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AI tutors are most useful when they adapt to each student's strengths, weaknesses, and preferred guidance, but evidence about which guidance works for which student is sparse, slow, and costly to collect from real learners. Student simulators can provide this signal as a proxy, yet existing approaches are limited: state-tracking models fit student behavior but struggle to process explanations or corrections, while LLM role-play follows guidance fluently but does not reliably match the competence of the student being imitated. We present StudentSim, a training framework that turns sparse per-student data into individualized simulators through pooled training followed by per-student specialization. The resulting simulators both mirror a student's own responses and update them under tutor guidance. We also introduce StudentSimEval, a standardized protocol covering 60 students across chess, second-language English writing, and mathematics, using public learner datasets with de-identified records shared for research. StudentSimEval measures behavioral fidelity (F), or how well a simulator matches a student's responses, and guidance responsiveness (R), or how readily it updates under tutor guidance, with all methods fit and evaluated on the same records. Across all three domains, StudentSim outperforms GPT-5.4 on both metrics. In chess, StudentSim reaches F=0.51 and R=0.91, compared with 0.23 and 0.72 for GPT-5.4 and 0.45 and 0.27 for Maia2. As a proof of concept, using StudentSim as a reward model for tutor reinforcement learning produces a chess tutor that expert humans rate as more accurate, better-guided, and more personalized than a no-RL baseline and a tutor trained against a GPT-5.4 simulator reward. Code is available at https://github.com/microsoft/StudentSim.

We present Qwen-Drive-1.0, an initial step towards a vision-language foundation model for autonomous driving. Qwen-Drive-1.0 retains the architecture of the pretrained vision-language model (VLM) and integrates 3D perception, visual question answering, and motion planning within a unified framework. An external bird's-eye-view (BEV) perception head jointly performs 3D object detection, semantic occupancy prediction, and BEV map segmentation. It serves as a probe of the 3D information accessible from the shared representations and provides an explicit, inspectable interface to 3D scene structure. A Planning Expert conditions on shared VLM representations to generate future ego trajectories. A staged training recipe combines driving supervision with general-purpose vision-language data to acquire driving-specific competence while helping preserve broad visual understanding and instruction-following capabilities. Experiments demonstrate strong 3D perception and driving scene understanding while largely preserving general vision-language capability. Comprehensive evaluations across open-loop, pseudo-closed-loop, and closed-loop settings further show highly competitive motion-planning performance.

Looped Transformers increase effective depth by iterating a shared block of layers, but most evaluations compare at fixed model size, conflating architectural advantage with extra FLOPs. We study looping on Mixture-of-Experts Transformers while closely matching per-token FLOPs, total non-embedding parameters, and KV cache. Through a series of ablations, we arrive at a recipe we call SMELT (Sparse MoE Transformer, middle layers Loop Twice), which loops the middle half of layers twice while matching the unlooped Baseline on all three budgets. We scale SMELT across four sizes up to 54B non-embedding parameters and fit a separate Chinchilla-style scaling law for each architecture. SMELT's loss drops faster with compute, saving 6.8--18.0\% of training FLOPs on the compute-optimal frontier. The advantage transfers to downstream benchmarks beyond what validation loss predicts, is largest on Code, and grows with sample length and the number of in-context examples. Mechanistic analysis shows that the second visit reduces the attention sink and redirects mass toward content-relevant tokens, an inductive bias that may underlie the observed performance gains. These results show that looping can improve Transformers even under budget matching, offering a practical recipe that turns depth reuse into measurable gains.

Multimodal GUI agents have emerged as a promising paradigm for digital task automation, yet transitioning from benchmark-oriented models to dependable real-world applications remains challenging due to limited environment coverage, brittle task construction, and unreliable reward verification. In this work, we present UI-Venus-2, a general-purpose foundation GUI agent designed to operate across mobile, web, and desktop environments through a unified closed-loop reasoning-action framework. To bridge the gap toward practical deployment, we jointly scale three critical dimensions: (1) Environments, expanding coverage to more than 170 multilingual mobile apps and native desktop operating systems; (2) Tasks, employing a deep-research pipeline for function-grounded instruction generation; and (3) Verification, adopting trace-level and sample-level evaluators with visual keypoints and multi-model voting to ensure reliable RL signals for training. Furthermore, we integrate safety-aware mechanisms to ensure controlled execution of consequential actions. By offering a capable, efficient, and open-source foundation, UI-Venus-2 advances the field toward more generalizable, verifiable, and self-reflective agents for real-world applications.

We present H3-World, an efficient framework that turns the 33B MiniMax-H3 video generator into an interactive world model. Our key finding is that, as large video generators become more capable, language is emerging as a natural interface for control. MiniMax-H3, for example, already supports zero-shot control of character behavior and camera motion through natural-language instructions. Building on this, H3-World turns this coarse language interface into precise, temporally grounded world control, without introducing dedicated action modules. Specifically, we represent each action as a structured combination of character and camera instructions, and align them with the corresponding temporal video latents. To make the control temporally precise, we further introduce temporal attention routing, which restricts each instruction to its intended time interval and reduces control leakage across actions. Importantly, H3-World directly reuses the semantic representations learned during large-scale video pretraining and requires only lightweight adaptation. With only 8,000 gameplay samples, 10,000 LoRA optimization steps, and 0.199% trainable parameters, H3-World achieves effective character and camera control while preserving strong generation quality. It also generalizes to unseen scenarios. These results show that the control capabilities emerging in large video generators can be efficiently transformed into interactive world control.

Robotic manipulation faces a fundamental scaling challenge: robust generalization demands broad physical experience, yet action-labeled robot trajectories are expensive to collect and inherently limited in diversity. Egocentric videos offer a far more scalable source of embodied experience, capturing object interactions, contact dynamics, tool use, and long-horizon behaviors across diverse environments. The central challenge is how to convert this abundant but action-free experience into effective robot control. We introduce ZimaBlue, a scalable framework for learning generalizable World Action Models (WAMs) from large-scale video. ZimaBlue follows a three-stage training curriculum: it first performs causal embodied video pre-training on large-scale human and robot egocentric videos, then grounds the learned visual dynamics in heterogeneous robot trajectories through video-action mid-training with a unified action representation, and finally specializes the model to a target robot for deployment. To make generative WAMs practical for real-time control, ZimaBluefurther adopts an asynchronous Slow-Fast dual-system architecture, where a high-capacity Slow world model provides generalizable spatiotemporal representations and a lightweight Fast branch enables 30 Hz action prediction on NVIDIA RTX 4090. On real-robot zero-shot evaluations, scaling from target-robot data alone to over 120,000 hours of embodied video improves success from 36.1% to 77.8%. ZimaBlue further delivers strong performance across multiple benchmarks, with particularly pronounced gains on unseen tasks.

Data-residency constraints force enterprises to self-host LLMs, but continuous adoption of newer models without decommissioning their predecessors expands the serving fleet, fragmenting a finite GPU pool. We consolidate traffic from over 200 internal applications onto a single model by closing quality gaps identified through production error analysis along three axes: instruction following, function-calling, and internal task distribution. Quality is tracked by offline benchmarks stratified to production traffic and scored by deterministic verifiers or calibrated LLM judges. Rather than optimising all objectives jointly, which introduces cross-domain reward interference, we train a separate GRPO expert per axis and merge them via two-stage SLERP. Each expert's reward exposes a distinct failure mode, namely semantic collapse, over-calling, and verbosity hacking, each requiring a domain-specific fix. In non-reasoning mode the recipe surpasses a {sim}7times larger by total parameters baseline on the in-house Arena with 69.6 to 65.8, instruction following with 0.85 to 0.83, and function-calling with 0.79 to 0.77, while lifting general dialogue benchmarks. The model absorbs 50% of platform traffic, 116M requests per month, at a fraction of the serving cost.

A central bottleneck in multi-hop Question Answering (QA) is that the granularity at which a question is expressed often differs from the granularity at which corpus evidence is retrievable. Existing methods address this mismatch by imposing fixed graph structures over the corpus, by iteratively reformulating the query, or by executing a generated program over it, but these strategies do not explicitly decide when a query unit is already supported by evidence and when it should be refined. We formulate this bottleneck as retrievable granularity discovery and introduce Hi-Q, an evidence-conditioned framework for hierarchical query refinement. At each query node, a resolution operator tests whether retrieved evidence supports the current query unit; resolved nodes terminate, while unresolved nodes are expanded by a dependency-preserving binary operator and checked by a semantic coverage verifier. Hi-Q therefore grows a query tree whose topology is determined by corpus support signals rather than by a fixed decomposition template or a pre-built graph. We evaluate Hi-Q on three multi-hop QA benchmarks, primarily under full-corpus retrieval, where dependent evidence must be located among open-domain distractors rather than within a small annotated pool. In this setting Hi-Q reaches 52.3 EM and 64.0 F1 averaged over the three benchmarks, ahead of the iterative retrieval baseline IRCoT by 15.1 EM / 18.2 F1 on that same average, and ahead of the graph-based RAG baseline PropRAG by 11.5 EM / 12.0 F1 on MuSiQue-full, without corpus-wide graph construction. In the restricted supporting/distractor setting used by prior work, Hi-Q likewise attains the best accuracy, with 57.9 EM and 69.3 F1 on average, ahead of PropRAG by 5.6 EM / 3.9 F1 and IRCoT by 13.7 EM / 15.8 F1. The project page is available at https://hi-q-project.github.io/.

Multimodal Large Language Models (MLLMs) are strong perceivers of images and video. We ask how far that reach extends into acting: dropping an MLLM directly into a drone's control loop, with its entire action space declared solely in the prompt. Recent systems approach this setting but increasingly narrow the model's decision-making. We widen it back. We introduce DroneCATS-Agent, an architecture where the MLLM is a swappable component, and DroneCATS, a benchmark treating the model as the independent variable. Beyond merely flying toward a pixel, our agent entrusts the model to yaw and search, deliberate when unsure, and self-declare arrival---all without fine-tuning or function-calling schemas. Evaluating frontier and open models across four core capabilities---approaching a visible target, tracking a moving one, searching outside the initial view, and commanding a multi-drone fleet---reveals that even the simplest embodied settings are far from solved. Crucially, to identify what breaks first at the edge, our roster scales down to 2B parameters. The findings expose a stark paradox: it is not the flying that fails. Small open models often navigate into the success radius more reliably than frontier models, yet lose the episode by declaring arrival prematurely or not at all. Multi-drone commanding amplifies this divide, with small models failing by blindly copying a single coordinate across distinct views. Viewed as vision-language-action agents, the models' spatial perception holds up, but their action protocol does not. What separates a deployable edge model from a frontier model is not navigation, but the discipline to sustain a declared protocol and emit the correct terminating action. The open problem is closing this gap at onboard compute costs---yielding a fast model that plans persistently and knows exactly when it is done---and DroneCATS is built to measure that distance.

While unified multimodal models (UMMs) jointly perform visual understanding and generation within a single model, functional unification does not guarantee learning synergy: the two objectives may reinforce each other, compete for capacity, or merely coexist. We investigate their relationship at the representation, task, and system levels in a controlled, structurally native setting without pretrained vision priors. At the representation level, we find that each objective provides useful signal to the other: generation enriches the visual features learned for understanding, while understanding strengthens vision--language alignment for generation. However, when both objectives are forced through the same computation path, one tends to dominate. A task-decoupled architecture that specializes conflicting visual computation while preserving semantic interaction avoids this asymmetric degradation. At the task level, through three case studies, we find positive bidirectional transfer when understanding and generation tasks rely on shared knowledge. At the system level, we show that an end-to-end UMM outperforms a matched planner--executor pipeline on complex tasks that explicitly require both image understanding and generation. Together, these results show that the value of UMMs extends beyond a unified interface: appropriate specialization, shared task knowledge, and end-to-end optimization can turn coexistence into synergy.

Long-horizon complex tasks require foundation models to accumulate information, maintain internal states, and adapt over extended interactions. Safety should be an intrinsic property of the model itself, rather than a behavioral constraint relying solely on external safeguards or post-hoc alignment such as supervised fine-tuning. This motivates Safety from Within, where safety-relevant capabilities are represented and invoked through the model's native computation. We present Safin-1, a family of foundation models realizing this principle through memory routing and state evolution. Safin-1 is built on Memory-Anchor Routing across Context History (MARCH), a network architecture that maintains structured memory states and selectively retrieves relevant historical information through content-conditioned routing. It supports test-time adaptation of persistent capability states without repeatedly modifying the backbone, enabling controlled specialization over a shared foundation. We investigate this interface on downstream safety tasks through a Safety State, demonstrating effective state-based adaptation with substantial safety improvements. More broadly, the routed-state interface unifies contextual memory and persistent capability adaptation within the model's native computation, reframing memory from a passive record of prior context into an active substrate for maintaining and evolving model behavior. Evaluations across general capabilities, long-context understanding, retrieval, and efficiency further validate Safin-1. These findings provide a path toward safety as a state-native and adaptively maintainable capability. This work is only an initial architectural exploration of Safety from Within, and substantial further work is needed to realize this broader vision.

LLM judges are widely used to evaluate agentic tool-calling systems, yet their reliability on structured, dependency-driven workflows remains largely unexamined. We present AgentJudgeBench, the first benchmark to systematically study LLM-as-a-judge reliability for agentic tool-calling over workflow DAGs, as distinct from the broader LLM-as-a-judge task of open-ended text or preference evaluation. The benchmark comprises 3,808 instances spanning six DAG topologies and three difficulty tiers, evaluated with five generators (3B-70B open-weight models and GPT-5.4) and six judges (20B to frontier scale) under paired with- and without-ground-truth conditions. Judge alignment degrades monotonically with task difficulty, 1.5x faster without ground truth, and on hard queries without ground truth all six judges converge to a narrow 77-82% band regardless of scale, revealing a structural ceiling driven primarily by task difficulty, though its height is partly prompt-dependent for weaker generators, that model capacity alone cannot overcome. Ground-truth exposure is not uniformly beneficial: it reduces alignment for GPT-5.4 (1.5 pp) and Gemini-2.5-Pro (3.9 pp), consistent with over-anchoring. Among mitigation strategies, chain-of-thought reasoning and judge temperature both have negligible effect, while structured evaluation rubrics improve alignment by up to 6.5 pp but do not generalize uniformly across judge-generator pairs. With ground truth, QwQ-32B best matches the programmatic reference, while a human validation study identifies GPT-OSS-120B as the most human-aligned judge; without it, frontier judges lead only marginally within the shared ceiling. These results expose fundamental limitations of current LLM judges and yield practical guidelines for reliable evaluation in agentic systems.

Self-play is an effective paradigm for language-model self-evolution, but without guidance, solver performance can plateau or decline across rounds. Unguided methods steer question generation with signals such as difficulty, learnability, or diversity. These signals keep questions challenging and varied but do not specify which unresolved reasoning weaknesses later rounds should target. Guided methods obtain direction from external task resources, including human examples, document corpora, or specified difficulty targets, and therefore rely on task information supplied outside the self-play loop. We show that the needed direction can instead be derived from the solver's own failure history. We introduce DiagEvo, whose diagnostician extracts recurring error causes from this history and stores them in a hierarchical error-cause memory. The memory groups related causes under skill nodes and tracks each as Active or Mastered according to self-consistency on targeted questions. The challenger uses these states and recurrence counts to balance cause-targeted generation with free exploration. Double-confidence filtering retains intermediate-difficulty questions only when the most common solver answer has a clear vote lead. DiagEvo derives its curriculum from information produced during self-play, without external task resources. With the default 4B diagnostician, DiagEvo outperforms every baseline in mean accuracy across all nine benchmarks for each of the three solvers: Qwen3-4B, Qwen3-8B, and OctoThinker-8B. On Qwen3-8B, it reaches 72.3% mean accuracy across five mathematical reasoning benchmarks, 4.5 percentage points above R-Zero. Its mean accuracy across all nine benchmarks is 57.4%, 1.1 percentage points above DARC. Ablations show that the hierarchical error-cause memory and double-confidence filtering both contribute to these gains.

This paper studies autonomous software development, in which LLM-based coding agents transform high-level requirements into complete, functional, and usable software systems without human intervention. We introduce Harness-of-Harness (HoH), a framework that enables coding agents to continually improve software during autonomous development. HoH operates on existing coding-agent harnesses, and organizes their executions into iterative planning-coding-testing loops. To sustain improvement across loops, HoH balances repair with capability growth, scopes development into small and verifiable increments, separates implementation-time testing from independent evaluation, and constrains verifiable outputs rather than prescribing agent workflows. It progressively exposes deliverables, role-specific tools, and skills, encourages reuse rather than recreation, and maintains versioned project histories. On GameCraft-Bench, FrontierSWE, and ProgramBench, three harness-model pairs (Codex with GPT-5.5, OpenCode with DeepSeek-V4-Pro, and Pi with MiniMax-M3), HoH consistently outperforms the corresponding standalone harnesses, achieving an average relative gain of 52.25 percent and a maximum gain of 82.86 percent after three iterations. In a multi-day deployment with more than 70 iterations, HoH autonomously develops a first-person-shooter game, featuring a coherent storyline, fully implemented core mechanics, human-playable experience, polished visuals and integrated audio. Github: https://github.com/Flesymeb/HarnessOfHarness Project Page: https://flesymeb.github.io/HarnessOfHarness/

Multimodal memory offers a scalable interface for long-video question answering, but existing methods often retrieve captions, frames, transcripts, summaries, or graph facts as isolated fragments. Although searchable, such fragments are not generation-ready: language models must reconstruct cross-modal and temporal alignments at inference time, when context is limited and attribution is difficult. We propose EM^2Mem, an event-centric multimodal memory framework that binds heterogeneous evidence to event anchors during memory construction. Each event-indexed memory cell aligns multimodal records, temporal context, graph-linked relations, semantic facts, and provenance, enabling compact evidence readout over grounded multimodal events rather than modality-specific fragments. Across three long-video QA benchmarks, EM^2Mem improves average accuracy over the strongest memory baseline by 2.0, 2.4, and 3.7 points, improves strict event-level Top-5 evidence recall by 7.0 points, and reduces per-query latency by 4.67 times and total inference tokens by 63.66% (The code will be integrated into https://github.com/zjunlp/LightMem).

Ryan Gould / Bloomberg : Sources: Thoma Bravo-backed Proofpoint is in talks to acquire cybersecurity company Varonis; Varonis rose 10.4% on Wednesday, giving it a market value of ~$5.4B —  Thoma Bravo-backed Proofpoint Inc. is in talks to acquire cybersecurity company Varonis Systems Inc., according to people familiar with the matter.

@artificialanlys : Muse Spark 1.3 with max reasoning, in limited preview for partners, scores 62 on the Artificial Analysis Intelligence Index, behind only Fable 5.1 and Opus 5 —  Meta has released Muse Spark 1.3, their fourth Muse Spark model release in five months. Muse Spark 1.3 (max), which is in limited preview for Meta's partners, scores 62 on the Artificial Analysis Intelligence Index, behind only Claude Fable 5.1 and Claude Opus 5. The variant available now, Muse Spa...

Dina Bass / Bloomberg : HPE reports Q3 revenue up 34% YoY to $12.2B, vs. $11.9B est., forecasts FY 2026 and FY 2027 sales growth above est., signs a $3.5B server deal with a cloud firm —  Hewlett Packard Enterprise Co. boosted its sales outlook for this fiscal year and next, saying artificial intelligence workloads …

Jordan Novet / CNBC : Broadcom reports Q3 revenue up 86% YoY to $29.59B, vs. $29.36B est., AI semiconductor revenue up 221% to $16.7B, and forecasts Q4 revenue below estimates —  Broadcom shares dropped 5% in extended trading on Wednesday after the chipmaker issued disappointing guidance for the current quarter.

MarketWatch : Snowflake reports Q2 revenue up 35% YoY to $1.55B, vs. $1.48B est., and forecasts Q3 and FY 2027 product revenue above estimates; SNOW jumps 20%+ after hours —  Companies looking to build AI tools on top of their data are turning to Snowflake, driving robust revenue growth

Courtney Rozen / Reuters : Letter: OpenAI told two House Democrats that its engineers are developing “automated shutdown capabilities” for AI systems —  OpenAI told two House Democrats that its engineers are developing “automated shutdown capabilities” for AI systems, according to a company letter reviewed by Reuters …

Mark Zuckerberg / @finkd : Mark Zuckerberg says Meta's Watermelon model and Muse Spark open weights are “coming soon” —  Muse Spark 1.3 is rolling out today with frontier performance almost too cheap to meter. This is the biggest jump we've made so far on coding and agentic work. Try it in Muse Code and our API. Next up 🍉 and Muse Spark open weights releases coming soon.

Ina Fried / Axios : Meta rolls out Muse Spark 1.3 in Muse Code and Meta Model API, saying it significantly improves coding and agentic performance, at the same price as Spark 1.2 —  Meta on Wednesday released Muse Spark 1.3, an update it says significantly improves performance in coding and agentic tasks.

Bloomberg : Uber is shutting down services in Nigeria and Uganda from September 2 after a global review of its operations; it launched in Nigeria in 2014 and Uganda in 2016 —  US ride-hailing giant Uber Technologies Inc. is shutting down its services in Nigeria and Uganda from September 2, following a global review of its operations.

Sarah Perez / TechCrunch : X says US payouts for its Original Content Rewards and Subscriptions will now be handled through X Money —  Elon Musk's social network X on Wednesday said that starting today, all of its U.S. creator payouts will be handled through X Money, the app's payments service.

Ian Thomas / CNBC : Thyme Care, which focuses on filling cancer treatment gaps via a virtual navigation platform, raised a $125M Series E at a $2B+ valuation, up from $1B+ in 2025 —  Despite recent advancements in oncology, the branch of medicine focused on the study, prevention, diagnosis and treatment of cancer …

Maria Curi / Axios : Commerce Secretary Howard Lutnick says “we trust Anthropic” as they are “back on the right side” with the administration and that “they've done what we asked” —  - “They've done what we asked. … - “Really excited for our conversation …

Maria Armental / Wall Street Journal : Huskeys, which uses agentic AI to help companies block AI-driven attacks, raised a $27M Series A led by Blackstone Innovations Investments at a $100M+ valuation —  The company, valued at more than $100 million, uses agentic AI to block threats  —  Blackstone is placing an early bet on Huskeys …

Barratt Dewey / Tectonic Defense : The US Army awards $192M in contracts to Palantir and Anduril to produce eight TITAN AI-powered mobile ground stations, to be delivered over the next 18 months —  Watch out, world.  The US Army's kill chain is going mobile.  —  On Tuesday, the Army awarded two contracts worth a combined $192M …

Stevie Bonifield / The Verge : MrBeast, whose YouTube channel hit 500M subscribers in June, will put Gemini, Google Health, and the Fitbit Air in his videos as part of a multiyear Google deal —  One of YouTube's biggest creators has a new deal to promote Gemini and Google Health.

全世界正在快速发展太阳能这一廉价电力来源，但各地发展并不均衡。以欧洲为例，除了罗马尼亚，其它欧洲国家几乎没有增长，原因是欧洲大陆电网的局限性。发电高峰期间电网拥堵，储能不足，导致能源浪费、负电价，甚至出现电网故障和灾难性停电风险，欧洲各国正竞相增加电网储能容量以解决这些问题。罗马尼亚太阳能装机量在 2025 年增长了 45%，该国在太阳能建设早期就对电池储能进行了投资。除此之外，巴基斯坦的屋顶太阳能和电池安装速度超过了其他任何市场，巴西、智利、萨尔瓦多、摩洛哥、肯尼亚和纳米比亚在清洁能源转型方面都已超越了美国。

根据发表在《Frontiers in Public Health》期刊上的一项研究，全世界每年有近五成农民因农药中毒。研究发现每年约有 4.02-4.33 亿农民和农场工人因意外急性农药中毒。全球农业人口约为 9.34 亿，这意味着每年约有 46% 的农民农药中毒。中毒人数最多的地区是南亚，其次是东南亚和东非。中毒率最高的国家是西非布基纳法索，该国近 84 %的农民和农场工人曾遭受农药中毒。研究还发现，每年约有 11,000 人死于农药中毒，其中近 60% 发生在印度。研究指出，农药的使用量仍在持续增长，2023 年的使用量达到 380 万吨，比 1990 年翻了一番。

Steam 平台一周内上架的游戏数量首次突破 700 款，但大部分游戏无人问津。根据 ICO Partners CEO Thomas Bidaux 提供的数据，上周共有 720 款游戏在 Steam 平台上架。其中 520 款游戏的评论数在 0-9 条之间，190 款游戏的评论数超过 10 条，95 款游戏的评论数超过 50 条，62 款游戏超过 100 条，10 款游戏的评论数超过 1000 条。数据存在重叠，评论数超过 1000 条的游戏会被同时计入 50+ 和 100+ 类别中。Steam 平台在 2026 年上半年上架了约 12000 款游戏，同比增长 19%，游戏销售收入达到了 111 亿美元。

根据稳定版内核维护者 Greg Kroah-Hartman 制作的幻灯片，Linux 7.x 系列的 CVE 数量已从 7.1 的逾千增加到 7.2 的逾 1500，按照这一趋势，下个版本 7.3 将会超过 2000。这一切并非是因为 Linux 内核安全性变差，而是因为 AI 辅助安全检测工具对内核源代码的自动扫描发现了大量 bug，大部分 bug 并不严重属于低危级别。AI 工具产生了大量报告，而要从这些报告中发现有用的信息需要维护者耗费大量精力和时间。内核网络系统的维护者 Jakub Kicinski 表示他们有点不堪重负了。为了减少旧代码的 bug 报告，内核维护者们开始移除大量基本上无人使用的驱动代码。Linux 7.3 移除了旧的 SGI 和 IBM 驱动代码，此举旨在减少维护成本，因为这些历史悠久的代码被 AI 工具发现了大量 bug，而维护者有义务调查和修复这些 bug。

联合国环境规划署（UNEP）2 日发布报告，警告称地球变暖导致全球平均气温较工业革命前上升的幅度将在数年内超过 1.5 度，灾害、健康危害等风险将上升至危机水平。报告指出若要减轻负面影响，重要的是将超过 1.5 度的持续时间缩至最短。报告称，若保持当前的气候变暖对策，到 2100 年全球平均气温将上升约 2.6 度。即使《巴黎协定》框架下各国自主设定的温室气体减排目标全部达成，排放量和森林等吸收量相抵达到净零，预计气温仍将上升约 1.8 度，估计 10 年内将超过 1.5 度。一旦超过 1.5 度，极端的气象现象将增多，小岛被淹没的可能性升高。报告还称，人类健康、粮食生产、基础设施以及经济也将受到严重影响。据称如果本世纪内最高上升 3 度，四分之一以上的冰川将消失，海平面将上升 9-13 厘米。参与撰写报告的　英国埃克塞特大学教授理查德·贝茨以 8 月尼泊尔和中国边境发生泥石流为例强调，“遗憾的是，这种事因气候变暖更易发生。需要采取措施遏制气温上升。”

OpenAI 的 ChatGPT/Codex 桌面应用被发现捆绑了完整的 LibreOffice 副本、完整的 Python 安装包、完整的 Node.js 安装包，以及 Poppler 和 git 等常用工具，这些文件位于名为 codex-primary-runtime 的文件夹内，占据了数 GB 的空间。此前 Anthropics 的 Claude 桌面应用被发现捆绑了 10GB 的虚拟机。

鳄梨/牛油果树是雌雄同株，它的花既能作为雌蕊接收花粉，也能作为雄蕊释放花粉。鳄梨树会在一天中数次切换性别。为了避免自花授粉，半数鳄梨树（被称为A 型）被设定为早上开雌蕊，下午开雄蕊，B 型树则与之相反。根据发表在 PNAS 期刊上的一项研究，科学家分析了数百棵鳄梨树的基因组，发现名为 SDMYB 的基因与 A/B 型树开花模式密切相关。该基因有两个等位基因，一个显性一个隐性，A 型树有一个显性一个隐性，B 型树有两个隐性。这种遗传机制并非鳄梨树所独有，其它 26 种相近树种也有相同的两个版本基因。

所有东西都在慢慢涨价，包括订阅服务。历史悠久的自由软件和开源新闻网站 LWN 宣布它将从 9 月 15 日起上调订阅费用。LWN 从 2002 年起采用订阅模式，过去 24 年只涨价过两次，最近一次是 2022 年。根据美国官方的数据，2022 年以来消费者价格通胀率累计接近 20%，因此 LWN 的订阅价格也将上调 20%。 订阅级别： Starving hacker $6.00 Professional hacker $11.00 Project leader $19.00 Maniacal supporter $55.00

Starman Holding 以 2.85 亿美元现金收购了知名运动相机制造商 GoPro 几乎所有股份。股东将保留公司 10% 的股份；YouTube 主播 Mark“Markiplier”Fischbach 此前收购了 GoPro 8.5% 的股份——但不清楚是否还会继续持续股份。Starman 的业务相当复杂，涉及多个领域，包括手机壳品牌 Incipio、Incase、Griffin 和 Survivor，曾获得新冠病毒检测合同的 Digital Gadgets，Onward Brands 等。根据递交到 SEC 的文件，名为 Action Acquisitions LLC 的公司将成为 GoPro 的母公司。

Mozilla 释出了 Firefox 155。从该版本起 Firefox 将每隔两周发布一个新版本。Firefox 155 主要变化包括： 地址栏中新增 Firefox 已拦截广告追踪器的数量；容器重排序；mailto: 链接仅在用户明确操作后打开；用于登录酒店 Wi-Fi 的域名 detectportal.firefox.com 改为 firefox-portal-detection.com；Smart Window 对所有美国、加拿大和法国用户可用；bug 修正和安全补丁，等等。

苹果在 Google 之后将其地图服务中的安大略湖（Lake Ontario）更名为美国湖（Lake America）。和 Google Maps 类似，这一改名主要针对美国地区的用户，加拿大地区用户仍然看到的是安大略湖，而世界其它地方的用户则同时看到两个名字。此前 Apple Maps 也以类似的方式将墨西哥湾改名为美国湾。与此同时，拒绝改名的美国地图服务 MapQuest 则成为美国 App Store 下载量最高的地图应用。

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

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

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

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