OrangeBot.AI Digest — 2026-09-01

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

Your multimedia mentor that takes you from mid to great

AI courses you can watch, quiz through, and create

CLI based Zero-Knowledge Architecture for code repositories

Google docs for AI agents, and you can close your laptop

The first open-source price index for GPU compute

Reshape any website with plain words via Sider extension

Multitrack synth, sampler, drum machine, and looper for iOS.

The research workspace built around Gemini Notebook

Connect Cosmic agents to any service with an MCP server

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.

We describe the architecture and ablations of Qwen3.8-Flash-Next, a sparse mixture-of-experts model with 125B parameters, 6B activated per token, and additional 51B parameters of n-gram embedding tables held off the accelerator. On fourteen pre-training benchmarks the model leads the 397B-A17B predecessor on eight and trails it on the rest by at most 2.6 points, at 1/3 the activated parameters, 1/3 the training tokens, and roughly 1/9 the training FLOPs. Token mixing uses a layer-wise hybrid of Gated DeltaNet (GDN) and global attention, with one full-attention layer in every four; at continued-pretraining time those full-attention layers are replaced by Qwen Sparse Attention (QSA), which scores context at micro-block granularity with a compressed lightweight indexer. The residual stream is widened to four branches and read through an elementwise gate, a design we call the Gated Residual (GR). Capacity is added outside the backbone by a single n-gram embedding layer whose tables are prefetched from host memory. We evaluate every candidate change along three axes: loss together with downstream benchmarks; the cost of the change in training, prefill and decode; and its effect on the optimal hyperparameters and training stability. Loss and downstream accuracy do not always move together: enlarging the n-gram vocabulary lowers loss monotonically while downstream accuracy saturates. The architecture and the Muon optimizer together shift the optimal learning rate and batch size upwards, render batch-size warmup unnecessary, and substantially improve stability under stress tests. Loss, benchmarks, efficiency and stability form one design problem. Solved jointly, they yield a recipe that is simultaneously more efficient, more capable and more stable.

We present CogEvol, a family of models trained specifically for Learning Environment Generation: turning a course brief into a finished learning artifact (structured-JSON slides or self-contained interactive HTML pages) in a single pass. Across 220k production requests, CogEvol completes a slide in a median of 17 seconds and an interactive page in 59, replacing minutes-long multi-turn agent scaffolding. Reliability is enforced rather than hoped for: a production-grounded data pipeline turns real failures into 53,687 verified SFT samples, and a hybrid rule-plus-VLM reward drives GRPO-based RL, hardened after we caught and fixed a reward-hacking episode that produced visually convincing but unplayable games. CogEvol-27B scores 83.7 on slide quality and 63.7 on a 500-case interactive-HTML benchmark with 26.9x fewer parameters than flagship coding models, and, in collaboration with the OpenMAIC team, serves their live production traffic. CogEvol-4B is released openly under the Apache 2.0 license at https://github.com/CogEvol/CogEvol-4B; external flagships are measured on the same suites under the identical harness. Scaffold editing cuts interactive-page generation cost by a further ~76%, and the full stack runs on domestic Ascend accelerators at application-level parity with A800 GPUs, lowering the unit cost of AI-native education at scale.

While Large language models (LLMs) incorporate user personalization signals to improve usability and helpfulness, they increasingly shift from providing balanced, informative responses toward optimizing for user satisfaction when conditioned on personal context such as conversation history, inferred preferences, and user profiles. Specifically, we identify three emerging risks: (1) irrelevant personalization, where models reference personal information in unnecessary contexts; (2) preference narrowing, where models reinforce informational echo chambers; and (3) sycophantic bias, where models agree excessively with user opinions. As a result, models may reference personal information in contexts where it is unnecessary, inadvertently collapse response diversity, or agree excessively with user opinions. Despite the growing use of personalization in AI assistants, there has been limited systematic evaluation of its potential side effects. To bridge this gap, we propose PRISK, a dynamic evaluation framework with automated data generation and tailored metrics that uncovers systematic limitations in current LLM personalization and how personalized information shapes its responses. Our empirical analysis across 13 LLMs demonstrates the presence of user profiles and retrieved memories consistently exacerbates biases, resulting in an average drop of 45.9% in irrelevant personalization, 41.7% in preference narrowing and 61.7% in sycophantic bias.

Embodied navigation requires agents to translate heterogeneous goals and visual observations into actions across tasks, environments, and robot embodiments. Modern vision-language models (VLMs) already encode spatial priors for visual grounding, spatial reasoning, and pointing, but these capabilities are rarely elicited directly for robot control. Existing navigation systems instead rely on task- or embodiment-specific components, fragmenting perception, reasoning, and action while offering limited generalization. Here we present LightNav-0, a compact generalist embodied navigation model that elicits the spatial intelligence of a pretrained VLM and aligns it with navigation, without task-specific prediction heads. LightNav-0 represents diverse navigation tasks through a unified token interface: dual-channel pointing expresses task-, scene-, and embodiment-agnostic spatial intent, while a residual vector-quantized action tokenizer maps this intent to precise, embodiment-specific trajectories. Together with temporally aware visual history compression, ER mid-training, supervised fine-tuning, and reinforcement learning, this formulation supports instruction following, open-vocabulary object navigation, and visual tracking within a single model. The navigation training corpus spans 2K+ scenes and 4K+ hours of embodied navigation data. LightNav-ER, the embodied-reasoning checkpoint used to initialize LightNav-0, attains the highest complete-set average across 8 embodied-reasoning benchmarks, while LightNav-0 achieves state-of-the-art monocular success rates across all 10 public navigation simulation settings. Real-world evaluations further demonstrate zero-shot generalization across robot embodiments, diverse scenes, and static and dynamic targets. These results establish compact VLMs as a unified and transferable backbone for generalist embodied navigation.

Large language models (LLMs) achieve strong performance on mathematical reasoning benchmarks, yet the mathematically meaningful skills underlying their reasoning remain underexplored. We introduce SHAPE, a framework that analyzes Chain-of-Thought (CoT) trajectories through two lenses developed in mathematics education: (1) semantic spaces: the model's evolving mathematical interpretations of a problem (e.g., algebraic, geometric), and (2) heuristics: the specific mathematical actions taken within those spaces (e.g., simplifying the problem, working backward). We first use SHAPE to analyze the reasoning patterns of various models. Our findings reveal that the mathematical heuristics employed by a model better explain final answer correctness than traditional CoT features. Furthermore, models are likely to reach correct solutions by concentrating their reasoning effort within a few semantic spaces rather than exploring many disparate ones -- a pattern consistent with human behavior. Next, we utilize the SHAPE lens to evaluate whether post-training truly enhances mathematical proficiency. We find that reinforcement learning induces mode-seeking in heuristic usage. Lastly, we post-train LLMs by promoting diverse heuristics and demonstrate its effectiveness in improving accuracy. Overall, SHAPE provides a theoretically-grounded diagnostic framework for decoding LLM reasoning and offers a new path toward post-training LLMs for math reasoning. The code for our model is available at https://github.com/holi-lab/SHAPE-of-CoT

Organizations often develop and maintain portfolios of related applications: independently deployable codebases that share substantial domain logic, interface patterns, or operational conventions. As LLM coding agents are increasingly used to generate and maintain such software, a naive application-by-application workflow duplicates shared logic across codebases and allows prolonged agentic maintenance to accumulate verbosity, dead code, and structural erosion. We introduce the Super Library Agent problem, where an agent sequentially generates a portfolio of N related applications while maintaining a shared Super Library of reusable cross-application components. A minimal sequential scaffold can in principle extract shared code and migrate applications to the evolving library, but in practice suffers from low extraction recall and fragile dependency migration. We address these failures with candidate-guided extraction over code chunk summaries, pre-extraction codebase consolidation, and context-aware migration using extraction traces and call-graph information. Across WebGen-Bench and PaperBench, our method preserves application functionality while significantly reducing redundancy and token footprint (verbosity, token length) over zero-shot, and avoiding the structural erosion introduced by naive library construction, with additional reductions in LOC and MDL. Our code is available at https://github.com/sbigstar0310/super-library-agent.

Recent advances in large reasoning models (LRMs) have shown that reinforcement learning with verifiable rewards (RLVR) can substantially improve reasoning in mathematics and code, where outcomes can be checked automatically. Extending this progress to open-ended and agentic tasks remains difficult because reliable rewards are harder to obtain and direct human supervision cannot keep pace with the scale and complexity of model-generated experience. This paper studies how LRMs can continue to improve as human supervision gradually recedes from the learning loop. We examine two connected dimensions of this problem. The reward axis traces the development from per-instance human judgments to reusable verifiers and rewards that operate even without human feedback. The experience axis examines how learning can progress from human-curated tasks and environments toward self-generated curricula, constructed environments, and autonomous co-evolution. We connect these dimensions through a five-level ladder from L0 to L4 that identifies which parts of the learning process remain under continued human control. Our analysis further highlights the risks introduced by increasingly autonomous rewards and experience generation, including reward hacking, feedback drift, curriculum collapse, and environment errors. Consequently, we also provide the evaluation around three complementary objects: policy capability, feedback fidelity, and experience quality. This analysis provides a structured account of current approaches to scaling LRMs beyond human supervision and the open problems involved in developing self-sustaining learning systems toward superintelligence. Furthermore, we maintain a continuously updated https://github.com/visitworld123/Awesome-Scaling-LRM-Beyond-Human-Supervision{GitHub repository} to track the latest advances.

Strategic deception by LLM and VLM agents has emerged as a central AI alignment and safety concern. Social-deduction games (where each player holds a hidden role and communicates with others to deduce identities) serve as the canonical testbed, particularly in multi-agent settings. Existing testbeds, however, are text-only and run on a single fixed agent configuration, missing the non-verbal sensorimotor channels treated as core by deception taxonomies and leaving it ambiguous whether an observed behavior reflects the underlying model or the surrounding harness. We introduce MineAmongUs, a 3D multimodal Among Us sandbox where imposter agents must deceive crewmates through joint verbal and non-verbal action. We also propose ARIA, a configurable VLM-agent harness that exposes five cognitive-component ablation axes; and an atom- and arc-level annotation scheme grounded in deception taxonomies and operationalized at scale by an LLM-as-a-Judge reaching near-human atom-labeling agreement. Empirical results show that VLM agents pursue imposter wins through joint verbal and non-verbal deception, with non-verbal channels emerging as the more decisive winning contributors across both harness ablation and cross-VLM evaluation. Taken together, our work opens a new path for embodied VLM-agent alignment research.

Interactive world models extend video generation from offline clip synthesis toward persistent simulation of interactive virtual worlds, enabling applications in games, robotics, embodied agents, and XR. Achieving stable long-horizon interactive generation, however, remains challenging, as the model must simultaneously preserve scene geometry, dynamic consistency, and camera control while supporting real-time autoregressive generation. Building upon Matrix-Game 3.0, we present Matrix-Game 3.5, as shown in Figure 1, which advances real-time interactive world generation toward geometry-aware and long-horizon consistent simulation through three key improvements. First, we propose a unified geometry-aware memory framework, whose patch-memory and tiled-PRoPE components introduce no additional learnable parameters, combining explicit 3D patch retrieval with projective camera conditioning to enable geometry-consistent camera control and faithful long-horizon scene recall. Second, we introduce a static-dynamic disentangled world representation that separately models static scene geometry and dynamic subjects, preserving both geometric consistency and subject identity throughout long-horizon generation. Third, we develop a two-stage progressive real-time distillation framework that converts a bidirectional diffusion model into a few-step causal generator through Perceptual Flow Matching and curriculum based Self-Rollout DMD, enabling minute-long real-time interactive generation. Extensive experiments demonstrate that, with a unified training corpus spanning Unreal simulation environments, open-world games, and internet videos, MatrixGame 3.5 achieves strong performance in long-horizon scene recall, precise camera control, subject consistency, prompt-driven world generation, and stable real-time open-world interaction.

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.

Mark Gurman / Bloomberg : Apple updates Apple Maps to show Lake America for US users, Lake Ontario for Canadian users, and both for the rest of the world, following Trump's EO —  Apple Inc. renamed Lake Ontario “Lake America” on its maps service, following a similar move by Alphabet Inc.'s Google, in the wake of an order from US President Donald Trump.

Business Insider : Memo: Alexandr Wang says Meta is switching from Google Chat to Slack for internal communications as Slack is the “strongest platform available today for agents” —  - Meta is migrating to Slack for internal communications, a memo from AI chief Alexandr Wang says.

Jordan Novet / CNBC : Dell reports Q2 revenue up 58% YoY to $46.97B, vs. $44.92B est., and forecasts $192B in FY 2027 revenue, vs. $172.67B est.; DELL jumps 9%+ after hours —  Dell Technologies shares moved 9% higher in extended trading on Tuesday after the computer maker reported results and a forecast that easily cleared Wall Street expectations.

Ina Fried / Axios : OpenAI says Astra is its first model to reach its “Critical” cyber threshold and warns safeguards may mistakenly flag legitimate activity as cyber misuse —  OpenAI said Tuesday that it plans to release its latest model — Astra — soon, but its most advanced cybersecurity features …

Wired : OpenAI says it plans to publicly release a version of Astra “soon” but will make its advanced cyber capabilities available only to select partners —  The company will give select partners early access to its Astra AI model—so they have time to shore up their defenses.

Anna Tong / Forbes : Sources: AfterQuery, which sells coding and finance training data to AI labs, has hit a valuation of $3.2B, up from $300M in April, and is profitable —  Twenty three year-old Spencer Mateega pivoted his YC startup into the fastest unicorn in the accelerator's history, fueling …

Meta AI Research : Meta launches Muse Voice Transcribe, MSL's first real-time audio perception model, with streaming automatic speech recognition, trained with 70+ languages —  Experience Muse Voice Transcribe in real time … We're excited to introduce Muse Voice Transcribe, the first real …

Stephanie Palazzolo / The Information : Wafer, which makes AI agents that optimize open-source models for a business's workload, raised a $40M Series A, a source says at a $200M+ valuation —  Developers and chip designers are increasingly upbeat about the benefits of using AI to supercharge the process of designing and optimizing AI chips …

World Labs : Fei-Fei Li's World Labs unveils Atlas, a multimodal world model that generates image/video frames with pixel-perfect camera control and reconstructs them in 3D —  World models generate, reconstruct, and simulate any possible world.  They understand how worlds appear, behave …

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。

中国国家能源局星期二公布的最新数据显示，截至今年 7 月底，全国光伏发电装机容量达到 12.86 亿千瓦，略高于 12.85 亿千瓦的煤电装机容量。目前光伏占全国发电总装机容量的31.5%。今年前七个月，全国光伏发电量超过 8024 亿千瓦时，同比增长 15.5%，相当于全国每八度电中约有一度来自光伏。能源局预计，未来五年中国光伏产业投资将超过 2 万亿元人民币。

Windows 上的流行图像编辑软件 Paint.NET 释出了 v5.2 Alpha (build 9739)，实验性的加入了对 Wine/Linux 的支持。想要尝试的 Linux 用户需要：1）使用便捷式版本，安装程序无法工作；WINE 版本至少为 v11.14；添加一个注册表项 wine reg add "HKCU\Software\Wine\DllOverrides" /v d3dcompiler_47 /d native /f，防止 Wine 用自身版本覆盖 DLL；已安装 DXVK； 用 /wine 命令行参数运行。

德国科学家研究显示，作为一种自然发生的现象，带电雨滴能分解经过保护性涂层处理的表面。这一此前被忽视的腐蚀机制有助于寻找新方法保护如汽车、船舶、建筑和文化遗址等金属物体。腐蚀是户外金属制品和结构面临的一个经济和安全问题。之前认为，雨水造成的腐蚀主要是因为雨滴具有物理腐蚀性或带有来自污染物的酸性。虽然既往研究已证明水滴在滑过植物叶子和窗玻璃这类常见表面时会带电，但这种电荷对金属表面的腐蚀作用却被忽视了。马普学会高分子研究所的研究人员分析了电中性水滴如何带电并腐蚀不同材料。他们将水滴到四种常见表面——植物叶子、PVC（聚氯乙烯）泡沫板、聚苯乙烯玻璃，和常用疏水涂层PFOTS（全氟辛基三乙氧基硅烷）。这些水滴随后会滑到有聚四氟乙烯涂层的铜上。研究者发现，水滴带上了很微弱的电荷（0.2-2纳库仑）。在3000个水滴从这些表面滴到有聚四氟乙烯涂层的铜上后，观察发现这一涂层会分解，导致底下的金属被腐蚀。如果水滴不带电，就不会观察到表面损伤。

在 Google Maps 以极快的速度屈服特朗普的行政令，将安大略湖（Lake Ontario）更名为美国湖（Lake America）之后，公开宣布拒绝改名的美国地图导航服务 MapQuest 立即赢得了公众的支持，其移动应用下载量飙升，排在苹果美国 App Store 非游戏类榜单第四，总排名第八，成为美国排名第一的地图导航应用。MapQuest 也成为苹果加拿大 App Store 下载量总排名第二的应用。该公司表示，自决定保留安大略湖名称以来，其应用的下载量增加了数十万次，使用量更是飙升至正常水平的 50 倍。Sensor Tower 的第三方数据也显示，8 月 24 日开始的一周内 MapQuest 全球和美国应用总下载量分别为 18.4 万次和 16.2 万次，均较前一周增长逾 10 倍。在美国 8 月 26-30 日间 MapQuest 应用的日下载量环比增长了 128%。

Google 从其扩展商店 Chrome Web Store 移除了包括 uBlock Origin 在内的所有 Manifest V2 扩展。Chrome 用户已安装的 Manifest V2 扩展目前还能使用，但不会再收到任何更新，卸载之后将无法再重新安装。Chrome Web Store 将只提供 Manifest v3 扩展，Manifest v3 相比 Manifest v2 的一大区别是移除了 blockingWebRequest 和 declarativeNetRequest，限制了广告屏蔽扩展的功能，uBlock Origin 是基于 Manifest V2，它没有 Manifest V3 版本，但有一个基于 V3 的精简版本。用户如果想要继续使用 uBlock Origin 只能改用 Firefox 以及另一款基于 Chromium 的浏览器 Brave。

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。