OrangeBot.AI Digest — 2026-02-05

The Open-Source Multimodal AI Agent Stack: Connecting Cutting-Edge AI Models and Agent Infra

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In this report, we introduce ERNIE 5.0, a natively autoregressive foundation model desinged for unified multimodal understanding and generation across text, image, video, and audio. All modalities are trained from scratch under a unified next-group-of-tokens prediction objective, based on an ultra-sparse mixture-of-experts (MoE) architecture with modality-agnostic expert routing. To address practical challenges in large-scale deployment under diverse resource constraints, ERNIE 5.0 adopts a novel elastic training paradigm. Within a single pre-training run, the model learns a family of sub-models with varying depths, expert capacities, and routing sparsity, enabling flexible trade-offs among performance, model size, and inference latency in memory- or time-constrained scenarios. Moreover, we systematically address the challenges of scaling reinforcement learning to unified foundation models, thereby guaranteeing efficient and stable post-training under ultra-sparse MoE architectures and diverse multimodal settings. Extensive experiments demonstrate that ERNIE 5.0 achieves strong and balanced performance across multiple modalities. To the best of our knowledge, among publicly disclosed models, ERNIE 5.0 represents the first production-scale realization of a trillion-parameter unified autoregressive model that supports both multimodal understanding and generation. To facilitate further research, we present detailed visualizations of modality-agnostic expert routing in the unified model, alongside comprehensive empirical analysis of elastic training, aiming to offer profound insights to the community.

The deployment of Large Language Models (LLMs) faces a critical bottleneck when handling lengthy inputs: the prohibitive memory footprint of the Key Value (KV) cache. To address this bottleneck, the token pruning paradigm leverages attention sparsity to selectively retain a small, critical subset of tokens. However, existing approaches fall short, with static methods risking irreversible information loss and dynamic strategies employing heuristics that insufficiently capture the query-dependent nature of token importance. We propose FASA, a novel framework that achieves query-aware token eviction by dynamically predicting token importance. FASA stems from a novel insight into RoPE: the discovery of functional sparsity at the frequency-chunk (FC) level. Our key finding is that a small, identifiable subset of "dominant" FCs consistently exhibits high contextual agreement with the full attention head. This provides a robust and computationally free proxy for identifying salient tokens. %making them a powerful and efficient proxy for token importance. Building on this insight, FASA first identifies a critical set of tokens using dominant FCs, and then performs focused attention computation solely on this pruned subset. % Since accessing only a small fraction of the KV cache, FASA drastically lowers memory bandwidth requirements and computational cost. Across a spectrum of long-context tasks, from sequence modeling to complex CoT reasoning, FASA consistently outperforms all token-eviction baselines and achieves near-oracle accuracy, demonstrating remarkable robustness even under constraint budgets. Notably, on LongBench-V1, FASA reaches nearly 100\% of full-KV performance when only keeping 256 tokens, and achieves 2.56times speedup using just 18.9\% of the cache on AIME24.

Recent advancements in Large Language Models (LLMs) have largely focused on depth scaling, where a single agent solves long-horizon problems with multi-turn reasoning and tool use. However, as tasks grow broader, the key bottleneck shifts from individual competence to organizational capability. In this work, we explore a complementary dimension of width scaling with multi-agent systems to address broad information seeking. Existing multi-agent systems often rely on hand-crafted workflows and turn-taking interactions that fail to parallelize work effectively. To bridge this gap, we propose WideSeek-R1, a lead-agent-subagent framework trained via multi-agent reinforcement learning (MARL) to synergize scalable orchestration and parallel execution. By utilizing a shared LLM with isolated contexts and specialized tools, WideSeek-R1 jointly optimizes the lead agent and parallel subagents on a curated dataset of 20k broad information-seeking tasks. Extensive experiments show that WideSeek-R1-4B achieves an item F1 score of 40.0% on the WideSearch benchmark, which is comparable to the performance of single-agent DeepSeek-R1-671B. Furthermore, WideSeek-R1-4B exhibits consistent performance gains as the number of parallel subagents increases, highlighting the effectiveness of width scaling.

Multimodal Process Reward Models (MPRMs) are central to step-level supervision for visual reasoning in MLLMs. Training MPRMs typically requires large-scale Monte Carlo (MC)-annotated corpora, incurring substantial training cost. This paper studies the data efficiency for MPRM training.Our preliminary experiments reveal that MPRM training quickly saturates under random subsampling of the training data, indicating substantial redundancy within existing MC-annotated corpora.To explain this, we formalize a theoretical framework and reveal that informative gradient updates depend on two factors: label mixtures of positive/negative steps and label reliability (average MC scores of positive steps). Guided by these insights, we propose the Balanced-Information Score (BIS), which prioritizes both mixture and reliability based on existing MC signals at the rollout level, without incurring any additional cost. Across two backbones (InternVL2.5-8B and Qwen2.5-VL-7B) on VisualProcessBench, BIS-selected subsets consistently match and even surpass the full-data performance at small fractions. Notably, the BIS subset reaches full-data performance using only 10% of the training data, improving over random subsampling by a relative 4.1%.

Omni-modal Large Language Models (Omni-LLMs) have demonstrated strong capabilities in audio-video understanding tasks. However, their reliance on long multimodal token sequences leads to substantial computational overhead. Despite this challenge, token compression methods designed for Omni-LLMs remain limited. To bridge this gap, we propose OmniSIFT (Omni-modal Spatio-temporal Informed Fine-grained Token compression), a modality-asymmetric token compression framework tailored for Omni-LLMs. Specifically, OmniSIFT adopts a two-stage compression strategy: (i) a spatio-temporal video pruning module that removes video redundancy arising from both intra-frame structure and inter-frame overlap, and (ii) a vision-guided audio selection module that filters audio tokens. The entire framework is optimized end-to-end via a differentiable straight-through estimator. Extensive experiments on five representative benchmarks demonstrate the efficacy and robustness of OmniSIFT. Notably, for Qwen2.5-Omni-7B, OmniSIFT introduces only 4.85M parameters while maintaining lower latency than training-free baselines such as OmniZip. With merely 25% of the original token context, OmniSIFT consistently outperforms all compression baselines and even surpasses the performance of the full-token model on several tasks.

This work introduces Hybrid Sparse Attention (HySparse), a new architecture that interleaves each full attention layer with several sparse attention layers. While conceptually simple, HySparse strategically derives each sparse layer's token selection and KV caches directly from the preceding full attention layer. This architecture resolves two fundamental limitations of prior sparse attention methods. First, conventional approaches typically rely on additional proxies to predict token importance, introducing extra complexity and potentially suboptimal performance. In contrast, HySparse uses the full attention layer as a precise oracle to identify important tokens. Second, existing sparse attention designs often reduce computation without saving KV cache. HySparse enables sparse attention layers to reuse the full attention KV cache, thereby reducing both computation and memory. We evaluate HySparse on both 7B dense and 80B MoE models. Across all settings, HySparse consistently outperforms both full attention and hybrid SWA baselines. Notably, in the 80B MoE model with 49 total layers, only 5 layers employ full attention, yet HySparse achieves substantial performance gains while reducing KV cache storage by nearly 10x.

Despite rapid progress in autoregressive video diffusion, an emerging system algorithm bottleneck limits both deployability and generation capability: KV cache memory. In autoregressive video generation models, the KV cache grows with generation history and quickly dominates GPU memory, often exceeding 30 GB, preventing deployment on widely available hardware. More critically, constrained KV cache budgets restrict the effective working memory, directly degrading long horizon consistency in identity, layout, and motion. To address this challenge, we present Quant VideoGen (QVG), a training free KV cache quantization framework for autoregressive video diffusion models. QVG leverages video spatiotemporal redundancy through Semantic Aware Smoothing, producing low magnitude, quantization friendly residuals. It further introduces Progressive Residual Quantization, a coarse to fine multi stage scheme that reduces quantization error while enabling a smooth quality memory trade off. Across LongCat Video, HY WorldPlay, and Self Forcing benchmarks, QVG establishes a new Pareto frontier between quality and memory efficiency, reducing KV cache memory by up to 7.0 times with less than 4% end to end latency overhead while consistently outperforming existing baselines in generation quality.

Deploying humanoid robots in real-world settings is fundamentally challenging, as it demands tight integration of perception, locomotion, and manipulation under partial-information observations and dynamically changing environments. As well as transitioning robustly between sub-tasks of different types. Towards addressing these challenges, we propose a novel task - EgoActing, which requires directly grounding high-level instructions into various, precise, spatially aware humanoid actions. We further instantiate this task by introducing EgoActor, a unified and scalable vision-language model (VLM) that can predict locomotion primitives (e.g., walk, turn, move sideways, change height), head movements, manipulation commands, and human-robot interactions to coordinate perception and execution in real-time. We leverage broad supervision over egocentric RGB-only data from real-world demonstrations, spatial reasoning question-answering, and simulated environment demonstrations, enabling EgoActor to make robust, context-aware decisions and perform fluent action inference (under 1s) with both 8B and 4B parameter models. Extensive evaluations in both simulated and real-world environments demonstrate that EgoActor effectively bridges abstract task planning and concrete motor execution, while generalizing across diverse tasks and unseen environments.

Recent advances in autonomous LLM agents demonstrate their ability to improve performance through iterative interaction with the environment. We define this paradigm as Test-Time Improvement (TTI). However, the mechanisms under how and why TTI succeed or fail remain poorly understood, and existing evaluation metrics fail to capture their task optimization efficiency, behavior adaptation after erroneous actions, and the specific utility of working memory for task completion. To address these gaps, we propose Test-time Improvement Diagnostic Evaluation (TIDE), an agent-agnostic and environment-agnostic framework that decomposes TTI into three comprehensive and interconnected dimensions. The framework measures (1) the overall temporal dynamics of task completion and (2) identifies whether performance is primarily constrained by recursive looping behaviors or (3) by burdensome accumulated memory. Through extensive experiments across diverse agents and environments, TIDE highlights that improving agent performance requires more than scaling internal reasoning, calling for explicitly optimizing the interaction dynamics between the agent and the environment.

Simulating deformable objects under rich interactions remains a fundamental challenge for real-to-sim robot manipulation, with dynamics jointly driven by environmental effects and robot actions. Existing simulators rely on predefined physics or data-driven dynamics without robot-conditioned control, limiting accuracy, stability, and generalization. This paper presents SoMA, a 3D Gaussian Splat simulator for soft-body manipulation. SoMA couples deformable dynamics, environmental forces, and robot joint actions in a unified latent neural space for end-to-end real-to-sim simulation. Modeling interactions over learned Gaussian splats enables controllable, stable long-horizon manipulation and generalization beyond observed trajectories without predefined physical models. SoMA improves resimulation accuracy and generalization on real-world robot manipulation by 20%, enabling stable simulation of complex tasks such as long-horizon cloth folding.

Diffusion Large Language Models (dLLMs) have emerged as a promising alternative to purely autoregressive language models because they can decode multiple tokens in parallel. However, state-of-the-art block-wise dLLMs rely on a "remasking" mechanism that decodes only the most confident tokens and discards the rest, effectively wasting computation. We demonstrate that recycling computation from the discarded tokens is beneficial, as these tokens retain contextual information useful for subsequent decoding iterations. In light of this, we propose Residual Context Diffusion (RCD), a module that converts these discarded token representations into contextual residuals and injects them back for the next denoising step. RCD uses a decoupled two-stage training pipeline to bypass the memory bottlenecks associated with backpropagation. We validate our method on both long CoT reasoning (SDAR) and short CoT instruction following (LLaDA) models. We demonstrate that a standard dLLM can be efficiently converted to the RCD paradigm with merely ~1 billion tokens. RCD consistently improves frontier dLLMs by 5-10 points in accuracy with minimal extra computation overhead across a wide range of benchmarks. Notably, on the most challenging AIME tasks, RCD nearly doubles baseline accuracy and attains up to 4-5x fewer denoising steps at equivalent accuracy levels.

Reinforcement learning (RL) has become a cornerstone for fine-tuning Large Language Models (LLMs), with Proximal Policy Optimization (PPO) serving as the de facto standard algorithm. Despite its ubiquity, we argue that the core ratio clipping mechanism in PPO is structurally ill-suited for the large vocabularies inherent to LLMs. PPO constrains policy updates based on the probability ratio of sampled tokens, which serves as a noisy single-sample Monte Carlo estimate of the true policy divergence. This creates a sub-optimal learning dynamic: updates to low-probability tokens are aggressively over-penalized, while potentially catastrophic shifts in high-probability tokens are under-constrained, leading to training inefficiency and instability. To address this, we propose Divergence Proximal Policy Optimization (DPPO), which substitutes heuristic clipping with a more principled constraint based on a direct estimate of policy divergence (e.g., Total Variation or KL). To avoid huge memory footprint, we introduce the efficient Binary and Top-K approximations to capture the essential divergence with negligible overhead. Extensive empirical evaluations demonstrate that DPPO achieves superior training stability and efficiency compared to existing methods, offering a more robust foundation for RL-based LLM fine-tuning.

Recent DiT-based text-to-image models increasingly adopt LLMs as text encoders, yet text conditioning remains largely static and often utilizes only a single LLM layer, despite pronounced semantic hierarchy across LLM layers and non-stationary denoising dynamics over both diffusion time and network depth. To better match the dynamic process of DiT generation and thereby enhance the diffusion model's generative capability, we introduce a unified normalized convex fusion framework equipped with lightweight gates to systematically organize multi-layer LLM hidden states via time-wise, depth-wise, and joint fusion. Experiments establish Depth-wise Semantic Routing as the superior conditioning strategy, consistently improving text-image alignment and compositional generation (e.g., +9.97 on the GenAI-Bench Counting task). Conversely, we find that purely time-wise fusion can paradoxically degrade visual generation fidelity. We attribute this to a train-inference trajectory mismatch: under classifier-free guidance, nominal timesteps fail to track the effective SNR, causing semantically mistimed feature injection during inference. Overall, our results position depth-wise routing as a strong and effective baseline and highlight the critical need for trajectory-aware signals to enable robust time-dependent conditioning.

While recent advances in neural representations and generative models have revolutionized 3D content creation, the field remains constrained by significant data processing bottlenecks. To address this, we introduce HY3D-Bench, an open-source ecosystem designed to establish a unified, high-quality foundation for 3D generation. Our contributions are threefold: (1) We curate a library of 250k high-fidelity 3D objects distilled from large-scale repositories, employing a rigorous pipeline to deliver training-ready artifacts, including watertight meshes and multi-view renderings; (2) We introduce structured part-level decomposition, providing the granularity essential for fine-grained perception and controllable editing; and (3) We bridge real-world distribution gaps via a scalable AIGC synthesis pipeline, contributing 125k synthetic assets to enhance diversity in long-tail categories. Validated empirically through the training of Hunyuan3D-2.1-Small, HY3D-Bench democratizes access to robust data resources, aiming to catalyze innovation across 3D perception, robotics, and digital content creation.

High-quality scientific illustrations are crucial for effectively communicating complex scientific and technical concepts, yet their manual creation remains a well-recognized bottleneck in both academia and industry. We present FigureBench, the first large-scale benchmark for generating scientific illustrations from long-form scientific texts. It contains 3,300 high-quality scientific text-figure pairs, covering diverse text-to-illustration tasks from scientific papers, surveys, blogs, and textbooks. Moreover, we propose AutoFigure, the first agentic framework that automatically generates high-quality scientific illustrations based on long-form scientific text. Specifically, before rendering the final result, AutoFigure engages in extensive thinking, recombination, and validation to produce a layout that is both structurally sound and aesthetically refined, outputting a scientific illustration that achieves both structural completeness and aesthetic appeal. Leveraging the high-quality data from FigureBench, we conduct extensive experiments to test the performance of AutoFigure against various baseline methods. The results demonstrate that AutoFigure consistently surpasses all baseline methods, producing publication-ready scientific illustrations. The code, dataset and huggingface space are released in https://github.com/ResearAI/AutoFigure.

Substack 通知用户数据泄漏。数据泄漏事件发生在 2025 年 10 月，但 Substack 直到本周才发现。CEO Chris Best 表示，未经授权的第三方访问了部分用户数据，包括邮箱地址、电话号码和其他内部元数据，信用卡号、密码和财务信息未被访问。Substack 未透露有多少用户受到影响。本周一有黑客在 BreachForums 论坛上泄露了一个 Substack 数据库，包含 697,313 条数据记录。Substack 非常受记者和内容创作者的欢迎，截至 2025 年 3 月有 500 万付费订阅用户。

CIA 宣布停止出版 World Factbook（世界概况或世界各国纪实年鉴），它没有解释原因，可能与特朗普政府削减政府机构的预算有关。World Factbook 是 CIA 的调查报告，发布世界各国及地区的概况，例如人口、地理、政治及经济等各方面的统计数据。CIA 是在 1975 年首次向公众发表该报告的非机密版本，1997 年起开始有线上版本。报告中的统计数据、地图以及图片等内容的之版权皆属于公有领域，任何人都可以无需 CIA 批准而自由引用或转载，只需注明资料来源即可，因此其数据被记者和学者广泛引用。

台积电董事长兼首席执行官（CEO）魏哲家表示，考虑在目前在熊本县建设的第二工厂生产日本国内首批 3 纳米制程最尖端半导体。台积电正在熊本县菊阳町建设第二工厂，原计划生产 6 纳米制程半导体，今后将就变更计划展开磋商。毗邻第二工厂用地的第一工厂目前生产 12 至 28 纳米制程半导体，已于 2024 年 12 月启动量产。

卡特中心宣布，麦地那龙线虫病正接近根除，根据初步统计数据，2025 年全球感染病例仅 10 例。如果能彻底根除，那么它将是天花之后被人类根除的第二种疾病。麦地那龙线虫（Dracunculus medinensis）是一种通过水传播的寄生线虫。如果人饮用了被麦地那龙线虫污染的水，寄生虫会钻入肠道在人体内移动。感染者起初没有症状。大约一年后，母虫会在下肢的皮肤上形成水疱，大约八周后一条意大利面条长度的虫体会从水疱中钻出。除了剧痛之外，麦地那龙线虫病还会导致继发感染和败血症等并发症，造成暂时性或永久性残疾。麦地那龙线虫根除计划于 1986 年启动，当时非洲和亚洲 21 个国家估计有 350 万例病例，2024 年病例数降为 15 例，2025 年的 10 例分别为：乍得 4 例，埃塞俄比亚 4 例，南苏丹 2 例。要彻底根除还必须消灭动物感染病例，2025 年动物感染病例有数百例：乍得（147 例）、马里（17 例）、喀麦隆（445 例）、安哥拉（70 例）、埃塞俄比亚（1 例）和南苏丹（3例）。

Copilot 是微软 AI 战略的核心，但相比 OpenAI 的 ChatGPT 或 Google 的 Gemini，微软面临的一大问题是它无法留住用户，Copilot 的使用体验非常糟糕。微软上周表示它售出了 1500 万个 Microsoft 365 Copilot“席位（seats，即用户）”，而 Microsoft 365 业务的总付费席位逾 4.5 亿，也就是说微软的 Microsoft 365 订户只有 3.3% 会购买 Copilot。该公司去年底表示，其第一方平台上的 Copilot 月活跃用户逾 1.5 亿。Google Gemini 的月活用户数逾 6.5 亿，ChatGPT 周活跃用户约 9 亿。未公开数据显示，Copilot 用户愈来愈倾向于选择竞品。根据市场调研公司 Recon Analytics 对美国超过 15 万名受访者的调查，从去年 7 月到今年 1 月底，将 Copilot 作为首选工具的订户比例从 18.8% 降至 11.5%。选择 Google Gemini 作为首选工具的付费用户比例从 12.8% 升至 15.7%。改用其它工具的前 Copilot 用户称，其它工具质量更好，Copilot 用户表示其使用体验差且使用限制也多。ChatGPT 和 Gemini 用户都比 Copilot 用户有更高的付费意愿。很多公司即使购买了 Copilot 席位，真实使用比例也只有 10%。Copilot 还存在让人困惑的多个不同版本，以及互操作性问题。

流媒体服务的平台独占导致了用户想看的内容分散在不同服务上，订阅所有服务对大部分用户而言是不经济也不可行的，这种状况导致了一站式盗版流媒体服务再次流行起来。美国这个最大的娱乐市场也涌现了基于电视盒的盗版流媒体服务。盗版流媒体的核心是两款中国公司制造的电视盒———SuperBox 和 vSeeBox。电视盒本身没有任何盗版服务，因此可以合法销售，但它们会引导用户下载官方应用商店没有的盗版流媒体应用。vSeeBox 会引导到 Heat，而 SuperBox 引导到 Blue TV。这些应用允许用户观看 6000-8000 个频道，包括付费体育频道和数百个地方电视台。美国公司正在努力打击这些盗版服务。

微软在 1 月 26 日释出紧急更新修复 Office 高危漏洞 CVE-2026-21509，不到 48 小时俄罗斯黑客组织就对补丁进行了逆向工程，开始利用该漏洞发动大规模钓鱼攻击，入侵多个国家的外交、海事和交通机构。安全公司 Trellix 的研究人员发现，钓鱼攻击持续了 72 小时，被称为 APT28 aka Fancy Bear、Sednit、Forest Blizzard 和 Sofacy 的黑客组织向主要位于东欧的 9 个国家发送了至少 29 封恶意电邮。被攻击的国家包括了波兰、斯洛文尼亚、土耳其、希腊、阿联酋、乌克兰、罗马尼亚和玻利维亚，目标组织包括国防部（40%）、运输/物流运营商（35%）和外交机构（25%）。攻击者利用尚未修复的漏洞安装了两种新后门程序 BeardShell 或 NotDoor。BeardShell 主要用于侦察，运行在内存中不会在硬盘上留下痕迹，NotDoor 则是监控电子邮件文件夹的 VBA 宏。

根据发表在《European Journal of Neuroscience》期刊上的一项研究，韩国釜山国立大学医学院的研究人员分析了参加体检的 233 名健康男性的正电子发射断层成像（PET）数据，他们的平均年龄为 43 岁，家庭年均收入为 61,319 美元，平均受教育年限为 13-14 年。研究人员结合家庭收入和教育水平数据发现，高收入家庭的男性大脑尾状核、壳核、前扣带回、海马和杏仁核区域有更高的葡萄糖代谢。这些大脑区域直接或间接参与了大脑奖赏回路。这是一项相关而非因果研究。教育水平与大脑区域代谢模式无关。

微软兑现了承诺，在 Windows 中集成了著名的系统监控工具 Sysmon。Sysmon 是 Sysinternals 套件的一部分，被用于管理和监控 Windows 系统，微软在 2006 年收购了 Sysinternals。直接在操作系统中集成 Sysmon 将简化管理员的工作。该功能已发布在 Windows Insider builds 26300.7733 (Dev channel) 和 26220.7752 (Beta channel)中，默认没有启用，用户需要使用 PowerShell 启用，而在启用前需要先卸载已安装的 Sysmon。

NYT 关注了去年引发广泛热议的被称为“中国版 N 号房”事件的 Telegram 偷拍群组 MaskPark。报道称：借助 Telegram 的匿名性、隐藏摄像头的普及和中国在线支付应用程序的便利性，中国女性和女孩偷拍影像的庞大交易正蓬勃发展。人们分享和交易女友、妻子、亲戚和熟人的照片和视频——这种做法在中文被称为“偷拍出卖”。他们还交换陌生人的此类视频。贩卖者常宣称这些影像来自隐蔽摄像头和手机拍摄，或者是黑入监控系统获取的。其中大量涉及偷拍女性裙底的内容，即所谓的“裙底偷拍”。

因内存和固态硬盘短缺，Valve 将 Steam Machine、Steam Frame 和 Steam Controller 的发售时间从 2026 年第一季度推迟到上半年（实际上就是第二季度）。因 AI 热，内存和固态硬盘供不应求，价格飙升。Valve 表示，不断上涨的零部件价格和有限的供应迫使其重新评估发货计划和定价策略。Valve 此前曾表示，Steam Machine 将定位为入门级 PC。

癌症是全世界疾病和死亡的主因。WHO 国际癌症研究所的研究人员分析了 2022 年 185 个国家 36 种不同癌症的病例数据，纳入了 30 种已知的、可改变的癌症风险因素如吸烟、饮酒和感染，估算了与每种风险因素直接相关的病例比例。研究人员认为逾三分之一新癌症病例是可预防的。2022 年全球新增癌症病例共 1870 万例。其中约 38%（即 710 万例）可归因于可避免因素。吸烟是导致癌症的首因，约占可预防病例的 15%，其次是感染（10%）和饮酒（3%）。肺癌、胃癌和宫颈癌占可预防癌症病例的半数。

天文学家通过凌日现象发现一颗大小十分接近地球的系外行星 HD 137010b。此行星环绕位于天秤座的 K 型恒星 HD 137010 公转，距离地球约 146 光年。恒星 HD 137010 的光谱分类为 K3.5V，估计年龄约 48-100 亿年，磁场活跃程度较弱，为一颗年老且相对平静的主序星。由凌日现象推算，HD 137010b 的行星半径约为地球的 1.06 倍，公转周期约为 355 天，来自母星的辐射能量约为地球的 29%，位置接近宜居带的外缘。若大气组成与地球或火星相近，推测其表面平均温度可能比南极更冷。但若拥有更浓密的大气层，则可能存在液态水，在行星上诞生生命的机会相对较高。

俄罗斯安全公司卡巴斯基发表了 Notepad++ 去年遭受供应链攻击的分析报告。安全研究人员发现，攻击者在 2025 年 7-10 月之间使用了三条感染链攻击十几台电脑，这些电脑属于：越南、萨尔瓦多和澳大利亚的个人；菲律宾的一家政府机构；萨尔瓦多的一家金融机构；越南的一家 IT 服务提供商。攻击者在 2025 年 7 月下旬首次部署了 Notepad++ 的恶意更新程序。恶意程序由合法的 Notepad++ 更新程序 GUP.exe 启动，启动之后会向攻击者发送包含系统信息的“心跳（heartbeat）”包，随后开始执行第二阶段的负荷。

开源办公软件 LibreOffice 项目释出了最新的 v26.2 版本。一大变化是取消了社区版品牌。LibreOffice 26.2 主要变化包括：Writer 改进拼写检查对话框和文档更改跟踪，优化段落首尾对齐等；Calc 支持连接器，支持 xmlMaps.xml，改进排序对话框选项，支持 Excel 2007+ 的 Biff12 贴板格式，性能改进；Base 支持多用户，改进 Chart 的 3D 图表性能，更快的 SVG 图形渲染速度，等等。