OrangeBot.AI Digest — 2025-12-03

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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。

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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

We introduce DeepSeek-V3.2, a model that harmonizes high computational efficiency with superior reasoning and agent performance. The key technical breakthroughs of DeepSeek-V3.2 are as follows: (1) DeepSeek Sparse Attention (DSA): We introduce DSA, an efficient attention mechanism that substantially reduces computational complexity while preserving model performance in long-context scenarios. (2) Scalable Reinforcement Learning Framework: By implementing a robust reinforcement learning protocol and scaling post-training compute, DeepSeek-V3.2 performs comparably to GPT-5. Notably, our high-compute variant, DeepSeek-V3.2-Speciale, surpasses GPT-5 and exhibits reasoning proficiency on par with Gemini-3.0-Pro, achieving gold-medal performance in both the 2025 International Mathematical Olympiad (IMO) and the International Olympiad in Informatics (IOI). (3) Large-Scale Agentic Task Synthesis Pipeline: To integrate reasoning into tool-use scenarios, we developed a novel synthesis pipeline that systematically generates training data at scale. This methodology facilitates scalable agentic post-training, yielding substantial improvements in generalization and instruction-following robustness within complex, interactive environments.

Large language models are powerful generalists, yet solving deep and complex problems such as those of the Humanity's Last Exam (HLE) remains both conceptually challenging and computationally expensive. We show that small orchestrators managing other models and a variety of tools can both push the upper bound of intelligence and improve efficiency in solving difficult agentic tasks. We introduce ToolOrchestra, a method for training small orchestrators that coordinate intelligent tools. ToolOrchestra explicitly uses reinforcement learning with outcome-, efficiency-, and user-preference-aware rewards. Using ToolOrchestra, we produce Orchestrator, an 8B model that achieves higher accuracy at lower cost than previous tool-use agents while aligning with user preferences on which tools are to be used for a given query. On HLE, Orchestrator achieves a score of 37.1%, outperforming GPT-5 (35.1%) while being 2.5x more efficient. On tau2-Bench and FRAMES, Orchestrator surpasses GPT-5 by a wide margin while using only about 30% of the cost. Extensive analysis shows that Orchestrator achieves the best trade-off between performance and cost under multiple metrics, and generalizes robustly to unseen tools. These results demonstrate that composing diverse tools with a lightweight orchestration model is both more efficient and more effective than existing methods, paving the way for practical and scalable tool-augmented reasoning systems.

Current video generation techniques excel at single-shot clips but struggle to produce narrative multi-shot videos, which require flexible shot arrangement, coherent narrative, and controllability beyond text prompts. To tackle these challenges, we propose MultiShotMaster, a framework for highly controllable multi-shot video generation. We extend a pretrained single-shot model by integrating two novel variants of RoPE. First, we introduce Multi-Shot Narrative RoPE, which applies explicit phase shift at shot transitions, enabling flexible shot arrangement while preserving the temporal narrative order. Second, we design Spatiotemporal Position-Aware RoPE to incorporate reference tokens and grounding signals, enabling spatiotemporal-grounded reference injection. In addition, to overcome data scarcity, we establish an automated data annotation pipeline to extract multi-shot videos, captions, cross-shot grounding signals and reference images. Our framework leverages the intrinsic architectural properties to support multi-shot video generation, featuring text-driven inter-shot consistency, customized subject with motion control, and background-driven customized scene. Both shot count and duration are flexibly configurable. Extensive experiments demonstrate the superior performance and outstanding controllability of our framework.

We present MG-Nav (Memory-Guided Navigation), a dual-scale framework for zero-shot visual navigation that unifies global memory-guided planning with local geometry-enhanced control. At its core is the Sparse Spatial Memory Graph (SMG), a compact, region-centric memory where each node aggregates multi-view keyframe and object semantics, capturing both appearance and spatial structure while preserving viewpoint diversity. At the global level, the agent is localized on SMG and a goal-conditioned node path is planned via an image-to-instance hybrid retrieval, producing a sequence of reachable waypoints for long-horizon guidance. At the local level, a navigation foundation policy executes these waypoints in point-goal mode with obstacle-aware control, and switches to image-goal mode when navigating from the final node towards the visual target. To further enhance viewpoint alignment and goal recognition, we introduce VGGT-adapter, a lightweight geometric module built on the pre-trained VGGT model, which aligns observation and goal features in a shared 3D-aware space. MG-Nav operates global planning and local control at different frequencies, using periodic re-localization to correct errors. Experiments on HM3D Instance-Image-Goal and MP3D Image-Goal benchmarks demonstrate that MG-Nav achieves state-of-the-art zero-shot performance and remains robust under dynamic rearrangements and unseen scene conditions.

This paper presents DualCamCtrl, a novel end-to-end diffusion model for camera-controlled video generation. Recent works have advanced this field by representing camera poses as ray-based conditions, yet they often lack sufficient scene understanding and geometric awareness. DualCamCtrl specifically targets this limitation by introducing a dual-branch framework that mutually generates camera-consistent RGB and depth sequences. To harmonize these two modalities, we further propose the Semantic Guided Mutual Alignment (SIGMA) mechanism, which performs RGB-depth fusion in a semantics-guided and mutually reinforced manner. These designs collectively enable DualCamCtrl to better disentangle appearance and geometry modeling, generating videos that more faithfully adhere to the specified camera trajectories. Additionally, we analyze and reveal the distinct influence of depth and camera poses across denoising stages and further demonstrate that early and late stages play complementary roles in forming global structure and refining local details. Extensive experiments demonstrate that DualCamCtrl achieves more consistent camera-controlled video generation, with over 40\% reduction in camera motion errors compared with prior methods. Our project page: https://soyouthinkyoucantell.github.io/dualcamctrl-page/

AI self-evolution has long been envisioned as a path toward superintelligence, where models autonomously acquire, refine, and internalize knowledge from their own learning experiences. Yet in practice, unguided self-evolving systems often plateau quickly or even degrade as training progresses. These failures arise from issues such as concept drift, diversity collapse, and mis-evolution, as models reinforce their own biases and converge toward low-entropy behaviors. To enable models to self-evolve in a stable and controllable manner while minimizing reliance on human supervision, we introduce R-Few, a guided Self-Play Challenger-Solver framework that incorporates lightweight human oversight through in-context grounding and mixed training. At each iteration, the Challenger samples a small set of human-labeled examples to guide synthetic question generation, while the Solver jointly trains on human and synthetic examples under an online, difficulty-based curriculum. Across math and general reasoning benchmarks, R-Few achieves consistent and iterative improvements. For example, Qwen3-8B-Base improves by +3.0 points over R-Zero on math tasks and achieves performance on par with General-Reasoner, despite the latter being trained on 20 times more human data. Ablation studies confirm the complementary contributions of grounded challenger training and curriculum-based solver training, and further analysis shows that R-Few mitigates drift, yielding more stable and controllable co-evolutionary dynamics.

Despite recent progress in multimodal agentic systems, existing approaches often treat image manipulation and web search as disjoint capabilities, rely heavily on costly reinforcement learning, and lack planning grounded in real tool-execution traces. To address these limitations, we present Skywork-R1V4, a 30B (A3B) parameter multimodal agentic model that unifies multimodal planning, active image manipulation ("thinking with images"), deep multimodal search, and, most critically, interleaved reasoning that dynamically alternates between visual operations and external knowledge retrieval. Trained solely via supervised fine-tuning on fewer than 30,000 high-quality, planning-execution-consistent trajectories and validated through stepwise consistency filtering, Skywork-R1V4 achieves state-of-the-art results across perception and multimodal search benchmarks: it scores 66.1 on MMSearch and 67.2 on FVQA, surpassing Gemini 2.5 Flash on all 11 metrics. Skywork-R1V4 exhibits emergent long-horizon reasoning at inference time, successfully orchestrating more than 10 tool calls to solve complex, multi-step tasks. Our results demonstrate that sophisticated agentic multimodal intelligence can be achieved through carefully curated supervised learning alone, without any reliance on reinforcement learning.

Achieving fully autonomous driving systems requires learning rational decisions in a wide span of scenarios, including safety-critical and out-of-distribution ones. However, such cases are underrepresented in real-world corpus collected by human experts. To complement for the lack of data diversity, we introduce a novel and scalable simulation framework capable of synthesizing massive unseen states upon existing driving logs. Our pipeline utilizes advanced neural rendering with a reactive environment to generate high-fidelity multi-view observations controlled by the perturbed ego trajectory. Furthermore, we develop a pseudo-expert trajectory generation mechanism for these newly simulated states to provide action supervision. Upon the synthesized data, we find that a simple co-training strategy on both real-world and simulated samples can lead to significant improvements in both robustness and generalization for various planning methods on challenging real-world benchmarks, up to +6.8 EPDMS on navhard and +2.9 on navtest. More importantly, such policy improvement scales smoothly by increasing simulation data only, even without extra real-world data streaming in. We further reveal several crucial findings of such a sim-real learning system, which we term SimScale, including the design of pseudo-experts and the scaling properties for different policy architectures. Our simulation data and code would be released.

LLMs and Agents have achieved impressive progress in code generation, mathematical reasoning, and scientific discovery. However, existing benchmarks primarily measure correctness, overlooking the diversity of methods behind solutions. True innovation depends not only on producing correct answers but also on the originality of the approach. We present InnoGym, the first benchmark and framework designed to systematically evaluate the innovation potential of AI agents. InnoGym introduces two complementary metrics: performance gain, which measures improvement over the best-known solutions, and novelty, which captures methodological differences from prior approaches. The benchmark includes 18 carefully curated tasks from real-world engineering and scientific domains, each standardized through resource filtering, evaluator validation, and solution collection. In addition, we provide iGym, a unified execution environment for reproducible and long-horizon evaluations. Extensive experiments show that while some agents produce novel approaches, their lack of robustness limits performance gains. These results highlight a key gap between creativity and effectiveness, underscoring the need for benchmarks that evaluate both.

Despite progress in video-to-audio generation, the field focuses predominantly on mono output, lacking spatial immersion. Existing binaural approaches remain constrained by a two-stage pipeline that first generates mono audio and then performs spatialization, often resulting in error accumulation and spatio-temporal inconsistencies. To address this limitation, we introduce the task of end-to-end binaural spatial audio generation directly from silent video. To support this task, we present the BiAudio dataset, comprising approximately 97K video-binaural audio pairs spanning diverse real-world scenes and camera rotation trajectories, constructed through a semi-automated pipeline. Furthermore, we propose ViSAudio, an end-to-end framework that employs conditional flow matching with a dual-branch audio generation architecture, where two dedicated branches model the audio latent flows. Integrated with a conditional spacetime module, it balances consistency between channels while preserving distinctive spatial characteristics, ensuring precise spatio-temporal alignment between audio and the input video. Comprehensive experiments demonstrate that ViSAudio outperforms existing state-of-the-art methods across both objective metrics and subjective evaluations, generating high-quality binaural audio with spatial immersion that adapts effectively to viewpoint changes, sound-source motion, and diverse acoustic environments. Project website: https://kszpxxzmc.github.io/ViSAudio-project.

Diffusion models have achieved remarkable success in image generation, yet their deployment remains constrained by the heavy computational cost and the need for numerous inference steps. Previous efforts on fewer-step distillation attempt to skip redundant steps by training compact student models, yet they often suffer from heavy retraining costs and degraded generalization. In this work, we take a different perspective: we accelerate smartly, not evenly, applying smaller speedups to early semantic stages and larger ones to later redundant phases. We instantiate this phase-aware strategy with two experts that specialize in slow and fast denoising phases. Surprisingly, instead of investing massive effort in retraining student models, we find that simply equipping the base model with lightweight LoRA adapters achieves both efficient acceleration and strong generalization. We refer to these two adapters as Slow-LoRA and Fast-LoRA. Through extensive experiments, our method achieves up to 5 acceleration over the base model while maintaining comparable visual quality across diverse benchmarks. Remarkably, the LoRA experts are trained with only 1 samples on a single V100 within one hour, yet the resulting models generalize strongly on unseen prompts.

Recent advances in video large language models have demonstrated strong capabilities in understanding short clips. However, scaling them to hours- or days-long videos remains highly challenging due to limited context capacity and the loss of critical visual details during abstraction. Existing memory-augmented methods mitigate this by leveraging textual summaries of video segments, yet they heavily rely on text and fail to utilize visual evidence when reasoning over complex scenes. Moreover, retrieving from fixed temporal scales further limits their flexibility in capturing events that span variable durations. To address this, we introduce WorldMM, a novel multimodal memory agent that constructs and retrieves from multiple complementary memories, encompassing both textual and visual representations. WorldMM comprises three types of memory: episodic memory indexes factual events across multiple temporal scales, semantic memory continuously updates high-level conceptual knowledge, and visual memory preserves detailed information about scenes. During inference, an adaptive retrieval agent iteratively selects the most relevant memory source and leverages multiple temporal granularities based on the query, continuing until it determines that sufficient information has been gathered. WorldMM significantly outperforms existing baselines across five long video question-answering benchmarks, achieving an average 8.4% performance gain over previous state-of-the-art methods, showing its effectiveness on long video reasoning.

Vision-language-action (VLA) models have shown remarkable capabilities in robotic manipulation, but their performance is sensitive to the action chunk length used during training, termed horizon. Our empirical study reveals an inherent trade-off: longer horizons provide stronger global foresight but degrade fine-grained accuracy, while shorter ones sharpen local control yet struggle on long-term tasks, implying fixed choice of single horizons being suboptimal. To mitigate the trade-off, we propose a mixture of horizons (MoH) strategy. MoH rearranges the action chunk into several segments with different horizons, processes them in parallel with a shared action transformer, and fuses outputs with a light linear gate. It has three appealing benefits. 1) MoH exploits long-term foresight and short-term precision jointly within a single model, improving both performance and generalizability to complex tasks. 2) MoH is plug-and-play for full-attention action modules with minimal training or inference overhead. 3) MoH enables dynamic inference with adaptive horizons, which selects stable actions through cross-horizon consensus, achieving 2.5times higher throughput than baselines while preserving superior performance. Extensive experiments over flow-based policies π_0, π_{0.5}, and one-step regression policy π_{reg} demonstrate that MoH yields consistent and significant gains on both simulations and real-world tasks. Notably, under mixed-task setting, π_{0.5} with MoH reaches a new state-of-the-art with 99% average success rate on LIBERO after only 30k training iterations. Project page: https://github.com/Timsty1/MixtureOfHorizons

Quantization to low bitwidth is a standard approach for deploying large language models, however, a few extreme weights and activations stretch the dynamic range and reduce the effective resolution of the quantizer. A common mitigation approach is to apply some fixed orthogonal transforms, such as Hadamard matrices, before quantization, which typically reduces the dynamic range. Yet, these transforms ignore the statistics of the data, and their optimality is currently not understood. In this work, we derive, for the first time, closed-form optimal linear blockwise transforms for joint weight-activation quantization using standard data-free quantizers for common numerical formats. Specifically, we provide derivations of the optimal adaptive (data-aware) transforms for round-to-nearest (RTN), AbsMax-scaled block quantizers for both integer and floating-point formats. The resulting construction, which we call WUSH, combines a Hadamard backbone with a data-dependent component based on second-order moments, yielding a non-orthogonal transform that is provably optimal under mild assumptions and remains structured for efficient implementation. Preliminary experimental results show that our approach consistently improves upon the Hadamard transform for common formats.

Latent-space modeling has been the standard for Diffusion Transformers (DiTs). However, it relies on a two-stage pipeline where the pretrained autoencoder introduces lossy reconstruction, leading to error accumulation while hindering joint optimization. To address these issues, we propose PixelDiT, a single-stage, end-to-end model that eliminates the need for the autoencoder and learns the diffusion process directly in the pixel space. PixelDiT adopts a fully transformer-based architecture shaped by a dual-level design: a patch-level DiT that captures global semantics and a pixel-level DiT that refines texture details, enabling efficient training of a pixel-space diffusion model while preserving fine details. Our analysis reveals that effective pixel-level token modeling is essential to the success of pixel diffusion. PixelDiT achieves 1.61 FID on ImageNet 256x256, surpassing existing pixel generative models by a large margin. We further extend PixelDiT to text-to-image generation and pretrain it at the 1024x1024 resolution in pixel space. It achieves 0.74 on GenEval and 83.5 on DPG-bench, approaching the best latent diffusion models.

《绝地潜兵 2（HELLDIVERS 2）》开发商 Arrowhead Game Studios 释出最新更新，将 PC 版本的游戏容量从 154GB 减少到 23GB，瘦身高达 85%。Arrowhead 此前曾在官方博客上解释了为什么 PC 版本的容量如此之大，原因是 PC 版本包含了大量重复数据，旨在加快机械硬盘上游戏的加载速度，而游戏机使用的是固态硬盘，因此主机版本的容量没有这么大。今天绝大部分 PC 使用的硬盘已从机械硬盘过渡到固态硬盘，Arrowhead 估计只有 12% 的《绝地潜兵 2》玩家仍然使用机械硬盘。

安全公司 Koi Security 披露了被称为 ShadyPanda 的攻击者利用浏览器扩展感染了 430 万 Chrome 和 Edge 用户。攻击者采取了长线方案，首先通过合法应用吸引积累用户群，然后通过后续更新植入恶意代码。攻击者的活动分为多个阶段，第一阶段是在扩展中嵌入联盟营销追踪代码，拦截电商平台购物链接嵌入自己的联盟营销代码获取佣金；第二阶段是劫持搜索和窃取 cookie；第三阶段植入远程访问后门变成间谍软件窃取敏感浏览器数据。 受影响的扩展包括了 Clean Master、以及 Infinity 和 WeTab 等。WeTab 开发商随后发表声明，称 Clean Master 扩展已被该公司出售，与 WeTab 和 Infinity 已经没有关联，而 WeTab 和 Infinity 并没有恶意代码，

Google DeepMind 在 2020 年 11 月宣布了它的 AI 工具 AlphaFold2，2021 年发布了 AlphaFold2 代码和数据库。问世五年来，AlphaFold2 不仅改变了结构生物学的研究方式，也推动了计算生物学的进步。不过将其生物学洞见转化为药物开发等实际应用仍需时间。AlphaFold 数据库目前已收录超过 2.4 亿个结构预测，覆盖绝大多数已知蛋白质，为全球 100 多个国家的 330 万名研究者提供支持。如今科学家已利用 AlphaFold2 设计应对抗生素耐药性的方案、寻找疟疾等疾病的新疗法，并深入理解疾病机制、加速靶向药物开发。

日本游戏公司面临切换商业字体的难题，因为原来提供低价商业字体服务的 Fontworks LETS 在被美资 Monotype 收购之后价格上涨逾 50 倍。Fontworks 以前的年费为 6 万日元（约 380 美元），现在 Monotype 的方案是年费为 2.05 万美元，而且还限制安装量最高为 2.5 万。对大部分商业游戏而言这是完全不切实际的限制。英语游戏可使用系统自带的 UI 字体、廉价的商业字体或开源字体，但日语字符数量庞大，制作高质量字体极其困难且成本高昂，变更日语字体的工作量将会非常大。已有游戏开始改用 DynaFont 的商业字体。

一辆 Waymo 无人驾驶出租车在旧金山碾过了一只未拴绳的狗，而几周前该公司的另一辆无人驾驶出租车撞死了一只备受邻居喜爱的猫。目前不清楚狗的状况。事故发生在 Scott 和 Eddy 街的交叉路口附近。一名自称是乘客的 Reddit 用户发帖称其孩子目睹了整个过程，表示当时他们一家参加完一场圣诞树点灯仪式后回家。美国国家公路交通安全管理局的记录显示，自 2021 年以来 Waymo 无人驾驶出租车至少卷入了 14 起动物碰撞事故。Waymo 发言人对此事表达了遗憾，表示会吸取教训，同时强调该公司的事故伤亡率远低于人类司机。人类司机每年都会在驾车过程中撞到数百万只动物。

美国威斯康星州议员以保护儿童的名义起草了 AB 105/SB 130 法案，要求提供性相关内容的网站验证访客年龄同时屏蔽 VPN 访问。法案寻求大幅扩大对未成年人有害材料的定义，试图将人体解剖、性行为和生殖等方面的描述和讨论囊括在内。保守派们显著扩大了内容审查的范围。该法案已经在州众议院获得通过，目前正在参议院进行讨论。如果该法案成为法律，威斯康星州将成为美国第一个禁止 VPN 访问特定内容的州。

蓝箭航天本周三执行了朱雀三号遥一运载火箭的首飞任务，火箭成功入轨，但第一级回收失败，根据视频推测是某台发动机爆炸了，蓝箭尚未就此发表新闻稿。朱雀三号一二级箭体直径 4.5 米，整流罩直径 5.2 米，全箭长 66.1 米，起飞质量约 570 吨，起飞推力超过 750 吨，采用不锈钢作为箭体主结构材料，一子级配备九台天鹊-12A液氧甲烷发动机，设计可在执行轨道发射任务后自主高精度返回，在回收场实现软着陆并重复使用。火箭如果是一次性使用其有效载荷为 11,800 公斤，如果尝试回收第一级则有效载荷为 8,000 公斤。相比下 SpaceX 的 Falcon 9 能将 22,800 公斤负荷发射到低地球轨道。

俄罗斯上周五发射了其新一代洲际导弹 RS-28，结果导弹飞行高度甚至没有超过 1200 米就翻滚坠毁在导弹发射井附近，在卫星地图上留下了一个明显的弹坑，其爆炸声据报道在数英里外都能听到。RS-28 萨尔马特设计取代由乌克兰制造和维护的旧洲际导弹 R-36M2 撒旦，因此又被称撒旦二号。RS-28 可携带 10 个重型或者 16 个轻型分导式核弹头，也可以与高超声速武器的配合使用，2022 年 4 月进行了首次成功发射，但之后的数次发射一直以失败告终。鉴于 R-36M2 即将退役，RS-28 发射失败将进一步加剧俄罗斯核武库战备状态的不确定性。

根据土耳其的一项小型研究，猫咪会根据性别表现出不同行为，它们发现男性需要更用力的喵喵叫才能引起他们的注意。研究人员记录了 31 只猫迎接主人回家的行为，人类佩戴了摄像头记录下了猫的表现。研究人员从数百个视频中分析出了 22 种不同行为。其中一种行为差异与性别相关：当男性进门时猫咪的叫声明显更多。此前的研究发现，女性通常更倾向于与宠物进行语言互动，更擅长解读猫咪叫声中细微的差别。相比下男性更为迟钝。也就是说，猫认识到对于男性轻轻摇尾巴是不够的。

一名男子在接受干细胞移植治疗血癌后，成为全球第七位摆脱艾滋病病毒（HIV）的人。他也是 7 位患者中第二位接受非抗HIV干细胞移植的人。这一案例进一步证明，治愈 HIV 或许并不需要依赖抗 HIV 干细胞。此前已有 5 人通过接受捐赠者的干细胞摆脱了 HIV，这些捐赠者 CCR5 基因的两个拷贝均发生突变。该基因编码的蛋白质是 HIV 感染免疫细胞的“通道”。这让科学家得出结论：携带两个突变拷贝可使免疫细胞完全缺失 CCR5 蛋白，这是治愈HIV的关键。但 2024 年被称为“日内瓦病人”的第六位患者在接受不含 CCR5 突变的干细胞移植后，病毒阴性期超过两年。最新案例则为“日内瓦病人已治愈”的观点提供了更强支撑。一名男子于 2015 年 10 月接受干细胞移植治疗白血病。当时 51 岁的患者同时感染了 HIV。治疗过程中，他接受了化疗以摧毁体内绝大多数免疫细胞，为供体干细胞生成健康的免疫系统腾出空间。理想情况下，男子本应接受抗 HIV 干细胞，但由于没有合适的供体，医生使用了携带一个正常拷贝和一个突变拷贝的 CCR5 基因干细胞。移植期间，他一直接受 HIV 治疗的标准方法——抗逆转录病毒疗法（ART）。在移植约 3 年后，男子选择停止服用 ART 药物。停药后不久，研究团队在他的血液样本中未发现任何病毒痕迹。截至目前，他已保持病毒阴性状态长达 7 年 3 个月，达到“治愈”的标准。在 7 位已摆脱 HIV 的患者中，该男子的病毒阴性期时间排名第二，最长者已保持约 12 年无病毒状态。研究结果意味着，治愈 HIV 的干细胞移植供体范围可能比我们想象的更广。

美国的版权保护期长达 95 年。随着 2026 年 1 月 1 日的到来，1930 年出版的美国作品将进入公有领域。其中的知名作品包括了：奥斯卡获奖影片《西线无战事》，希区柯克(Alfred Hitchcock)的《Juno and the Paycock》和《Murder!》，1930 年版本的米老鼠，哈米特（Dashiell Hammett）的侦探小说《马耳他之鹰》， 阿加莎·克里斯蒂的侦探小说《寓所谜案》，威廉·福克纳的《我弥留之际》，第一部丁丁历险记漫画 《丁丁在苏联》以及第二部《丁丁在刚果》，卡夫卡 《城堡》 的首部英文译本，等等。

FreeBSD 项目释出了 v15.0。主要变化包括：支持使用 pkg 包管理器安装 base 系统；OpenZFS 2.4.0-rc4；原生支持 inotify(2) 接口；OpenSSL 升级到最新的长期支持版 v3.5.4；OpenSSH 10.0p2；Open Container Initiative (OCI)镜像；等等。

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。