Weekly Digest — 2026-W36

Sell ad space on your laptop

Create and edit precision motion graphics & video with chat

Drive the full SDLC with AI agents, step by step

See what new sites are built with, the week they launch

Turn public buying signals into sales pipeline

A ball for boring meetings to keep you busy

Every model with a unified interface. Privacy with proof.

Performance monitoring built for Expo and React Native

The phone that literally has a gimbal built in

Build one customer-facing AI agent and launch it everywhere

A node-based free open source terminal manager

A native AI agent that does real work on your Mac

Loop Engineering is emerging as a practice for organizing development work around coding agents. Instead of writing each prompt by hand, practitioners design loops that monitor progress, assign work, run checks, and decide what the agent should do next. Even with a capable coding agent, a loop may trust a stale progress note, skip needed verification, spend its budget in the wrong direction, or stop before the task is safe to submit. Yet the final outcome of one end-to-end run cannot tell whether success or failure reflects the loop's guidance or the coding agent's ability to carry out the task. We introduce LoopArena, a benchmark for evaluating how well one model can guide a separate coding agent through a long-running task. The model under evaluation is the Controller: after each coding round, it receives a structured summary of the run and instructs a separate, fixed coding agent, the Worker, on what to do or verify next, or decides whether to stop. LoopArena evaluates this ability in three complementary settings that differ in execution scope and cost. Type I scores next-step Loop Contract selection through execution-validated questions without running the Worker at evaluation time. Type II executes repeated control over a selected slice of a full task, while Type III evaluates the paired full task from its original state. On full tasks, the best observed Strict Success Rate is 24.69\%, leaving substantial room for improvement in long-horizon loop control. Across Controllers, the paired reduction in estimated inference cost averages 64.4\%, and Type II produces a similar ordering under the main Core criterion (Spearman's \(ρ=0.9747\)). We release the benchmark data and evaluation code at https://github.com/AMAP-ML/LoopArena .

Equipping Large Language Models (LLMs) with multi-turn tool-calling capabilities is essential for building autonomous agents. However, progress is fundamentally limited by the reliance on full-length trajectory imitation. For tasks involving multiple order-independent sub-goals, the optimal solution space forms a vast combinatorial diamond lattice. Forcing this rich topology into monolithic trajectories causes a severe topological collapse, indiscriminately penalizing valid alternative explorations and severely degrading policy diversity. To address this, we propose DART-SD (Diamond-topology Aware Retrieval and Tuning for Self-Distillation), a novel framework that shifts the paradigm from global forcing to topology-guided localized correction. DART-SD first models the execution process as a converging Interaction-State Transition Graph (ISTG), faithfully capturing the inherent diamond topology of successful and failed exploratory paths. During autonomous rollouts, the framework identifies the Critical Topological Breakpoint (CTB) and retrieves success-supported recovery references. Finally, we introduce a progressive self-distillation paradigm through CTB-guided localized supervision, ensuring that the training loss is calculated exclusively on the generated recovery steps while strictly protecting the valid reasoning prefix from destructive gradient updates. Experiments on complex multi-turn tool-calling benchmarks demonstrate that DART-SD significantly outperforms traditional full-trajectory baselines.

Scaling robot data is crucial for building generalist Vision-Language-Action (VLA) models, yet robot trajectories are harder to scale than web-scale image-text data because embodied collection is costly and sparsely covers the physical world. This makes representation quality a central bottleneck: under a fixed robot-data budget, continued pre-training must turn limited trajectories into transferable visual-action knowledge rather than merely fit actions. We propose VLAct, a VLA-oriented VLM backbone trained on broad, heterogeneous, multi-embodiment robot data before task-specific fine-tuning. VLAct preserves the broad VLM prior and encourages shared action semantics across embodiments through VLM-prior preservation, multi-head continuous action co-supervision, and a partially unified cross-embodiment action layout, while allowing task-specific action heads during fine-tuning. Across simulation, real-world, and unseen-embodiment transfer, VLAct consistently improves downstream performance under fixed fine-tuning protocols. On LIBERO-Plus and RoboTwin 2.0, VLAct surpasses industrial VLA systems including ABot-M0 and LingBot-VLA, achieving success rates of 82.6% and 92.5%. On RoboDojo, VLAct ranks sixth among all policies by success rate and outperforms all explicitly designated world-action model (WAM) entries on both metrics. Most notably, on RoboCasa-GR1, an unseen humanoid embodiment, VLAct using only 20% of downstream trajectories outperforms the full-data GR00T-N1.6 baseline. These results are obtained using fully open-source data and only a 16-GPU training setup, showing that representation-centric continued pre-training can deliver highly competitive performance under a modest compute budget and is an important independent axis of VLA progress beyond data scaling.

Generative models can turn natural-language prompts into images, text, code, and other content, lowering the cost of producing drafts and components. Their practical impact increasingly depends on whether those pieces can become complete, dependable deliverables. This survey examines agentic artifact creation, which we define as stateful construction in which an AI system materially constructs or revises a deliverable and intermediate observations redirect later work. Functionally, the process links an operational representation of the artifact, a construction policy, and runtime verification whose feedback can redirect later actions. We reviewed 259 works available through August 20, 2026: 230 systems meeting this definition and 29 benchmarks of agentic artifact construction. We compare six artifact families, then analyze application settings and evaluation practice as separate dimensions. Across families, construction challenges reflect not only modality but also how tightly decisions are coupled and whether failures become visible while they remain repairable. Decomposition can reduce local complexity while increasing coordination and reassembly costs. Learned judges may add little independent evidence when they share the generator's preferences or blind spots. We formulate principles for keeping commitments and responsibility explicit, turning feedback into targeted repair, and revalidating affected state after change. We also identify opportunities for sustaining coherent, accountable control as artifacts, creator intent, and construction systems evolve. A curated paper list is available at https://github.com/GeminiLight/awesome-agentic-artifact-creation.

Physical understanding and reasoning depend on forming compact and generalizable representations of the world. While modern vision-language models can recognize and explain diverse physical events, they often lack explicit representations of the underlying mechanisms-such as object states, physical parameters, and governing dynamics-needed for reliably reasoning how the world evolves and responds to interventions. In this work, we introduce Code-as-World, a paradigm that represents physical worlds through executable world representations. By expressing physical composition, dynamic evolution, and visual appearance as executable code, Code-as-World provides a compact, quantitatively grounded, and controllable abstraction of the physical world. To construct such representations from multimodal observations, such as natural-language descriptions or real-world videos, we develop an agentic discovery loop inspired by abductive reasoning, where an agent proposes, executes, renders, verifies, and iteratively refines executable world hypotheses. As a concrete application, we use verified executable worlds to provide scalable physical supervision for training vision-language models on quantitative physical reasoning. Experiments show that Code-as-World-VL achieves state-of-the-art performance on QuantiPhy and surpasses leading proprietary models, highlighting the potential of executable world representations as a scalable foundation for physical intelligence.

Self-evolving language models have recently emerged as a promising path toward superintelligence, with the advantage of reducing the cost of human supervision. While considerable progress has been made in verifiable domains, self-evolution in unverifiable domains remains substantially less explored. We propose Judge co-adaptation from Zero data (J-Zero), a unified Challenger--Solver--Judge co-evolution framework that supports self-improvement across both domains. The Challenger and Solver co-evolve through an adversarial interaction: the Challenger generates increasingly difficult tasks, while the Solver learns to produce higher-quality responses to them. In parallel, the Judge co-adapts using preference pairs whose ordering is known in advance from how each response was produced, i.e., the Solver's answer over the Challenger's, and its decomposed-and-recombined answer over its one-shot answer, rather than from the Judge's own scores. J-Zero outperforms the baselines by an average of 4.2 points on verifiable and 8.0 points on unverifiable domains, and continues to improve through at least ten iterations, whereas the baselines degrade after two.

On-policy distillation (OPD) offers dense token-level supervision as an alternative to the sparse outcome-level advantages of reinforcement learning with verifiable rewards (RLVR). However, the teacher scores student-generated trajectories that are inherently off-policy for it, so the reliability of its supervision, and hence the source of the student's improvement, remains unclear. We quantitatively analyze teacher supervision during OPD training and find substantial noise whose prevalence increases with teacher scale. Surprisingly, the student policy is insensitive to such noise, converging to comparable performance regardless of whether noisy supervision is retained or removed. Does OPD distill at all? By analyzing what drives its gains, we find that learning concentrates on low log-probability tokens, and using a single fixed negative advantage matches the performance of teacher-provided ones. This suggests that OPD works largely by suppressing low log-probability tokens, which requires no teacher. These findings motivate On-Policy Self-Adaptation (OPSA), a supervision-free method using entropy-adaptive negative advantages. It assigns stronger learning signals to high-entropy positions, suppressing tail tokens, and evenly redistributing probability mass among head tokens. Compared with the base Qwen3-1.7B, OPSA improves Avg@32 by 35.41 points on AIME24, corresponding to a 263\% relative gain, and more than doubles Pass@32 across all three benchmarks. It also outperforms OPD by 16.77 points in Avg@32 on AIME24. Extensive experiments and analyses across model families and tasks further demonstrate its effectiveness and generalizability.

Recent video generators often omit audio or synthesize it in a separate stage, limiting reciprocal modeling of visual dynamics and acoustic events. We present DreamX-Creator 1.0, a compact native joint audio-video generation system centered on a 7B generator. Conditioned on a first frame and a text prompt, the generator jointly denoises modality-specialized audio and video streams. The streams are processed independently in the first half of the network and coupled in the latter half through Gated Cross-Modal Attention, whose token- and head-wise output gates modulate each active cross-modal attention-head output. A unified Audio-Video Data System constructs and filters temporally coherent clips, produces structured multimodal annotations, and organizes clips into capability-oriented data pools. Progressive Joint Training comprises two audio-video pre-training stages followed by High-Quality Finetuning. Audio-Video Reinforcement Learning further post-trains the generator with Modality-Aware Multimodal Feedback that routes video-, audio-, and cross-modal feedback to the corresponding streams. For high-resolution output, our Autoregressive 1-Step 2K Refinement pipeline adapts a bidirectional multi-step teacher into an autoregressive multi-step refiner and distills it into a student requiring one denoising evaluation per temporal chunk. Overall, DreamX-Creator 1.0 achieves native, synchronized audio-video generation with performance competitive with state-of-the-art open-source systems. By releasing our compact 7B generator and 2K Refiner, we seek to democratize native audio-video generation and provide an accessible foundation for future research in unified audio-video generative modeling.

Composable scene modeling aims to recover a real indoor scene as complete, editable object assets arranged as observed, giving robot simulation and embodied AI a simulation-ready replica of the real environment whose objects can be manipulated individually. Existing pipelines decompose the task into three steps---parse the observations into instances, generate an asset for each, and place each asset back---but every step presumes an input that a cluttered capture rarely provides: accurate instance geometry, unoccluded views, and assets that accurately match the observations. We propose Lucida, which keeps this order but redistributes the requirements, so each step consumes only what a real capture reliably provides and precision is reached at the end of the pipeline rather than demanded at its start. Lucida parses the video into a scene graph whose nodes carry per-instance multi-view evidence, generates a complete asset for each instance from its evidence, and places assets with GizmoAct, a VLM policy that casts placement as multi-turn GUI interaction, manipulating the object's gizmo in a closed loop and deciding itself when alignment is reached. Across scene-level 3D object detection, object pose estimation, and scene reconstruction, Lucida improves mAP over Boxer by 69% on R2S-Scene, raises [email protected] from 57.8% to 83.4% on CA-1M, and increases scene F-Score from 0.794 for SAM3D to 0.924.

Latent generative models typically follow a two-stage pipeline, training a variational autoencoder for reconstruction and then a generative model on the frozen latent space. Since reconstruction-optimized latents are not necessarily generation-friendly, jointly training both models is an appealing alternative. However, direct end-to-end training remains challenging, as it is prone to latent collapse and faces a generation-reconstruction conflict. We revisit this problem by analyzing how different objectives shape the latent space and identify two key insights. First, the entropy term in the Kullback-Leibler divergence objective is essential for preventing collapse: reconstruction and prior fitting tend to shrink the posterior, while entropy preserves non-degenerate latent uncertainty. Second, reconstruction and generation exhibit asymmetric learning dynamics: reconstruction is fast and strongly supervised, whereas generation is slower and harder to optimize. Based on these insights, we achieve the first direct end-to-end training without latent collapse and propose GenFirst, a simple generation-before-reconstruction strategy. The generative objective first shapes the latent space under weak reconstruction pressure, after which reconstruction is progressively strengthened to recover visual details. We validate GenFirst with continuous autoregressive priors with exact likelihoods and SiT priors with implicit likelihoods. With our end-to-end objective and GenFirst, SiT achieves a gFID of 0.97 with CFG and 1.45 without CFG on ImageNet-256, while MMDiT reaches a GenEval score of 0.90 on text-to-image generation. Beyond image generation, we extend the framework to shared visual latents for generation and representation learning, and to continuous unified text-image generation. These results demonstrate the generality of stable end-to-end latent learning across generative priors and modalities.

While low-rank adaptation (LoRA) is widely used for parameter-efficient model adaptation, how to regularize its training dynamics for stable and effective optimization remains underexplored. Because LoRA initializes the up-projection to zero, its early optimization dynamics are largely governed by the down-projection. Building on this observation, we introduce Normalized Low-Rank Adaptation (NoRA), a simple yet effective method that normalizes the down-projection matrices during training. We further show that the same normalization can be applied only at initialization, improving standard LoRA without requiring repeated normalization throughout training. Across pretraining, supervised finetuning, and reinforcement learning, NoRA consistently accelerates convergence, improves performance and training stability, and mitigates catastrophic forgetting. These benefits require neither additional trainable parameters nor inference-time computation, making NoRA a simple and broadly applicable enhancement to LoRA.

Research planning is the decisive capability of AI scientists. Yet a research plan admits no verifiable answer, so reinforcement learning lacks the environment it requires: tasks paired with a critic. Rubrics extracted from scientific papers can supply the critic. Existing pipelines, however, draw the question and the criteria from the same content, so the reward can be earned by paraphrase. The rubric is further compressed into a single scalar per rollout. We introduce PaperGym, a unified framework that turns each research paper into a complete training environment. PaperGym exploits the structure of a paper: the question is synthesized from the research goal and background, while the criteria are derived from the method and experiments. The criteria span methodological innovation and experimental design, and criterion leakage falls to 3.7%, versus 11.90% to 34.10% in existing datasets. Training uses the rubric twice: first as privileged context for OPSD's self-teacher, then as the reward for GRPO. Across Qwen3-1.7B/4B/8B, this schedule outperforms supervised fine-tuning, either stage alone, and the reverse ordering, improving five-benchmark averages by +5.6, +5.0, and +4.8 points. With the recipe held fixed, models trained on PaperGym-20k win 58.1% of three-way comparisons, against 28.2% for RubricHub Science. The trained Qwen3-8B reaches 73.48 on ResearchQA, above the far larger Kimi K2.6. We release the pipeline, the 20,000-instance corpus PaperGym-20k, and the benchmarks PaperGym-Innov and PaperGym-Design.

Ryan Gillespie / Orlando Sentinel : Florida says it is revoking permits for Flock cameras and similar license-plate readers on state roads and no longer intends to issue such permits —  Florida is revoking permits for Flock cameras and similar license-plate readers on state roads, dealing a blow to local law enforcement agencies …

Chance Miller / 9to5Mac : Filing: Apple claims a former iPhone engineer used a confidential Apple circuit schematic in his work at OpenAI, evidence is being destroyed, and more —  Apple has filed a new document in its ongoing lawsuit against OpenAI as it continues to push for expedited discovery.

Lucinda Shen / Axios : Sources: AI sales and marketing startup Clay is raising a round led by Wellington at a $7B pre-money valuation, up from $5B via an employee tender in January —  Clay, an AI startup for sales and marketing teams, is raising new money led by Wellington at a $7 billion pre-money valuation, Axios has learned.

Kirsten Korosec / TechCrunch : The Pentagon launches ChatGPT Mil and Grok for Government on its GenAI.mil platform, giving its 3M personnel access to AI tools “tailored to warfighter needs” —  The Pentagon has launched versions of OpenAI's ChatGPT and xAI's Grok, giving 3 million civilian and military personnel access …

About Amazon : Amazon says the FTC's complaint over Sponsored Ads cites no evidence of consumer price increases or advertiser harm, and misunderstands how advertisers operate —  The FTC today filed a misguided lawsuit claiming Amazon misled advertisers about its Sponsored Ads pricing and auction.

Amanda L. Gordon / Bloomberg : Sources: Polymarket will raise $1B led by 1789 Capital at a $21B post-money valuation; Donald Trump Jr. is a partner at 1789 and adviser to Polymarket —  Venture capital firm 1789 Capital is leading a new funding round for Polymarket that will give the prediction-markets platform a $21 billion post-money valuation.

Ryan Whitwam / Ars Technica : Google rolls out its September Android Drop, with remembered items in Find Hub, Guided vision in Gemini Live, Motion Assist to reduce motion sickness, and more —  Let's be frank: Some of Google's recent Android feature Drops have been duds, offering little more than an expansion of Gemini summaries and chat functions.

Mark Gurman / Bloomberg : Filing: Apple says John Ternus will get a compensation package worth ~$58M in FY 2027, while Tim Cook's role as executive chairman will pay ~$47M —  Apple Inc. said that new Chief Executive Officer John Ternus will get a compensation package worth about $58 million in fiscal 2027 …

Erin Woo / Wall Street Journal : Sources: Google plans to release Gemini 3.8 Flash as soon as Wednesday; Gemini 4 has done well on pre-training evals but still needs to complete post-training —  Internal tests of Gemini 3.8 Flash show progress in an area where the company has lagged behind Anthropic and OpenAI.

Reuters : The US urged G20 members to avoid writing entirely new AI regulations, and instead focus on writing rules for “novel” situations that involve the tech —  The U.S. pressed G20 members on Tuesday to take a hands-off approach to AI regulation and avoid creating new rules …

Samantha Subin / CNBC : Palo Alto Networks reports Q4 revenue up 34% YoY to $3.41B, vs. $3.35B est., and acquires Console, which provides AI-powered IT service management —  Palo Alto Networks surpassed fiscal fourth-quarter estimates as mounting artificial intelligence risks boost demand for its cybersecurity tools.

Ben Patterson / PCWorld : Claude Fable 5.1 and Mythos 5.1 are Anthropic's first models to watermark text outputs; a detection API is available to eligible groups as required under EU law —  Anthropic has announced the arrival of its latest Claude models, and just as it promised last month, the new models will add invisible watermarks to all their text replies.

自由软件视频编辑器项目 OpenShot 释出了 v4.0 版本。主要新特性包括：新色彩视图；新录制视图：将麦克风、屏幕、Web 摄像头和系统音频直接添加到项目中，每个音源保持独立且可编辑；10 种新特效；使用本地大模型选择和跟踪对象；更简洁的原生时间线；更快的特效和编辑速度；智能的创意工作流程；扩展 Qt 6 支持，改进了与较新 Linux 发行版的兼容性，为 Android 和其它平台奠定了基础。

加州参议院和众议院批准了年龄验证法案 Assembly Bill 1856。在递交给州长批准之后法案预计于 2027 年 1 月 1 日生效。法案豁免了 Linux 和 BSD 等开源操作系统。法案要求，如果操作系统有账户设置功能，那么系统提供商须提供一个界面，在账户设置期间要求输入设备主用户的出生日期、年龄或两者兼有。操作系统通过相对一致的实时 API 向受监管的应用商店和应用开发商提供数字年龄信号。该信号不显示精确的出生日期，而是四个年龄段之一：13 岁以下、13-15 岁、16-17 岁或 18 岁及以上。对 2027 年 1 月 1 日之前的设备，操作系统提供商必须在 2027 年 7 月 1 日之前提供界面让账户持有人提供所需的年龄信息。

Linus Torvalds 宣布释出 Linux 7.3-rc1，关闭了 7.3 的合并窗口，正式版预计将在十月底释出。Linux 7.3 的主要特性包括：Ryzen AI Halo LED/RGB 驱动、继续即将推出的 AMD Zen 6 的支持工作、KSMBD 兼容 Apple Time Machine 备份、内核驱动初步支持 2026 年款 Steam Controller、合并 FailFS、Intel Xe3P Nova Lake 集显支持稳定、改进了显存容量有限的系统的游戏性能、改进 SMP 降低延迟提升实时性能、等等。

上周末 Steam 平台逾 12TB 数据泄露，涵盖了该平台 2003-2013 年之间几乎所有的游戏。这些数据是通过一个公开访问的 API 获取的，但不清楚是近期访问还是早就下载但直到上周才公开。相关数据来自被称为 Steam2 的内容分发系统，2013 年 Steam2 被 SteamPipe 系统所取代，因此数据仅限于 2013 年前。泄露的数据包括了Valve 和第三方发行商发布的热门游戏的早期版本、原型版本和试玩版本，其中包括《传送门2》的被删减内容，被取消的《半条命2：第三章》的部分文件。

Google 过去一年对其搜索结果的展示方式进行了两次重大改变。其一是搜索结果链接，以前你将鼠标悬停在搜索结果上会在浏览器底部看到网站链接，现在显示的是 google.com/goto + 一串看起来随机的字符串。搜索结果中的 AI Overview 引用的链接也是采用此类展示方式。其二是用户以前可以在搜索词末尾添加 &num=100，可以在一个页面上显示前 100 个搜索结果，如今这一快捷方式被取消了，Google 强制只展示最多 10 个搜索结果，意味着你想要看前 100 个结果需要点击 10 次。

科学家早就知道，植物叶片表面分布着许多微小的气孔。当温度升高时，这些微小的孔隙会“张嘴”，让水分蒸发，从而带走热量，就像人出汗能降温一样，但气孔这一植物“散热器”背后的分子调控机制，一直是个未解之谜。该通路的核心是一种名为“泛素特异性蛋白酶24”（UBP24）的蛋白质。他们发现，当高温来袭，植物体内的激酶会“唤醒”UBP24，导致其分子电荷发生变化，使其变得更加稳定。在更稳定的状态下，UBP24 有助于“保护”并激活其他参与维持气孔开口的蛋白质，使植物的蒸发冷却系统在热应激时保持活跃。通俗来说，UBP24 就像“空调”上灵敏的温控开关。类似的故事并非只在植物身上上演。科学家还发现，啤酒酵母中的一种相关蛋白质也依靠相似原理应对热应激。酵母与植物分属不同物种，生活方式大相径庭，且两者之间存在数亿年的进化差距，却在细胞层面使用了相似的“散热逻辑”。

周二公布的一项民调显示，七成韩国民众表示支持出台限制青少年使用社媒的政策。这项民意调查访问了 1000 名年龄在 14-58 岁之间的受访者。调查结果显示，70.7% 的受访者支持，29.3% 的受访者反对。占总调查人数五分之一的青少年受访者中，59% 反对，41% 支持。当被问及实施此类限制的合适年龄时，16.8% 选择了 15 岁，14.9% 选择了 18 岁，13.1% 选择了 11 岁及以下。大多数受访者表示，即使出台此类政策，在限制青少年使用社媒方面仍然存在局限性，青少年用户可能会盗用他人账号或转向其它不受限制的平台，因此 59.8% 的受访者认为，平台应采取更多措施营造安全的社媒使用环境。

8 月 28 日 20:57 UTC 左右，一个不相关网络 BGP 路由通告了 Softaculous 使用的 Hetzner IP 段，将部分原本发送到 Softaculous 系统的流量劫持到攻击者控制的服务器。Hetzner 是 Softaculous 的上游基础设施供应商，而 Softaculous 则是一家为 Web 托管服务商提供软件的公司，它的 Virtualizor 控制面板被管理员用于部署和管理 VPS。这次 BGP 路由劫持影响了 Virtualizo 更新服务器以及客户和计费网站。攻击者还从 Let's Encrypt CA 获取了有效的 TLS 证书，Let's Encrypt 的自动域名所有权验证也被劫持到了攻击者控制的 IP。Softaculous 于 8 月 29 日 08:50 UTC 向 Hetzner 报告了事件，Hetzner 随后通过发布相同的路由通告遏制了问题。但攻击者于 20:00 UTC 再次了长达 10 小时的路由劫持。8 月 30 日 05:50-06:10 UTC 路由通告被撤回，劫持停止。Softaculous 建议在攻击期间登陆过的用户立即重置密码，以及重置所有重用该密码的账户。同一时间段内输入过银行卡信息的客户也应检查其账单。攻击者在此期间推送了一个恶意的 Virtualizor 更新包，它建议所有 Virtualizor 用户检查其服务器并轮换凭证。

为弄清动物进入冬眠时大脑的变化，研究人员首先在实验室中诱导叙利亚仓鼠冬眠。两个月里，他们把动物笼舍中开灯的时间缩短，以模拟秋季。然后在接下来的两个月中，将温度降至约4摄氏度，以模拟冬季。 在人造冬季中，动物开始冬眠——体温下降，“在窝里缩成一团”。冬眠持续2到8周，其间仓鼠睡眠状态在微觉醒和深度蛰伏之间循环。团队收集了刚进入深度蛰伏的仓鼠的大脑，并将其与刚从蛰伏中短暂醒来或完全未冬眠的仓鼠的大脑进行比较。结果发现，一种名为Fos的蛋白质高水平表达，表明下丘脑视前区存在活动。该区域参与调节体温、睡眠和其他重要功能。研究人员发现，仓鼠冬眠中活跃的POA神经元特定亚群，似乎与小鼠蛰伏状态中鉴定出的神经元相同。抑制这些神经元会延迟仓鼠重新进入蛰伏，而激活这些神经元则引发仓鼠筑巢行为并使其体温下降。这是体温虽不如自然冬眠时那么低，但也远低于平时，仅13摄氏度。在小鼠中激活这些细胞也能使体温降低，但幅度较小。这组POA细胞可能是进化遗留下来的“关闭键”，使早期温血哺乳动物能够降低维持体温的高能量成本。

欧盟委员会周一表示，OpenAI 的 ChatGPT 将需要遵守更严格的欧盟法规，否则将面临罚款。聊天机器人 ChatGPT、社媒论坛 Reddit 和游戏平台 Roblox 被欧盟网络安全法规《Digital Services Act（DSA）》归类为“超大型在线平台”。该认定意味着这些服务面临额外的义务，如删除非法内容、保护未成年人的隐私和安全，如果未能遵守规定，将面临最高全球收入 6% 的罚款。欧盟的决定标志着 DSA 的适用范围进一步扩大到生成式 AI 领域。此前 X 的 AI 聊天机器人 Grok 已因违反 DSA 而受到调查。这三大服务在欧盟的月活用户数都已超过 4500 万，达到了 DSA 规定的加强审查门槛。它们需要在 12 月底前履行额外义务。

2025 年 11 月太阳释放了多个 X 级耀斑，耀斑还伴随着引发地磁风暴的日冕物质抛射。地球上的居民在此期间目睹了绚丽的极光，极光的范围甚至延伸至低纬度地区。对太阳风暴期间收集的数据的分析发现，美国上空的大气层出现了大范围的、横跨东西海岸的扰动，其规模前所未见。它导致部分地区的 GPS 定位偏差超过 10 米。如此大的偏差足以影响精准农业和自动驾驶汽车的运作。GPS 信号穿过电离层时会被扭曲和衍射，导致抵达接收器时的信号强度快速波动，这种现象被称为振幅闪烁（amplitude scintillation）。闪烁并不罕见，通常发生在两极和赤道，中纬度地区被认为相对安全。但去年底的太阳超级风暴改变了这一切。美国大陆西经 80-120 度之间的大片区域出现了强振幅闪烁。如此大范围的强振幅闪烁以前从未看到过。

民营商业航天公司星河动力于 9 月 1 日 10 时在酒泉东风商业航天创新试验区智神星系列专用发射工位成功发射了其中型火箭智神星一号。智神星一号是基于已投入使用的小型火箭谷神星一号，为两级构型，全长 52 米，芯级直径 3.35 米。智神星一号类似 Falcon 9，使用煤油和液氧作为推进剂，也采用类似的方式回收方式，第一次飞行没有尝试回收。火箭能将 5 吨重的有效载荷送入 400 公里高的近地轨道，或将 3 吨的有效载荷送入 700 公里高的太阳同步轨道，其有效载荷小于 Falcon 9。智神星一号设计回收使用次数不少于 25 次。星河动力正在智神星一号基础上研发重型版本，类似 Falcon Heavy，设计能将 17.5 吨重的有效载荷送入近地轨道。