OrangeBot.AI Digest — 2026-02-02

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.

Neovim AI agent done right

Termux - a terminal emulator application for Android OS extendible by variety of packages.

Agent Orchestration Command Center

Connect your devices into a secure WireGuard®-based overlay network with SSO, MFA and granular access controls.

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

A fast, single-binary qBittorrent web UI: manage multiple instances, automate torrent workflows, and cross-seed across trackers.

AI agent toolkit: coding agent CLI, unified LLM API, TUI & web UI libraries, Slack bot, vLLM pods

📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG

The best ChatGPT that $100 can buy.

The official source code repository for the calibre ebook manager

Large language models (LLMs) are increasingly used as tool-augmented agents for multi-step decision making, yet training robust tool-using agents remains challenging. Existing methods still require manual intervention, depend on non-verifiable simulated environments, rely exclusively on either supervised fine-tuning (SFT) or reinforcement learning (RL), and struggle with stable long-horizon, multi-turn learning. To address these challenges, we introduce ASTRA, a fully automated end-to-end framework for training tool-augmented language model agents via scalable data synthesis and verifiable reinforcement learning. ASTRA integrates two complementary components. First, a pipeline that leverages the static topology of tool-call graphs synthesizes diverse, structurally grounded trajectories, instilling broad and transferable tool-use competence. Second, an environment synthesis framework that captures the rich, compositional topology of human semantic reasoning converts decomposed question-answer traces into independent, code-executable, and rule-verifiable environments, enabling deterministic multi-turn RL. Based on this method, we develop a unified training methodology that integrates SFT with online RL using trajectory-level rewards to balance task completion and interaction efficiency. Experiments on multiple agentic tool-use benchmarks demonstrate that ASTRA-trained models achieve state-of-the-art performance at comparable scales, approaching closed-source systems while preserving core reasoning ability. We release the full pipelines, environments, and trained models at https://github.com/LianjiaTech/astra.

The NVFP4 lower-precision format, supported in hardware by NVIDIA Blackwell GPUs, promises to allow, for the first time, end-to-end fully-quantized pre-training of massive models such as LLMs. Yet, existing quantized training methods still sacrifice some of the representation capacity of this format in favor of more accurate unbiased quantized gradient estimation by stochastic rounding (SR), losing noticeable accuracy relative to standard FP16 and FP8 training. In this paper, improve the state of the art for quantized training in NVFP4 via a novel unbiased quantization routine for micro-scaled formats, called MS-EDEN, that has more than 2x lower quantization error than SR. We integrate it into a novel fully-NVFP4 quantization scheme for linear layers, called Quartet II. We show analytically that Quartet II achieves consistently better gradient estimation across all major matrix multiplications, both on the forward and on the backward passes. In addition, our proposal synergizes well with recent training improvements aimed specifically at NVFP4. We further validate Quartet II on end-to-end LLM training with up to 1.9B parameters on 38B tokens. We provide kernels for execution on NVIDIA Blackwell GPUs with up to 4.2x speedup over BF16. Our code is available at https://github.com/IST-DASLab/Quartet-II .

Large reasoning models (LRMs) achieve remarkable performance by leveraging reinforcement learning (RL) on reasoning tasks to generate long chain-of-thought (CoT) reasoning. However, this over-optimization often prioritizes compliance, making models vulnerable to harmful prompts. To mitigate this safety degradation, recent approaches rely on external teacher distillation, yet this introduces a distributional discrepancy that degrades native reasoning. We propose ThinkSafe, a self-generated alignment framework that restores safety alignment without external teachers. Our key insight is that while compliance suppresses safety mechanisms, models often retain latent knowledge to identify harm. ThinkSafe unlocks this via lightweight refusal steering, guiding the model to generate in-distribution safety reasoning traces. Fine-tuning on these self-generated responses effectively realigns the model while minimizing distribution shift. Experiments on DeepSeek-R1-Distill and Qwen3 show ThinkSafe significantly improves safety while preserving reasoning proficiency. Notably, it achieves superior safety and comparable reasoning to GRPO, with significantly reduced computational cost. Code, models, and datasets are available at https://github.com/seanie12/ThinkSafe.git.

Reinforcement Learning with Verifiable Rewards (RLVR) has become a cornerstone for unlocking complex reasoning in Large Language Models (LLMs). Yet, scaling up RL is bottlenecked by limited existing verifiable data, where improvements increasingly saturate over prolonged training. To overcome this, we propose Golden Goose, a simple trick to synthesize unlimited RLVR tasks from unverifiable internet text by constructing a multiple-choice question-answering version of the fill-in-the-middle task. Given a source text, we prompt an LLM to identify and mask key reasoning steps, then generate a set of diverse, plausible distractors. This enables us to leverage reasoning-rich unverifiable corpora typically excluded from prior RLVR data construction (e.g., science textbooks) to synthesize GooseReason-0.7M, a large-scale RLVR dataset with over 0.7 million tasks spanning mathematics, programming, and general scientific domains. Empirically, GooseReason effectively revives models saturated on existing RLVR data, yielding robust, sustained gains under continuous RL and achieving new state-of-the-art results for 1.5B and 4B-Instruct models across 15 diverse benchmarks. Finally, we deploy Golden Goose in a real-world setting, synthesizing RLVR tasks from raw FineWeb scrapes for the cybersecurity domain, where no prior RLVR data exists. Training Qwen3-4B-Instruct on the resulting data GooseReason-Cyber sets a new state-of-the-art in cybersecurity, surpassing a 7B domain-specialized model with extensive domain-specific pre-training and post-training. This highlights the potential of automatically scaling up RLVR data by exploiting abundant, reasoning-rich, unverifiable internet text.

Test-Time Training offers a promising way to improve the reasoning ability of large language models (LLMs) by adapting the model using only the test questions. However, existing methods struggle with difficult reasoning problems for two reasons: raw test questions are often too difficult to yield high-quality pseudo-labels, and the limited size of test sets makes continuous online updates prone to instability. To address these limitations, we propose TTCS, a co-evolving test-time training framework. Specifically, TTCS initializes two policies from the same pretrained model: a question synthesizer and a reasoning solver. These policies evolve through iterative optimization: the synthesizer generates progressively challenging question variants conditioned on the test questions, creating a structured curriculum tailored to the solver's current capability, while the solver updates itself using self-consistency rewards computed from multiple sampled responses on both original test and synthetic questions. Crucially, the solver's feedback guides the synthesizer to generate questions aligned with the model's current capability, and the generated question variants in turn stabilize the solver's test-time training. Experiments show that TTCS consistently strengthens the reasoning ability on challenging mathematical benchmarks and transfers to general-domain tasks across different LLM backbones, highlighting a scalable path towards dynamically constructing test-time curricula for self-evolving. Our code and implementation details are available at https://github.com/XMUDeepLIT/TTCS.

State-of-the-art embedding models are increasingly derived from decoder-only Large Language Model (LLM) backbones adapted via contrastive learning. Given the emergence of reasoning models trained via Reinforcement Learning with Verifiable Rewards (RLVR), a natural question arises: do enhanced reasoning translate to superior semantic representations when these models serve as embedding initializations? Contrary to expectation, our evaluation on MTEB and BRIGHT reveals a **null effect**: embedding models initialized from RLVR-tuned backbones yield no consistent performance advantage over their base counterparts when subjected to identical training recipes. To unpack this paradox, we introduce **H**ierarchical **R**epresentation **S**imilarity **A**nalysis (HRSA), a framework that decomposes similarity across representation, geometry, and function levels. HRSA reveals that while RLVR induces irreversible latent manifold's local geometry reorganization and reversible coordinate basis drift, it preserves the global manifold geometry and linear readout. Consequently, subsequent contrastive learning drives strong alignment between base- and reasoning-initialized models, a phenomenon we term **Manifold Realignment**. Empirically, our findings suggest that unlike Supervised Fine-Tuning (SFT), RLVR optimizes trajectories within an existing semantic landscape rather than fundamentally restructuring the landscape itself.

Despite the non-autoregressive potential of diffusion language models (dLLMs), existing decoding strategies demonstrate positional bias, failing to fully unlock the potential of arbitrary generation. In this work, we delve into the inherent spectral characteristics of dLLMs and present the first frequency-domain analysis showing that low-frequency components in hidden states primarily encode global structural information and long-range dependencies, while high-frequency components are responsible for characterizing local details. Based on this observation, we propose FourierSampler, which leverages a frequency-domain sliding window mechanism to dynamically guide the model to achieve a "structure-to-detail" generation. FourierSampler outperforms other inference enhancement strategies on LLADA and SDAR, achieving relative improvements of 20.4% on LLaDA1.5-8B and 16.0% on LLaDA-8B-Instruct. It notably surpasses similarly sized autoregressive models like Llama3.1-8B-Instruct.

Despite rapid advances in autonomous AI scientists powered by language models, generating publication-ready illustrations remains a labor-intensive bottleneck in the research workflow. To lift this burden, we introduce PaperBanana, an agentic framework for automated generation of publication-ready academic illustrations. Powered by state-of-the-art VLMs and image generation models, PaperBanana orchestrates specialized agents to retrieve references, plan content and style, render images, and iteratively refine via self-critique. To rigorously evaluate our framework, we introduce PaperBananaBench, comprising 292 test cases for methodology diagrams curated from NeurIPS 2025 publications, covering diverse research domains and illustration styles. Comprehensive experiments demonstrate that PaperBanana consistently outperforms leading baselines in faithfulness, conciseness, readability, and aesthetics. We further show that our method effectively extends to the generation of high-quality statistical plots. Collectively, PaperBanana paves the way for the automated generation of publication-ready illustrations.

While Chain-of-Thought (CoT) significantly enhances the performance of Large Language Models (LLMs), explicit reasoning chains introduce substantial computational redundancy. Recent latent reasoning methods attempt to mitigate this by compressing reasoning processes into latent space, but often suffer from severe performance degradation due to the lack of appropriate compression guidance. In this study, we propose Rendered CoT-Guided variational Latent Reasoning (ReGuLaR), a simple yet novel latent learning paradigm resolving this issue. Fundamentally, we formulate latent reasoning within the Variational Auto-Encoding (VAE) framework, sampling the current latent reasoning state from the posterior distribution conditioned on previous ones. Specifically, when learning this variational latent reasoning model, we render explicit reasoning chains as images, from which we extract dense visual-semantic representations to regularize the posterior distribution, thereby achieving efficient compression with minimal information loss. Extensive experiments demonstrate that ReGuLaR significantly outperforms existing latent reasoning methods across both computational efficiency and reasoning effectiveness, and even surpasses CoT through multi-modal reasoning, providing a new and insightful solution to latent reasoning. Code: https://github.com/FanmengWang/ReGuLaR.

Reinforcement learning with verifiable rewards has emerged as a powerful paradigm for training intelligent agents. However, existing methods typically employ binary rewards that fail to capture quality differences among trajectories achieving identical outcomes, thereby overlooking potential diversity within the solution space. Inspired by the ``sweet spot'' concept in tennis-the racket's core region that produces optimal hitting effects, we introduce Sweet Spot Learning (SSL), a novel framework that provides differentiated guidance for agent optimization. SSL follows a simple yet effective principle: progressively amplified, tiered rewards guide policies toward the sweet-spot region of the solution space. This principle naturally adapts across diverse tasks: visual perception tasks leverage distance-tiered modeling to reward proximity, while complex reasoning tasks reward incremental progress toward promising solutions. We theoretically demonstrate that SSL preserves optimal solution ordering and enhances the gradient signal-to-noise ratio, thereby fostering more directed optimization. Extensive experiments across GUI perception, short/long-term planning, and complex reasoning tasks show consistent improvements over strong baselines on 12 benchmarks, achieving up to 2.5X sample efficiency gains and effective cross-task transferability. Our work establishes SSL as a general principle for training capable and robust agents.

Character image animation aims to synthesize high-fidelity videos by transferring motion from a driving sequence to a static reference image. Despite recent advancements, existing methods suffer from two fundamental challenges: (1) suboptimal motion injection strategies that lead to a trade-off between identity preservation and motion consistency, manifesting as a "see-saw", and (2) an over-reliance on explicit pose priors (e.g., skeletons), which inadequately capture intricate dynamics and hinder generalization to arbitrary, non-humanoid characters. To address these challenges, we present DreamActor-M2, a universal animation framework that reimagines motion conditioning as an in-context learning problem. Our approach follows a two-stage paradigm. First, we bridge the input modality gap by fusing reference appearance and motion cues into a unified latent space, enabling the model to jointly reason about spatial identity and temporal dynamics by leveraging the generative prior of foundational models. Second, we introduce a self-bootstrapped data synthesis pipeline that curates pseudo cross-identity training pairs, facilitating a seamless transition from pose-dependent control to direct, end-to-end RGB-driven animation. This strategy significantly enhances generalization across diverse characters and motion scenarios. To facilitate comprehensive evaluation, we further introduce AW Bench, a versatile benchmark encompassing a wide spectrum of characters types and motion scenarios. Extensive experiments demonstrate that DreamActor-M2 achieves state-of-the-art performance, delivering superior visual fidelity and robust cross-domain generalization. Project Page: https://grisoon.github.io/DreamActor-M2/

Recent GRPO-based approaches built on flow matching models have shown remarkable improvements in human preference alignment for text-to-image generation. Nevertheless, they still suffer from the sparse reward problem: the terminal reward of the entire denoising trajectory is applied to all intermediate steps, resulting in a mismatch between the global feedback signals and the exact fine-grained contributions at intermediate denoising steps. To address this issue, we introduce DenseGRPO, a novel framework that aligns human preference with dense rewards, which evaluates the fine-grained contribution of each denoising step. Specifically, our approach includes two key components: (1) we propose to predict the step-wise reward gain as dense reward of each denoising step, which applies a reward model on the intermediate clean images via an ODE-based approach. This manner ensures an alignment between feedback signals and the contributions of individual steps, facilitating effective training; and (2) based on the estimated dense rewards, a mismatch drawback between the uniform exploration setting and the time-varying noise intensity in existing GRPO-based methods is revealed, leading to an inappropriate exploration space. Thus, we propose a reward-aware scheme to calibrate the exploration space by adaptively adjusting a timestep-specific stochasticity injection in the SDE sampler, ensuring a suitable exploration space at all timesteps. Extensive experiments on multiple standard benchmarks demonstrate the effectiveness of the proposed DenseGRPO and highlight the critical role of the valid dense rewards in flow matching model alignment.

This work highlights that video world modeling, alongside vision-language pre-training, establishes a fresh and independent foundation for robot learning. Intuitively, video world models provide the ability to imagine the near future by understanding the causality between actions and visual dynamics. Inspired by this, we introduce LingBot-VA, an autoregressive diffusion framework that learns frame prediction and policy execution simultaneously. Our model features three carefully crafted designs: (1) a shared latent space, integrating vision and action tokens, driven by a Mixture-of-Transformers (MoT) architecture, (2) a closed-loop rollout mechanism, allowing for ongoing acquisition of environmental feedback with ground-truth observations, (3) an asynchronous inference pipeline, parallelizing action prediction and motor execution to support efficient control. We evaluate our model on both simulation benchmarks and real-world scenarios, where it shows significant promise in long-horizon manipulation, data efficiency in post-training, and strong generalizability to novel configurations. The code and model are made publicly available to facilitate the community.

We present RM-RF, a lightweight reward model for run-free evaluation of automatically generated unit tests. Instead of repeatedly compiling and executing candidate tests, RM-RF predicts - from source and test code alone - three execution-derived signals: (1) whether the augmented test suite compiles and runs successfully, (2) whether the generated test cases increase code coverage, and (3) whether the generated test cases improve the mutation kill rate. To train and evaluate RM-RF we assemble a multilingual dataset (Java, Python, Go) of focal files, test files, and candidate test additions labeled by an execution-based pipeline, and we release an associated dataset and methodology for comparative evaluation. We tested multiple model families and tuning regimes (zero-shot, full fine-tuning, and PEFT via LoRA), achieving an average F1 of 0.69 across the three targets. Compared to conventional compile-and-run instruments, RM-RF provides substantially lower latency and infrastructure cost while delivering competitive predictive fidelity, enabling fast, scalable feedback for large-scale test generation and RL-based code optimization.

Large Language Models (LLMs) are typically evaluated for safety under single-shot or low-budget adversarial prompting, which underestimates real-world risk. In practice, attackers can exploit large-scale parallel sampling to repeatedly probe a model until a harmful response is produced. While recent work shows that attack success increases with repeated sampling, principled methods for predicting large-scale adversarial risk remain limited. We propose a scaling-aware Best-of-N estimation of risk, SABER, for modeling jailbreak vulnerability under Best-of-N sampling. We model sample-level success probabilities using a Beta distribution, the conjugate prior of the Bernoulli distribution, and derive an analytic scaling law that enables reliable extrapolation of large-N attack success rates from small-budget measurements. Using only n=100 samples, our anchored estimator predicts ASR@1000 with a mean absolute error of 1.66, compared to 12.04 for the baseline, which is an 86.2% reduction in estimation error. Our results reveal heterogeneous risk scaling profiles and show that models appearing robust under standard evaluation can experience rapid nonlinear risk amplification under parallel adversarial pressure. This work provides a low-cost, scalable methodology for realistic LLM safety assessment. We will release our code and evaluation scripts upon publication to future research.

几天前才出现的太阳黑子区域 AR4366 在 24 小时内释放出 17 个 M 级耀斑和 3 个 X 级耀斑，其中包括一个 X8.11 级耀斑。这是过去二十年最强耀斑之一，是当前 25 太阳周期的第三强的耀斑。太阳目前处于活跃期，AR4366 非常不稳定，意味着会爆发更多高强度耀斑。

日本反盗版组织 CODA(文化产品海外流通促进机构) 宣布，上海警方去年 11 月拘留了一名广西男子，该男子被控运营了最大的动漫盗版网站 BATO.TO。BATO.TO 不只是一个网站，它包含了 xbato.com、bato.to 和 mangapark.io 等 60 个网站。这名男子已获释，他承认运营了这些网站，未来将面临正式诉讼。警方已扣押该男子的电脑，还在继续调查，分析服务器确定更多运营者身份。在该男子拘留之后，BATO 网站仍然继续运营了一段时间，直到 1 月 19 日全部关闭。被侵权的日本出版商包括了角川集团、讲谈社、集英社、小学馆和史克威尔艾尼克斯。CODA 北京办事处应这些出版商要求向公安局提起刑事诉讼。它还寻求了腾讯旗下公司的合作。BATO 旗下网站的月访问量达到 3.5 亿次，从 2022 年 10 月到 2025 年 10 月总访问量 72 亿次。

Blue Origin 宣布暂停 New Shepard 项目两年，但此举可能意味着其亚轨道太空旅游的永久终结。New Shepard 火箭和太空船自 2015 年投入使用至今共完成了 38 次发射，除一次外全部成功，将 98 人送入太空体验亚轨道飞行。为何 Blue Origin 要终止其成立至今持续时间最长的项目？CEO Dave Limp 表示要将人力和资源投入到载人登月项目上。Blue Origin 员工对此举也颇感意外，因为上一次亚轨道飞行是在 8 天前将六人送入太空，该公司还有 4 枚处于不同阶段的 New Shepard 火箭以及两艘正在建造中的太空船，它还在去年讨论过扩展发射场。然而该项目一直处于亏损状态，有逾 500 名员工投入在该项目，分散了其精力和资源。

2025 年 10 月比特币创下了 123,742 美元的记录，但四个月后跌至 76000 美元，币值从峰值下跌了四成。彭博认为这一波跌势不是出于恐慌而是买家、动能和信心的缺失引起的。下跌没有明显的导火索，纯粹是需求减弱、流动性减少，其价值与更广泛的市场无关联。即使最近几周黄金白银价格剧烈波动，加密货币也未出现任何震荡。比特币 1 月下跌近 11%，连续第四个月下跌——这是自 2018 年以来最长的连跌纪录。社交媒体上也对止跌缺乏乐观情绪。主流买家的信心正在减弱，许多买家在高价买入后都处于亏损状态。

根据发表在《Journal of Sport and Health Science》期刊上的一项研究，坚持规律的有氧运动有助于保持大脑年轻。研究显示，坚持一整年有氧运动的成年人大脑看起来比那些没有改变活动习惯的人年轻近一岁。研究使用 MRI 扫描结果估算大脑的生物学年龄。130 名年龄在 26-58 岁之间的健康成年人参与了研究。参与者被随机分配到中等至高强度有氧运动组或常规对照组。运动组的参与者每周在实验室完成两次 60 分钟的监督锻炼，在家中进行额外的锻炼以达到每周约 150 分钟的有氧运动量。研究人员在研究开始时和 12 个月后分别使用 MRI 扫描测量了大脑结构，通过峰值摄氧量（VO2peak）评估了心肺功能。一年后两组有明显差异：运动组参与者大脑年龄显著下降，对照组大脑年龄略有上升。平均而言运动组大脑年龄下降了约 0.6 岁，对照组大脑年龄增加了约 0.35 岁——但该结果不具有统计显著性。两组的大脑年龄相差了一岁。

中国计划 2028 年至 2029 年间择机向日地 L5 点发射“羲和二号”。羲和是《山海经》中的太阳之母，是《楚辞》中驾车控制太阳东升西落的神，也是中国古代观测天象与制定历法的官职。2021 年 10 月，我国成功发射首颗太阳探测科学技术试验卫星“羲和号”，正式步入空间探日时代。近 5 年后，“羲和二号”正式启动。“羲和号”环绕地球运行，“羲和二号”则不是。“羲和号”科学与应用系统总设计师、南京大学天文与空间科学学院教授李川介绍，太阳和地球有 5 处引力平衡点，L1、L2、L3 在日地连线上，L4、L5 则在地球环绕太阳运行的轨道上，各自与太阳、地球构成边长约 1.5 亿公里的等边三角形，如果将地球公转方向视作“前方”，L5 在地球的“身后”。“截至目前，人类发射的太阳探测器已有70多颗，绝大多数分布在日地连线上，少数环绕太阳运行，还没有探测器在日地 L5 点驻留。因此，‘羲和二号’将给人类研究太阳提供一个全新的‘旁观者’视角。”李川说，身处引力平衡点，“羲和二号”无需消耗过多能量就能维持轨道稳定，设计寿命长达 7 年。

苹果能为上市超过 10 年的设备释出安全更新，Android 生态系统呢？Android 有 AI 新贵英伟达。Android 厂商中只有三星和 Google 对其旗舰设备提供七年的软件更新，但英伟达对支持 10 年前发布的电视盒 Shield TV 的热情仍然高涨。英伟达在 2015 年发布了第一代 Shield，主打游戏功能；2017 年和 2019 年发布的新版本则更侧重于流媒体服务。它不便宜，起售价高达 200 美元，在电视盒中属于高端产品，它至今仍然在为 2015 年的产品提供操作系统和安全更新，运行的 Android 版本从 5.0 一直升级到了 Android 11，没有其它 Android 设备能提供如此长时间的全面支持。英伟达仍然在生产 2019 款的 Shield，因为消费者仍然在购买它。

Wiki Education 是一个致力于招募维基百科新编辑的组织，贡献了英文维基百科约 19% 的新活跃编辑，该组织分享了 ChatGPT 自 2022 年 11 月发布以来生成式 AI 对内容编辑的影响。Wiki Education 支持的大部分新编辑没有使用生成式 AI，以 2025 年秋季为例，6357 位新编辑中只有 217 位（占 3%）收到了多次使用 AI 编辑内容的警告。Wiki Education 表示它的原则是“维基百科编辑绝不应将 ChatGPT 等生成式 AI 聊天机器人的输出复制粘贴到维基百科条目中”。在 ChatGPT 发布前它没有观察到使用 AI 编辑的迹象，自 ChatGPT 发布后 AI 使用率开始逐年增加。它使用 Pangram 检测 AI 生成内容，对标记为 AI 生成的文章的分析显示，只有 7% 的文章包含有虚假的引用来源，但信息源存在并不意味着 AI 的引用是正确的，大模型的统计逻辑经常会导致张冠李戴似是而非，这种错误更难识别出来，分析显示逾三分之二的 AI 文章包含此类错误。

根据国家统计局上个月公布的数据，2025 年中国全国企业就业人员每周平均工时为 48.6 小时，在连续上涨 9 年后，终于小幅回落。国家统计局数据显示，中国劳工工时自 2016 年起不断上涨，在 2023 和 2024 年达新高，每周平均工时达 49 小时。2025 年除了 1 月其余 11 个月每周平均工时都低于 2024 年同期。中国人民大学中国就业研究所所长曾湘泉表示，2025 年企业就业人员每周平均工时下降，一方面可能是工时吸收经济压力的调节机制接近极限—工时不可能不断上涨；另一方面，官方“反内卷”和劳动者权益保障力度持续加强，也带来积极影响。曾湘泉表示，在网约车司机、外卖骑手等职业的劳动时长屡屡突破生理极限的同时，电力、燃气等垄断型行业的员工极少出现工时过长的情况。他还指出，如果将工资因素考虑在内，较长的工时可能反映了就业质量的低下；例如，许多低收入劳动者虽然每周工作高达 50—60 小时，但收入仍难以满足基本生活需求；这说明此类岗位普遍缺乏足够的劳动保护和福利支持，劳动者只能通过延长工作时间弥补工资的不足，也就是被迫“以时间换收入”。

European Open Source Academy 的 2025 年开源卓越奖得主、cURL 维护者 Daniel Stenberg 宣布了 2026 年的开源卓越奖得主、稳定版 Linux 内核维护者 Greg Kroah-Hartman。他表示：很难夸大 Greg 在 Linux 上的工作的重要性。在软件领域，创新总能抢占头条，但稳定性却默默守护着生命和生计。每一部 Android 手机、每一台 Web 服务器、每一个运行 Linux 的关键系统，都依赖于 Greg 精益求精的工作。正因为他的努力，医院、银行、政府和个人在使用 Linux 时，才能安心无忧。他的工作代表着最高形式的服务：不求浮华，坚持不懈，却不可或缺。

CERN 获得 10 亿美元私人捐赠用于建造未来环形对撞机（Future Circular Collider，FCC），这是该机构 72 年历史上首次有私人和慈善基金支持其大型项目。FCC 有望成为大强子对撞机（LHC）的继任者。FCC 设计建造一条长约 90.7 公里的巨型隧道——其长度三倍于 LHC——平均深度约为地下 200 米。FCC 方案是 CERN 下一代对撞机的首选方案，将于 2026 年 5 月递交给 CERN 理事会，如果理事会在 2028 年批准该方案，那么 FCC 电子正电子对撞机（FCC-ee）的建造将于 2030 年启动，2047 年投入运行。

负责评选诺贝尔和平奖的挪威诺贝尔研究所在安全部门帮助下完成了内部调查，证实遭到了黑客入侵。2025 年诺贝尔和平奖得主、委内瑞拉反对派领导人 Maria Corina Machado 的名字提前泄露可能是黑客攻击所致。在去年 10 月 Machado 的名字公开前几小时，预测平台 Polymarket 上有关她的投注激增，她此前并不被认为是和平奖的热门人选。

GNU 国际化与本地化函数库 gettext 在历经逾 30 年开发之后终于释出了具有象征意义的 1.0 版本。gettext 主要优势是将编程和翻译分开。GNU gettext 1.0 的主要变化包括：改进 PO(Portable Object)文件处理，新 po-fetch 程序从网上的翻译项目提取已翻译的 PO 文件，新预翻译程序 msgpre 和 spit，改进 Ocaml 和 Rust语言支持；等等。msgpre 和 spit 可通过本地安装的大模型去实现机器翻译，msgpre 应用于整个 PO 文件，而 spit 则是单则信息。

以隐私为卖点的搜索引擎 DuckDuckGo 调查了其用户对 AI 功能的态度，结果显示用户压倒性多数的反对 AI：在 175,354 名参与投票的用户中，九成用户表示不希望使用 AI。DuckDuckGo 为此推出了两个版本：反 AI 用户可选择访问 noai.duckduckgo.com，想要 AI 功能的用户可访问 yesai.duckduckgo.com。用户还可以在主站设置中禁用 AI 摘要、AI 生成图像以及 Duck.ai 聊天机器人。

YouTube 证实调整了平台体验，阻止非付费用户访问后台播放功能。在确认前，三星浏览器 Samsung Internet、Brave、Vivaldi 甚至 Microsoft Edge 用户通过社交网络报告后台视频播放功能失效。后台视频播放对移动用户而言是一项有用且方便的功能，用户可以在关闭手机屏幕或最小化浏览器窗口的情况下听视频的声音，YouTube 上有很多适合听的内容，如音乐和播客。但从一周前开始，用户报告关闭手机屏幕或最小化浏览器窗口后音频播放停止了。YouTube 官方证实这是有意为之，称后台播放功能只提供给付费订阅用户。