OrangeBot.AI Digest — 2026-02-04

A Claude Code plugin that automatically captures everything Claude does during your coding sessions, compresses it with AI (using Claude's agent-sdk), and injects relevant context back into future sessions.

Skills Catalog for Codex

Master Claude Code Hooks

ChatDev 2.0: Dev All through LLM-powered Multi-Agent Collaboration

Anki is a smart spaced repetition flashcard program

Contrib repository for the OpenTelemetry Collector

⚡️ GenBI (Generative BI) queries any database in natural language, generates accurate SQL (Text-to-SQL), charts (Text-to-Chart), and AI-powered business intelligence in seconds.

Agent Orchestration Command Center

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

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

Truly independent web browser

Visualize, collaborate, and evolve the software architecture with always actual and live diagrams from your code

Large Language Models (LLMs) have achieved remarkable success in source code understanding, yet as software systems grow in scale, computational efficiency has become a critical bottleneck. Currently, these models rely on a text-based paradigm that treats source code as a linear sequence of tokens, which leads to a linear increase in context length and associated computational costs. The rapid advancement of Multimodal LLMs (MLLMs) introduces an opportunity to optimize efficiency by representing source code as rendered images. Unlike text, which is difficult to compress without losing semantic meaning, the image modality is inherently suitable for compression. By adjusting resolution, images can be scaled to a fraction of their original token cost while remaining recognizable to vision-capable models. To explore the feasibility of this approach, we conduct the first systematic study on the effectiveness of MLLMs for code understanding. Our experiments reveal that: (1) MLLMs can effectively understand code with substantial token reduction, achieving up to 8x compression; (2) MLLMs can effectively leverage visual cues such as syntax highlighting, improving code completion performance under 4x compression; and (3) Code-understanding tasks like clone detection exhibit exceptional resilience to visual compression, with some compression ratios even slightly outperforming raw text inputs. Our findings highlight both the potential and current limitations of MLLMs in code understanding, which points out a shift toward image-modality code representation as a pathway to more efficient inference.

Language agents have shown strong promise for task automation. Realizing this promise for increasingly complex, long-horizon tasks has driven the rise of a sub-agent-as-tools paradigm for multi-turn task solving. However, existing designs still lack a dynamic abstraction view of sub-agents, thereby hurting adaptability. We address this challenge with a unified, framework-agnostic agent abstraction that models any agent as a tuple Instruction, Context, Tools, Model. This tuple acts as a compositional recipe for capabilities, enabling the system to spawn specialized executors for each task on demand. Building on this abstraction, we introduce an agentic system AOrchestra, where the central orchestrator concretizes the tuple at each step: it curates task-relevant context, selects tools and models, and delegates execution via on-the-fly automatic agent creation. Such designs enable reducing human engineering efforts, and remain framework-agnostic with plug-and-play support for diverse agents as task executors. It also enables a controllable performance-cost trade-off, allowing the system to approach Pareto-efficient. Across three challenging benchmarks (GAIA, SWE-Bench, Terminal-Bench), AOrchestra achieves 16.28% relative improvement against the strongest baseline when paired with Gemini-3-Flash. The code is available at: https://github.com/FoundationAgents/AOrchestra

This work stems from prior complementary observations on the dynamics of Chain-of-Thought (CoT): Large Language Models (LLMs) is shown latent planning of subsequent reasoning prior to CoT emergence, thereby diminishing the significance of explicit CoT; whereas CoT remains critical for tasks requiring multi-step reasoning. To deepen the understanding between LLM's internal states and its verbalized reasoning trajectories, we investigate the latent planning strength of LLMs, through our probing method, Tele-Lens, applying to hidden states across diverse task domains. Our empirical results indicate that LLMs exhibit a myopic horizon, primarily conducting incremental transitions without precise global planning. Leveraging this characteristic, we propose a hypothesis on enhancing uncertainty estimation of CoT, which we validate that a small subset of CoT positions can effectively represent the uncertainty of the entire path. We further underscore the significance of exploiting CoT dynamics, and demonstrate that automatic recognition of CoT bypass can be achieved without performance degradation. Our code, data and models are released at https://github.com/lxucs/tele-lens.

Automating AI research differs from general software engineering due to computationally expensive evaluation (e.g., model training) and opaque performance attribution. Current LLM-based agents struggle here, often generating monolithic scripts that ignore execution costs and causal factors. We introduce MARS (Modular Agent with Reflective Search), a framework optimized for autonomous AI research. MARS relies on three pillars: (1) Budget-Aware Planning via cost-constrained Monte Carlo Tree Search (MCTS) to explicitly balance performance with execution expense; (2) Modular Construction, employing a "Design-Decompose-Implement" pipeline to manage complex research repositories; and (3) Comparative Reflective Memory, which addresses credit assignment by analyzing solution differences to distill high-signal insights. MARS achieves state-of-the-art performance among open-source frameworks on MLE-Bench under comparable settings, maintaining competitiveness with the global leaderboard's top methods. Furthermore, the system exhibits qualitative "Aha!" moments, where 63% of all utilized lessons originate from cross-branch transfer, demonstrating that the agent effectively generalizes insights across search paths.

While Large Language Models (LLMs) excel at short-term tasks, scaling them to long-horizon agentic workflows remains challenging. The core bottleneck lies in the scarcity of training data that captures authentic long-dependency structures and cross-stage evolutionary dynamics--existing synthesis methods either confine to single-feature scenarios constrained by model distribution, or incur prohibitive human annotation costs, failing to provide scalable, high-quality supervision. We address this by reconceptualizing data synthesis through the lens of real-world software evolution. Our key insight: Pull Request (PR) sequences naturally embody the supervision signals for long-horizon learning. They decompose complex objectives into verifiable submission units, maintain functional coherence across iterations, and encode authentic refinement patterns through bug-fix histories. Building on this, we propose daVinci-Agency, which systematically mines structured supervision from chain-of-PRs through three interlocking mechanisms: (1) progressive task decomposition via continuous commits, (2) long-term consistency enforcement through unified functional objectives, and (3) verifiable refinement from authentic bug-fix trajectories. Unlike synthetic trajectories that treat each step independently, daVinci-Agency's PR-grounded structure inherently preserves the causal dependencies and iterative refinements essential for teaching persistent goal-directed behavior and enables natural alignment with project-level, full-cycle task modeling. The resulting trajectories are substantial--averaging 85k tokens and 116 tool calls--yet remarkably data-efficient: fine-tuning GLM-4.6 on 239 daVinci-Agency samples yields broad improvements across benchmarks, notably achieving a 47% relative gain on Toolathlon. Beyond benchmark performance, our analysis confirms...

Existing methods for human motion control in video generation typically rely on either 2D poses or explicit 3D parametric models (e.g., SMPL) as control signals. However, 2D poses rigidly bind motion to the driving viewpoint, precluding novel-view synthesis. Explicit 3D models, though structurally informative, suffer from inherent inaccuracies (e.g., depth ambiguity and inaccurate dynamics) which, when used as a strong constraint, override the powerful intrinsic 3D awareness of large-scale video generators. In this work, we revisit motion control from a 3D-aware perspective, advocating for an implicit, view-agnostic motion representation that naturally aligns with the generator's spatial priors rather than depending on externally reconstructed constraints. We introduce 3DiMo, which jointly trains a motion encoder with a pretrained video generator to distill driving frames into compact, view-agnostic motion tokens, injected semantically via cross-attention. To foster 3D awareness, we train with view-rich supervision (i.e., single-view, multi-view, and moving-camera videos), forcing motion consistency across diverse viewpoints. Additionally, we use auxiliary geometric supervision that leverages SMPL only for early initialization and is annealed to zero, enabling the model to transition from external 3D guidance to learning genuine 3D spatial motion understanding from the data and the generator's priors. Experiments confirm that 3DiMo faithfully reproduces driving motions with flexible, text-driven camera control, significantly surpassing existing methods in both motion fidelity and visual quality.

World models have emerged as a critical frontier in AI research, aiming to enhance large models by infusing them with physical dynamics and world knowledge. The core objective is to enable agents to understand, predict, and interact with complex environments. However, current research landscape remains fragmented, with approaches predominantly focused on injecting world knowledge into isolated tasks, such as visual prediction, 3D estimation, or symbol grounding, rather than establishing a unified definition or framework. While these task-specific integrations yield performance gains, they often lack the systematic coherence required for holistic world understanding. In this paper, we analyze the limitations of such fragmented approaches and propose a unified design specification for world models. We suggest that a robust world model should not be a loose collection of capabilities but a normative framework that integrally incorporates interaction, perception, symbolic reasoning, and spatial representation. This work aims to provide a structured perspective to guide future research toward more general, robust, and principled models of the world.

Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a key approach for enhancing LLM reasoning.However, standard frameworks like Group Relative Policy Optimization (GRPO) typically employ a uniform rollout budget, leading to resource inefficiency. Moreover, existing adaptive methods often rely on instance-level metrics, such as task pass rates, failing to capture the model's dynamic learning state. To address these limitations, we propose CoBA-RL, a reinforcement learning algorithm designed to adaptively allocate rollout budgets based on the model's evolving capability. Specifically, CoBA-RL utilizes a Capability-Oriented Value function to map tasks to their potential training gains and employs a heap-based greedy strategy to efficiently self-calibrate the distribution of computational resources to samples with high training value. Extensive experiments demonstrate that our approach effectively orchestrates the trade-off between exploration and exploitation, delivering consistent generalization improvements across multiple challenging benchmarks. These findings underscore that quantifying sample training value and optimizing budget allocation are pivotal for advancing LLM post-training efficiency.

Distribution matching distillation (DMD) aligns a multi-step generator with its few-step counterpart to enable high-quality generation under low inference cost. However, DMD tends to suffer from mode collapse, as its reverse-KL formulation inherently encourages mode-seeking behavior, for which existing remedies typically rely on perceptual or adversarial regularization, thereby incurring substantial computational overhead and training instability. In this work, we propose a role-separated distillation framework that explicitly disentangles the roles of distilled steps: the first step is dedicated to preserving sample diversity via a target-prediction (e.g., v-prediction) objective, while subsequent steps focus on quality refinement under the standard DMD loss, with gradients from the DMD objective blocked at the first step. We term this approach Diversity-Preserved DMD (DP-DMD), which, despite its simplicity -- no perceptual backbone, no discriminator, no auxiliary networks, and no additional ground-truth images -- preserves sample diversity while maintaining visual quality on par with state-of-the-art methods in extensive text-to-image experiments.

Recent advances in large language models (LLMs) have enabled software engineering agents to tackle complex code modification tasks. Most existing approaches rely on execution feedback from containerized environments, which require dependency-complete setup and physical execution of programs and tests. While effective, this paradigm is resource-intensive and difficult to maintain, substantially complicating agent training and limiting scalability. We propose SWE-World, a Docker-free framework that replaces physical execution environments with a learned surrogate for training and evaluating software engineering agents. SWE-World leverages LLM-based models trained on real agent-environment interaction data to predict intermediate execution outcomes and final test feedback, enabling agents to learn without interacting with physical containerized environments. This design preserves the standard agent-environment interaction loop while eliminating the need for costly environment construction and maintenance during agent optimization and evaluation. Furthermore, because SWE-World can simulate the final evaluation outcomes of candidate trajectories without real submission, it enables selecting the best solution among multiple test-time attempts, thereby facilitating effective test-time scaling (TTS) in software engineering tasks. Experiments on SWE-bench Verified demonstrate that SWE-World raises Qwen2.5-Coder-32B from 6.2\% to 52.0\% via Docker-free SFT, 55.0\% with Docker-free RL, and 68.2\% with further TTS. The code is available at https://github.com/RUCAIBox/SWE-World

In this technical report, we present SWE-Master, an open-source and fully reproducible post-training framework for building effective software engineering agents. SWE-Master systematically explores the complete agent development pipeline, including teacher-trajectory synthesis and data curation, long-horizon SFT, RL with real execution feedback, and inference framework design. Starting from an open-source base model with limited initial SWE capability, SWE-Master demonstrates how systematical optimization method can elicit strong long-horizon SWE task solving abilities. We evaluate SWE-Master on SWE-bench Verified, a standard benchmark for realistic software engineering tasks. Under identical experimental settings, our approach achieves a resolve rate of 61.4\% with Qwen2.5-Coder-32B, substantially outperforming existing open-source baselines. By further incorporating test-time scaling~(TTS) with LLM-based environment feedback, SWE-Master reaches 70.8\% at TTS@8, demonstrating a strong performance potential. SWE-Master provides a practical and transparent foundation for advancing reproducible research on software engineering agents. The code is available at https://github.com/RUCAIBox/SWE-Master.

Parallel thinking has emerged as a promising paradigm for reasoning, yet it imposes significant computational burdens. Existing efficiency methods primarily rely on local, per-trajectory signals and lack principled mechanisms to exploit global dynamics across parallel branches. We introduce 2D probing, an interface that exposes the width-depth dynamics of parallel thinking by periodically eliciting intermediate answers from all branches. Our analysis reveals three key insights: non-monotonic scaling across width-depth allocations, heterogeneous reasoning branch lengths, and early stabilization of global consensus. Guided by these insights, we introduce Parallel-Probe, a training-free controller designed to optimize online parallel thinking. Parallel-Probe employs consensus-based early stopping to regulate reasoning depth and deviation-based branch pruning to dynamically adjust width. Extensive experiments across three benchmarks and multiple models demonstrate that Parallel-Probe establishes a superior Pareto frontier for test-time scaling. Compared to standard majority voting, it reduces sequential tokens by up to 35.8% and total token cost by over 25.8% while maintaining competitive accuracy.

Nowadays, training and evaluating DeepResearch-generated reports remain challenging due to the lack of verifiable reward signals. Accordingly, rubric-based evaluation has become a common practice. However, existing approaches either rely on coarse, pre-defined rubrics that lack sufficient granularity, or depend on manually constructed query-specific rubrics that are costly and difficult to scale. In this paper, we propose a pipeline to train human-preference-aligned query-specific rubric generators tailored for DeepResearch report generation. We first construct a dataset of DeepResearch-style queries annotated with human preferences over paired reports, and train rubric generators via reinforcement learning with a hybrid reward combining human preference supervision and LLM-based rubric evaluation. To better handle long-horizon reasoning, we further introduce a Multi-agent Markov-state (MaMs) workflow for report generation. We empirically show that our proposed rubric generators deliver more discriminative and better human-aligned supervision than existing rubric design strategies. Moreover, when integrated into the MaMs training framework, DeepResearch systems equipped with our rubric generators consistently outperform all open-source baselines on the DeepResearch Bench and achieve performance comparable to that of leading closed-source models.

Recent advancements in multimodal reward models (RMs) have significantly propelled the development of visual generation. Existing frameworks typically adopt Bradley-Terry-style preference modeling or leverage generative VLMs as judges, and subsequently optimize visual generation models via reinforcement learning. However, current RMs suffer from inherent limitations: they often follow a one-size-fits-all paradigm that assumes a monolithic preference distribution or relies on fixed evaluation rubrics. As a result, they are insensitive to content-specific visual cues, leading to systematic misalignment with subjective and context-dependent human preferences. To this end, inspired by human assessment, we propose UnifiedReward-Flex, a unified personalized reward model for vision generation that couples reward modeling with flexible and context-adaptive reasoning. Specifically, given a prompt and the generated visual content, it first interprets the semantic intent and grounds on visual evidence, then dynamically constructs a hierarchical assessment by instantiating fine-grained criteria under both predefined and self-generated high-level dimensions. Our training pipeline follows a two-stage process: (1) we first distill structured, high-quality reasoning traces from advanced closed-source VLMs to bootstrap SFT, equipping the model with flexible and context-adaptive reasoning behaviors; (2) we then perform direct preference optimization (DPO) on carefully curated preference pairs to further strengthen reasoning fidelity and discriminative alignment. To validate the effectiveness, we integrate UnifiedReward-Flex into the GRPO framework for image and video synthesis, and extensive results demonstrate its superiority.

Reranking is a critical component of modern retrieval systems, which typically pair an efficient first-stage retriever with a more expressive model to refine results. While large reasoning models have driven rapid progress in text-centric reranking, reasoning-based reranking for video retrieval remains underexplored. To address this gap, we introduce RANKVIDEO, a reasoning-based reranker for video retrieval that explicitly reasons over query-video pairs using video content to assess relevance. RANKVIDEO is trained using a two-stage curriculum consisting of perception-grounded supervised fine-tuning followed by reranking training that combines pointwise, pairwise, and teacher confidence distillation objectives, and is supported by a data synthesis pipeline for constructing reasoning-intensive query-video pairs. Experiments on the large-scale MultiVENT 2.0 benchmark demonstrate that RANKVIDEO consistently improves retrieval performance within a two-stage framework, yielding an average improvement of 31% on nDCG@10 and outperforming text-only and vision-language reranking alternatives, while more efficient.

FT 报道了中国的一种选拔有天赋少年人才进行特殊培养的特教模式，此类特培的最早例子当属中国科技大学的少年班，过去二十年还出现了清华姚班、北大图灵班等特殊培养班。这些特培班为 AI 和科技公司输送了核心技术人才。中科大少年班培养的 3167 名毕业生中，18%-20% 留在学界，逾 200 人成为国内外名校和科研机构教授。去年初引发广泛关注的 DeepSeek 其逾百名研发团队大多数都来自这些特培班。今天中国每年有 500 万 STEM 专业毕业生，相比之下美国约 50 万。在 2025 年中国派出的 23 名参加国际科学奥林匹克竞赛的学生有 22 名获得了金牌。

根据发表在《Milbank Quarterly》期刊上的一项研究，哈佛、杜克和密歇根大学的研究人员认为，超加工食品（Ultra-processed foods）与香烟的相似之处远多于与水果或蔬菜的相似之处，需要更严格的监管。超加工食品是经过工业化生产、通常使用乳化剂或人工色素和香精的食品，如软饮料、薯片和饼干。研究人员称，超加工食品和香烟的生产过程存在相似之处，制造商都在努力优化产品“剂量”以及对人体奖赏通路的作用速度。宣传食品“低脂”或“无糖”都是在误导消费者，类似 1950 年代宣传香烟的过滤嘴是一种保护性创新，实际上几乎没有任何实质性益处。研究人员认为应该借鉴烟草管理去监管超加工食品。

西班牙首相 Pedro Sanchez 周二表示，计划禁止 16 岁以下未成年人使用社交媒体，社交平台需要引入年龄验证系统。他表示要保护儿童远离数字狂野西部。澳大利亚于去年 12 月成为首个禁止 16 岁以下儿童使用社交媒体的国家，英法等国正在考虑采取类似年龄限制措施。Sanchez 称西班牙将于下周提出一项法案，追究社交媒体高管对非法和仇恨言论内容的责任，将算法操纵和放大非法内容定为犯罪行为。

巴黎检方突击搜查 X 在法办公室。执行搜查的是网络犯罪部门，欧洲刑警组织协助。搜查与 2025 年 1 月启动的调查相关，这次调查涉及对 X 算法及其推荐内容的投诉。巴黎检方还传唤了马斯克（Elon Musk）以及 X 前 CEO Linda Yaccarino，要求 4 月出席听证会。检方在声明中称，X 平台流传深度伪造的色情视频以及否认纳粹大屠杀的内容。检方还宣布将退出 X 平台，将通过 LinkedIn 和 Instagram 与外界沟通。

工信部发布了新的强制性安全标准《汽车车门把手安全技术要求》，禁止电动汽车使用隐藏式门把手，成为世界上首个禁止这种设计的国家。这种特斯拉推广的设计因一系列致命事件而面临全球监管机构的审查。新规定要求在中国销售的汽车必须配备机械释放车门外把手。新规将于 2027 年 1 月 1 日起开始实施。已获得型式批准的车型，应于 2029 年 1 月前修改其设计以符合要求。在此之前，中国国内发生多起引发高度关注的事故，其中包括两起小米电动汽车起火事故。事故中车门疑似因断电而无法打开，造成车内人员既无法逃生，也无法获救，最终身亡。

乌克兰和 SpaceX 最近合作阻止俄罗斯无人机使用 Starlink 发动攻击。乌克兰国防部表示，乌克兰的 Starlink 用户在不久之后将被要求登记其终端，未来经过验证和登记的 Starlink 终端将被加入到白名单，能继续在乌克兰境内访问卫星互联网，未登记的终端将被断开连接。俄罗斯通过黑市交易获得了 Starlink 终端，它的 Molniya-2 无人机的攻击型号和侦察型号通过配备 Starlink 实现超视距的控制和数据传输，在更远的距离上进行精确打击。Molniya-2 被发现使用了 F8 迷你 PC ，运行正版授权的 Windows 11 操作系统。

AI 热导致内存和固态硬盘价格不断上涨，也迫使 PC 组装厂商不断调整价格应对主要零部件价格的上涨。树莓派宣布了两个月内的第二次价格上调。所有配备 2GB 以上内存的 Raspberry Pi 4 和 Raspberry Pi 5 都将涨价。2GB 内存版本上涨 10 美元，4GB 内存上涨 15 美元，8GB 内存上涨 30 美元，16GB 内存版本将大幅上涨 60 美元。16GB 版本的 Pi 5 如今售价高达 205 美元，而树莓派之类的单板电脑曾以低价著称。

马斯克（Elon Musk）旗下的火箭公司正式宣布收购他旗下的 AI 公司 xAI，这也意味着作为 xAI 一部分的社交网络 X/Twitter 也将成为 SpaceX 家族的一员。SpaceX 是马斯克目前最成功的公司，预计今年 IPO，其估值超过 1 万亿美元。马斯克声称他计划使用 SpaceX 的火箭为 xAI 发射部署 100 万个轨道数据中心。他的很多言论不能当真。

Phoronix 评测了龙芯科技 LoongArch 架构 12 核 24 线处理器 3B6000 在 Linux 下的性能，并与 AMD 和英特尔推出的主流 CPU 以及树莓派的 Raspberry Pi 500+ 进行了对比。3B6000 于 2025 年推出，支持 DDR4 ECC 内存，其开发板售价为 3000 元。测试结果并不出人意料，3B6000 性能要远逊色于 AMD Zen 5 架构的处理器如 9600x，但显著强于 ARM 架构的 Pi 500+。

根据发表在 PNAS 期刊上的一项研究，犹他大学的研究人员分析了近一个世纪的人类头发样本，发现自 1970 年代禁止含铅汽油等含铅产品以来，头发中的铅含量减少到峰值期间的百分之一。铅是强效神经毒素。研究人员使用了犹他州居民身上收集的头发样本，部分样本保存在家族数代人的剪贴簿中。分析发现，头发铅含量在 1916-1969 年间达到峰值的 100 ppm，1990 年降至 10ppm，2024 年降至 1ppm 以下。这一下降趋势与美国淘汰含铅汽油的进程基本一致。

芬兰交通管理局（Fintraffic）准备今年夏天推出新交通信号灯系统，该系统将赋予紧急车辆优先通行权，允许救护车、消防车和其它紧急救援车辆通过“绿波”式的交通信号灯通过路口。名为“national traffic light priority system”的交通灯优先系统能识别紧急车辆的位置，自动将交通信号灯切换为绿灯方便其快速通行。 新系统将于 7 月正式投入使用，但由于需要对交通系统进行测试和更新，因此在全国范围内推广还需要时间。

Mozilla 宣布为 Firefox 用户提供禁用所有 AI 功能的选项。从 2 月 24 日推出的 Firefox 148 起，用户可以在设置中启用“Block AI enhancements”选项，一旦启用，用户将不会看到任何现在或未来 AI 功能的弹出窗口或提醒。新的 AI 控制选项还允许用户单独管理各项 AI 功能。Mozilla 新 CEO Anthony Enzor-DeMeo 表示，AI 应该始终是一种选择，可以关闭，让用户知道如何运作，能带来什么价值。

知情人士透露，微软正在重新评估 Windows 11 的 AI 战略，收缩或移除 Windows 内置应用与 AI 应用 Copilot 的集成。过去几个月 Windows 深度整合 AI 遭遇了用户的强烈反对。微软正在评估记事本和画图等应用中的 Copilot 功能，可能会完全移除相关功能或移除 Copilot 标识以提供更简洁的用户体验。微软已经暂停了在其它内置应用中引入 Copilot 按钮的工作。早先引发争议的 Windows Recall 功能也在接受评估，微软内部认为目前的实现方案是失败的，探索重新设计或重命名该功能，但没有完全放弃。

公安部发布《网络犯罪防治法（征求意见稿）》，意见反馈截止时间 2026 年 3 月 2 日。根据《征求意见稿》：第十五条 任何个人和组织制作、销售、提供具有下列功能的设备、软件、工具、服务的，应当到公安机关、电信等主管部门备案，并登记购买者、使用者的真实身份信息： （一）具有批量控制网络账号、上网线路、智能终端等功能的； （二）具有网络虚拟定位功能的； （三）具有侵入、控制计算机信息系统功能的； （四）其他由省级以上公安机关会同电信等主管部门认定的，可能被大量用于网络违法犯罪的设备、软件、工具、服务。 第二十四条 任何个人和组织不得违反国家有关规定，实施网络产品安全漏洞发现、收集、发布等违法犯罪活动，或者散布、传播重要信息系统的设计方案、网络拓扑、核心源代码等可能危害网络安全的信息。 第二十五条 未经省级以上网信部门、公安机关批准或者行业主管部门、运营者授权，任何个人、组织不得对网络安全等级保护第三级（含）以上的网络开展网络安全漏洞探测、渗透性测试等可能影响网络安全的活动。 未经设区的市级以上网信部门、公安机关批准或者行业主管部门、运营者授权，任何个人、组织不得对网络安全等级保护第二级（含）以下的网络开展网络安全漏洞探测、渗透性测试等可能影响网络安全的活动。 依法或者经批准、授权开展的，应当在活动实施五个工作日前向县级以上公安机关报告。法律、行政法规另有规定的，从其规定。

去年 12 月 Notepad++ 发布安全警告，它遭遇了流量劫持，部分地区的更新程序被植入恶意程序。调查发现，Notepad++ 更新程序 WinGUp 的流量被劫持到恶意服务器，下载恶意可执行文件。现在 Notepad++ 公布了最新调查结果：其流量被国家支持黑客劫持。托管 https://notepad-plus-plus.org/update/getDownloadUrl.php 的服务器在 2025 年 6 月遭到入侵，这次入侵在 2025 年 9 月 2 日因服务器更新而被阻止，但攻击者仍然持有 Notepad++ 内部服务凭据直至 12 月 2 日，允许攻击者将部分 https://notepad-plus-plus.org/getDownloadUrl.php 的流量重定向到他们控制的服务器，返回篡改后的 URL。攻击者是专门针对 Notepad++。