OrangeBot.AI Digest — 2026-01-15

Eigent: The Open Source Cowork Desktop to Unlock Your Exceptional Productivity.

NVR with realtime local object detection for IP cameras

An agentic skills framework & software development methodology that works.

eBPF-based Networking, Security, and Observability

An open-source, cross-platform terminal for seamless workflows

Ultralytics YOLO 🚀

🤖 The free, Open Source alternative to OpenAI, Claude and others. Self-hosted and local-first. Drop-in replacement for OpenAI, running on consumer-grade hardware. No GPU required. Runs gguf, transformers, diffusers and many more. Features: Generate Text, MCP, Audio, Video, Images, Voice Cloning, Distributed, P2P and decentralized inference

Cross-platform, customizable ML solutions for live and streaming media.

The visual editor for React

Source code for the X Recommendation Algorithm

Self-evolution methods enhance code generation through iterative "generate-verify-refine" cycles, yet existing approaches suffer from low exploration efficiency, failing to discover solutions with superior complexity within limited budgets. This inefficiency stems from initialization bias trapping evolution in poor solution regions, uncontrolled stochastic operations lacking feedback guidance, and insufficient experience utilization across tasks. To address these bottlenecks, we propose Controlled Self-Evolution (CSE), which consists of three key components. Diversified Planning Initialization generates structurally distinct algorithmic strategies for broad solution space coverage. Genetic Evolution replaces stochastic operations with feedback-guided mechanisms, enabling targeted mutation and compositional crossover. Hierarchical Evolution Memory captures both successful and failed experiences at inter-task and intra-task levels. Experiments on EffiBench-X demonstrate that CSE consistently outperforms all baselines across various LLM backbones. Furthermore, CSE achieves higher efficiency from early generations and maintains continuous improvement throughout evolution. Our code is publicly available at https://github.com/QuantaAlpha/EvoControl.

Deep research systems are widely used for multi-step web research, analysis, and cross-source synthesis, yet their evaluation remains challenging. Existing benchmarks often require annotation-intensive task construction, rely on static evaluation dimensions, or fail to reliably verify facts when citations are missing. To bridge these gaps, we introduce DeepResearchEval, an automated framework for deep research task construction and agentic evaluation. For task construction, we propose a persona-driven pipeline generating realistic, complex research tasks anchored in diverse user profiles, applying a two-stage filter Task Qualification and Search Necessity to retain only tasks requiring multi-source evidence integration and external retrieval. For evaluation, we propose an agentic pipeline with two components: an Adaptive Point-wise Quality Evaluation that dynamically derives task-specific evaluation dimensions, criteria, and weights conditioned on each generated task, and an Active Fact-Checking that autonomously extracts and verifies report statements via web search, even when citations are missing.

Large Language Model (LLM) Agents exhibit inherent reasoning abilities through the collaboration of multiple tools. However, during agent inference, existing methods often suffer from (i) locally myopic generation, due to the absence of lookahead, and (ii) trajectory instability, where minor early errors can escalate into divergent reasoning paths. These issues make it difficult to balance global effectiveness and computational efficiency. To address these two issues, we propose meta-adaptive exploration with LLM agents https://github.com/exoskeletonzj/MAXS, a meta-adaptive reasoning framework based on LLM Agents that flexibly integrates tool execution and reasoning planning. MAXS employs a lookahead strategy to extend reasoning paths a few steps ahead, estimating the advantage value of tool usage, and combines step consistency variance and inter-step trend slopes to jointly select stable, consistent, and high-value reasoning steps. Additionally, we introduce a trajectory convergence mechanism that controls computational cost by halting further rollouts once path consistency is achieved, enabling a balance between resource efficiency and global effectiveness in multi-tool reasoning. We conduct extensive empirical studies across three base models (MiMo-VL-7B, Qwen2.5-VL-7B, Qwen2.5-VL-32B) and five datasets, demonstrating that MAXS consistently outperforms existing methods in both performance and inference efficiency. Further analysis confirms the effectiveness of our lookahead strategy and tool usage.

Scientific reasoning relies not only on logical inference but also on activating prior knowledge and experiential structures. Memory can efficiently reuse knowledge and enhance reasoning consistency and stability. However, existing benchmarks mainly evaluate final answers or step-by-step coherence, overlooking the memory-driven mechanisms that underlie human reasoning, which involves activating anchors and attractors, then integrating them into multi-step inference. To address this gap, we propose A^3-Bench~ https://a3-bench.github.io, a benchmark designed to evaluate scientific reasoning through dual-scale memory-driven activation, grounded in Anchor and Attractor Activation. First, we annotate 2,198 science reasoning problems across domains using the SAPM process(subject, anchor & attractor, problem, and memory developing). Second, we introduce a dual-scale memory evaluation framework utilizing anchors and attractors, along with the AAUI(Anchor--Attractor Utilization Index) metric to measure memory activation rates. Finally, through experiments with various base models and paradigms, we validate A^3-Bench and analyze how memory activation impacts reasoning performance, providing insights into memory-driven scientific reasoning.

In this report, we introduce DASD-4B-Thinking, a lightweight yet highly capable, fully open-source reasoning model. It achieves SOTA performance among open-source models of comparable scale across challenging benchmarks in mathematics, scientific reasoning, and code generation -- even outperforming several larger models. We begin by critically reexamining a widely adopted distillation paradigm in the community: SFT on teacher-generated responses, also known as sequence-level distillation. Although a series of recent works following this scheme have demonstrated remarkable efficiency and strong empirical performance, they are primarily grounded in the SFT perspective. Consequently, these approaches focus predominantly on designing heuristic rules for SFT data filtering, while largely overlooking the core principle of distillation itself -- enabling the student model to learn the teacher's full output distribution so as to inherit its generalization capability. Specifically, we identify three critical limitations in current practice: i) Inadequate representation of the teacher's sequence-level distribution; ii) Misalignment between the teacher's output distribution and the student's learning capacity; and iii) Exposure bias arising from teacher-forced training versus autoregressive inference. In summary, these shortcomings reflect a systemic absence of explicit teacher-student interaction throughout the distillation process, leaving the essence of distillation underexploited. To address these issues, we propose several methodological innovations that collectively form an enhanced sequence-level distillation training pipeline. Remarkably, DASD-4B-Thinking obtains competitive results using only 448K training samples -- an order of magnitude fewer than those employed by most existing open-source efforts. To support community research, we publicly release our models and the training dataset.

General-purpose Large Vision-Language Models (LVLMs), despite their massive scale, often falter in dermatology due to "diffuse attention" - the inability to disentangle subtle pathological lesions from background noise. In this paper, we challenge the assumption that parameter scaling is the only path to medical precision. We introduce SkinFlow, a framework that treats diagnosis as an optimization of visual information transmission efficiency. Our approach utilizes a Virtual-Width Dynamic Vision Encoder (DVE) to "unfold" complex pathological manifolds without physical parameter expansion, coupled with a two-stage Reinforcement Learning strategy. This strategy sequentially aligns explicit medical descriptions (Stage I) and reconstructs implicit diagnostic textures (Stage II) within a constrained semantic space. Furthermore, we propose a clinically grounded evaluation protocol that prioritizes diagnostic safety and hierarchical relevance over rigid label matching. Empirical results are compelling: our 7B model establishes a new state-of-the-art on the Fitzpatrick17k benchmark, achieving a +12.06% gain in Top-1 accuracy and a +28.57% boost in Top-6 accuracy over the massive general-purpose models (e.g., Qwen3VL-235B and GPT-5.2). These findings demonstrate that optimizing geometric capacity and information flow yields superior diagnostic reasoning compared to raw parameter scaling.

Vision-Language-Action (VLA) tasks require reasoning over complex visual scenes and executing adaptive actions in dynamic environments. While recent studies on reasoning VLAs show that explicit chain-of-thought (CoT) can improve generalization, they suffer from high inference latency due to lengthy reasoning traces. We propose Fast-ThinkAct, an efficient reasoning framework that achieves compact yet performant planning through verbalizable latent reasoning. Fast-ThinkAct learns to reason efficiently with latent CoTs by distilling from a teacher, driven by a preference-guided objective to align manipulation trajectories that transfers both linguistic and visual planning capabilities for embodied control. This enables reasoning-enhanced policy learning that effectively connects compact reasoning to action execution. Extensive experiments across diverse embodied manipulation and reasoning benchmarks demonstrate that Fast-ThinkAct achieves strong performance with up to 89.3\% reduced inference latency over state-of-the-art reasoning VLAs, while maintaining effective long-horizon planning, few-shot adaptation, and failure recovery.

We propose OpenVoxel, a training-free algorithm for grouping and captioning sparse voxels for the open-vocabulary 3D scene understanding tasks. Given the sparse voxel rasterization (SVR) model obtained from multi-view images of a 3D scene, our OpenVoxel is able to produce meaningful groups that describe different objects in the scene. Also, by leveraging powerful Vision Language Models (VLMs) and Multi-modal Large Language Models (MLLMs), our OpenVoxel successfully build an informative scene map by captioning each group, enabling further 3D scene understanding tasks such as open-vocabulary segmentation (OVS) or referring expression segmentation (RES). Unlike previous methods, our method is training-free and does not introduce embeddings from a CLIP/BERT text encoder. Instead, we directly proceed with text-to-text search using MLLMs. Through extensive experiments, our method demonstrates superior performance compared to recent studies, particularly in complex referring expression segmentation (RES) tasks. The code will be open.

The development of large language models (LLMs) has achieved superior performance in a range of downstream tasks, including LLM-based retrieval-augmented generation (RAG). The quality of generated content heavily relies on the usefulness of the retrieved information and the capacity of LLMs' internal information processing mechanism to incorporate it in answer generation. It is generally assumed that the retrieved information is relevant to the question. However, the retrieved information may have a variable degree of relevance and usefulness, depending on the question and the document collection. It is important to take into account the relevance of the retrieved information in answer generation. In this paper, we propose OpenDecoder, a new approach that leverages explicit evaluation of the retrieved information as quality indicator features for generation. We aim to build a RAG model that is more robust to varying levels of noisy context. Three types of explicit evaluation information are considered: relevance score, ranking score, and QPP (query performance prediction) score. The experimental results on five benchmark datasets demonstrate the effectiveness and better robustness of OpenDecoder by outperforming various baseline methods. Importantly, this paradigm is flexible to be integrated with the post-training of LLMs for any purposes and incorporated with any type of external indicators.

Experience intervention in web agents emerges as a promising technical paradigm, enhancing agent interaction capabilities by providing valuable insights from accumulated experiences. However, existing methods predominantly inject experience passively as global context before task execution, struggling to adapt to dynamically changing contextual observations during agent-environment interaction. We propose ExpSeek, which shifts experience toward step-level proactive seeking: (1) estimating step-level entropy thresholds to determine intervention timing using the model's intrinsic signals; (2) designing step-level tailor-designed experience content. Experiments on Qwen3-8B and 32B models across four challenging web agent benchmarks demonstrate that ExpSeek achieves absolute improvements of 9.3% and 7.5%, respectively. Our experiments validate the feasibility and advantages of entropy as a self-triggering signal, reveal that even a 4B small-scale experience model can significantly boost the performance of larger agent models.

Vision-Language Models (VLMs) have shown remarkable performance in User Interface (UI) grounding tasks, driven by their ability to process increasingly high-resolution screenshots. However, screenshots are tokenized into thousands of visual tokens (e.g., about 4700 for 2K resolution), incurring significant computational overhead and diluting attention. In contrast, humans typically focus on regions of interest when interacting with UI. In this work, we pioneer the task of efficient UI grounding. Guided by practical analysis of the task's characteristics and challenges, we propose FocusUI, an efficient UI grounding framework that selects patches most relevant to the instruction while preserving positional continuity for precise grounding. FocusUI addresses two key challenges: (1) Eliminating redundant tokens in visual encoding. We construct patch-level supervision by fusing an instruction-conditioned score with a rule-based UI-graph score that down-weights large homogeneous regions to select distinct and instruction-relevant visual tokens. (2) Preserving positional continuity during visual token selection. We find that general visual token pruning methods suffer from severe accuracy degradation on UI grounding tasks due to broken positional information. We introduce a novel PosPad strategy, which compresses each contiguous sequence of dropped visual tokens into a single special marker placed at the sequence's last index to preserve positional continuity. Comprehensive experiments on four grounding benchmarks demonstrate that FocusUI surpasses GUI-specific baselines. On the ScreenSpot-Pro benchmark, FocusUI-7B achieves a performance improvement of 3.7% over GUI-Actor-7B. Even with only 30% visual token retention, FocusUI-7B drops by only 3.2% while achieving up to 1.44x faster inference and 17% lower peak GPU memory.

While LLM-based agents have shown promise for deep research, most existing approaches rely on fixed workflows that struggle to adapt to real-world, open-ended queries. Recent work therefore explores self-evolution by allowing agents to rewrite their own code or prompts to improve problem-solving ability, but unconstrained optimization often triggers instability, hallucinations, and instruction drift. We propose EvoFSM, a structured self-evolving framework that achieves both adaptability and control by evolving an explicit Finite State Machine (FSM) instead of relying on free-form rewriting. EvoFSM decouples the optimization space into macroscopic Flow (state-transition logic) and microscopic Skill (state-specific behaviors), enabling targeted improvements under clear behavioral boundaries. Guided by a critic mechanism, EvoFSM refines the FSM through a small set of constrained operations, and further incorporates a self-evolving memory that distills successful trajectories as reusable priors and failure patterns as constraints for future queries. Extensive evaluations on five multi-hop QA benchmarks demonstrate the effectiveness of EvoFSM. In particular, EvoFSM reaches 58.0% accuracy on the DeepSearch benchmark. Additional results on interactive decision-making tasks further validate its generalization.

Large Language Model (LLM) training often optimizes for preference alignment, rewarding outputs that are perceived as helpful and interaction-friendly. However, this preference-oriented objective can be exploited: manipulative prompts can steer responses toward user-appeasing agreement and away from truth-oriented correction. In this work, we investigate whether aligned models are vulnerable to Preference-Undermining Attacks (PUA), a class of manipulative prompting strategies designed to exploit the model's desire to please user preferences at the expense of truthfulness. We propose a diagnostic methodology that provides a finer-grained and more directive analysis than aggregate benchmark scores, using a factorial evaluation framework to decompose prompt-induced shifts into interpretable effects of system objectives (truth- vs. preference-oriented) and PUA-style dialogue factors (directive control, personal derogation, conditional approval, reality denial) within a controlled 2 times 2^4 design. Surprisingly, more advanced models are sometimes more susceptible to manipulative prompts. Beyond the dominant reality-denial factor, we observe model-specific sign reversals and interactions with PUA-style factors, suggesting tailored defenses rather than uniform robustness. These findings offer a novel, reproducible factorial evaluation methodology that provides finer-grained diagnostics for post-training processes like RLHF, enabling better trade-offs in the product iteration of LLMs by offering a more nuanced understanding of preference alignment risks and the impact of manipulative prompts.

Recent advances in world models have shown promise for modeling future dynamics of environmental states, enabling agents to reason and act without accessing real environments. Current methods mainly perform single-step or fixed-horizon rollouts, leaving their potential for complex task planning under-exploited. We propose Imagine-then-Plan (ITP), a unified framework for agent learning via lookahead imagination, where an agent's policy model interacts with the learned world model, yielding multi-step ``imagined'' trajectories. Since the imagination horizon may vary by tasks and stages, we introduce a novel adaptive lookahead mechanism by trading off the ultimate goal and task progress. The resulting imagined trajectories provide rich signals about future consequences, such as achieved progress and potential conflicts, which are fused with current observations, formulating a partially observable and imaginable Markov decision process to guide policy learning. We instantiate ITP with both training-free and reinforcement-trained variants. Extensive experiments across representative agent benchmarks demonstrate that ITP significantly outperforms competitive baselines. Further analyses validate that our adaptive lookahead largely enhances agents' reasoning capability, providing valuable insights into addressing broader, complex tasks.

We present TranslateGemma, a suite of open machine translation models based on the Gemma 3 foundation models. To enhance the inherent multilingual capabilities of Gemma 3 for the translation task, we employ a two-stage fine-tuning process. First, supervised fine-tuning is performed using a rich mixture of high-quality large-scale synthetic parallel data generated via state-of-the-art models and human-translated parallel data. This is followed by a reinforcement learning phase, where we optimize translation quality using an ensemble of reward models, including MetricX-QE and AutoMQM, targeting translation quality. We demonstrate the effectiveness of TranslateGemma with human evaluation on the WMT25 test set across 10 language pairs and with automatic evaluation on the WMT24++ benchmark across 55 language pairs. Automatic metrics show consistent and substantial gains over the baseline Gemma 3 models across all sizes. Notably, smaller TranslateGemma models often achieve performance comparable to larger baseline models, offering improved efficiency. We also show that TranslateGemma models retain strong multimodal capabilities, with enhanced performance on the Vistra image translation benchmark. The release of the open TranslateGemma models aims to provide the research community with powerful and adaptable tools for machine translation.

在 Reddit 之前，最流行的社交新闻平台是 Digg。在 Reddit 发展良好的今天，Digg 又复活了，它采用了类似 Reddi 的基于兴趣的社区模式，但 Reddi 的内容更丰富，用户规模更庞大，为什么要来 Digg？这可能是未来几个月 Digg 面临的问题。Digg 的创始人 Kevin Rose 和 Reddit 联合创始人 Alexis Ohanian 去年联合多家风投收购了 Digg，他们认为可以在 AI 的帮助下解决社交媒体环境中的混乱和有毒问题，将尝试利用零知识证明验证用户身份，要求加入特定产品社区的用户验证是否拥有相关产品，希望利用这些方法建立一个更值得信任的社区。

欧洲曾在很长时间里力推数字支付，将数字支付作为一种打击逃税和洗钱的方法，但在体验到数字支付的问题之后，欧洲又开始重新发现现金的价值。欧盟去年 12 月做出了禁止商家拒收现金的决定，逆转了数字化支付政策。2024 年欧洲的商家有 12% 完全拒收现金，而三年前这一比例是 4%。荷兰有逾三分之一电影院不再收现金。欧元区的现金交易比例也从 2016 年的 79% 降至 2024 年的 52%，其中瑞典比例最高，九成的交易通过数字支付完成，而现金交易比例不到 GDP 1%，相比下日本是 22%。改变数字支付政策是出于对挣扎于数字支付的老年人和贫困人口的担忧。去年春天西班牙发生了大规模停电事故，影响到居民的食物购买，增加了对数字金融系统弹性不足的担忧。欧盟官员同时也担心过度依赖于美国支付巨头 Visa 和万事达卡，它现在建议公民储备足够的现金应对一周没有电力或网络的情况。

乌干达于 1 月 15 日举行全国大选，选举总统和议会，而现任总统 Yoweri Museveni 将竞选其第七个任期，他自 1986 年起一直统治乌干达。在选举前的 1 月 13 日，政府电信监管机构 Uganda Communications Commission 以传播虚假信息、选举舞弊和煽动暴力为由切断了互联网。Netblocks 的监测显示网络自 13 日起被切断，目前投票已经开始，而网络中断仍然在持续。

将近十年时间里，伊朗尝试构建了一个被称为 National Information Network 的互联网平行版本，它又被称为清真互联网（halal internet）。但清真互联网失败了，被证明存在太多漏洞。因此伊朗采取了完全切断互联网的信息控制方法，这就是它上周四采取的措施。这次网络封锁是如此彻底，以前被列入白名单的政府自己内部使用的 SIM 卡也无法访问互联网。突破封锁主要靠走私入境的 Starlink，但 Starlink 也受到严重干扰。伊朗用户还利用现有的简陋互联网基础设施将政府批准的电邮网络变成与外界沟通的消息服务，一种纯文本浏览器服务也有助于绕过封锁。

安全公司 Checkpoint 的研究人发现了设计针对云端主机的 Linux 恶意程序 VoidLink。VoidLink 包含逾 30 个模块，攻击者能根据受感染机器的具体需求定制功能。VoidLink 能检测被感染机器是否托管在 AWS、GCP、Azure、阿里巴巴和腾讯等主流云服务。开发者还计划未来版本添加对华为、DigitalOcean 和 Vultr 等云服务的支持。研究人员猜测，随着越来越多的企业将工作负载迁移到 Linux 系统、云基础设施和应用程序部署环境，攻击者也随着将攻击目标扩大到此类环境。VoidLink 的界面为中文操作者进行了本地化，表明它可能源自中文开发环境。源代码中的符号和注释表明，VoidLink 仍在开发之中。Checkpoint 未发现任何实际感染，意味着其开发尚未完成。

漫画《呆伯特（Dilbert ）》作者 Scott Adams 因前列腺癌去世，享年 68 岁。《呆伯特》是讽刺格子间职场文化的作品，在其鼎盛时期曾同时在 2000 家日报上连载。在漫画中，呆伯特是一位缺乏社交技能的工程师，他的上司“老板”则是糟糕上司的典型代表，总是让工程师的工作变得艰难。Scott Adams 在生命的最后二十年从漫画转向政界，日益直言不讳的表达其保守观点和对特朗普的支持。他因为种族主义言论而导致《呆伯特》漫画被大部分主流媒体停止刊登。在去世前几天他还上直播讽刺抗议 ICE 的自由派女性。

内存价格暴涨，而这一涨势预计会持续到 2027 年，主要 PC 厂商已经考虑降低中低端 PC 产品的内存容量，比如 32GB DDR5 降至 16GB 甚至 8GB。内存和硬盘成本的上升最终也将会转嫁到消费者身上。内存短缺也冲击了 AI PC 这一概念。如果内存规格降低，那么本地 AI 功能也将受到影响，所以如今也没多少人谈论 AI PC。IDC 的经理 Jitesh Ubran 认为内存价格可能到 2027 年才能稳定下来，到供应充足还需要一段时间。

根据 Netblocks 以及 Cloudflare Rader 的监测，伊朗全国断网六天。自 2025 年 12 月起，伊朗发生了一系列抗议活动，起因是民众对通货膨胀飙升、食品价格上涨以及伊朗里亚尔大幅贬值感到不满。示威活动最初由店主和市场商贩发起，进入新年后，抗议规模日益扩大。维基百科数据显示，目前有逾万人死亡，逾 1.6 万人被捕。

NASA 研究人员报告，北极野火数量在上升。相比过去几十年，野火规模更大、温度更高、持续时间更长。这一趋势与该地区气候变化密切相关。北极升温速度几乎是全球平均水平的四倍，降雨和降雪的减少，土壤湿度的下降，都让地表更容易燃烧。闪电是北极野火的主要点火源，而北极闪电的频率也在增加。北美北极地区的火灾面积平均约为 20 世纪中期的两倍。低强度火灾通常不会造成太大影响，但高强度火灾则可能造成严重影响。

美国商务部周二提出了关于 AI 半导体出口管理的新规则方案，将以获得许可为前提，允许部分 AI 半导体对华出口。设想主要面向在中国开展业务的西方企业，对总部设在中国大陆和澳门的中国企业的出口仍为“原则上禁止”。商务部要求英伟达等出口企业对中国的出口数量不得超过其在美国国内总出货量的 50%。新规还要求出口企业优先考虑美国国内 AI 半导体需求者的订单，将美国国内剩余的部分用于对华出口。目的是，即使向中国出口 AI 半导体，也要确保中国的 AI 开发能力不会超过美国。对于在中国方面接收产品的客户，还增加了严格进行身份验证的条件。为了证明出口产品的性能在 H200 以下，还必须接受美国国内的第三方机构的样品性能测试。

哈瓦那综合症首次引起公众注意是在 2016 年，美国驻古巴哈瓦那大使馆的几十名外交官抱怨身体不适。症状包括偏头痛、恶心、记忆力减退和头晕。此后维也纳、巴黎、日内瓦等地的美国驻外外交人员、官员和家庭成员也报告了类似的症状。根据美国五角大楼的调查，它在拜登政府期间斥资数百万美元购买了一种设备，测试后认为它可能与哈瓦那综合症相关。这种设备包含了俄罗斯制造的零部件，但不是完全来自俄罗斯。这种设备可以装进一个背包里。有关该设备的更多细节没有披露。

2023 年 Google Chrome 移除了对实验性的 JPEG-XL 图像格式的支持。JPEG-XL 是免专利新的图像格式。Google 此举引发了很多争议，因为 Chrome/Chromium 占据了九成市场份额，它是 Web 标准事实上的仲裁者。到了 2025 年事情有了戏剧性转变。Google 改变了主意，开始恢复对 JPEG-XL 图像的支持，去年 12 月 Chrome/Chromium 代码库合并了 Rust 语言开发的 JPEG-XL 图像解码器 jxl-rs，本周基于 jxl-rs 的 JPEG-XL 图像解码功能默认启用。

Wine 11.0 正式释出。主要变化包括：全新的 WoW64 模式，支持 32 位甚至 16 位应用在 64 位前缀下以更简洁的方式运行；支持 Linux 6.14 的 NTSYNC 内核模块，显著改进游戏和多线程应用性能；统一的 Wine 二进制文件；支持 Vulkan API 1.4.335；通过 Direct3D 实现硬件加速的 H.264 解码；改进 Wine Wayland 驱动，支持剪贴板操作、IME 和创建非矩形窗口；等等。

台媒报道，一加科技创始人兼 CEO、OPPO 高级副总裁兼首席产品官刘作虎，涉嫌未经主管机关许可，便绕道香港在台设立分公司，实则为深圳母公司从事手机软件研发与人才招揽，利用港商名义规避法律审查，6 年间砸下 7293 万美元，大规模挖角台湾 70 多位顶尖研发工程师。检方依违反两岸人民关系条例将台籍郑姓、林姓 2 名干部提起公诉；刘作虎则另行通缉。起诉书指出，深圳万普拉斯（OnePlus）公司董事长刘作虎为组建台湾研发团队，与郑女、林男达成协议。自 2014 年起，先在香港成立「一加公司」，隔年再以港商身分来台设立分公司（后更名为声赫公司）。林男在侦讯时供称，他受刘作虎指示担任研发部门负责人，陆续面试并聘雇了 70 多位台湾研发工程师。尽管这群工程师领的是台湾分公司的薪水，但其开发出的软件全数应用在 OnePlus 手机上，且行政、财务等营运细节，皆须向深圳母公司的主管汇报，年终奖金发放甚至要经过刘作虎点头才能决定。

Mozilla 释出了 Firefox 147。主要变化包括：Apple Silicon 设备上启用 WebGPU；通过为硬件解码视频启用零拷贝播放改进 AMD GPU 系统上的视频播放性能，使之与 Intel 和 NVIDIA GPU 系统相当；支持 Safe Browsing V5 协议，Enhanced Tracking Protection(ETP)设为严格的用户将默认启用本地网络访问限制，网站要访问本地网络资源需要得到用户的明确同意；支持 Freedesktop.org XDG Base Directory Specification；改进了 GNOME 桌面环境下分数比例显示渲染；修复多个沙箱逃逸漏洞，等等。