OrangeBot.AI Digest — 2026-11-03

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Computer graphics is built on code. Large language model (LLM) agents can now generate graphics code and iteratively refine it through various feedback. This capability opens the door to a potentially new paradigm of computer graphics, with graphics algorithms increasingly built around agentic systems to automate progressively complex tasks. Over the past years, research on agentic graphics has expanded rapidly across a diverse range of tasks. This rapid growth makes it increasingly difficult for manual literature reviews to keep pace with new developments. At the same time, similar ideas are often described using different terminology across these tasks, further complicating efforts to develop a systematic understanding of the field. In this report, we address these challenges through two contributions. First, we introduce a unified taxonomy for characterizing agentic systems across diverse computer graphics tasks. Second, we develop an agentic literature system that can assist human researchers in analyzing existing and newly published papers at scale. Building on these contributions, we analyze emerging research trends and discuss open challenges in coordinating agents and tools across graphics tasks, designing interfaces and representations for human-agent collaboration, sustaining long-horizon planning and refinement, and reliably evaluating interactive graphics systems.

Real-time long-video generation requires continuous output with consistent appearance and natural motion. Existing chunk-wise autoregressive methods face challenges in both generation efficiency and long-video quality: serialized denoising and history-cache refresh require two forward passes even with one-step denoising, and longer rollouts can degrade quality and cause camera drift. We propose Wave Forcing (WF), which co-designs generation, training, and execution for continuous video output with multi-step denoising. WF uses chunk-wise causal attention to access earlier, cleaner chunks before their denoising is complete, and distributes staggered denoising and cache-refresh stages across full-model replicas to achieve 1F1C: one output chunk per concurrent forward round after pipeline fill. To adapt the model to this continuous generation process, we develop a three-stage training procedure that successively introduces mixed-noise denoising, motion supervision, and causal adaptation using self-generated history. To execute this process efficiently, Wave Runtime (WaveRT) uses Wave Parallelism (WP) for early K/V delivery and communication-computation overlap, together with optimized kernels and pipelined VAE decoding. Experiments on Wan2.1-T2V-1.3B show that 5-step WF maintains overall quality comparable to Rolling Forcing while achieving higher Dynamic Degree on both short and long videos. Our system efficiency experiments show that 4-step WF achieves 147.6 DiT FPS and 134.8 end-to-end FPS on eight H200 GPUs, yielding 7.07x the single-GPU end-to-end throughput. In DiT-only tests with matched SageAttention+W8A8 backends, denoising steps, and GPU counts, WP delivers up to 2.53x the throughput of the faster of the Ulysses and Ring context-parallel baselines across six model scales (1.3B-14B).

Despite recent advances in CUAs, their development remains constrained by the limited diversity, controllability, and reliability of interaction data. Moreover, conventional optimization pipelines lack iterative feedback, while existing recursive improvement approaches often rely on self-generated experiences, limiting ability expansion and yielding diminishing returns. To address these challenges, we introduce CUA-Master, a nested double-loop recursive improvement framework. At the system level, an outer world-data-model flywheel continuously incorporates open-world corpus and leverages evolving model weaknesses to guide subsequent data construction and optimization iterations. At the data level, an inner flywheel couples open-world seeding and autocatalytic propagation. The former mines authentic workflows as seed tasks from instructional videos, and employs visual-semantic dual-path trajectory reconstruction; The latter transforms these seeds into reusable primitives for controllable synthesis of diverse, complex, and verifiable tasks. To enable efficient and reliable trajectory collection, we further introduce a feedback-guided snapshot-rollback retry mechanism in various real environments. Extensive experiments demonstrate that CUA-Master-27B achieves competitive performance across GUI-only and GUI-CLI hybrid benchmarks, scoring 80.2 on OSWorld Verified, 36.9 on OSWorld 2.0, 43.9 on WeaveBench, and 25.0 on Agents' Last Exam. Our framework also substantially improves the quality and efficiency of data synthesis, and exhibits continuous gains across recursive iterations.

Rapid advances in cowork agents are shifting human-AI collaboration toward real-world automation, delivering software, designs, and other artifacts. However, such productivity work demands both high-quality delivery on complex productivity tasks and continued monitoring and decision-making over long horizons, a combination that remains an open challenge. We introduce CoworkOS, a general-purpose harness for long-horizon work that separates task strategy from execution management: the model decides actions, delegation, and acceptance, while the runtime maintains state and feedback. Its three-layer architecture enables direct execution or delegation to Subagents, synchronously or asynchronously, under stable responsibilities. Through a shared application layer, humans and agents inspect and revise the same evolving documents, presentations, CAD models, and Blender scenes, so human-in-the-loop participation can enter both through conversation and through direct modification of the work itself. To sustain continuity across agents and sessions, CoworkOS further introduces hierarchical workspaces and persistent semantic memory, so working files, key decisions, and long-running Job logs remain shareable, reusable, and resumable after interruption. Across 12 benchmarks and 6 categories of real-world applications, CoworkOS proves highly competitive with frontier agent applications such as Codex and DeepSeek Harness. As a sustained-work case study, CoworkOS uses GLM-5.3 as its backbone to conduct RSI-driven automated post-training on Qwen3.6-35B-A3B. Over five rounds, the model's average score across eight benchmarks rose from 33.19 to 42.95 (+9.76). Looking ahead, we believe CoworkOS opens a meaningful path toward general cowork-agent intelligence.

Video generation is shifting from end-to-end synthesis toward agentic generation, where planning, tool use, and feedback enable coherent, controllable multi-shot videos. In parallel, video world models provide action-conditioned observations and predicted futures, serving as interactive environments for agents. We study their intersection through the agent harness: an executable runtime that constructs context, exposes tools, maintains state, routes feedback, and manages budgets around video models. This report examines three directions. First, agentic video generation, where harnesses coordinate scripting, shot planning, asset management, production, and editing, alongside the evaluation challenges of these systems. Second, agentic world modeling, where harnesses mediate observation, control, execution, and state. We discuss hybrid designs in which game engines or agent-written programs maintain world state while video models render observations, and how agents and world models can mutually evaluate and improve one another. Third, recursive self-improvement (RSI) in video model development, distinguishing output revisions, updates to harness state, generator parameters, and evaluators, and changes to the improvement procedure itself. We identify how these improvements feed back into subsequent development. Across these directions, we analyze essential agent capabilities, including audiovisual perception, long-horizon planning, memory, code-based interaction, feedback-guided control, skill reuse, and multi-agent coordination. We conclude with challenges in autonomous video production, lifelong memory, programmable hybrid control, autonomous model development, provenance, cost accounting, and evaluation linking video quality and task performance to sustained improvements in models and harnesses.

Robot-use Agents (RUAs) have shown promising zero-shot manipulation capabilities, but reliance on frontier, proprietary models limits research into further training. To democratize RUA research, we introduce uni-robot-agent, a unified harness for competitive open-weight robot use that supports further training. Without privileged inputs, an out-of-the-box Qwen3.8-Flash-Next achieves a RoboDojo Score of 31.39, surpassing the published GPT-6 Astra-RoboProbe result of 28.97 and many VLA/WAM baselines, and performs competitively on 19 real-world tasks. Our analysis across model generations, families, and sizes links effective robot use to four general capabilities, suggesting that future RUA training should develop them jointly.

Coding agents solve repository issues through long sequences of exploration, editing, and validation. These trajectories are increasingly used as post-training data, so how they are collected shapes what the trained model learns. A common approach is to sample multiple trajectories independently for each issue and then select or curate them. However, as more trajectories are sampled independently, later trajectories become increasingly redundant, even though earlier trajectories could guide where subsequent collection effort should be spent. A natural way to use this history is to expose it to the coding agent as advice. But training on such trajectories can make the model rely on advice that is unavailable at test time, creating a train-test mismatch. Instead, we argue that history should guide where collection effort is spent, while the coding agent decides what to generate. We introduce SWE-Trail, a trajectory collector that uses earlier trajectories to guide subsequent collection while keeping that history outside the coding agent's context. During a new rollout, SWE-Trail uses history to identify a state where additional collection effort may be useful. There, the coding agent proposes candidate next actions from its ordinary context, and SWE-Trail briefly executes them from the same checkpoint to decide which route continues. We evaluate collection approaches by training student models on the trajectories they produce. For a 124B student initialized from Ling-3.0-flash-base, SWE-Trail improves mean resolution over independent sampling by 2.33 and 1.60 percentage points on SWE-bench Multilingual and SWE-bench Pro, respectively. Without history, the same pipeline produces more resolved trajectories than independent sampling, but improves by only 0.11 points on Multilingual. These results show the value of using history to guide collection without exposing it to the coding agent.

Scaling computer-use agents from individual actions to complex software work requires experience that connects decisions to complete outcomes. We present NorthCUA, an integrated system for turbocharging computer-use agents through experience at scale. Its Experience Flywheel supplies diverse executable tasks to the NorthCUA Harness, which sustains long interactions, records decisions, and checks task outcomes. The Flywheel uses these records to curate verified trajectories. For unsolved tasks, recursive self-improvement evolves reusable skills and preserves verified milestones so later attempts build on earlier progress. Staged Learning combines this experience with grounding data from the Flywheel to develop GUI competence through mid-training and long-horizon supervised fine-tuning. The policy then learns from fresh attempts in the same Harness through joint desktop-web reinforcement learning, closing the loop between experience production and policy improvement. The resulting Nano, Mini, and Pro family achieves state-of-the-art results among open-source models across GUI grounding, desktop execution, and Web interaction under the reported evaluation settings. For more responsive Web workflows, NorthCUA-Ultrafast pairs direct action selection with open-ended generation on pre-RL Nano, offering a measured quality-latency tradeoff. Together, these advances establish NorthCUA as a high-performing model family and a reusable foundation for agents that carry complex software work from intent to completion.

Recent advances in large (vision) language models have enabled computer-use agents that automate digital workflows via fine-grained keyboard and mouse operations. However, such agents remain fragile in long-horizon settings, where errors accumulate and executed actions leave unintended side effects, making the ability to recover from failures critical. Yet, existing remedies remain inadequate: environment resets are infeasible in proprietary settings, action replay is prohibitively expensive over long horizons, and API-based rollback is limited to web environments, leaving model-based recovery largely unexplored. To address this gap, we present ReTrace, a scalable evaluation framework for systematically studying recovery behavior in computer-use agents. We formalize Transition-Consistent Recovery: identical downstream actions from the recovered and target states must yield identical outcomes. We operationalize this criterion through goal-oriented action replay following controlled error injection. Using ReTrace, we present the first systematic study of autonomous, model-based recovery, revealing that (i) ideal recovery can unlock model's latent task completion capabilities, (ii) current computer-use agents fail to exploit their latent recovery capabilities, and (iii) accurate model-based recovery yields measurable end-to-end gains. Together, these findings establish recovery as a fundamental capability for building reliable computer-use agents. Our code and dataset is available at https://github.com/BangProx/ReTRACE.

Traditional image cropping assumes the photograph already contains the composition worth keeping, and asks only where to crop. A full 360^{circ} scene offers no such guarantee: the viewing direction in a scene has to be discovered before anything can be framed. We present PhotoGuider, a measure-then-frame method that answers both questions with compact learned scorers over one shared set of composition-aware features. The designed features are extracted using foundation and task-specific vision models and structured into four photographic tokens: Subject, Composition, Aesthetics, and Integrity. Our MLLM explainer is fine-tuned to generate composition-related explanations conditioned on images and measured token assessments. To our knowledge, PhotoGuider is the first method to combine explicit composition-based direction selection in 360^{circ} panoramas with refined framing, camera-parameter output for native re-rendering, and measurement-conditioned explanations. On two standard 2D cropping benchmarks, PhotoGuider achieves the highest average mIoU performance against ground-truth crops. In a blind ranking study, 120 participants ranked three cropping methods over 100 panoramic scenes from the public Poly Haven HDRI library, placing our framings first in 53.2% of cases, against 27.3% and 19.5% for two strong alternatives. Project page: https://ntuneillee.github.io/research/photoguider/

On-policy distillation (OPD) trains a student on its own generations by matching the teacher's token probabilities. These probabilities entangle two kinds of information: which tokens the teacher prefers, and how confident it is. The latter reflects the teacher's own capacity and calibration, which a smaller student may be unable to reproduce. Because the teacher temperature changes these values without changing the ordering, OPD is also sensitive to the temperature choice. We ask whether the teacher's ordering of candidate tokens is enough, and propose Order-Only On-Policy Distillation (O^{3}PD). At each student-generated prefix, O^{3}PD trains the student to reproduce the order of the teacher's top-K tokens, without fitting their probability values. The objective is therefore invariant to the teacher temperature and needs no temperature tuning. Across six teacher--student pairs, O^{3}PD outperforms OPD on the average over HMMT25, AIME24, and AIME25 in every pair, by up to 2.9 points, with gains of up to 5.6 points on AIME25. Other ranking losses yield similar gains, so the benefit comes from ordinal supervision itself. Analysis shows that O^{3}PD departs further from the teacher's probabilities than OPD, yet reproduces its ranking more faithfully and yields a stronger student. The ranking, not the probability values, thus carries the knowledge that on-policy distillation needs to transfer.

Multimodal large language models (MLLMs) can generate detailed video captions, yet they may overemphasize minor details while overlooking essential context and key events. We argue that not all visual evidence deserves equal attention, as the importance of visual information depends on the content of each video. Motivated by this observation, we propose Reviso, an agentic video captioning framework that learns what to observe and how to gather relevant visual evidence. From a low-frame-rate global preview, Reviso constructs a hierarchical Context--Focus--Points (CFP) observation plan that identifies the video context, prioritizes key aspects, and defines concrete observation targets and corresponding tools. Guided by this plan, Reviso performs goal-directed active perception by selectively revisiting temporal segments and combining tools for fine-grained inspection. It interprets the acquired evidence, updates its plan as needed, and integrates global and local information to generate the final caption. To learn this observation strategy, we develop a verification-guided pipeline that constructs high-quality training trajectories through factual verification and teacher-guided refinement. We further train Reviso using Group Relative Policy Optimization (GRPO), incorporating a key-fact reward for caption quality, a Plan Alignment Reward (PAR) for identifying important observation targets, and an Unproductive Tool-Call Penalty (UTCP) that adjusts observation-cost penalties based on caption quality. Experiments across four video captioning benchmarks show that Reviso outperforms the evaluated open-source baselines, while its generated captions better support downstream video question answering. These findings highlight the effectiveness and potential of priority-guided active observation for video captioning.

Research taste, the judgment of what to read, which problems to pursue, and which methods to try, increasingly shapes the work of AI scientists, yet it remains far less understood than their ability to execute research. We introduce IdeaTaste, a benchmark that scores an LLM by how closely the research proposals it writes from a literature snapshot frozen at 2025 match the work the same communities published in 2026. Our experiments span six contemporary language models and four research areas from established to emerging fields, covering more than 90{,}000 proposals in total. LLM agents fall well short of human communities in both the literature they cite and the ideas they state, reaching about 26\% of the human level on citation matching and only 18\% on idea matching. Their populations can cite as diversely as a community and take up its tasks and methods, yet rarely arrive at the concrete approaches and research plans it pursues. Neither richer field context nor larger populations substantially close this gap. In blinded expert review, AI proposals trail the 2026 papers least in presentation and most in expected success, echoing their gap in concrete approaches, and those whose ideas no human paper shares fare worst, suggesting weaker directions rather than overlooked opportunities. These results indicate that the main bottleneck in AI research taste lies less in finding the directions a community works on than in turning them into the concrete approaches it goes on to pursue. We release IdeaTaste as an open, continuously updated leaderboard (https://cxcscmu.github.io/IdeaTaste) that benchmarks contemporary AI proposals against future publication cycles.

Prediction errors from video world models are often treated as physical surprise, but they can also reflect appearance, static content, motion, or ordinary prediction difficulty. We present an evaluation framework that separates what frozen representations contain, how they organize appearance and physical differences, and what changes a model's own prediction error. On IntPhys2, a benchmark of physically possible and impossible events, we compare representations from latent prediction, masked pixel reconstruction, and simple visual statistics. Appearance remains easy to decode, while V-JEPA2 exposes modest information about whether events are possible or impossible. Looking at the latent geometry, appearance changes become smaller compared with differences between videos, their directions line up more consistently across scenes, and their overlap with physical changes decreases in deeper layers. Across action-conditioned models, incorrect actions raise prediction error, while appearance changes also produce substantial responses. In Physion++ video clips, repeating a single frame can produce higher error than the original clip, while changing color throughout the clip can lower it. Using Kubric, a simulation and rendering framework, we find that impossible events can produce lower error than possible events. A fixed error threshold also incorrectly flags many possible events as impossible. A model can therefore represent physical information and respond to incorrect actions without its native prediction error reliably measuring physical surprise.

Embedding models underpin virtually every modern AI system, from search engines to recommendation systems. Despite shared mathematical foundations, research on embedding models remains fragmented by modality: the communities develop largely in isolation, seldom exchanging insights. We argue that this fragmentation obscures a deeper convergence. Driven by contrastive learning, Transformer architectures, and a granularity shift, embedding research across modalities is converging toward a common set of principles, even as modality-specific challenges remain underexplored. To reason across this convergence without conflating it, we propose a framework of three progressively harder capabilities: coverage, comparability, and compositionality. Three paths toward unification satisfy these in turn: transformation broadens coverage, alignment enables comparability, and joint embedding, the terminus we advocate, aims at compositionality. This position paper traces the resulting evolution, providing the first unified, cross-cutting treatment of omnimodal embedding research.

Rashi Shrivastava / Forbes : Traversal, which provides AI agents for site reliability engineering, raised $120M co-led by Kleiner Perkins, Redpoint, and Khosla at a $1B valuation —  Traversal has raised $120 million in funding to help companies like American Express and Pepsi troubleshoot site outages.  —  for The Prompt

Reuters : Block reports Q3 revenue up 10% YoY to $6.7B, above est., Cash App gross profit up 27% to $2.07B, raises its full-year profit outlook; XYZ jumps 5%+ after hours —  Jack Dorsey-led Block (XYZ.N) raised its full-year gross profit outlook on Tuesday after posting a rise in third-quarter earnings …

David Swan / The Age : Canva says it is restructuring, which will impact 1,271 workers, or 22% of its workforce, as it merges its product, growth, and platform teams —  Canva will cut more than 1000 jobs in a major restructure in response to slowing growth at the design giant.  —  Co-founder and chief executive …

Tobias Mann / The Register : Revolut says it cut AI coding inference costs by ~90% in less than a year by making self-hosted GLM models its default over commercial tools like Claude Code —  Revolut says that in less than a year it's managed to slash its AI coding inference costs by roughly 90 percent by making self-hosted models …

Reuters : Super Micro reports Q1 revenue up 298% YoY to $19.95B, vs. $16.39B est., and forecasts Q2 revenue above estimates; SMCI jumps 7%+ after hours —  Super Micro Computer (SMCI.O) forecast second-quarter revenue above Wall Street estimates on Tuesday as strong demand for its AI-optimized servers boosted sales …

Katie Tarasov / CNBC : AMD reports Q3 revenue up 45% YoY to $13.38B, vs. $13.09B est., Data Center revenue up 97% to $8.5B, forecasts Q4 revenue above est.; AMD jumps 7%+ after hours —  Advanced Micro Devices reported third-quarter earnings on Tuesday that beat Wall Street's expectations, with revenue jump 45% …

Jason Schreier / Bloomberg : Sources: Ubisoft and at least two other gaming companies offered to acquire Arkane Studios from Microsoft, which is exploring options before a December deadline —  Microsoft's gaming unit is looking to divest the French company behind ‘Dishonored’ and 'Marvel's Blade'

Anthropic : Anthropic releases FlashProve, an open-source prototype that cryptographically verifies an AI response came from a given model, not one that a router swapped in —  Roy Rinberg (Anthropic), Alex Allain (Anthropic); Alessandro Chiesa (EPFL, StarkWare), Miranda Christ (MIT, RESI), Rahul Dalal …

Mike Allen / Axios : OpenAI hires Jill Hazelbaker, Uber's president and chief corporate affairs officer, as its new chief corporate affairs officer, reporting to Greg Brockman —  Jill Hazelbaker, Uber's president and chief corporate affairs officer, is moving to OpenAI in the new role of chief corporate affairs officer …

Bloomberg : Apple launches iOS 27.3 beta, adding Apple Intelligence in China with an Alibaba-powered content filter, and watchOS 27.3 beta, with Siri Recap and Live Rewind —  Apple Inc. rolled out the first developer beta versions of iOS 27.3 and watchOS 27.3 on Tuesday, putting new features …

Karen Freifeld / Reuters : Sources: US officials have held up approval for US chipmaking equipment needed by SK Hynix to expand in China, complicating its plans for new plants in New York —  US officials have held up approval for American chipmaking equipment needed by SK Hynix (000660.KS) to expand production in China …

Samantha Subin / CNBC : CrowdStrike debuts an AI kill switch in Falcon Guardian to stop internal AI agents that go rogue, and announces a Falcon Guardian integration with Claude Cowork —  CrowdStrike is betting that it has a solution to rogue artificial intelligence.  —  The cybersecurity vendor on Tuesday launched …

Apple Developer : Apple opens Siri AI to all developers, letting any app on iOS 27.3 beta and other 27.3 OS betas bring its content and actions into Siri AI by using App Intents —  Starting today with the iOS 27.3, iPadOS 27.3, macOS 27.3, and visionOS 27.3 betas, a new Developer Preview enables any developer …

Steven Lee Myers / New York Times : How OpenAI, Anthropic, and others are trying to prevent anyone from using their AI tools to disrupt the US midterms, including Anthropic's first-ever “war room” —  OpenAI, Anthropic and other tech companies have vowed to protect Tuesday's elections from efforts to undermine democracy.

Bloomberg : Sources: Meta eyes $20B+ from a potential cross-border investment-grade bond offering, its first major debt sale in six months and its debut offering in euros —  Meta Platforms Inc. is eyeing at least $20 billion from a potential cross-border investment-grade bond offering …

《纽约时报》、《芝加哥论坛报》和《奥兰多哨兵报》等美国出版商起诉 Brave Software，指控其未经许可抓取网站内容，并帮助用户绕过付费墙。Brave Software 被控将其抓取内容的索引数据库 Brave Index 提供给 Anthropic、Perplexity、Mistral、xAI 和 Cohere 等 AI 公司，通过 Paywall blocker 功能帮助用户绕过数百家新闻媒体的付费墙。《纽约时报》发言人 Graham James 在一份声明中表示：“我们支持合乎道德且负责任的使用与开发 AI，但 Brave 公然蔑视现行版权法，对《纽约时报》多次要求其停止侵权行为的诉求置之不理。我们提起此项诉讼，旨在追究 Brave 的责任——它窃取了我们的内容，不仅用于支撑其自身产品，还将其提供给部分全球最知名、资源最雄厚的生成式 AI 公司。”

Google 宣布美国 K-12 学校学生使用的软件将默认关闭所有 AI 功能，除非学校管理人员自己选择启用。此举旨在回应教育工作者和家长的诉求，他们希望在孩子与 AI 互动上有更多的掌控权。今天 AI 功能已成为 Google 商用搜索引擎的标配，Google 负责教育的副总裁 Chris Phillips 表示，搜索与 AI 已深度融合，如果学校禁用了 AI，那么学生将无法使用搜索。美国 K-12 学校广泛使用 Google 的软件，包括 Gmail、Docs 以及作业平台 Classroom。Google 的 Chromebook 是美国学校最广泛使用的笔记本电脑。

Fedora Linux 项目释出了 v45。Fedora Linux 项目同时释出了 Fedora Workstation、Fedora KDE Plasma Desktop、Fedora Server、Fedora Cloud、Fedora CoreOS 和 Fedora IoT，以及 Atomic Desktops 等众多衍生版本。Fedora Linux 45 的主要变化包括：支持通过 Anaconda 或 Kickstart 在 Stratis 存储上进行安装，移除 NetworkManager 不再维护的 VPN 插件，GNOME 和 KDE ​​Plasma 桌面环境的最新版本，完善 Wayland 支持，改进分数缩放，gcc 16.2、binutils 2.47、glibc 2.44、gdb 17.2、Fontconfig 2.18、LLVM 23 等开发工具的新版本。更多变化可浏览 ChangeSet。

血细胞并不容易形成化石。大多数血细胞在死亡后不久便会分解，这使得找到令人信服的、非常古老的血液痕迹非常困难。然而澳大利亚科廷大学的研究团队在 PNAS 上发表论文称，凭借多条证据，他们在巴西一只 1.15 亿年前的恐龙遗骸中发现了最古老的红细胞化石。这项研究的对象是巴西恐龙“阿拉塔龙”的骨骼化石。研究人员估计，这只恐龙体长约 3 米、重约 34 公斤，死亡后遗体沉入水底，微生物生长并将其掩埋，从而保存了下来。在显微镜下，研究人员在这只恐龙的骨骼中发现化石细胞的迹象：这些结构呈圆形，中心凹陷，每个细胞平均长 23 微米、宽 20 微米，嵌在曾经让血管穿过骨骼的通道中。下一步则是寻找化学线索，以证明这些结构确实含有血细胞，而不仅仅是形似血细胞的矿物质。在他们进行的近 30 项试验中，有一项是将骨样本研磨成粉后用激光照射，从样本散射回来的光显示出血红素的存在。血红素是一种环状化合物，其中的铁元素有助于红细胞运输氧气。研究人员还发现了大量降解形式的血红素。

轻量级桌面环境 Enlightenment 项目释出了 v0.28.0。主要变化包括：初步支持 NetBSD，支持 Network Manager，原生支持 PipeWire，新的剪贴板管理器，电池管理、AC 轮询等相关代码进行重写，新增设置电池阈值和支持 ACPI 电源配置等等，以及大量 bug 修复和更改。

全球在建核电站已增至 79 个机组，达到 40 年来的高水平。据世界核协会统计，2026 年全球有 11 个新核电机组开工建设，截至 10 月 16 日，在建核电机组数量达到 79 个。这是 1986 年（91 个）以来的高位。1986 年是切尔诺贝利核电站事故发生的年份，此后全球新建核电机组数量有所减少。如果将预计未来 15 年内投入运营的正在规划的核电机组也计算在内，数量将达到 201 个。目前全球在运核电机组共有 443 个。即使考虑到因老化而退役的核电机组，到 2040 年前后，全球在运核电机组数量仍有可能超过 600 个。目前全球在建的79个核电机组中，仅中国就有38个，约占总数的一半，其次是印度（8个）和俄罗斯（7个）。从处于规划阶段的核电机组来看，中国同样遥遥领先，达到 42 个，其次是俄罗斯（23个）和印度（14个）。

Tor Browser 16.0 释出，它是首个基于 Firefox ESR 153 的稳定版本。主要变化包括：Linux 版本支持 64 位 ARM 架构，重新设计设置，名为通用窗口标题（generic window titles）的功能有助于防止其它应用窥探用户的浏览器活动，Android 版本使用 Google 的 Material Design 3 更新了界面，停止支持 32 位 x86 Linux 系统，等等。更多变化可浏览更新日志。

拯救失败的 NASA Neil Gehrels Swift 天文台将于 11 月 4 日重返大气层。但目前还不清楚其坠落地点以及它会有哪些组件会在穿过大气层后仍然幸存下来。Swift 天文台重 1.5 吨，于 2004 年发射升空，旨在研究高能伽马射线暴的起源。过去 22 年受大气阻力的影响 Swift 天文台的轨道高度在缓慢下降，过去两年活跃的太阳活动加速了其坠落。今年 7 月 NASA 曾尝试了一次拯救任务，但任务失败。10 月 20 日其轨道高度降至 275 公里，其使命正式结束。Swift 天文台的轨道经过了澳大利亚、非洲、印度、东南亚以及中南美洲上空，最有可能的坠落地点是海洋。

挪威、瑞典和芬兰等北欧国家传统上经历的热浪强度低于欧洲大陆的其它地区。然而在近年的极端天气事件中，例如 2026 年的热浪，欧洲大部分地区都观测到了异常高温，40 °C 的高温天气也出现在比以往更靠北的地区。根据发表在《npj Natural Hazards》期刊上的一项研究，芬兰科学家报告，北欧出现 40°C 高温不再像以前认为的那样不可能发生。研究人员估算，2025 年北欧某个地点 40 °C 的年发生率为 0.53%，比 1981 年的概率增加了 6 倍。如果以迄今为止各国已观测到的最高气温纪录作为标准，那么在当前气候条件下，挪威突破本国历史最高气温纪录的年概率最高达到 10.9%。研究结果表明，北欧国家应当做好应对远高于历史观测记录所显示的极端峰值气温的准备。

专注于报道俄罗斯太空新闻的媒体 Russianspacewe 称，位于 Progress（进步号）气闸舱和 Zvezda（星辰号）服务舱之间的 PrK 模块已被永久关闭。NASA 以及俄罗斯航天局尚未正式宣布这一消息，但早在 6 月就有消息称俄罗斯计划退役漏气的国际空间站 PrK 模块。PrK 模块因结构裂缝导致的漏气过去几年一直困扰着国际空间站，今年初漏气问题一度被认为已经修复，但 6 月报告漏气再次加剧，该模块的裂缝总数达到 16 处。俄罗斯货运飞船将改为与 Poisk 舱对接。计划于 11 月 24 日发射的 Progress MS-36 货运飞船将通过 Poisk 舱卸载货物。

小行星灭绝了恐龙，鳄鱼为何幸存？科学家或许解开了这个困扰6600万年的谜团。一项大规模化石与古气候分析表明，鳄鱼偏爱温暖环境的习性，让它们在小行星撞击后仍能栖身于条件尚可的热带“避难所”。研究团队发现，鳄鱼及其远古近亲在长达 2.5 亿年的演化史中，始终“追着温暖气候走”，而非适应剧烈变化的温度。地球变暖时，它们扩散到新区域，最远抵达南极洲；气温回落时，它们再退回热带。研究分析了 7000 多块化石、400 多种现存与灭绝物种的记录，并结合电脑模型，重建了 2.45 亿年间地球气候与地理的变迁。结果显示，小行星撞击后，热带和亚热带地区对鳄鱼近亲依然宜居，而许多恐龙的生存环境却在不断恶化。只有在极端气候模拟条件下，这些潜在的避难所才会消失。研究人员还模拟了 6600 万年前撞击发生后的地球状况，发现赤道附近地区可能仍足够温暖，足以庇护鳄鱼近亲的幸存种群，让它们得以存续并逐步恢复。

男性想要长寿的话可随时尝试阉割。对包括人类在内的哺乳动物，切除睾丸有助于延长雄性的寿命。一项新研究发现，与此前的认知不同，阉割手术并不一定要在生命早期进行。对年长雄鼠进行去势处理，可使其寿命延长逾 30%。这一发现颠覆了以往对于睾丸如何影响衰老的既有观念。在人类等物种中，雌性的寿命显著长于雄性。以美国为例，男女之间的平均寿命差距为 6 年。男性也更容易罹患与衰老相关的疾病如癌症和心脏病。这一现象背后原因尚不明确，有证据表明雄性的短寿与睾丸相关。多项研究表明去势能普遍带来寿命延长。在最新研究中，研究人员对不同年龄同一品系的雄鼠进行去势处理，时间点分别是在青春期前、青春期刚过，以及成年期。结果显示这三组小鼠的寿命均长于对照组。出人意料的是成年期接受去势手术的小鼠获益最大。手术使其寿命延长了约 33%；相比之下在青春期前或青春期后接受手术的小鼠，寿命分别延长了 19% 和 26%。成年雄鼠受益于去势的原因尚不清楚，但研究结果促使研究人员给四岁雄狗做绝育手术。

在法国之后、比利时、瑞典、卢森堡和爱尔兰多家超市的冷冻和冷藏设备遭遇类似的网络攻击。比利时和卢森堡有约 40 家 Delhaize 超市受影响，瑞典 ICA 和 Coop 的多家门店受影响，爱尔兰有 13 家 SuperValu 门店受影响。瑞典的一家 ICA 门店称，其冷冻设备的温度被篡改设定到 50 摄氏度。一家 Delhaize 门店称它损失了价值 10 万欧元的商品。法资超市家乐福表示位于比利时的门店有 15 家受到影响，法国受影响超市都使用了丹麦公司 Danfoss 的制冷控制器，运行的是已停止支持的型号。目前不清楚最新受影响的超市是否使用了 Danfoss 控制器。

昆明动物所的研究人员对高黎贡山 62 种鸣禽的历史标本与当代样本进行全基因组比较发现，过去半个世纪内其遗传多样性平均下降 43%。高黎贡山面积仅占我国陆地面积的 0.36%，却孕育了占中国鸟类物种总数 1/3 以上的鸟类，且位于喜马拉雅山地、印缅地区和中国西南山地三大生物多样性热点交汇地带，保护基础较好。研究团队进一步分析发现，扩散能力是造成物种间遗传多样性下降差异的重要因素：扩散能力强的物种遗传多样性降幅相对较小，扩散能力弱的降幅更大。研究同时发现，种群数量越多的物种遗传多样性下降情况越严重。

以提供隐私服务著称的 Proton 公司正全力推动工程师转向采用 AI，CEO Andy Yen 还在内部 Slack 频道训斥了提出安全顾虑的员工，称是“失败者的思维”。Proton 甚至计划对员工进行重新面试，要求员工说明他们如何使用 AI。如果员工没有充分使用 AI 他们可能会面临被解雇。在引发公众抗议之后，Proton 取消了重新面试。尽管如此，Proton 公布了一份 AI 宣言：“首先工程文化和工程师心态需要发生转变。你们的工作性质已经变了。你们的任务不再是写代码，LLM 也不是为了帮助你们更快、更好地写代码。你们的工作，是帮助 LLM 更快更好地写代码。”