分析显示，2025 年没有一部新的原创剧能进入十大最受欢迎的流媒体节目之列。这是尼尔森自 2020 年以来发布流媒体数据以来首次出现该情况。数据还显示，由广告支持的免费流媒体服务增长速度超过了付费流媒体服务。YouTube 是美国电视上观看量最高的流媒体服务，超过了 Netflix 和亚马逊总和。Netflix 在热门剧上仍然具有优势，在尼尔森每周十大热门原创节目榜单中占了约三分之二。但其主导地位正逐渐消失——该公司的流媒体观看份额占比降至 20% 以下。迪士尼流媒体服务份额三年以来停滞不前，而亚马逊则在迎头赶上。2025 年观看量最高的原创剧是《鱿鱼游戏》终季，之后是《星期三》第二季和《爱情岛》的最新季。