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Unified conditional image generation requires a single model to preserve different aspects of a visual input while producing a target image that follows textual guidance. Pretrained video models provide a promising foundation by encoding visual transformations across time, yet existing approaches typically assign the condition and target to fixed temporal positions. We observe that condition-target relationships vary substantially across generation tasks, while video models naturally encode visual transformations at different temporal scales. This motivates us to model conditional generation through input-adaptive virtual temporal relations. We propose FrameForge, which captures two complementary aspects of such relations. Mixture of Temporal Experts (MoTE) models how far the target lies from the condition by dynamically selecting temporal RoPE experts with different virtual intervals. Chain of Temporal Reasoning (CoTR) models how the condition is transformed into the target through a latent intermediate state, with a reasoning-bottleneck attention mask encouraging information propagation through this pathway. Experiments on DreamBench, COCO2017, and GEdit-Bench show that FrameForge achieves superior performance in subject-driven generation, canny-to-image generation, and image editing among the compared methods.

In recent years, agentic orchestration has been widely applied to solve complex human tasks. However, existing orchestration learning still faces key challenges, including hand-designed curricula, advantage collapse when a task's rollouts all reach the same outcome, and unreliable evaluation signals that judge each skill by one noisy score. To address these challenges, we propose R^2Orch, a new paradigm of Recursive Self-Play Orchestration: an inner loop of self-play that improves the orchestrator on self-chosen tasks, and an outer loop that improves how the inner loop improves. To support this paradigm, we introduce Recursive Learnability, a training objective that rewards a Proposer for choosing tasks whose rollouts keep reaching different outcomes as skills are withdrawn. Built on this objective, we further propose Skill Withdrawal, in which the outer loop withdraws each skill once a controlled evaluation within self-play shows that the orchestrator performs as well without it. Moreover, the two loops form one cycle: failures are distilled into skills, skills into parameters, and withdrawals lead the Proposer to new failures, so the orchestrator moves toward zero-skill deployment. Experimental results on twelve datasets show that R^2Orch consistently outperforms baselines across question answering, math, code, and interactive tasks. Our code is available at https://github.com/beita6969/R2Orch.

Progress toward artificial general intelligence (AGI) has largely relied on scaling a resource-intensive executive, System-2, through larger models, more data and compute, longer reasoning, and more agents. We ask whether the next stage may come instead from co-designing it with System-1, a lightweight, semantically aware predictor that evaluates candidate interactions before execution, so that System-2 acts only on those worth their cost. Our answer is a qualified yes. We study conditional interaction, a predict--gate--execute--update loop: System-1 estimates each candidate's utility, System-2 executes those admitted by an adaptive threshold, and the outcomes update System-1. The loop spans training, test-time reasoning, adaptive decision-making, and agentic tool use, with utility and cost defined by the application. Under ideal screening and equal execution costs, execution compute falls by a factor approaching 1/ρ_u, where ρ_u is the density of utility-relevant interactions (cost-weighted when costs differ); prediction errors, exploration, and distribution shift reduce the saving. We bound selection regret by predictor error and the utility margin. These results motivate our central hypothesis: ρ_u is a measurable coordinate of scaling efficiency and makes informative compute a better description of scaling than total compute.

Foundation models are becoming remarkably reliable at high-level reasoning, yet they remain too slow and costly to serve as fine-grained controllers. Conversely, fast policies like VLAs and whole-body controllers offer more efficient reflexes, but struggle to generalize. While pairing a deliberative brain (foundation models) with a reactive cerebellum (fast policies) seems natural for embodied systems, two obstacles remain: methods are built and scored on incompatible stacks, and their asynchronous coordination is difficult. For example, an agent that calls the VLA in the agentic loop can stall the fast execution, while a VLA that calls the agent on errors tends to re-trigger fallbacks repeatedly or completely miss silent failures. To address this, we propose REA (Routed Embodied Agent), a unified runtime where a deliberative System 2, an embodied router System 1 and reactive System 0 policies cooperate under a single golden rule - no running policy waits on the brain. Instead of querying the brain at each failure, REA employs a route memory that stores each policy's measured success ahead of time, a boundary-triggered judge gate that consults the brain on policy execution, and a shared-budget escalation ladder for bounded recovery. Evaluated on 3,000 test episodes across 60 LIBERO-Pro tasks, REA reaches 95.9% success and beats the best single-agent baseline in its own policy pool (94.7%) while reducing control steps by 25% and control time by 17%. In a separate 100-episode RoboTwin 2.0 study, REA achieves 84% success with VLA policies and 86% with VLA+code, compared with 80% for the strongest single VLA baseline MEDex. These results demonstrate learned and programmatic policies operating within the same runtime. REA also transfers to BEHAVIOR-1K and a real Unitree G1 humanoid, achieving the highest observed success among the compared methods.

A capable voice assistant must do more than recognize words; it must understand requests, reason over context, sustain conversations, and act through tools. We present Apriel Voice 27B Preview, an open speech-language model that unifies these capabilities within a shared reasoning backbone while retaining text and image input modalities. Built on the post-trained Gemma 4 26B-A4B checkpoint, Apriel Voice directly processes speech and generates both responses and tool calls without requiring a separate dialogue model. We introduce a two-stage training procedure to adapt the model to speech. The first stage establishes the speech interface, while the second jointly adapts the projector and backbone through supervised fine-tuning, text replay, and source-dependent cross-entropy and forward Kullback--Leibler divergence supervision. Automatically constructed speech-conditioned dialogues provide conversational training data, while local teacher serving, overlapping teacher and student computation, and fused full-vocabulary losses enable efficient online distillation. In thinking mode, Apriel Voice achieves a score of 98.90 on Big Bench Audio, 81.13 on Speech-BFCL, and a task success rate of 65.51% on τ-Voice, with a mean latency of 1.91 seconds. It also achieves 51.27% English EVA accuracy and 74.13% experience pass@1. Finally, we deploy Apriel Voice 27B Preview on a custom serving platform to support interactive voice interactions.

Short-video search needs compact query rewriters that recover users' personalized intent from ambiguous queries under strict latency constraints. Existing LLM-based methods have improved rewriting by using synthetic reasoning and subsequent consumption-behavioral feedback. However, overemphasizing subsequent consumption can favor engaging content over the user's immediate need, causing intent drift even when rewritten queries are grounded in behavioral history. We propose WeFair, Fidelity-Aware Alignment from Human Feedback for Personalized Query Rewriting in WeChat short-video search. WeFair uses human feedback according to how directly it reflects personalized intent, assigning feedback fidelity to a progression from demonstration to preference to verification. We first mine actual queries and user reformulations from search sessions, and then screen them for contextual support. In the demonstration stage, a supervised fine-tuning curriculum progresses from large-scale context-to-query learning to reasoning and direct prediction on human reformulation and preservation. In the preference stage, direct preference optimization learns relative preferences from subsequent consumption feedback. In the verification stage, group relative policy optimization rewards exact agreement with reformulation or preservation targets to constrain intent drift. Further analysis shows that broad supervision benefits the full curriculum supervised training, while strict verification achieves better performance than auxiliary rewards. In offline experiments, WeFair-2B outperforms strong rewriting baselines in overall accuracy and intent preservation. An online A/B test shows relative increases of 2.593% in effective video-view rate and a 2.350% relative reduction in query reformulation rate. WeFair-2B has been deployed in the WeChat short-video search system, which serves over 100 million queries per day.

Fine-tuning Large Language Models (LLMs) on instruction data can quietly erode their safety, even when no training example looks harmful. Because this safety-degrading data looks benign, standard content filters struggle to catch it. We tackle this problem by developing SimpleLoRIF, a scalable gradient embedding method that identifies fine-tuning examples whose gradients align with unsafe behavior. By simplifying a low-rank attribution method and restricting backpropagation to the final four transformer blocks, SimpleLoRIF can embed datasets on the order of a million examples, boosting gradient attribution from a small-scale analysis tool into a practical filter for full post-training datasets. We first validate it on emergent misalignment, where we find that gradients can connect insecure-code training data to the broadly harmful behaviors that appear after fine-tuning, with AUC as high as 0.865 in the gradient space of misaligned models. We then use SimpleLoRIF to build SafeTulu, a version of Tulu 3 with the 20% of examples most gradient-aligned with unsafe responses removed. Training on SafeTulu reduces GCG attack success rate by up to 22% across Qwen, Llama, and Olmo, outperforming both LLM-as-a-judge and prior gradient-based filtering, while largely preserving or even improving capability. Finally, we analyzed the removed data and identify possibly under-studied sources of safety degradation, such as tool-execution failures and narrative arithmetic. We release SafeTulu along with a Hugging Face Space that lets researchers build their own customized versions of SafeTulu to fine-tune on, such as by changing the removal fraction or filtering only certain subsets.

Unified Multimodal Models (UMMs) achieve remarkable success on diverse multimodal understanding and generation tasks, yet they still struggle to effectively leverage language-based chain-of-thought reasoning for image generation. As a result, existing reasoning-augmented UMMs often fail to inherit the test-time scaling and fine-grained controllability observed in large language models. We attribute this failure to a fundamental modality mismatch: reasoning is performed in a discrete token space, whereas image generation operates in a continuous space. To bridge this gap, we introduce a Latent Reasoning framework in Continuous space (LARC), which alleviates the token-level bottleneck by reasoning over continuous hidden states. Specifically, the training framework consists of two stages: (i) curriculum supervised fine-tuning (SFT), and (ii) information-gain reinforcement learning (RL). First, the curriculum SFT stage gradually converts token-level reasoning into latent reasoning. Subsequently, the information-gain RL stage self-evolves latent reasoning without ground truth supervision. Experiments across diverse text-to-image generation benchmarks show that LARC consistently improves generation quality, prompt following, and compositional fidelity. Notably, LARC achieves state-of-the-art performance on GenEval with a 7%p gain over BAGEL and demonstrates clear test-time scaling behavior, outperforming token-based generation under both parallel and sequential test-time scaling strategies.

Recent advances in video world models have enabled the generation of increasingly realistic and interactive virtual environments, opening new possibilities for player-customizable gameplay. Agentic frameworks further advance this direction by decoupling explicit game logic and controllable world representations from visual generation. However, existing approaches support only simple game settings and predefined motion patterns, generalize poorly to unseen environments and characters, and offer limited real-time interactivity. In this paper, we introduce Wizard, an agentic game world model that enables real-time, precise control over character motions while generalizing zero-shot across novel scenes, characters, and actions. To achieve this, we construct a diverse dataset spanning 750 scenes and 178 characters, incorporating skeleton and geometry information as structured representations. We leverage these representations as intermediate proxies to condition a video generation model, together with an adaptive masking mechanism that improves alignment between the conditioning signals and generated video rollouts. Furthermore, we introduce an agentic framework that explicitly maintains and updates game states, allowing players to freely configure their virtual worlds and exercise fine-grained control over character motions while preserving visual quality and gameplay consistency. By combining agentic control with structured proxy-based generation, our framework unlocks new possibilities for player-driven game creation with real-time, generalizable, and mechanically consistent world simulation.

Vision-Language-Action (VLA) models have demonstrated promising capabilities in robotic manipulation by integrating visual perception, language understanding, and action generation. Recent advances have further incorporated future prediction to enhance the understanding of environmental dynamics and task progression. However, existing methods often model short-term dynamics and long-term task objectives separately, while directly incorporating predicted futures into action policies without sufficiently considering their relevance to current action requirements. To address these limitations, we propose HiFOR, a hierarchical foresight framework with action-guided world retrieval. Specifically, Hierarchical Foresight Prediction jointly predicts short-term state evolution, semantic milestones, and final goal states through a Hierarchical Dependency Mask (HDM), establishing dependencies across temporal horizons. Building upon these predictions, Structure-Aware Latent World Modeling organizes future latent representations along the horizon, view, and spatial dimensions to facilitate fine-grained retrieval. Furthermore, Action-Guided Factorized World Retrieval adaptively retrieves relevant future information from the structured latent world based on evolving action representations during denoising, providing targeted guidance for action generation. Extensive experiments on two simulation benchmarks and six real-world robotic manipulation tasks demonstrate the effectiveness of our approach.

Direct-policy World Action Models (WAM) have demonstrated the benefits of decoupling action generation from explicit future visual prediction for efficient inference in autonomous driving and embodied control. However, they still require tens of denoising steps to achieve competitive action quality, incurring substantial latency and hindering real-time deployment. Simply reducing the number of denoising steps will collapse the nonlinear multi-step denoising trajectory into coarse updates, substantially degrading action quality. To reduce the denoising steps while enabling real-time action generation, we present an effective world action model with one-step action generation, namely OSWAM, for autonomous driving and embodied control. We argue that it is the absence of structured action priors in random noise initialization that forces direct-policy WAMs to recover coherent action sequences through iterative denoising. Hence, we construct imagination tokens to initialize the diffusion process, providing physical priors and action-conditioned representations. We further introduce privileged information distillation with future conditioning to enhance future-aware action generation. As a result, OSWAM can efficiently and effectively generate high quality actions in just one diffusion step. Our experiments demonstrate that OSWAM achieves comparable or even better results in both autonomous driving and embodied control than direct-policy WAM and Joint-WAM that require tens of steps. It obtains approximately 3times speedup over direct-policy WAM baselines and supports real-time compiled inference with latency as low as 40ms on an NVIDIA RTX 5090 GPU. The project page is available at https://gwenzhang.github.io/OSWAM/

Dense 3D tracking from monocular video requires establishing point correspondences over time, estimating their depth, and inferring their trajectories during occlusion. Existing methods typically learn these tasks jointly, a demanding objective given the scarcity of 4D supervision. We show that strong pretrained 2D trackers and 3D geometry models provide an effective foundation: lifted trajectories, with occluded segments simply interpolated, already rival dedicated 3D trackers using the same geometry. However, lifted trajectories inherit correspondence and depth errors, and under occlusion, their depth comes from the occluder rather than the tracked point. We introduce ReLift, an efficient feed-forward framework that recasts dense 3D tracking as geometric trajectory repair and completion. At its core, a trajectory corrector alternates attention along and across trajectories to preserve reliable observations, repair corrupted positions, and complete missing motion. Using only geometric inputs allows us to train directly on 3D trajectories with simulated lifting errors, decoupling training from images and from any specific tracker or geometry model. Concentrating 4D supervision on trajectory refinement yields a reusable model that consistently improves lifted trajectories across trackers and geometry providers without retraining, so it benefits directly from future advances in either component. On WorldTrack, ReLift achieves state-of-the-art mean accuracy, improving on the strongest baselines by 2.1 APD on all points and 3.0 on dynamic points, with faster configurations remaining competitive with leading 3D trackers while running faster than 4RC. Visit our supplementry page for more examples and code.

Real-world forgery is inherently heterogeneous: it manifests not only as digital manipulation but also as physical spoofing, and is carried not only by images but also by videos. Despite substantial progress, a unified anti-forgery foundation model covering such a broad threat spectrum remains absent. In this paper, we present Venus-DefakerOne2, which substantially extends the forensic capability boundary along both the forgery-form and media-type dimensions. Trained on tens of millions of open-source and in-house samples, it incorporates two key designs. Tide-Style-Tuning alternates LoRA and full-parameter fine-tuning to balance computational efficiency, generalization, and hard-sample fitting while mitigating catastrophic forgetting. Entropy-based Test Adaptation determines the optimal prediction modality by analyzing prediction entropy. Venus-DefakerOne2 achieves new state-of-the-art performance across a comprehensive suite of benchmarks, including DeepFake faces (96.7%), AI-generated images (93.3%), tampered documents (87.6%), manipulated natural images (89.6%), physical spoofs (93.1%), and AI-generated videos (96.0%). It significantly outperforms both specialized expert models and its predecessor, DefakerOne, establishing itself as a powerful and versatile foundation model for binary-decision forensic tasks.

Multi-agent systems (MAS) require both effective coordination and specialized task execution. Existing prompt optimization approaches typically couple these functions within agent-specific prompts. We introduce Coordinator-Executor Prompt Optimization (CEPO), a general framework for improving prompt optimization in MAS. CEPO separates system-level coordination from agent-level execution through a shared coordinator prompt and agent-specific executor prompts, and guides their updates according to their respective roles. This structure allows shared coordination rules to be refined consistently across agents while keeping execution instructions separately adjustable. CEPO provides the prompt structure and optimization guidance, while existing prompt optimizers perform the underlying search. Extensive experiments spanning 11 benchmarks across four domains demonstrate that applying CEPO to four existing prompt optimizers consistently improves their performance. Ablations and qualitative analyses support the proposed design, while controlled studies on a document-understanding MAS further demonstrate improved optimization budget efficiency and performance scaling with the number of agents.

Training acceleration methods for diffusion transformers, such as REPA and dispersive regularization, are commonly explained by additional supervision, spatial-structure priors, or improved feature geometry. We revisit this view and uncover a striking internal change: the AdaLN-Zero residual gate α, which directly controls how conditioned updates enter the residual stream, is substantially reorganized by these methods. This observation motivates our hypothesis that acceleration arises, at least in part, from reshaping the residual gating pathway rather than solely from the feature structures explicitly imposed by auxiliary objectives. Supporting this view, accelerated models are markedly more sensitive to perturbations of α, and their gate geometry reflects semantic relationships among classes. We further introduce the normalized participation ratio (nPR) to characterize the channel-wise organization of the gates and find that, across REPA teachers, projector architectures, and radial-objective targets, broader gate participation accompanies better generation quality. Guided by this insight, we propose a simple teacher-free radial objective that promotes broader gate participation through feature-norm regularization, without pairwise feature comparisons. On ImageNet 256times256 with SiT-B/2, our method improves FID over dispersive loss while increasing mean gate participation over timesteps and classes, further supporting the link between gate organization and training acceleration. Our results identify residual-gate reorganization as a measurable axis of diffusion training acceleration and suggest a simple direction for further acceleration.

Julie Bort / TechCrunch : Anthropic S-1 claims it may “imminently meet” its RSI threshold and may fail to identify “significant acceleration” in its development until after it's occurred —  While much attention has focused on the updated financials Anthropic revealed in its revised S-1A on Monday …

Jason Shubnell / The Block : Brazil's largest crypto exchange Mercado Bitcoin halts withdrawals after a breach involving $46M+ in assets, per Arkham; Tether froze 4.5M+ USDT in response —  The exchange paused deposits and withdrawals after the incident, while Tether froze more than 4.5 million USDT linked to the attacker.

Wall Street Journal : Sources: institutional investors told Anthropic that its IPO valuation should be around $1.5T to $1.6T, down from estimates of $2T, citing a tech stock selloff —  Institutional investors who plan to buy shares in IPO show concern following a selloff in tech stocks

Loree Seitz / The Wrap : Apple reports Q4 revenue from Services, which includes the App Store, Apple TV, Apple Music, iCloud, and more, grew 10% YoY to $31.57B, vs. $31.58B est. —  The tech conglomerate beat Wall Street expectations with revenue hitting $114.48 billion  —  Getting your Trinity Audio player ready...

Marcus Mendes / 9to5Mac : Apple Q4: iPhone up 17% YoY to $57.58B, vs. $56.54B est., Mac up 24% to $10.8B, iPad down 10% to $6.2B, and Wearables, Home, and Accessories down 8% to $8.3B —  Apple sees Q4 revenue climb 12% year over year  —  Last year, for its fourth fiscal quarter, Apple reported $102.47 billion in revenue …

Apple : Apple reports Q4 revenue up 12% YoY to $114.48B, vs. $113.39B est., China revenue up 21% to $17.5B, vs. $18.9B est., in John Ternus' first earnings report —  September quarter records for total company revenue, iPhone revenue, and EPS Services revenue reaches new all-time high CUPERTINO …

Samantha Subin / CNBC : Palantir reports Q3 revenue up 96% YoY to $2.31B, above $2.18B est., US commercial revenue up 148% YoY to $984M, and raises FY 2026 revenue guidance to ~$8.53B —  Palantir beat Wall Street's third-quarter estimates after the bell on Monday, with U.S. commercial revenue topping government …

Todd Spangler / Variety : Roku Q3: revenue up 24% YoY to $1.5B, Platform revenue up 23% YoY to $1.3B, ad revenue up 23% YoY to $727.4M, and subscriptions revenue up 23% YoY to $580.9M —  For the third quarter, Roku handily topped Wall Street earnings expectations, with top-line growth of 24% to $1.50 billion in revenue.

Rikiya Takehi : Researchers say LLMs can be good at natively reading and writing neural network weights; GPT-6 Astra edited Qwen3.5-0.8B's weights to make it better at math —  TL;DR … If you were asked to handcraft a neural network that plays Go, what would you do?  If you were handed raw neural network weights …

Liam Proven / The Register : Memo: Linux distribution vendor SUSE is laying off ~10% of its employees, saying “automation and AI are fundamentally shifting how software companies operate” —  One in ten employees at SUSE are facing the chop due to “automation and AI,” according to a memo dispatched by management …

OpenAI : OpenAI launches the Grid Protectors Program, a US effort for its future data center campuses meant “to reduce electricity demand when the grid is under strain” —  Today we're launching the Grid Protectors Program, a national effort to support affordable, reliable electricity …

John Werner / Forbes : Tutor Intelligence, which trains robot foundation models and builds AI warehouse robots, raised a $75M Series B led by a16z, taking its total funding to $117M —  This voice experience is generated by AI.  Learn more. … When you look at some of what robotics companies are doing today …

Debby Wu / Bloomberg : DeepSeek releases a “creator mode” that allows DeepSeek Harness users to quickly create custom plugins, like a 3D model renderer and a video editing workbench —  DeepSeek has unveiled a new option to quickly create custom plugins that help users to tackle a range of complex tasks …

Bruno Ferreira / Tom's Hardware : Anthropic confirms new Claude Code limits “to steer it away from recreating someone else's specific game”, saying IP infringement is against its usage policy —  Meanwhile, we now have direct reports of reverse engineering efforts being blocked, while online complaints grow.

Ram Iyer / TechCrunch : Tel Aviv-based 42AI, which is developing a “behavioral world model” that tries to predict how humans will act, emerges from stealth with a $62.5M seed —  Here's a question straight out of sci-fi: What if AI could predict what you were going to do before you decided to, even in a completely new situation?

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。”

Valve 公布了 2026 年 10 月的 Steam 硬件和软件调查，结果显示 Steam 用户中使用 Linux 比例占 3.95%，过去几个月它的份额一直在 3% 以上，最高超过 5%，这一波动可能与简体中文用户增加或减少相关。凡是简体中文用户数增长的月份 Linux 份额就会下降 Windows 份额会上升。2026 年 10 月简体中文用户减少 9.41% 至 21.98%，英文用户增长 4.84% 至 39.04%。其它数字包括：Windows 10 仍然有 21.11%，玩家使用英特尔 CPU 的比例占 52.63%，AMD 占 47.37%。

骨骼塑造着体型，也影响着衰老。根据发表在《Cell Metabolism》期刊上的一项研究，小鼠实验发现骨骼在机械应力下——锻炼或其它身体活动中间发生的推、拉、扭转等——会触发骨骼释放名为骨细胞（osteocytes）的力感知细胞，促使其分泌出有抗衰老作用的化学物质。研究团队甚至通过轻微挤压小鼠腿骨，成功使其寿命延长了近 3 个月，相当于人类寿命延长了约 9 年。研究负责人、南方医科大学的 Xiaochun Bai 表示，骨骼并非仅是一个被动的支撑框架；它是一个活跃的、能对机械力做出反应的器官，在很大程度上决定了全身衰老的进程。研究人员尚未在人体中验证这一效应，因此它未必适用于人类。过去几十年的研究表明，体育锻炼及其它形式的身体活动与更长的寿命密切相关，这一现象背后的机制尚不清楚。Bai 指出大多数科学家此前都在肌肉中寻找线索，却忽略了骨骼。

Linus Torvalds 在内核邮件列表上宣布释出 Linux 7.4-rc1，关闭合并窗口，正式版本预计在圣诞节前发布。Linux 7.4 共包含了 18,591 个非合并（non-merge）变更集，这一数字创下了纪录；有 2,678 名开发者为 v7.4 贡献了代码，其中 625 人为首次。Torvalds 表示他乐见 AI 相关工作在合并窗口期间大量涌入，Linux 7.4 合并窗口合并的补丁规模高于通常的版本，显示 AI 修复逐渐融入常规开发流程，他认为这是一种更健康的模式。Linux 7.4 的新特性包括：Radeon iGPU 支持 AMD PerfOpt 帮助增强 AI/LLM 性能，进一步完善对 AMD Ryzen Zen 6 架构处理器的支持，默认启用 AMDGPU HDMI 2.1，英特尔新 Nova Lake 笔记本电脑支持 IPU8，更多高通 Snapdragon X2 笔记本电脑支持，EXT4 可选支持缓冲 IOma，等等。

Stack Overflow 自己的统计数据显示，它一个月内的新增问题数已经低于 1000。此事再次在社交媒体上引发了编程问答社区即将死亡的讨论。Stack Overflow 官方博客回应说，死亡预测夸大其词，混淆了获取知识方式的改变与知识本身——以及背后的公司——消失两个概念。AI 改变了开发者的行为。在 Stack Overflow 近期发布的《2026 年开发者调查》中，64% 的受访者表示，对于简单问题他们减少了访问 Stack Overflow 的频率。但对于更深入的问题，他们会访问该平台验证由 AI 生成的答案。Stack Overflow 依然是一个可以挑战既定假设、比较不同方法并验证解决方案的场所。此外网站上沉淀的知识，实际上也在为 AI 生态系统提供支撑。Stack Overflow 上经由人工验证的内容，被用于训练和优化模型、增强检索系统，提升 AI 助手及其他工具的可靠性。开发者和技术人员或许是通过不同界面——如 IDE——接触到一个答案，但其源头往往依然与过去一样——源自某位开发者在 Stack Overflow 上的分享。Stack Overflow 表示它已在为未来做准备，正将战略重心转向企业市场，推出 Stack Internal。

在因黑客攻击短暂暂停提现之后，币安于周日恢复了提现功能。11 月 1 日上午 6 点 - 下午 3:30 UTC 之间，币安处理了 46.65 亿美元的提现和 10.1 亿美元的充值，净流出 36.4 亿美元。根据截至 10 月 28 日的最新储备金证明（Proof of Reserves）数据，币安总资产为 1710 亿美元，此次净流出占其总资产的 2.1%。币安创始人兼前 CEO 赵长鹏在社交媒体上表示，“98% 的用户选择留下！”他表示大部分净流出发生在提现功能恢复后的最初几个小时内。

Reddit 的最新财报证实了众多网站的抱怨：Google AI Mode 未给网站带来流量。AI Mode 通过生成总结的方式响应用户的查询，而不是传统搜索的提供网站链接以及简要摘要。硅谷对 AI 的全面拥抱牺牲了构成互联网的无数网站。Reddit 联合创始人兼 CEO Steve Huffman 表示：“用户依然需要 Reddit，只是 Google 不再像过去那样将他们引导到 Reddit。”上季度来自 Google 搜索的引荐流量显著下滑。Google 向 AI Mode 及 AI overviews 的转型切断了原本流向 Reddit 的流量。Reddit 的回应和很多新闻网站类似：变得更封闭：Reddit 将强制要求所有用户登录，而众多新闻网站则拥抱了订阅模式。

Sierra Club 的分析显示，到 2030 年美国电力行业的二氧化碳排放量可能达到 21 亿公吨，比 2025 年的水平高出约 42%。这一预计增幅将抵消过去 15 年电力行业在减排上取得的成果。随着数据中心对资源的需求持续增长，AI 基础设施的电力需求正以清洁能源电网无法跟上的速度快速增长。为绕过缓慢的公用电网接入排队流程，部分科技公司建设了主要依赖化石燃料的自备发电设施。到 2030 年由数据中心开发商建设或委托建设的离网发电设施每年可能产生逾 4.6 亿公吨的二氧化碳，大致相当于墨西哥全年的碳排放量。

哈勃望远镜在轨运行 36 年之后仍然极具价值，但它的轨道高度已经大幅下降，如果不采取行动它将于 2030 年代中期前重返大气层。NASA 本周公布了一份 Request For Information(RFI)，向商业公司征询提升哈勃的轨道、进行维护或对其进行受控重返大气层的方案。方案递交截至日期 11 月 30 日。NASA 未承诺是否资助一次哈勃拯救任务，它想知道有多少公司对此有兴趣，以及成本多少。哈勃的轨道高度已从 2002 年的 583 公里降至目前的 470 公里，如果不进行干预，哈勃预计会在 2033 年或 2034 年坠入大气层。NASA 想要将哈勃的轨道高度重新提升至 600 公里，如果能做到，哈勃将能至少继续运行 25 年。

思科总裁兼首席产品官 Jeetu Patel 在接受《财富》杂志采访时称，该公司给予一名 27 岁工程师无限 tokens 去重写遗留代码（legacy code）。27 岁的 Padmanabhan Krishnamurthy 在 2023 年联合创办了实时翻译初创公司 EzDubs，2025 年该公司被思科收购，他随后加入了思科。两人曾在旧金山办公室有过谈话，Krishnamurthy 许诺只需一个两人团队就能重写思科的遗留代码。他给自己争取了四周时间来证明这一想法，最终目标是在 24 个月内重构思科 98% 的遗留代码。

OpenAI CEO Sam Altman 接受 YouTube 主播 Cheeky Pint 采访时谈到了 AI 行业目前所面临的全球算力供应问题。当话题不可避免转向内存时，Altman 指出，市场看不见的手失灵了，原因可能是并非每一块 DRAM 晶圆都直接流向数据中心，用于运行 AI 模型。他表示：“从全球资源配置角度看，让全球有限的 DRAM 供应分散在每个人的 PC 里且利用率可能仅有 5% 左右，这是非常糟糕的。在当前这种供应极度受限的环境下，我们理应追求云端算力的最大化利用。然而许多人的直觉却恰恰相反。”AI 芯片使用的内存主要是 HBM，和 PC 使用的 DDR4 或 DDR5 并不相同。内存短缺的主要问题是内存厂商将产能转移到了利润更高的 HBM 上。