OrangeBot.AI Digest — 2026-10-01

A smoke alarm that detects fire, not toast.

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A fast, native Jira client for Mac

The memory layer for your business

The help center I built after Intercom's search broke

AI agents that fix production before you wake up

The 2FA app you actually enjoy using

Say it once and your AI assistant plans the rest

Start, watch and answer AI coding CLIs from one desktop app

Share what your agents make, get feedback on the exact line

The PHP framework your AI agent can actually inspect

Team document collab with your terminal harness (cc, codex)

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A small, fast code editor written in assembly

Catch API keys the moment they appear, encrypted locally

Self-evolving search agents build their own training curricula by jointly optimizing a proposer that generates questions and a solver that answers them. This closed loop introduces a failure mode we call co-cheating: the proposer and solver increasingly agree on shared errors, so internal reward improves without a matching gain in external correctness. A post-hoc audit against source evidence shows co-cheating growing more severe over successive rounds of self-evolution, with pseudo-label correctness stagnating or declining even as the in-loop training signal improves. The most direct mitigation is to verify proposals before training: we introduce multi-sample verification (MSV), which queries the same model three times with the source and three times without it to decide task admission and replace unreliable pseudo-labels. MSV partially reduces false agreement but leaves substantial residual co-cheating and costs six extra labeler generations per candidate. These limitations motivate CrossFit, our main method: it partitions the proposer's source documents into groups A and B; questions generated from A are scored by an auxiliary solver trained only on B, and vice versa. The cross-fitted agreement determines proposer reward, so a same-source pseudo-label cannot be reproduced through the feedback solver, while the original solver's update rule is unchanged. Rerunning the loop with Qwen3.5-4B and Qwen3.5-9B, MSV reduces false-agreement mass from 6.1% to 5.7% and from 8.8% to 7.2%, whereas CrossFit reduces it to 3.0% and 3.7%. Replaying identical proposals with source-excluded feedback further reduces false agreement to 0.4% and 0.1%, isolating feedback ancestry from curriculum changes. Across seven downstream search benchmarks, CrossFit improves average performance over standard coupled self-evolution by 8.8 and 8.4 points and over Search-R1 by 8.7 and 7.8 points at 4B and 9B.

Latent visual reasoning (LVR) enables multimodal large language models (MLLMs) to perform intermediate computation in continuous latent tokens rather than expressing every reasoning step in words. However, unlike textual CoT, latent reasoning is not directly observable, making it difficult to supervise what latent tokens learn. In this work, we first conduct a thorough analysis of latent-token behavior and identify a latent evidence-credit gap: latent tokens respond only weakly to image perturbations that alter the correct answer. We hypothesize that this issue stems from the lack of explicit supervision during GRPO training. These findings suggest that a final-answer reward provides too little guidance on what visual evidence to preserve or how credit should be assigned across latent tokens. To bridge this gap, we propose ReaLVR, which brings visual-evidence supervision to the model's own free-running latent trajectories. ReaLVR contrasts correct and model-generated wrong answers to determine where stronger supervision is needed, and relevant and mismatched visual evidence to specify what to preserve. Across three model families, ReaLVR consistently outperforms evaluated LVR baselines, achieving the highest five-task average of 63.7% on Qwen2.5-VL-7B. Crucially, we are the first to scale visual reasoning in latent space, showing that our framework continues to deliver robust improvements at frontier model scales up to 235B. Further analyses show more question-sensitive latent-token positions, stronger alignment with relevant visual regions, and greater fixed-context dependence on the most attended latent tokens.

Terminal agents act through stochastic model generations, yet the ability to generate a useful action does not ensure its reliable execution. A poor command (e.g., wrong package install) can change the environment in ways that hinder subsequent progress, even when the model could generate a better alternative. We investigate whether allocating test-time compute at the model-harness boundary can improve action reliability and trajectory success, and what makes this allocation effective. To study these questions, we introduce Mid-Harness, which samples and verifies candidate actions before forwarding one for execution, while keeping the generator and harness unchanged. With a TMAX-9B generator, more action sampling yields little benefit under weak verification, whereas a capable verifier can exploit useful alternatives from the same generator. On TerminalBench-Lite, a GPT-5.6 Sol verifier raises Pass@1 from 50.00% for the base agent to 68.03% with 8 sampled actions. When the same TMAX-9B model serves as the verifier, pairwise verification performs best among the evaluated verification mechanisms. Distilling responses from the stronger verifier into TMAX-9B further improves Pass@1, while leaving the action generator unchanged. With TMAX-9B on TerminalBench-Lite, combining action and trajectory scaling reaches higher success at lower estimated token cost than generating more trajectories alone. Mid-Harness also improves performance across additional models, benchmarks, and harnesses. These findings identify action scaling as a promising target for test-time compute scaling in terminal agents.

Large language models (LLMs) are increasingly deployed as agents for multi-step decision-making, yet transfer poorly to unseen environments. World-model methods address this by training agents to predict future observations, at the cost of additional training and errors that compound when predictions are used for planning. However, for LLM agents operating in digital environments, much of this world knowledge is already internalized during pretraining, which shifts the problem from acquiring it to eliciting it. We argue that typical post-training provides little pressure for such elicitation, since supervision under a single goal at each visited state inadvertently drives policies to rely on superficial contextual habits. We introduce EVOKE, a post-training method that supplies this pressure through goal diversity at fixed states. Motivated by theory showing that an agent competent across diverse goals must encode a world model recoverable from its action preferences, EVOKE holds the environment state and interaction history fixed and ranks the same candidate actions under alternative goals, forcing action preferences to change, so that a policy relying on contextual habits or single-goal correlations cannot order them correctly. This implicitly elicits the policy's pretrained world knowledge to inform decisions. We evaluate EVOKE across diverse tasks in three backbones, demonstrating improved task performance, unseen environment generalization, and data efficiency. We further conduct controlled analyses to better understand what drives these gains. These findings offer a new perspective on eliciting internalized world knowledge for transferable action through direct decision supervision.

Masked diffusion language models (MDMs) admit flexible generation orders, making the unmasking strategy an inference decision. Existing methods vary in how they prioritize positions, control parallelism, restrict selection regions, revise predictions, or plan future denoising, yet it remains unclear when these choices should change during generation. We study this question through strategy reversals, where an alternative action becomes preferable to a fixed choice. We organize MDM inference into five axes--score, cardinality, region, commitment, and planning--and define adaptation opportunity as the one-step utility advantage of the best candidate action over a validation-selected fixed action. This view shows that adaptation value depends on both the frequency and magnitude of such reversals. Across three MDMs and ten tasks, adaptation opportunities are highly heterogeneous, with some regimes exhibiting concentrated and predictable one-step gains. This motivates selective adaptation: lightweight detectors calibrated on validation prompts identify high-opportunity states, capturing, for example, 56.9 percent of the candidate-set oracle opportunity by adapting only the top 10 percent of states on LLaDA-8B constrained JSON filling. Our transition-level results suggest that state adaptation is most useful when applied selectively rather than uniformly.

Language models can now prove theorems, but people still decide which problems to pursue. We ask whether a model's internal representations can help identify promising mathematical connections. We develop LANTERN, a fast, cost-efficient pipeline that uses a classifier over pretrained-model activations to rank candidate relations, followed by staged filtering, hypothesis generation, executable verification, and analytical checking. Applied to the On-Line Encyclopedia of Integer Sequences (OEIS), LANTERN ranked 50 million pairs among 10,000 frequently referenced sequences and produced 62 verified relations between pairs without an existing OEIS cross-reference. A content screen retained 13 relations worth presenting; nine of these are informative or insightful, including four which are entirely novel to the best of our knowledge: none appears in the OEIS or in our targeted literature search. The entire end-to-end process including classifier training, candidate ranking, filtering and verification took under 8 hours.

Vision-language models (VLMs) are increasingly used in place of human annotators, making it important that substitutability tests reflect the model rather than incidental evaluation conditions. We introduce MIST, the Misleading-Image Stress Test: 200 English sentences, each built around a phrase readable either figuratively or literally and shown with an aligned image depicting its reading, a misleading image depicting the opposite, or no image at all. The guidelines require the label to be decided from the sentence alone, so no image should change any answer. We expected each image to pull a judge's labels toward the sense it depicts, and neither kind did. Across thirteen VLM judges, an aligned image changed 20.5% of labels and a misleading one 19.4%, close for every judge and both above the 11.6% produced by deleting the ignore-the-image instruction with the image left in place. Yet only 37% of the labels that differ between the two images moved toward the sense shown, and agreement with our human annotators is unchanged whether the image is absent, aligned or misleading. The effect is smaller in the seven judges that pass the alt-test than in the six that never do, but present in all of them: what moves a judge is that an image is there, not which of the two it is, so a substitutability verdict describes a configuration as much as a model.

Reconstructing dynamic driving scenes from recorded sensor data supports closed-loop evaluation of autonomous driving systems by synthesizing observations beyond the original trajectory. Unlike cameras and LiDAR, radar measures radial velocity directly through Doppler. Yet existing radar novel-view synthesis fails to exploit this capability: methods addressing dynamic scenes reconstruct only range-azimuth tensors, while methods that render Doppler assume static scenes. Moreover, because radar processing spreads each reflection across multiple bins, existing representations absorb this spread into scene geometry, causing it to render incorrectly when the viewpoint moves. We present DyRAD, which models dynamic driving scenes using static background reflectors and motion-tracked dynamic point reflectors to render complete range-azimuth-Doppler (RAD) tensors. Reflector velocities are derived from object tracks and projected onto the line of sight, making Doppler both a rendered output and supervision for those tracks. Crucially, we render reflectors through a fixed analytic point-spread function (PSF) derived from the radar's signal-processing chain, preventing sensor-induced spread from being baked into the scene representation. Beyond improving scene reconstruction, this separation also enables zero-shot sensor-configuration transfer, allowing the same reconstructed scene to be rendered under different radar specifications without refitting. We evaluate DyRAD on RADIal, Boreas, and a synthetic benchmark across both on-path poses and displaced viewpoints untested by prior work. On RADIal, DyRAD recovers radar detections in 90.7% of reference-detected objects, compared with 26.9% for the strongest baseline.

Long-form subtitle translation requires reasoning over discourse and cultural context spanning episodes or entire series, while maintaining consistent terminology and style. Existing single-LLM methods are largely sentence-level, and multi-agent systems often use static workflows that do not adapt to scene complexity or production context. We propose SMART, a Self-evolving Multi-Agent system for long-foRm subtitle Translation. During test-time training, SMART builds persistent series-level memory and translates a subset of sentences through a dynamic router and Mixture-of-Agents layer with tools for terminology verification, subtitle constraint validation, and contextual retrieval. A judge-refiner loop scores candidates and uses textual critiques to update agent prompts and routing policies without retraining the underlying LLMs. During test-time inference, the evolved configuration translates the remaining series. We also introduce Subtitle Arena, covering 14 genres, 2--198 episodes per series, production years 1959--2023, and 15 target locales, together with SubMQM, a subtitle-adapted MQM framework with seven dimensions and 19 error categories. SMART achieves the best overall MQM score in all 15 Subtitle Arena directions, reducing average penalty by 6.9% over the strongest competing agent system. On the public MuSC benchmark, SMART obtains the best model result across all four language pairs and also achieves the best human-evaluation result, with an overall score of 4.50/5.

Recent advances in musculoskeletal modeling and reinforcement learning have enabled muscle-actuated agents to reproduce increasingly complex human motions. Yet these capabilities remain largely confined to flat ground, in part because motion datasets rarely include aligned terrain geometry and because retargeting terrain interactions to complex musculoskeletal bodies is challenging. We present TERRA, an end-to-end pipeline for terrain-aware retargeting and control of musculoskeletal locomotion. From kinematic trajectories alone, TERRA combines terrain priors, estimated contacts, and negative free-space evidence to recover task-relevant support geometry. TERRA further considers anatomical, tendon-continuity, and contact constraints during retargeting. Using the resulting motion-terrain pairs from five datasets, we successfully train a single muscle-actuated control policy on 9.4 hours of diverse locomotion. Across reconstruction, retargeting, and held-out tracking benchmarks, TERRA improves terrain accuracy, sharply reduces anatomical and interaction violations, and achieves the highest observed completion rate over supported terrain families. Overall, TERRA provides a practical route from scene-less motion data to muscle-actuated locomotion over diverse non-flat terrain. Project website: https://cnai.epfl.ch/terra/

We introduce LoopVL to study whether Loop Transformers can be effectively extended to vision- language models. LoopVL combines Module-Loop and Model-Loop computation to iteratively update a unified vision-language state through shared modules. We train LoopVL from scratch through language pre-training, multimodal training, and post-training. LoopVL outperforms a range of similarly sized and larger non-recurrent models on multimodal understanding and visual reasoning benchmarks. We also observe Visual Aha Moments in LoopVL, characterized by pronounced shifts in visual attention across loops. LoopVL provides practical evidence for recurrent vision-language modeling and offers an intuitive perspective on how shared parameters can support deeper multimodal computation over continuously evolving visual-language states.

Scientific software presents a demanding test for computer-using agents based on visual language models (VLMs): completing a research workflow requires interpreting specialized interfaces, manipulating scientific objects, and producing verifiable results. We thus introduce OSWorld-Science, a benchmark and evaluation environment that combines scientifically meaningful tasks, artifact-based evaluation, and an efficient agent harness for studying computer use in the scientific domain. The benchmark contains 12 VLMs and 146 high-quality tasks across several scientific domains and software configurations, covering workflows such as molecular drawing and retrosynthesis, pathology image analysis, statistical computing, and physical simulation. Tasks are developed through expert proposals and iterative human--AI co-design, with selection guided by scientific value and difficulty. Task-specific execution-based evaluators inspect application states and generated artifacts, including molecular structures, segmentation masks, plots, and numerical results, and award partial credit for incomplete outcomes. Our special harness integrates model adapters, interaction-loop control, and trajectory logging to support comparisons of models and interaction strategies. Our results show that current state-of-the-art VLMs with a strong harness still face challenges in addressing key questions in the scientific domains. We also analyze the benchmarking results across multi-linguistics, reasoning efforts, context length and other factors and derive several important conclusions and directions to assist future development. Overall, we provide an integrated framework connecting expert-defined scientific goals to verifiable software outcomes, enabling systematic evaluation of both agent capabilities and harness design in scientific workflows.

Reward models score responses from large language models (LLMs) and guide LLM training toward human preferences. However, reward models can favor superficial attributes such as length or confidence, leading LLMs to produce higher-scoring but not more correct responses. Existing mitigation methods either retrain the reward model or apply a fixed correction to one known bias, such as a preference for longer responses. Retraining requires additional data and computational resources, while existing editing methods require the target bias to be specified in advance and use a fixed edit for that bias. To this end, we propose BiasReducer, a lightweight framework that edits only the linear reward head and selects the relevant edits for each new dataset. First, BiasReducer uses a sparse autoencoder (SAE)-style encoder to learn which attributes (e.g., length and confidence) the reward model is sensitive to. Second, it learns how to reduce the reward model's dependence on each attribute by determining which direction to adjust the reward head and how much to adjust it. Third, for a new dataset, it ranks the attributes by their influence on reward scores, selects the relevant ones, and edits the reward model accordingly. BiasReducer consistently improves reward-model robustness to biases toward superficial response attributes. Across five reward models, BiasReducer-M improves the three benchmarks by 8.3, 18.0, and 6.9 percentage points on average, outperforming the two training-based baselines. The gains transfer downstream, reducing unnecessary verbosity and sycophancy while maintaining comparable judged quality.

Modern agentic systems combine an AI model with a harness that controls execution and environmental interactions. Harness design strongly affects long-horizon performance, yet its combinatorial search space demands substantial human effort that must be repeated as models change. Existing automated methods explore this space narrowly, optimizing only components such as prompts or skills or becoming trapped by fixed, exploitative search strategies. We introduce MILO (Meta-evolutionary Island Orchestration), a framework that co-evolves agent harnesses and the strategy used to discover them. MILO combines: (i) hierarchical lineage memory over island-based trees, using rejected mutations as negative evidence; (ii) per-island mutator agents that rewrite complete harnesses using global search history and parent-specific feedback; and (iii) an orchestrator that adapts search through lineage grafting and speciation, mutator reassignment and curriculum revision. Across Terminal-Bench 2.1, PaperBench, and DeepSWE, MILO-discovered harnesses outperform eight state-of-the-art harnesses and six search methods using frontier (Opus 4.8) and open-weight (gpt-oss-120b) models. With Opus 4.8, MILO improves resolution over its initial harness by +12.0%, +28.3%, and +10.3%, respectively, compared with best prior-search gains of +4.5%, +18.3%, and 0%. On Terminal-Bench 2.1, it achieves 86.1 pm 2.0%, exceeding the official leaderboard's top entry (83.8 pm 2.3%) while using 26\% fewer tokens than its initial harness. On EinsteinArena open problems, MILO improves best-known upper bounds for Erdős minimum-overlap (0.3808586 to 0.3808568) and the first and third autocorrelation inequalities (1.50274365 to 1.50274360; 1.45081 to 1.44889).

Looped transformers and Mixture-of-Experts (MoE) offer complementary routes to efficient scaling: recurrence increases computational depth at fixed parameters, while MoE sparsity expands total capacity at fixed active compute. Yet existing scaling laws model recurrence or sparsity in isolation. In this work, we introduce Loop Scaling Laws, the first scaling law to jointly model recurrence and sparsity alongside model size and data. At its core is a bounded, sparsity-conditional recurrence mapping that characterizes the effective-parameter gain from looping and how sparsity raises this gain. The laws predict the held-out loss of looped models more accurately than prior alternatives, and recover the standard dense and MoE scaling laws as special cases. Beyond prediction, the fitted laws provide a principled foundation for designing looped MoE models under compute and memory constraints. Downstream evaluations further demonstrate the complementary benefits of the two axes: sparsity delivers ~3x active-parameter efficiency, recurrence yields ~2x total-parameter efficiency on reasoning, and joint scaling further advances the performance frontier. As a practical extension, we show these gains hold at trillion-token scale: at matched training compute, a looped MoE with law-derived recurrence matches a ~2x larger non-looped MoE on the reasoning benchmarks, while enabling test-time scaling through recurrence.

Rachel Metz / Bloomberg : Source: some of the information that the three OpenAI employees allegedly mishandled pertained to OpenAI's infrastructure architecture —  OpenAI has parted ways with three employees for violating company policies on how private information should be handled, including for allegedly sharing it with an outside group.

Daniel Wiessner / Reuters : Lyft agrees to pay $272.5M to settle California claims that it mislabeled drivers as independent contractors rather than employees between 2016 and 2020 —  Lyft (LYFT.O) has agreed to pay $272.5 million to settle claims by the state of California and three of its largest cities …

Sarah Perez / TechCrunch : OpenAI launches two ChatGPT shopping features: a virtual try-on tool for clothing and accessories, and a Favorites feature to save products to a user's Library —  OpenAI is again experimenting with how its conversational AI assistant, ChatGPT, can help users as they shop online.

Martin Austermuhle / The Washington Sun : DoorDash pulls support for a GOP bill that would have limited DC's ability to write its own tax laws after widespread calls for locals to boycott the service —  The company still wants the D.C. Council to make changes to a new delivery fee taking effect Thursday.  — Copy

Josh Butler / The Guardian : Anthropic urges Australia to consider “conditional approval” for Big Tech to train models on copyrighted works, giving copyright holders a choice to opt out —  Maker of Claude claims AI could transform economy but ABC and SBS say the technology should be subject to media regulations

The Information : SoftBank made the final $10B investment in its $30B pledge to OpenAI's last funding round; source: Nvidia also made its final $10B investment in the round —  Nvidia and SoftBank have each made the final $10 billion investment in each of their $30 billion pledges to OpenAI's last funding round …

Mike Scarcella / Reuters : A US judge dismisses Chegg and Penske lawsuits alleging Google violated antitrust law by forcing them to allow content in AI Overviews, reducing web traffic —  Alphabet's Google has persuaded a US federal judge to dismiss lawsuits from education technology company Chegg …

Carl Franzen / VentureBeat : Strands Labs, AWS's experimental agent-development project, unveils Strands Decider 2B, a free, open-source Jev competitor fine-tuned from an Alibaba Qwen base —  The race to make AI agents cheaper and faster is moving beyond the models that write their answers.

Bailey Lipschultz / Bloomberg : Sources: Anthropic could start formal marketing for its IPO as soon as the week of November 9, putting it in line to begin trading before Thanksgiving —  Anthropic PBC is seeking to go public as soon as the middle of November, according to people familiar with the matter …

Jay Peters / The Verge : Sony launches Quick Spectral Super Resolution, a “new performance tier of AI upscaling” for the standard PS5 developed in collaboration with AMD —  Sony's new Quick Spectral Super Resolution AI upscaling is coming first to Marvel's Wolverine and Ghost of Y&omacrtei.

Sophie Austin / Associated Press : Gavin Newsom vetoes a CA bill banning secret recording with smart glasses in private places, saying its definition of wearable recording devices was too broad —  California Gov. Gavin Newsom vetoed legislation Wednesday that would have penalized Californians for using smart glasses …

Wall Street Journal : OpenAI says it “parted ways” with three researchers for violating its “handling sensitive” info policies; sources: they shared it with an AI safety organization —  OpenAI has parted ways with three researchers for allegedly sharing confidential company information …

Tom Warren / The Verge : Memo: Ryan Roslansky, who was promoted from LinkedIn CEO to the head of Office and Teams in 2025, is leaving, triggering another Microsoft executive reshuffle —  Ryan Roslansky is leaving after nearly 18 years at LinkedIn and Microsoft. … After nearly 18 years at LinkedIn and Microsoft, Ryan Roslansky is leaving the company.

Kevin McLaughlin / The Information : Sources: Anthropic has taken the unusual step of ending customers' discounts, which typically reach ~15%, once they hit the usage limits, forcing renegotiations —  Anthropic is flexing its muscle with enterprise customers by taking a hard line on discounts once those customers use up all the tokens they purchased.

Stephen Nellis / Reuters : Volantis, which aims to use vertical-cavity surface-emitting lasers, like those used by the iPhone's Face ID, to connect AI chips and memory chips, raised $88M —  San Francisco-based semiconductor startup Volantis on Thursday said it has raised $88 million in venture capital in an effort …

当前一代游戏机的模拟器通常需要较长时间才能成熟，但 PS5 的模拟器仅仅几个月时间就让许多 PS5 游戏能在 PC 平台上可玩。SharpEmu 从 5 月的极早 Alpha 阶段到现在具备加载真实游戏 eboot.bin 文件、执行原生 CPU 指令以及部分处理 GPU 相关功能的能力。已有 10 款游戏被标记为可玩，其中包括简单 2D 游戏如 Tetris Forever，也有复杂 3D 大作如 Astro Bot 和 Demon’s Souls 重制版。另一款 PS5 模拟器 KytyPS5 也于上周发布了首个公开版本，它是 PS4 模拟器 Kyty 的扩展版，能启动 2D 游戏及部分 3D 游戏，包括使用虚幻引擎 4/5、Unity 以及自研引擎开发的作品。有 134 款游戏标记为可玩，但很多存在严重 bug。

一名玩家购买了一个二手 CPU Ryzen 7 5800X3D，结果发现无法启动 Riot 工作室旗下的多款游戏，每次启动游戏就被踢出，在联络了 Riot 的客服之后才知道该 CPU 被列入了封禁黑名单，因为其前任主人有作弊行为。Riot 工作室旗下的所有游戏都受到影响，其中包括了 Valorant、League of Legends、Teamfight Tactics、Legends of Runeterra、2XKO 等。Riot 使用了内核级反作弊系统 Vanguard，它深度嵌入在 Windows 内核模式中。暂时不清楚 Riot 是如何唯一标识 CPU 的，CPU 会向操作系统报告硬件 ID，但该硬件 ID 不是唯一标识符，只是告诉操作系统其 CPU 型号。它可能是通过 CPU 运行产生的独特指纹标记 CPU，每个 CPU 在运行时候都会有微小的差异。

为了提高生育率，新加坡试点推出了为公务员牵线搭桥的约会应用 FirstDate。新加坡的总和生育率已降至每名女性生育 0.87 个孩子，而十年前这一数字为 1.24。政府最近成立了一个专门研究生育率下降问题的工作组，预计该作组将在 2027 年初发表研究结果。FirstDate 面向 21-35 岁的单身人士，目前仅向公务员开放，申请截止日期为 10 月 5 日。用户无需浏览海量的个人资料，而是填写一份关于兴趣、习惯、价值观和偏好的问卷。FirstDate 会在每个周期内（即双方互相接受并预计见面所需的时间）为用户推送一个匹配对象。每项匹配结果都包含匹配度评分、对方的简介和一段个人留言。该服务使用了 Gale-Shapley 稳定婚姻算法。有公务员认为该应用的一大优势可能是被诈骗的可能性较低。

根据发表在 PLOS One 期刊上的一项研究，猫的数量与幸福感正相关，一个国家的猫越多，其幸福感通常越高。但主要通过猫传播的弓形虫感染率与幸福感负相关，弓形虫感染率较高的国家的幸福指数通常较低。两个关系似乎矛盾，但也可能与卫生、经济条件相关联。研究分析了 93 个国家和地区的数据，幸福指数平均值为 5.72，每万人拥有猫数量的平均值为 989 只，其中中国的幸福指数为 5.97，略高于平均水平；每万人拥有猫的数量为 376 只，显著低于平均值，中国的弓形虫感染率与平均水平相当。研究作者提出，猫对幸福感的影响可能与经济因素有关。也就是随着收入水平的提高，公共卫生条件会得到改善，虽然猫的数量增加，但感染风险不会有相应的增加。

由于索尼频繁更新 PS5 的固件，而大部分 PS5 越狱方法只针对特定固件版本的漏洞，因而这些越狱方法实用性相当有限。但情况在本周二发生了变化，名为 Relapse 的漏洞利用方法适用于最高固件版本 v13.6 的 PS5 游戏机，而 v13.6 是在今年 7 月释出的，意味着 PS5 只要不更新最新固件，就能成功越狱。Relapse 利用了 PS5 浏览器的一个已知的 WebKit 漏洞，提权获取内核的写入访问权限，安装 ELF 加载器去简化任意代码的运行。越狱后的 PS5 除了能备份游戏外还能运行模拟器以及 PS4 游戏的非官方 60 帧 MOD。

中国互联网络信息中心（CNNIC）发布了《生成式人工智能应用发展报告（2026）》，截至 2026 年上半年，我国生成式人工智能用户规模突破 7亿 人，普及率超 50%。76.0%的 用户表示自己会让生成式人工智能回答问题；使用生成式人工智能处理图片/视频、文本、工作总结/会议纪要/PPT的用户占比分别为 47.8%、37.6% 和 32.5%。数据显示，38.7%的网民近半年在网上购买过智能硬件设备。其中可穿戴设备和3C数码产品是我国网民接触智能硬件设备的首要入口。购买过智能可穿戴设备的网民比例为 20.2%；购买智能手机、平板电脑等3C数码产品的网民比例为18.2%。报告称，深度求索、月之暗面等本土企业先后发布多个万亿级参数开源大模型，全球主流大模型调用榜单上排名前六的模型全部来自中国团队。

新奥集团发表新闻稿，称其“玄龙-50U”装置实现氢硼聚变反应。新闻稿称：氢硼聚变具有无中子、燃料丰富易得、低成本等商业化优势，产物是氦（α粒子），但相对于氘氚聚变，反应温度及三乘积要求更高，反应条件更苛刻。本次新奥聚变团队通过高能中性束注入与射频波的协同，大幅提高了氢硼反应第一共振峰的非热平衡快质子份额，实现了大于 10^8/秒的氢硼聚变反应率，表明新奥氢硼聚变迈入燃烧等离子体相关实验阶段，是中国多路径聚变能发展的重大突破。来自全球多个国家科研院所与知名高校的十余位聚变权威专家就本次实验成果召开专题论证会，一致认为：本次实验实现了球形环装置中质子能谱及氢硼聚变反应产物α粒子的有效、可重复测量，探测方法可靠，可支撑氢硼聚变反应验证，是球形环氢硼聚变创新探索实践的里程碑突破，对全球磁约束氢硼反应的科学研究具有重要价值贡献。

过去几年极端气候频发，美国佛蒙特州每年都会因此发生十几次持续数小时的断电事故。当地电力公司 Green Mountain Power(GMP)记录到的 10 场最具有破坏性的飓风有 7 场发生在过去十年，造成了逾 2.25 亿美元的损失。为了应对气候变化导致的断电，该公司推出了分布式电池储能网络，向参与该网络的家庭出租两块电池，租期十年，每月费用为 55 美元。该州有超过 5,500 人参与了该家庭电池网络，半数家庭还安装了太阳能电池板。该项目目前提供约 110 MW 的电力，相当于一座中小型天然气发电厂的装机容量。美国其他州也有类似的电池储能网络。

文学家团队借助韦伯望远镜在距地球 500 光年的巨行星 HATS-6 b 大气中探测到水、甲烷、氨，同时发现这颗行星温度可能比标准推算温度低得多。这是透射光谱技术第二次在系外行星大气中检出水汽之外的氨信号。由于氨这类含氮分子在较冷的巨行星中本应比在炽热类木星行星中更为丰富，这一发现支持了一个判断：围绕 M 型矮星运行的行星可能在化学上构成独特群体。HATS-6 b 体积大致相当于木星，每 3 天绕一颗体积小的低温红矮星公转一周。按现有认识，小恒星周围的气体尘埃盘既缺乏足够物质，也缺乏足够时间积聚出木星、土星级别的行星。目前人类已在太阳系外发现 6000 多颗行星，多数与太阳系内行星毫无相似之处。团队表示，弄清它们由什么构成、怎样形成，是判断其他星系是否与太阳系有共同起源的前提。

微软宣布 WSL Containers GA，该工具为 Windows 11 开发者提供了一种通过 Windows Subsystem for Linux 构建、运行和部署 Linux 容器的内置方案。微软同时提供了容器管理工具 wslc.exe，GPU 支持、网络改进、健康检查、存储挂载、与 Microsoft Defender for Endpoint 和 Intune 的集成。微软还声称，当 Linux 环境访问存储在 Windows 中的文件时，性能最多可提升一倍。

日本人的身高在 1896～1996 年的 100 年间，男性增长约 14.6 厘米，女性增长了约 16 厘米。但 1990 年代之后升高增长日益乏力，相比下中韩平均身高则在快速增长。2019 年日本 19 岁男性平均身高约 172.1 厘米，女性约 158.5 厘米。韩国男性为 175.5 厘米，女性为 163.2 厘米，中国男性为 175.7 厘米，女性为 163.5 厘米。日本人口身高增长停滞有三种解释：其一是能量摄入减少，日本人均每日能量摄入量在 1995 年至 2023 年间缓慢下降并趋于停滞，中国和韩国的能量摄入则比日本高出约 760-800 千卡。其二是能量摄入问题可能对正在怀孕的女性产生影响，日本每 10 名新生儿中就有 1 人出生体重不足 2500 克，高于中韩。其三可能与婴幼儿时期的睡眠时长相关，日本婴儿（11.62 小时）显著低于中国（12.49小时）和韩国（11.9小时）。

日本总务省公布的 2025 年人口普查确定值显示，截至 2025 年 10 月 1 日，包含外国人在内的日本总人口为 122,972,528 人，较 2020 年的上次调查减少 3,173,571 人，降幅为 2.5%。自 2015 年的调查起连续3次负增长。65 岁以上人口在总人口中的占比（老龄化率）为 29.4%，创历史新高。较上次调查上升 0.9 个百分点。总人口中的日本人减少 3.5% 至 119,131,935 人，自 2010 年的调查起连续 4 次减少。居住在日本国内的外国人增加 39.8% 至 3,840,039 人，创历史新高。从国籍来看，越南和尼泊尔增幅显著。日本整体的家庭数量比上一次 2020 年调查增加了 149 万 4465 户，达到 5732 万 4619 户，创出有可比数据以来的新高。每户平均人数降至 2.10 人，降至最低水平。一人家庭达到 2368 万 9024 户，增加了 253 万 7982 户。其中增长最多的是 65 岁以上的老年人，占一人家庭总数的 34.4%。一人家庭中，男性有 24.6% 为 65 岁以上，女性有 44.7% 为 65 岁以上。一人家庭的比例达到 41.4%，首次超过4成。

世界正日益城市化，夜空的亮度每年都在增加10%。全球八成的人口生活在受光污染影响的夜空之下，真正的黑暗日益稀缺，对大部分人而言正变得遥不可及。享受黑暗的意义不止于观星。黑夜本身就是一个独特的生态环境。人造光会干扰依靠月光导航的动物：无论是误将停车场当成大海而迷途的幼龟，还是绕着灯泡团团转的飞蛾，都深受其害。萤火虫和青​​蛙需要黑暗环境完成求偶仪式；候鸟常因大城市的强光照射而偏离迁徙路线。研究表明，光污染正在破坏植物与授粉昆虫之间的关系，改变树木的开花时节，扰乱包括人类在内的所有生物的昼夜节律。无论是室内的灯光，还是夜间透过窗户射入的光线，研究都证实我们需要黑暗环境休息、恢复体力和保持健康。大多数光污染源自彻夜长明的路灯、建筑物、停车场和运动场，但来自天空本身的光污染威胁也日益增加。地球轨道上的卫星越来越多。

FBI 与荷兰合作逮捕了疑似 ShinyHunters 组织领导成员、24 岁的 Pepijn van der Stap。他是在 9 月 16 日左右被捕的，发生在 ShinyHunters 入侵 FBI 招聘网站 apply.fbijobs.gov 纂改网页窃取逾 2TB 雇员数据之前——有一种解释是 ShinyHunters 组织的领导者换人了，该组织在新领导人的管理下变得更激进，并试图将入侵 FBI 的行动嫁祸给被捕的 van der Stap。van der Stap 曾使用化名 Umbreon，其头像就是宝可梦 Umbreon。入侵 FBI 的 ShinyHunters 黑客在纂改网页时植入了宝可梦 Umbreon 的 ASCII 艺术图。接管 ShinyHunters 的据称是约旦的少年黑客 Rey。

据清华大学经济管理学院官方微信公号称，今年清华经管学院顾问委员会新增委员三人，新增接任委员五人。三位新增委员分别是黄仁勋、苏姿丰，以及瑞士百达集团高级管理合伙人百达铭（Marc Pictet）。五位新增接任委员分别是宝马集团董事长聂科维（Milan Nedeljković）、沃尔玛公司总裁兼首席执行官方威翰（John Furner）、可口可乐公司首席执行官柏瑞凯（Henrique Braun）、泛大西洋资本集团联席总裁、全球成长型股权投资负责人马丁·埃斯科瓦里（Martín Escobari），以及bp集团首席执行官梅格·奥尼尔（Meg O’Neill）。清华经管学院称，这五位接任委员所在公司的前任都曾是学院顾问委员会委员，因工作变动不再担任。清华经管学院现任顾问委员会主席为苹果董事会主席库克。马斯克（Elon Musk）、扎克伯格（Mark Zuckerberg）以及微软 CEO 纳德拉(Satya Nadella)都是委员。