OrangeBot.AI Digest — 2026-10-13

Autonomous assistant designed to handle meetings and email

See how simulated buyers react, right in your agent

The open-source personal agent that runs on your computer

Books and PDFs read aloud by neural voices, on device

Bring your ideas to life through the camera.

Orchestrate agent fleets with custom tools built on macOS

Free, open-source diagrams you and your agent edit together

AI-Native, Simple & Collaborative Wire Harness Design

A recorder for musicians. Every idea sounds finished.

Ship a launch video without opening After Effects

The AI agent that does the work on your files

Data permissions your AI agent can't talk its way past

Let one AI agent open and drive your other CLI agents

Native macOS mail app with one-click sign-in codes and AI

One console to run and monitor all your servers

Precise grounding matters. It specifies which object is the target and where that object is, even in clutter and for tiny objects, and it has to be fast enough for closed-loop control. Yet vision-language-action (VLA) and world-action models (WAMs) take perception from general-purpose vision-language and video-generation backbones, which still fail in these settings. We introduce GroundingPI, a 4B grounding foundation model that generates points and boxes as quantized coordinates in a shared vocabulary. Training combines multimodal and spatial pretraining, supervised fine-tuning, and reinforcement learning with GRPO, using supervision from public datasets and dedicated data engines. Against 44 baselines across 34 grounding benchmarks spanning 11 perceptual capabilities, GroundingPI establishes a new state of the art, averaging 73.68%, above the larger GPT-6 Astra (71.54%). As a downstream visual backbone, GroundingPI improves performance on robotic manipulation and autonomous driving. On RoboTwin 2.0, it outperforms every mainstream backbone we evaluate in all four out-of-distribution settings, by up to 24.8% relative to the strongest backbone. On RoboCasa-GR1, GroundingPI trained with 50% of the demonstrations outperforms those baselines trained with 75%. On nuScenes, used as the visual backbone, GroundingPI attains an average open-loop L2 error of 0.296 m. We systematically analyze GroundingPI's pretraining in scale and data composition. Downstream autonomous driving and robotic manipulation improve as the pretraining is scaled. Analyzing the data recipe across these 11 perceptual capabilities shows dense grounding's substantial benefits for both, and OCR's potential as a catalyst for perceptual learning. These results support grounding as a perceptual foundation, and dedicated perceptual pretraining as a promising direction for foundation models of physical intelligence.

Large Language Models (LLMs) increasingly ship with explicit "thinking modes", yet their counterpart, "no-thinking", has received far less attention. We study LLMs' no-thinking behavior along two axes. a. How to measure no-thinking? Prior work typically defines no-thinking through proxies such as a disabled thinking mode or the absence of long traces. These proxies are unreliable: disabled thinking modes may still emit reasoning, while long traces may contain filler rather than genuine inference. We instead normalize each response into a pre-answer trace and final answer, and evaluate it at three levels: (i) Empty-Thinking Rate for strict answer-only compliance; (ii) instruction-aware Question-Pre-answer Relevance for similarity between the question and pre-answer trace; and (iii) LLM-as-judge Explicit Inference Rate for visible explicit inference. Together, these metrics distinguish answer-only output, relevant but non-inferential text, and explicit inference. b. How does no-thinking vary across tasks and models? We evaluate six prompting interventions on six LLMs across Boolean, multiple-choice, and open-ended questions. We find that explicit no-think controls cannot reliably eliminate visible inference. Models instead exhibit "Thinking Inertia": explicit inference persists even under strict controls and becomes more prevalent as the answer space opens. Accuracy remains stable on Boolean and multiple-choice tasks, whereas open-ended tasks reveal a trade-off between answer-only compliance and task accuracy. Rewriting the same questions across answer spaces shows that supplying candidate answers makes answer-only responses easier to produce. These findings establish no-thinking as a non-trivial capability: stopping explicit reasoning cannot be assumed from model settings or instructions alone and deserves systematic evaluation alongside reasoning ability.

As the production of software passes into the hands of software itself, coding agents are increasingly commissioned to build agents fitted to their users' own workflows. This delegation carries a quiet assumption, that the delivered agent will faithfully serve the requirement it was built for, rather than merely appear to. No existing benchmark tests that assumption. We introduce AgentSWE, a benchmark for agent software engineering spanning three stages of the agent development lifecycle: Creation of a complete agent from a natural-language requirement, Editing of production agent codebases, and Optimization toward a measurable target. Each of its 25 tasks is framed as a commission rather than an exam: the builder iterates against a handful of illustrative cases, while acceptance exercises the frozen submission on held-out cases designed to span the full requirement, grading behavior against requirement-derived rubrics under hard pass/fail gates or, for Optimization, against the source benchmark's own objective. Evaluating a range of coding agents across models and harnesses, we find that none comes close to delivering what a requirement asks for: the gap between what a builder demonstrates and what it delivers is the norm, not the corner case. An accompanying empirical analysis of the failure trajectories identifies recurring failure modes, among them overfitting to the visible cases, hollow runtimes behind code that looks complete, and unsupported claims of completion, each a signal by which coding agents can learn to build the agents they are asked for.

Terminal agents are moving beyond local operations toward completing real-world professional tasks. Short trajectories can demonstrate commands and tool calls, but they do not fully show how agents accumulate evidence through exploration, use it to inform judgments, continually adjust strategies based on feedback, and turn intermediate results into final deliverables. High-quality long-horizon trajectories capture these agentic behavioral patterns and their connections within a single problem-solving process, providing richer experience for sustained autonomous work. However, environment synthesis may complete or simplify the key work underlying these patterns in advance and thus remove the need for exploration, implementation, and verification that the agent was meant to perform. To address this, we introduce TerminalHorizon, a fully automated data synthesis engine for long-horizon terminal agents. Starting from real-world professional work, it uses independent execution to identify key work omitted during construction and uses the resulting evidence to guide environment reconstruction. To extend these patterns across stages, it progressively expands tasks under the same goal, introducing new requirements that build on prior results and call for further exploration and improvement. Using this engine, we construct 1.5K task environments and perform supervised fine-tuning of Qwen3.5-27B on the resulting dataset to obtain TerminalHorizon-27B. We achieve substantial performance gains on all 12 benchmarks spanning coding-agent tasks, agentic tasks in real-world production settings, and general reasoning. Multiple RSI case studies, each with a 12-hour research budget, further demonstrate the model's ability to continue experimenting beyond an initial solution, improve its methods based on feedback, and retain effective improvements in its final deliverables.

Video interactive storytelling enables viewers to actively steer how a video unfolds. However, once we allow viewers to intervene during generation, a new challenge arises: The viewer's request can have latent dependencies on both the grounding source and the current rendered video state. These dependencies may not be explicitly stated in any individual input, but emerge only when the source, rendered history, and new viewer intent are considered jointly. Existing interactive video generation systems primarily emphasize following viewer instructions, while source-grounded video generation methods focus on aligning generated content with an external narrative or knowledge source. This leaves a fundamental question underexplored: What context should a generation model ground on during interactive continuation, and how can heterogeneous, unstructured inputs be transformed into such grounding context? In this work, we formulate contextual grounding as the process of transforming heterogeneous input context into an executable constraint model for video generation. To address this challenge, we introduce VIS-Ground, which performs Structured Context Abstraction to recover grounded states and cross-context dependencies, Generation Constraints Induction to project relevant dependencies into candidate-specific constraints, and Constrained Video Generation to enforce these constraints through planning, verification, revision, and rendering. Across three video generation backbones, VIS-Ground consistently achieves the highest overall composite score, reaching an average absolute improvement of 10.3 points over the strongest per-backbone baselines. Detailed analysis further shows gains across both narrative and knowledge grounding, and reveals remaining challenges in dependency extraction, and faithful realization during video rendering.

Autoregressive (AR) grounding models serialize spatial predictions, introducing sequential latency and imposing a causal order on output tokens. We view grounding as visual evidence extraction: objects, locations, and spatial relations are jointly constrained by the image and query, yet their dependencies do not imply an intrinsic left-to-right generation order. This distinction makes bidirectional diffusion a natural fit, allowing spatial hypotheses to emerge in parallel and be jointly refined through iterative denoising. We introduce GroundAnything, a 4B-parameter grounding foundation model that reconciles fast parallel decoding with precise localization through blockwise denoising. Training combines grounding pretraining from public datasets and dedicated data engines, direct AR-to-diffusion conversion with joint AR and diffusion objectives, supervised fine-tuning, and GRPO-based reinforcement post-training. Across 30 grounding benchmarks, our autoregressive variant, GroundAnything-VLM, establishes a new overall state of the art among similarly sized models at 72.42%, remaining competitive with GPT-6 Astra (71.35%). With entropy-guided decoding, GroundAnything also surpasses the prior state of the art at this scale, averaging 61.75% versus 53.32% for the fast MTP-based LocateAnything model. We further explore decoding strategies, showing that an optional self-speculative mode achieves a 4.51times speedup over the AR counterpart with a 0.74 percentage-point drop in COCO F1mIoU. Infrastructure experiments show that progressive inference optimizations translate parallel decoding into practical speedups. These support efficient visual grounding in latency-sensitive real-world systems.

Attention Residuals (AttnRes) enables content-dependent retrieval over network depth, which can be viewed as single-stream softmax attention along the depth axis. Inspired by the success of multi-head attention along the sequence axis, we introduce Multi-Stream Attention Residuals (MSAR), an extension of AttnRes that generalizes this mechanism to multi-head depth attention, yielding substantial performance gains with minimal additional overhead. MSAR draws stream diversity from sequence context through multi-scale causal convolutions, allowing different heads to retrieve historical representations through distinct contextual views while preserving the integrity of the hidden representation. The retrieved streams are combined through lightweight adaptive mixing to generate the single-stream layer input. By retaining single-stream sources and expanding them on demand, MSAR supports multi-stream computation without requiring persistent multi-stream storage or communication. Across all 12 downstream benchmarks at the 18B and 28B MoE scales, MSAR consistently outperforms prior residual variants, including standard residual connections, manifold-constrained Hyper-Connections (mHC), and AttnRes. Scaling-law experiments yield an iso-loss compute speedup of 1.58times over the standard residual baseline, compared with 1.35times for AttnRes and 1.30times for mHC. These results make MSAR a promising residual architecture for large-scale pre-training.

Personal Graphical User Interface (GUI) agents rely on interaction history to infer what a user wants from ambiguous instructions and to anticipate recurring routines. Existing approaches retrieve task-relevant history and append it to the policy's context, implicitly assuming that experience relevant to a task remains useful for each decision within it. We find that this help is largely spent at the first decision: retrieved history strongly improves the opening step of an episode, yet provides little sustained benefit over the remaining 90\% of steps, and offers weak guidance on whether a proactive suggestion is warranted. A relevant record may tell the agent where to begin, but not which past action applies to the current screen or whether a routine is due now. The underlying issue is that relevance does not imply applicability}: relevance is determined at the task level, whereas applicability depends on the situation at decision time. We therefore recast personalization as experience activation and introduce ExpActivator, a training-free framework that activates only the experience applicable to the current situation. During execution, ExpActivator matches each new screen to historical states in the frozen GUI backbone's latent space and supplies the corresponding action as a reference. Before execution, it activates a recurring intent only when the current time and scenario provide sufficient support, and otherwise abstains. Across four GUI backbones, ExpActivator improves within-trajectory step success by 28\% on average, achieves the best personalized execution on every backbone while using about one-fifth as many history tokens, and reaches approximately 2.3times the Matthews correlation coefficient of the strongest proactive baseline. Experience pays where it is activated, not where it is appended.

Historical street-view imagery records urban evolution, but uneven coverage leaves substantial gaps in historical records. Generating plausible past appearances requires restoring changed structures while preserving persistent scene content. We construct VIGOR-his, a decade-spanning cross-view dataset containing 43,653 location-level quadruplets across 11 cities on three continents. Its automated pipeline performs spatial pairing, consistency screening, change classification, and the generation and validation of satellite-based change descriptions and local editing instructions. Based on VIGOR-his, we propose CrossTimeEdit, a model that reformulates historical street-view generation as editing, using recent street views to constrain viewpoint and unchanged appearance and temporal satellite differences as change evidence. Starting from FLUX.2 [Klein] 4B, we train CrossTimeEdit through supervised fine-tuning (SFT) followed by online reinforcement learning (RL). We design three street-view editing criteria, namely Instruction Alignment (IA), Background Preservation (BP), and Quality and Physical Plausibility (QP), as both RL reward dimensions and evaluation metrics. We optimize this multi-reward objective using Within Group Relative Policy Optimization for flow-matching models (Flow-GRPO) with Group reward-Decoupled Normalization Policy Optimization (GDPO), which normalizes each reward dimension before aggregation. CrossTimeEdit improves overall performance across the three editing criteria by 17.12\% over the pretrained baseline and outperforms cross-view generation models in scene consistency, visual realism, and perceptual quality. The implementation code, dataset, and model weights are available at https://luhanwen67.github.io/CrossTimeEdit-release/.

When solving complex spatial reasoning tasks, ordinary humans naturally revisit the scenes from task-relevant viewpoints and mentally reconstruct the spatial layouts from those viewpoints. But can Large Vision-Language Models (LVLMs) proactively think in the same way? We term this ability Active Spatial Mentalization (ASM), an intrinsic component of visual intelligence that remains substantially underdeveloped in current LVLMs. To bridge this gap, we introduce SpatialASM, a scalable framework to internalize ASM into LVLMs through training-time spatial simulation. SpatialASM employs an allocentric novel-view generator to ground hypothetical viewpoints in visual evidence and produces two complementary forms of supervision: i) explicit ASM trajectories that teach LVLMs when and how to adaptively invoke view planning and imagination during spatial reasoning, and ii) automatically-constructed perspective-taking tasks that further strengthens viewpoint-conditioned scene imagination. On seven benchmarks spanning relatively perception-oriented spatial understanding to more challenging tasks requiring multi-image, cross-view and viewpoint-transformation reasoning, SpatialASM yields performance gain on LVLMs of four LVLM backbones in spatial reasoning while retaining their original general vision capabilities. The experimental results demonstrate that our approach improves average accuracy across the seven spatial benchmarks. And the synthesis of ASM trajectories provides a scalable pathway to alleviate the scarcity of spatial Chain-of-Thought (CoT) supervision for eliciting active viewpoint exploration and mental scene simulation.

We reconstruct high-fidelity 3D scene meshes from posed multi-view images without per-scene optimization, across scales ranging from single objects to large outdoor scenes. Per-scene optimization methods lack the learned 3D prior needed when observations are sparse or surfaces are glossy or transparent. Existing generative methods leverage such priors to complete geometry in sparsely observed regions, but typically operate at a fixed resolution over a limited spatial extent, trading spatial coverage against detail. Reconstructing a large scene therefore often requires partitioning it into independently processed overlapping local regions, making it difficult to maintain global geometric consistency. To address these issues, we propose T3lescope, which applies a single fixed-resolution generator across scene scales in an inference-time coarse-to-fine cascade. A coarse level establishes the scene layout, and finer levels perturb and denoise geometry inherited from the coarser level within progressively finer spatial cells to recover surface detail. The model is trained on individual cells at multiple scales and shares its weights across all levels, so no hierarchy is fixed during training, and the number of levels, cell scales, and cell locations are determined at inference time. On indoor, outdoor, and city-scale scenes, T3lescope outperforms feed-forward and generative baselines, matches or surpasses per-scene optimization, and recovers fine structures as well as glossy and transparent surfaces. These results show that our method generalizes across diverse scenes, view counts, and image resolutions. Project page: https://pfnet-research.github.io/t3lescope/

Vision-and-Language Navigation (VLN) has largely focused on a single agent following a single instruction, yet many real-world applications require teams of robots to tackle tasks beyond the capabilities of any individual agent. We present Systematic Multi-Agent Vision-and-Language Navigation, providing, to our knowledge, the first systematic formalization of multi-agent VLN as a constrained coordination problem: each mission consists of subtasks carrying dependency and resource constraints (presence locks and holding chains). A verified four-stage crafting pipeline instantiates the task as MAVLN, comprising 11,724 episodes across 145 scenes with teams of up to four agents under three instruction regimes, accompanied by tailored constraint-aware metrics. We further present TRISS, a coordination-ready navigation system coupling an LLM-based subtask scheduler, a shared topological memory that turns each agent's exploration into team knowledge, and a conflict-aware execution mechanism that realizes simultaneous intentions as collision-free routes. Extensive experiments establish TRISS as a comprehensive baseline and reveal substantial room for improvement across scheduling, planning, and execution, highlighting the challenges of coordinating under MAVLN task constraints. Project page: https://xyz9911.github.io/mavln.

As text-to-SQL systems move beyond demonstrations toward real-world deployment, ambiguity in user questions becomes a primary source of errors. Such ambiguities are often subtle, domain- or data-specific, and can silently cause system outputs to deviate from the user's true intent. Ambiguity is traditionally addressed through conversational clarification, which is often inefficient, cognitively demanding, and poorly aligned with real-world user workflows. We propose structured disambiguation, a new paradigm in which ambiguity is resolved through explicit, constrained interactions rather than free-form dialogue. We construct ARCS (Ambiguity Resolution Corpus for SQL), the first text-to-SQL benchmark featuring naturally occurring, unconstrained ambiguities over real-world databases, with complete annotations of all valid ambiguity points, interpretations, and SQL queries. Experimental results show that text-to-SQL remains challenging in the presence of ambiguity: gpt-6-sol achieves only 51% end-to-end execution accuracy, and no open-source model exceeds 27%.

Large language models have achieved remarkable advances in complex reasoning. However, scaling reasoning post-training with ever-larger corpora can substantially increase training cost without proportional performance gains. In this study, we therefore ask: how far can reasoning supervision be compressed without sacrificing capability? To this end, we propose an uncertainty-guided progressive contraction framework that concentrates supervision and optimization on the reasoning trajectories most valuable for learning. Specifically, rather than treating a teacher-generated chain-of-thought (CoT) as a single realized path, we construct uncertainty-profiled reasoning supervision by building Monte Carlo reasoning trees around intermediate reasoning states to expose process-level uncertainty. This uncertainty first contracts the original corpus into a compact teacher-informed pool. A second, learner-aware contraction then performs online routing during SFT, concentrating optimization according to the student's evolving learning state. Together, the two stages progressively contract both supervision and optimization, substantially reducing the student training budget. Experiments across diverse reasoning benchmarks and LLM families show that our method uses as little as 0.22% of the optimization budget of full-data SFT, over 450x less training exposure, while maintaining or improving reasoning performance. Notably, our framework generalizes well across heterogeneous student families, black-box teachers, and weak-to-strong transfer. This indicates that it is not only a way to accelerate training but can also enhance reasoning capability.

On-policy distillation (OPD) provides teacher supervision on student-generated trajectories, but a single trajectory realizes only one continuation at each decision, limiting supervision coverage under a finite sampling budget. We propose BranchOPD (Branching On-Policy Distillation), which selects informative decision points using student uncertainty and teacher-student disagreement, resamples student-policy continuations from shared prefixes, and applies token-level teacher supervision to both main trajectories and branches. BranchOPD operates at token-level decisions in mathematics and within-turn decisions in agent tasks. Shared-prefix reuse reduces student-generated token costs by approximately 19% on ALFWorld and 31% on WebShop relative to seven independent trajectories. We also introduce protocol-boundary repair (PBR), which uses targeted supervision under protocol constraints to mitigate repetition and wasted generation. BranchOPD outperforms Vanilla OPD and Multi-Rollout OPD (n=7) across all evaluated benchmarks, with gains over Vanilla OPD of 18.55 percentage points in mean mathematics accuracy over 16 sampled answers (avg@16) and 13.8 points in mean ALFWorld ID/OOD success. Mechanistic analyses and selection ablations show that branching at uncertain decisions expands subsequent behavior coverage and outperforms random selection and turn-initial branching.

The Information : A look at OpenAI's safety concerns over loop transformers before shipping its next major model; Microsoft accidentally confirmed GPT-6.1 Sol uses the technique —  OpenAI is grappling with fallout from its use of a controversial innovation in AI design as it works to get its next unreleased model …

Annelise Levy / Bloomberg Law : Apple v. OpenAI: a US judge partly grants Apple's motion for expedited discovery, ordering OpenAI to hand over forensic images of devices and other documents —  Apple Inc. convinced a federal judge in California to force OpenAI Inc. to expedite turning over documents concerning its alleged theft …

Dustin Volz / New York Times : The FBI says it worked with CrowdStrike to dismantle 40+ laptop farms across Texas used to help North Koreans get remote US tech jobs under stolen identities —  F.B.I. investigators worked with the cybersecurity firm CrowdStrike to dismantle so-called laptop farms that steal American identities to earn cash for Pyongyang.

Jon Hicks / GamesIndustry.biz : Xbox CEO Asha Sharma announces Xbox Labs, a small team to test emerging ideas, citing recent game ports and mashups, many of them built with AI tools —  New business unit is charged with testing new projects and judging them by user feedback, spurred by recent mashups and ports delivered with AI coding tools

The San Francisco Standard : Federal prosecutors say a Saudi national was arrested over bugging an OpenAI executive conference room; OpenAI found the audio recorder around September 30 —  An OpenAI engineer has been arrested on suspicion of hiding an audio recording device in an executive conference room …

@openainewsroom : OpenAI says the FBI arrested one of its staffers after the company found a hidden recording device in its offices and used its models to understand the device —  We discovered a sophisticated recording device that was hidden inside one of our offices. We used our models to understand the device and where it had come from, identifying that a specific employee had planted it a few days earlier. We referred the matter to the FBI, and we're grateful to the agen...

Stephanie Palazzolo / The Information : Sources: self-driving simulation software maker Applied Intuition has received investment offers that would value it at $30B+, up from $15B in June —  For the last three years, AI labs like OpenAI, Anthropic and xAI have sucked up a lot of investor attention—as well as nosebleed valuations.

Anthropic : Anthropic details Scion, a git replacement for its monorepo, built by seven engineers with Claude in five months, as daily merges rose from 2.5K in May to 30K+ —  As our merges per day grew more than tenfold, source control became our biggest development bottleneck.

Tom Hals / Reuters : A Delaware jury awards Qualcomm $2 in nominal damages after finding Arm breached its architectural license agreement and sought to disrupt Qualcomm's business —  Chip technology supplier Arm Holdings (O9Ty.F) breached a licensing agreement with Qualcomm (QCOM.O) and intentionally interfered …

Katie Roof / Business Insider : Sources: Decagon, which offers AI customer service agents and was last valued at $4.5B, hired a bank and is in early talks with buyers, including ServiceNow —  Buzzy AI agent startup Decagon could be the latest company swept up in a growing wave of AI dealmaking.

Ram Iyer / TechCrunch : Pluto Security, which monitors AI agents and vibe-coded apps, exits stealth with $42M, including a $34M Series A, and says it has hit $2M+ in ARR since April —  The problem with giving everyone in a company access to AI tools is that people will use them in unexpected ways.

Artie Beaty / ZDNET : Google says users with a standard account can now build personal Android apps for free in AI Studio and will be able to share them with friends in coming months —  It just got a whole lot easier to vibe code the exact Android app you need - no developer experience required.  —  ZDNET's key takeaways

Stevie Bonifield / The Verge : Meta expands its AI-powered “Your Algorithm” feature from Instagram to Facebook and Facebook Reels and says it is using LLMs more in its ranking systems —  Meta's bringing Instagram's “Your Algorithm” feature to Facebook, and using LLMs more to rank content.

Eduard Kovacs / SecurityWeek : Meta says hackers stole some code and personal data of roughly 1,500 people, most of them employees in the US, after compromising some of its GitHub repos —  Most of the affected individuals are Meta employees in the United States and they are being offered credit monitoring services.

John Gruber / Daring Fireball : Apple says HomeView's facial recognition works only if users have a “registered personal device” with Apple Intelligence and Siri AI on the same Wi-Fi network —  Apple Newsroom: … Your first question might be how this facial recognition to identify users works …

众所周知，工蜂蜇人之后会将螫针留在皮肤上自己飞走，而螫针会自行穿透皮肤组织泵送毒液。发表在 PNAS 期刊上的一项研究利用高速摄像技术揭示了蜜蜂螫针如何泵送毒液的过程。工蜂留下螫针的现象被称为自切（autotomy），自然界里相当罕见。螫针由三个互锁部件组成，共同构成了排毒管：一个向外扩展形成主泵室的管心针（stylet），以及两根尖锐的、可前后滑动的刺针（lancets），它们刺入组织并将毒液注入目标。研究人员利用镊子取下活工蜂的螫针，将其插入人工凝胶块，利用体视显微镜和高速彩色摄像技术捕捉了排毒动作。视频显示，连接在每根刺针上的瓣膜在前后滑动泵送时会改变形状。它们在向下冲程时像降落伞一样充气，通过类似活塞的动作形成密封，对泵室施加向下压力以挤出毒液；在向上冲程时则会瘪下去。当这些瓣膜在中间相遇时，它们的形状会再次改变，以便能彼此错开通过。

《巴黎协定》对全球气温上升幅度设定了一个比工业化前水平高 1.5°C 的阈值。现有气候模型预测这一阈值会在 2030 年代突破。但根据《npj Climate and Atmospheric Science》期刊上发表的一项研究，突破时间可能比预期的更快。对截至 2025 年的气温记录的分析显示，2011-2025 年间的全球变暖速率达到了每 10 年上升 0.40°C，比 1971-2025 年间每 10 年暖化速率 0.20°C 翻了一倍。这意味着 1.5°C 的阈值会在 2028 年突破，比此前的预测早了十年。暖化加速主要归因于东亚-北太平洋、欧亚中纬度地区以及南大西洋-南极洲区域变暖趋势的加剧。

代表美国游戏业的行业组织 Entertainment Software Association (ESA) 向美国贸易代表办公室提议将最近炙手可热的游戏破解黑客 voices38 列入恶名盗版市场名单。匿名黑客 voices38 在短时间内发布了一系列使用 Denuvo DRM 的游戏破解补丁，其中包括《霍格沃茨之遗（Hogwarts Legacy）》和《黑神话：悟空》。ESA 的理由是，游戏破解黑客在盗版生态系统中扮演着重要角色，没有破解，盗版网站就无法提供可运行的盗版游戏。ESA 认为将其列入名单有助于版权所有者打击破解活动。

著名基地建造与管理游戏《RimWorld》开发商 Ludeon Studios 宣布释出 v1.7 版本，将游戏引擎从 Unity 切换到自研的 Waylith Engine，性能提升了 2-4 倍。Ludeon Studios 称，游戏的外观和玩法完全一致。相比 v1.6 版本，v1.7 在启动速度、加载大量模组时的速度、地图生成、世界生成、每秒更新次数（TPS）以及几乎所有其它方面都有显著提升。开发商表示，一个多达 1500 个模组的模组包加载速度从 14 分钟缩短到 6 分钟。虽然引擎变了，但存档都能向前向后兼容，v1.6 的存档能在 v1.7 中正常加载，反之亦然。模组开发者无需为 v1.7 修改模组，只需要测试之后标记下兼容 v1.7 即可。

美国曾在 2000 年宣布消灭了麻疹，但因为虚假信息和接种率下降，麻疹疫情正卷土重来，今年至今麻疹病例总数已达到 4080 例，创 1991 年以来的最高纪录。其中宾夕法尼亚州的疫情最为严重，该州今年已经累计报告 1136 例病例，六人死亡。死者包括一名新生儿、一名 6 周大的婴儿、一名 18 岁青年、一名 20 岁青年、一名 40 岁成年人以及一名 39 岁女性。所有死者都未接种疫苗，两名婴儿尚未达到接种疫苗的年龄。宾夕法尼亚州的疫情无放缓迹象，过去一周新增 157 例病例。美国现任卫生部长 Robert F. Kennedy Jr. 是坚定的反疫苗者。

美国太阳能板生产商表示，他们即将迎来一项技术突破，这项突破可能颠覆整个行业，甚至威胁到中国在该行业的垄断地位。这些制造商正在竞相开发新型太阳能板，其发电量比目前市面上大多数同类产品高出 25%。如果成功，这一进步将使世界能以更少的土地和屋顶面积来满足迅速增长的电力需求。新技术被称为叠层太阳能，通过像层层蛋糕一样添加第二层和第三层材料，使太阳能电池板能够将更多的太阳辐射转化为电能。研究人员多年来一直致力于完善叠层太阳能电池板，包括 Tandem PV、First Solar 和韩华 Qcells 在内的几家公司表示，在成功测试这些设备并获得技术机构认证后，他们正准备很快将此类电池板投入商业销售。但新的叠层太阳能板及其制造商将面临许多挑战。电力生产商将不愿购买过于昂贵的新设备，除非制造商能让他们相信，叠层太阳能电池的性能衰减速度不会比传统太阳能电池更快。

WHO 发布报告称，俄罗斯西伯利亚伊尔库茨克州一所鼠疫防治研究中心的实验室工作人员本月因肺炎死亡，病因仍未查明，当地未报告鼠疫病例。由于存在关键的不确定因素，WHO 无法评估事件风险，无法判断俄罗斯的公共卫生措施是否足以防止不明疾病的传播。WHO 称它是在 10 月 2 日通过媒体报道获悉了实验室女研究员的死亡事件，它随后在 10 月 3 日、6 日、7 日和 8 日索取相关信息。俄罗斯于 10 月 6 日和 9 日回应了信息索取请求，称未记录到鼠疫病例。该研究员是在出现急性呼吸道病毒感染症状后，因严重肺炎死亡。医生将死因归类为病因不明的肺炎，多种高威胁病原体检测结果均为阴性。密接者也均未携带高威胁病原体。俄罗斯当局对接触者进行了 5500 次检测，所有接触者身上均未检测到实验室研究项目涉及的高威胁病原体。俄罗斯否认研究员的死因是鼠疫，称她接种了鼠疫疫苗，实验室没有发生鼠疫杆菌泄露事件。

生活在英国海外领地圣赫勒拿岛的巨龟乔纳森是已知最长寿的陆生动物，它估计是在 1832 年孵化的，目前年龄 194 岁。对其基因组的研究发现，它基因组中与线粒体功能相关的部分的活性堪比该物种 5 岁个体的水平。高效运转的线粒体可以为 DNA 修复系统供给更多能量，以对抗衰老带来的损伤。这或许就是它预估寿命达到 194 岁的原因。乔纳森是一只阿尔达布拉巨型陆龟，而阿尔达布拉巨龟的平均寿命约 80 年。研究团队将其与另外 4 只阿尔达布拉巨龟的 DNA 作比对，找到了287处专属于乔纳森的基因变异，其中 41 处变异被认为具有实际功能意义，会切实影响生物体的生理活动、生存状态与生命调控。有 12 处变异同样出现在其他长寿动物物种的基因数据库中。

Mozilla 释出了 Firefox 158。主要变化是默认启用了 JPEG XL 图像格式的支持。JPEG XL 是一种免版税的位图文件格式，支持有损和无损压缩。默认支持 JPEG XL 表明它不再是一项实验性功能，意味着其图像格式 .jxl 成为 WebP、JPEG、PNG 和 GIF 等图像格式之外的又一选择。图像请求的默认 HTTP Accept 请求头如今也支持了 JPEG XL 图像格式 image/jxl。其它变化包括点击表单字段或选择从来源填充时 Smart Window 能更快的填写表格，CSS 类型化算术支持使用 calc()等函数，等等。

黑客远程控制了法国数百家超市的联网制冷系统，部分冷藏和冷冻设备的温度被调高至 50C，导致食品变质，甚至引发火灾。法国下令对此展开调查。欧洲其它国家如西班牙、葡萄牙、波兰、德国和比利时也报告了类似的案件。在法国，家乐福报告有 80 家门店受到影响，不二价（Monoprix）有 5% 的门店遭到攻击，Les Mousquetaires 旗下约 150 家 Intermarché 超市的制冷设备温度被调高至 50C 并持续了六小时。超市的制冷系统通常由控制面板管理，这些控制面板负责调控全店的冷藏和冷冻设备，支持互联网访问，允许维护公司在任何地点进行远程调节。丹麦 Danfoss 公司提供的控制软件此前发现有漏洞。安全公司 Claroty 今年八月披露 Danfoss 的 AK-SM 800A 控制器存在的漏洞，允许攻击者绕过身份验证控制，在入侵设备上执行代码。该公司释出的软件更新修复了这些问题。Danfoss 声称受影响的超市设备运行的都是过时的固件，它一直建议客户更新到最新固件。

加州公司 Atomic Canyon 与核工业组织合作推出了针对核反应堆运营的 AI 助手 NIVA——代表 Nuclear Industry Virtual Assistant。该 AI 助手已进行试点，目前向美国全部 94 座核反应堆开放使用。另一家核能领域的 AI 初创公司 Nuclearn 表示其产品已服务于逾 65 家美国合作伙伴，其中包括传统核电站，也包括开发先进小型核反应堆的公司如 NuScale Power。Nuclearn 首席财务官兼联合创始人 Jerrold Vincent 指出，AI 是解决核工业最紧迫难题的理想工具。核反应堆运营面临的难题包括了繁琐的监管规定以及复杂的管理与维护系统，这恰恰是 AI 擅长处理的任务。核反应堆的运营需要处理大量文书工作。核电站会产生海量文档，用于证明符合监管要求，记录针对各种潜在问题的应对过程。Nuclearn 的 AI 产品能帮助核电站更高效检索数据库，更快向联邦政府提交文件，或查找过去是如何解决特定问题的，它称得上是核能领域的 ChatGPT。

亚马逊最近更新了一系列自有品牌产品线，其中自有品牌平板产品发生了重大变化。过去 15 年亚马逊一直销售 Fire 平板电脑，如今 Fire 品牌名被放弃，亚马逊改为推出 Alexa 平板，而且运行的是经过 Google 认证的 Android 操作系统。以前 Fire 平板运行的 Fire OS 是亚马逊定制的 Android 版本，亚马逊还提供了自己的应用商店，如今应用商店已经关闭，Alexa 平板运行的是完全 Google 化的 Android 操作系统，以及 Google Play 商店和其它常规应用。Alexa 平板共三款：Alexa Tablet 8 起售价为 230 美元，Tablet 11 起售价 330 美元，Tablet 12 Pro 起售价为 500 美元。前两款使用了联发科入门级处理器 8189，第三款使用了联发科的天玑 8400。该平板的一大亚马逊特色是用户可通过唤醒词 Alexa 与 AI 机器人进行对话。这些平板将于 10 月 14 日上市。

2026 年诺贝尔经济学奖授予了斯坦福大学的 Robert E. Hall 和哥伦比亚大学的 Michael Woodford，以表彰两人突破传统凯恩斯主义框架，从家庭和企业等微观主体行为出发建立经济模型，证明无数家庭和企业的日常决策最终会塑造整个经济运行方向。这套理论如今已成为全球央行决策的重要基础，也成为应对衰退、通胀和能源危机的重要工具。数十年来对凯恩斯模型的应用已暴露出其局限性，包括未能有效抑制失业，反而推高通货膨胀。

一项对数百家公司编程实践的研究发现，人工代码审查是 AI 编程工具整体效率的一大瓶颈，导致几乎没有证据表明企业使用 AI 工具能提高软件产量或减少就业。研究涵盖了 2021-2026 年 3 月间逾 700 家软件开发公司逾 70 万员工的 3 亿条工作事件（如 commits 和 pull requests）以及问题管理软件数据。公司在引入 AI 编程智能体后，代码总行数平均增加了 30%，commit 总数增加了 20%，pull requests 总数平均增加了 23%。但代码行数的增加并没有直接转化为软件产出的提升。原因在于代码审查流程，引入 AI 编程智能体后，代码审查的平均耗时明显延长。总体而言，引入 AI 智能体后，代码审查流程时间平均增加了 49%。引入 AI 智能体后，代码审查的员工人数增加了 14%。虽然 AI 智能体贡献了部分代码审查意见和 pull requests，绝大部分审查工作仍然是由人类完成的。

早在 1970 年代，面对石化产品在全球暖化中的作用，美国石化行业制定了一系列策略，旨在推迟气候行动以维护自身经济利益。行业组织 American Petroleum Institute 在 1980 年发布了一份政策手册，歪曲了科学研究结果并淡化全球变暖的严重性。研究发现，2008-2018 年间美国石化行业组织在与气候变化相关的政治活动上总共投入了约 34 亿美元。牲畜业排放的甲烷对全球暖化也有重要影响。发表在 PLOS Climate 期刊上的一项研究发现，美国主要畜牧业行业组织 National Cattlemen’s Association(NCA)采用了与石化行业相似的策略。美国在很长时间里是全球最大的牛肉生产国，最近几年被巴西超越。该组织早在 1989 年就知道了牲畜对全球变暖的贡献，但一直致力于淡化甲烷的排放问题、质疑气候科学，以及反对减少牛肉消费。