OrangeBot.AI Digest — 2025-10-27

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Large reasoning models have demonstrated strong problem-solving abilities, yet real-world tasks often require external tools and long-horizon interactions. Existing agent frameworks typically follow predefined workflows, which limit autonomous and global task completion. In this paper, we introduce DeepAgent, an end-to-end deep reasoning agent that performs autonomous thinking, tool discovery, and action execution within a single, coherent reasoning process. To address the challenges of long-horizon interactions, particularly the context length explosion from multiple tool calls and the accumulation of interaction history, we introduce an autonomous memory folding mechanism that compresses past interactions into structured episodic, working, and tool memories, reducing error accumulation while preserving critical information. To teach general-purpose tool use efficiently and stably, we develop an end-to-end reinforcement learning strategy, namely ToolPO, that leverages LLM-simulated APIs and applies tool-call advantage attribution to assign fine-grained credit to the tool invocation tokens. Extensive experiments on eight benchmarks, including general tool-use tasks (ToolBench, API-Bank, TMDB, Spotify, ToolHop) and downstream applications (ALFWorld, WebShop, GAIA, HLE), demonstrate that DeepAgent consistently outperforms baselines across both labeled-tool and open-set tool retrieval scenarios. This work takes a step toward more general and capable agents for real-world applications. The code and demo are available at https://github.com/RUC-NLPIR/DeepAgent.

Unified, generalizable semantic control in video generation remains a critical open challenge. Existing methods either introduce artifacts by enforcing inappropriate pixel-wise priors from structure-based controls, or rely on non-generalizable, condition-specific finetuning or task-specific architectures. We introduce Video-As-Prompt (VAP), a new paradigm that reframes this problem as in-context generation. VAP leverages a reference video as a direct semantic prompt, guiding a frozen Video Diffusion Transformer (DiT) via a plug-and-play Mixture-of-Transformers (MoT) expert. This architecture prevents catastrophic forgetting and is guided by a temporally biased position embedding that eliminates spurious mapping priors for robust context retrieval. To power this approach and catalyze future research, we built VAP-Data, the largest dataset for semantic-controlled video generation with over 100K paired videos across 100 semantic conditions. As a single unified model, VAP sets a new state-of-the-art for open-source methods, achieving a 38.7% user preference rate that rivals leading condition-specific commercial models. VAP's strong zero-shot generalization and support for various downstream applications mark a significant advance toward general-purpose, controllable video generation.

Group Relative Policy Optimization (GRPO) has shown strong potential for flow-matching-based text-to-image (T2I) generation, but it faces two key limitations: inaccurate advantage attribution, and the neglect of temporal dynamics of generation. In this work, we argue that shifting the optimization paradigm from the step level to the chunk level can effectively alleviate these issues. Building on this idea, we propose Chunk-GRPO, the first chunk-level GRPO-based approach for T2I generation. The insight is to group consecutive steps into coherent 'chunk's that capture the intrinsic temporal dynamics of flow matching, and to optimize policies at the chunk level. In addition, we introduce an optional weighted sampling strategy to further enhance performance. Extensive experiments show that ChunkGRPO achieves superior results in both preference alignment and image quality, highlighting the promise of chunk-level optimization for GRPO-based methods.

We tackle the challenge of generating the infinitely extendable 3D world -- large, continuous environments with coherent geometry and realistic appearance. Existing methods face key challenges: 2D-lifting approaches suffer from geometric and appearance inconsistencies across views, 3D implicit representations are hard to scale up, and current 3D foundation models are mostly object-centric, limiting their applicability to scene-level generation. Our key insight is leveraging strong generation priors from pre-trained 3D models for structured scene block generation. To this end, we propose WorldGrow, a hierarchical framework for unbounded 3D scene synthesis. Our method features three core components: (1) a data curation pipeline that extracts high-quality scene blocks for training, making the 3D structured latent representations suitable for scene generation; (2) a 3D block inpainting mechanism that enables context-aware scene extension; and (3) a coarse-to-fine generation strategy that ensures both global layout plausibility and local geometric/textural fidelity. Evaluated on the large-scale 3D-FRONT dataset, WorldGrow achieves SOTA performance in geometry reconstruction, while uniquely supporting infinite scene generation with photorealistic and structurally consistent outputs. These results highlight its capability for constructing large-scale virtual environments and potential for building future world models.

Discrete diffusion models have emerged as a promising direction for vision-language tasks, offering bidirectional context modeling and theoretical parallelization. However, their practical application is severely hindered by a train-inference discrepancy, which leads to catastrophic error cascades: initial token errors during parallel decoding pollute the generation context, triggering a chain reaction of compounding errors and leading to syntactic errors and semantic hallucinations. To address this fundamental challenge, we reframe the generation process from passive denoising to active refining. We introduce ReDiff, a refining-enhanced diffusion framework that teaches the model to identify and correct its own errors. Our approach features a two-stage training process: first, we instill a foundational revision capability by training the model to revise synthetic errors; second, we implement a novel online self-correction loop where the model is explicitly trained to revise its own flawed drafts by learning from an expert's corrections. This mistake-driven learning endows the model with the crucial ability to revisit and refine its already generated output, effectively breaking the error cascade. Extensive experiments demonstrate that ReDiff significantly improves the coherence and factual accuracy of generated content, enabling stable and efficient parallel generation far superior to traditional denoising methods. Our codes and models are available at https://rediff-hku.github.io/.

The lack of a concrete definition for Artificial General Intelligence (AGI) obscures the gap between today's specialized AI and human-level cognition. This paper introduces a quantifiable framework to address this, defining AGI as matching the cognitive versatility and proficiency of a well-educated adult. To operationalize this, we ground our methodology in Cattell-Horn-Carroll theory, the most empirically validated model of human cognition. The framework dissects general intelligence into ten core cognitive domains-including reasoning, memory, and perception-and adapts established human psychometric batteries to evaluate AI systems. Application of this framework reveals a highly "jagged" cognitive profile in contemporary models. While proficient in knowledge-intensive domains, current AI systems have critical deficits in foundational cognitive machinery, particularly long-term memory storage. The resulting AGI scores (e.g., GPT-4 at 27%, GPT-5 at 58%) concretely quantify both rapid progress and the substantial gap remaining before AGI.

Frontier reasoning models have exhibited incredible capabilities across a wide array of disciplines, driven by posttraining large language models (LLMs) with reinforcement learning (RL). However, despite the widespread success of this paradigm, much of the literature has been devoted to disentangling truly novel behaviors that emerge during RL but are not present in the base models. In our work, we approach this question from a different angle, instead asking whether comparable reasoning capabilites can be elicited from base models at inference time by pure sampling, without any additional training. Inspired by Markov chain Monte Carlo (MCMC) techniques for sampling from sharpened distributions, we propose a simple iterative sampling algorithm leveraging the base models' own likelihoods. Over different base models, we show that our algorithm offers substantial boosts in reasoning that nearly match and even outperform those from RL on a wide variety of single-shot tasks, including MATH500, HumanEval, and GPQA. Moreover, our sampler avoids the collapse in diversity over multiple samples that is characteristic of RL-posttraining. Crucially, our method does not require training, curated datasets, or a verifier, suggesting broad applicability beyond easily verifiable domains.

Scaling the context length of large language models (LLMs) offers significant benefits but is computationally expensive. This expense stems primarily from the self-attention mechanism, whose O(N^2) complexity with respect to sequence length presents a major bottleneck for both memory and latency. Fortunately, the attention matrix is often sparse, particularly for long sequences, suggesting an opportunity for optimization. Block-sparse attention has emerged as a promising solution that partitions sequences into blocks and skips computation for a subset of these blocks. However, the effectiveness of this method is highly dependent on the underlying attention patterns, which can lead to sub-optimal block-level sparsity. For instance, important key tokens for queries within a single block may be scattered across numerous other blocks, leading to computational redundancy. In this work, we propose Permuted Block-Sparse Attention (PBS-Attn), a plug-and-play method that leverages the permutation properties of attention to increase block-level sparsity and enhance the computational efficiency of LLM prefilling. We conduct comprehensive experiments on challenging real-world long-context datasets, demonstrating that PBS-Attn consistently outperforms existing block-sparse attention methods in model accuracy and closely matches the full attention baseline. Powered by our custom permuted-FlashAttention kernels, PBS-Attn achieves an end-to-end speedup of up to 2.75times in long-context prefilling, confirming its practical viability. Code available at https://github.com/xinghaow99/pbs-attn

GUI grounding, which maps natural-language instructions to actionable UI elements, is a core capability of GUI agents. Prior works largely treats instructions as a static proxy for user intent, overlooking the impact of instruction diversity and quality on grounding performance. Through a careful investigation of existing grounding datasets, we find a 23.3% flaw rate in their instructions and show that inference-time exploitation of instruction diversity yields up to a substantial 76% relative performance improvement. In this paper, we introduce the Instruction-as-Reasoning paradigm, treating instructions as dynamic analytical pathways that offer distinct perspectives and enabling the model to select the most effective pathway during reasoning. To achieve this, we propose a two-stage training framework: supervised fine-tuning (SFT) on synthesized, diverse instructions to instill multi-perspective reasoning, followed by reinforcement learning (RL) to optimize pathway selection and composition. Our resulting models, UI-Ins-7B and UI-Ins-32B, achieve state-of-the-art results on five challenging grounding benchmarks and exhibit emergent reasoning, selectively composing and synthesizing novel instruction pathways at inference. In particular, UI-Ins-32B attains the best grounding accuracy, scoring 87.3% on UI-I2E-Bench, 57.0% on ScreenSpot-Pro, and 84.9% on MMBench-GUI L2. Furthermore, our model demonstrates strong agentic potential, achieving a 74.1% success rate on AndroidWorld using UI-Ins-7B as the executor. Our in-depth analysis reveals additional insights such as how reasoning can be formulated to enhance rather than hinder grounding performance, and how our method mitigates policy collapse in the SFT+RL framework. All code and model checkpoints will be publicly released in https://github.com/alibaba/UI-Ins.

In this paper we show that visual diffusion models can serve as effective geometric solvers: they can directly reason about geometric problems by working in pixel space. We first demonstrate this on the Inscribed Square Problem, a long-standing problem in geometry that asks whether every Jordan curve contains four points forming a square. We then extend the approach to two other well-known hard geometric problems: the Steiner Tree Problem and the Simple Polygon Problem. Our method treats each problem instance as an image and trains a standard visual diffusion model that transforms Gaussian noise into an image representing a valid approximate solution that closely matches the exact one. The model learns to transform noisy geometric structures into correct configurations, effectively recasting geometric reasoning as image generation. Unlike prior work that necessitates specialized architectures and domain-specific adaptations when applying diffusion to parametric geometric representations, we employ a standard visual diffusion model that operates on the visual representation of the problem. This simplicity highlights a surprising bridge between generative modeling and geometric problem solving. Beyond the specific problems studied here, our results point toward a broader paradigm: operating in image space provides a general and practical framework for approximating notoriously hard problems, and opens the door to tackling a far wider class of challenging geometric tasks.

Prompt design plays a crucial role in text-to-video (T2V) generation, yet user-provided prompts are often short, unstructured, and misaligned with training data, limiting the generative potential of diffusion-based T2V models. We present RAPO++, a cross-stage prompt optimization framework that unifies training-data--aligned refinement, test-time iterative scaling, and large language model (LLM) fine-tuning to substantially improve T2V generation without modifying the underlying generative backbone. In Stage 1, Retrieval-Augmented Prompt Optimization (RAPO) enriches user prompts with semantically relevant modifiers retrieved from a relation graph and refactors them to match training distributions, enhancing compositionality and multi-object fidelity. Stage 2 introduces Sample-Specific Prompt Optimization (SSPO), a closed-loop mechanism that iteratively refines prompts using multi-source feedback -- including semantic alignment, spatial fidelity, temporal coherence, and task-specific signals such as optical flow -- yielding progressively improved video generation quality. Stage 3 leverages optimized prompt pairs from SSPO to fine-tune the rewriter LLM, internalizing task-specific optimization patterns and enabling efficient, high-quality prompt generation even before inference. Extensive experiments across five state-of-the-art T2V models and five benchmarks demonstrate that RAPO++ achieves significant gains in semantic alignment, compositional reasoning, temporal stability, and physical plausibility, outperforming existing methods by large margins. Our results highlight RAPO++ as a model-agnostic, cost-efficient, and scalable solution that sets a new standard for prompt optimization in T2V generation. The code is available at https://github.com/Vchitect/RAPO.

We unveil that internal representations in large language models (LLMs) serve as reliable proxies of learned knowledge, and propose RECALL, a novel representation-aware model merging framework for continual learning without access to historical data. RECALL computes inter-model similarity from layer-wise hidden representations over clustered typical samples, and performs adaptive, hierarchical parameter fusion to align knowledge across models. This design enables the preservation of domain-general features in shallow layers while allowing task-specific adaptation in deeper layers. Unlike prior methods that require task labels or incur performance trade-offs, RECALL achieves seamless multi-domain integration and strong resistance to catastrophic forgetting. Extensive experiments across five NLP tasks and multiple continual learning scenarios show that RECALL outperforms baselines in both knowledge retention and generalization, providing a scalable and data-free solution for evolving LLMs.

Video Large Language Models (VideoLLMs) extend the capabilities of vision-language models to spatiotemporal inputs, enabling tasks such as video question answering (VideoQA). Despite recent advances in VideoLLMs, their internal mechanisms on where and how they extract and propagate video and textual information remain less explored. In this study, we investigate the internal information flow of VideoLLMs using mechanistic interpretability techniques. Our analysis reveals consistent patterns across diverse VideoQA tasks: (1) temporal reasoning in VideoLLMs initiates with active cross-frame interactions in early-to-middle layers, (2) followed by progressive video-language integration in middle layers. This is facilitated by alignment between video representations and linguistic embeddings containing temporal concepts. (3) Upon completion of this integration, the model is ready to generate correct answers in middle-to-late layers. (4) Based on our analysis, we show that VideoLLMs can retain their VideoQA performance by selecting these effective information pathways while suppressing a substantial amount of attention edges, e.g., 58% in LLaVA-NeXT-7B-Video-FT. These findings provide a blueprint on how VideoLLMs perform temporal reasoning and offer practical insights for improving model interpretability and downstream generalization. Our project page with the source code is available at https://map-the-flow.github.io

Model merging is an efficient post-training strategy for integrating knowledge from multiple finetuned checkpoints of a shared foundation model. Existing methods operate in the parameter space, combining task vectors to mitigate conflicts, but remain constrained by parameter inconsistencies. We propose Functional Dual Anchors (FDAs), a framework that instead models the input-representation space. FDAs are synthetic inputs whose induced gradients align with task vectors, capturing task-specific functional shifts relative to the pretrained model. This perspective bridges joint multi-task training and post-hoc merging, offering both robustness and flexibility. We further introduce a principled initialization scheme and show that FDAs are complementary to parameter-space model merging. Comprehensive experiments demonstrate the effectiveness of FDAs in model merging.

We apply category theory to extract multimodal document structure which leads us to develop information theoretic measures, content summarization and extension, and self-supervised improvement of large pretrained models. We first develop a mathematical representation of a document as a category of question-answer pairs. Second, we develop an orthogonalization procedure to divide the information contained in one or more documents into non-overlapping pieces. The structures extracted in the first and second steps lead us to develop methods to measure and enumerate the information contained in a document. We also build on those steps to develop new summarization techniques, as well as to develop a solution to a new problem viz. exegesis resulting in an extension of the original document. Our question-answer pair methodology enables a novel rate distortion analysis of summarization techniques. We implement our techniques using large pretrained models, and we propose a multimodal extension of our overall mathematical framework. Finally, we develop a novel self-supervised method using RLVR to improve large pretrained models using consistency constraints such as composability and closure under certain operations that stem naturally from our category theoretic framework.

根据发表在《自然》期刊上的一项研究，在新冠疫情期间拯救数百万人生命的 mRNA 疫苗可能激活免疫系统识别和杀死癌细胞。研究人员调查了逾千名接受免疫疗法 immune checkpoint inhibitors 的晚期黑色素瘤和肺癌患者，该疗法通过阻断肿瘤细胞制造的用于关闭免疫细胞的蛋白质，让免疫系统能继续杀死癌细胞。研究发现，接受免疫治疗 100 天内接种辉瑞或 Moderna mRNA 新冠疫苗的患者，三年后存活的可能性是未接种任何一种疫苗的患者的两倍多。通常对免疫疗法反应不佳的肿瘤患者也效果显著，三年总生存率提高了近五倍。研究人员进一步调查发现，新冠 mRNA 疫苗就像警钟，触发人体免疫系统识别和杀死癌细胞，遏制癌症关闭免疫细胞的能力。组合使用疫苗和 immune checkpoint inhibitors，它们能协同释放免疫系统的全部力量杀死癌细胞。

生成式 AI 使用了不同许可证授权的 FOSS 软件代码进行了训练，当它们生成代码片段时，所有许可证、作者和上下文等相关信息都被剥离了。由于 AI 代码切断了人与代码之间的联系，这意味着下游开发者将无法遵守互惠许可条款。即使开发者怀疑一段 AI 代码来自开源许可证授权的代码，也无法确定其源项目，训练数据被抽象成数十亿统计权重，在法律上这相当于一个黑洞。AI 代码造成的伤害不限于法律上的不确定性，整个开源生态系统也面临风险。当 AI 吸收互联网上的一切并清洗时，模糊归属、所有权和互惠原则，所有现代社会赖以存在的关键基础设施都面临风险。

天文学家首次在银河系以外的冰层中，发现构成生命的化学基础物质。在大麦哲伦星系一颗新生恒星周围的冰层中，研究团队侦测到乙醇（ethanol）、乙醛（acetaldehyde）与蚁酸甲酯（methyl formate）等复杂有机分子，这是人类首次在银河系外的冰冻物质中找到这些化合物。此外，他们还首次在宇宙中发现固态冰的乙酸（acetic acid），在过去仅观测到以气态存在的乙酸。研究揭示生命化学的基础成分可能广泛存在于宇宙中，而非仅限于银河系之内。与银河系相比，大麦哲伦星系的金属量仅约为其三分之一至二分之一。所谓「金属」在天文学上指氦以外的所有元素，因此该星系的氧、碳、矽等含量相对贫乏。它的尘埃也较少，使光线更容易穿透，同时频繁的恒星诞生活动释放强烈紫外线辐射，这使得在此环境下形成复杂有机分子的机制更值得探究。

一项发表在 arXiv 的研究发现，AI 模型的谄媚程度比人类高 50%。该研究测试了 11 个广泛使用的大模型对 1.15 多万个咨询请求的响应情况，其中不乏涉及不当行为或有害行为的请求。包括 ChatGPT 和 Gemini 在内的AI聊天机器人，常常会鼓励用户、给出过度奉承的反馈，还会调整回应以附和用户观点，有时甚至会为此牺牲准确性。研究 AI 行为的科研人员表示，这种取悦他人的倾向即“谄媚性”，正影响着他们在科研中使用 AI 的方式，涵盖从构思创意、生成假设到推理分析等各类任务。arXiv 上的另一项研究旨在验证 AI 的谄媚性是否会影响其解决数学问题的能力。研究人员从今年举办的数学竞赛中选取了 504 道题目，对每道题的定理表述进行修改，植入不易察觉的错误，随后让 4 个大模型为这些存在缺陷的表述提供证明。测试结果显示，GPT-5 的谄媚性最低，仅 29% 的回答存在谄媚行为；而 DeepSeek-V3.1 的谄媚性最高，70% 的回答带有谄媚倾向。研究人员指出，尽管这些大模型具备识别数学表述中错误的能力，但它们“就是会默认用户的说法是正确的”。

面向开发者、AI工程师及技术决策者开放 除主论坛（大模型、物理 AI、机器人）和三大技术分论坛外，还将开放免费 NVIDIA Certified Associate（NCA）级别认证考试，常规费用960 元，参与本次活动将全额免除。 开放科目（三选一）： NCA-GENL：生成式 AI / 大语言模型开发 NCA-GENM：多模态生成式 AI（文本/图像/音频） NCA-AIIO：AI 基础设施与运维 名额有限，仅100个免费席位，抓紧报名 报名地址：https://developer.nvidia.cn/developer-day?ncid=pa-so-zdn-510609-vt16

比尔盖茨支持的核电公司 TerraPower 通过了美国核管理委员会的环评（Environmental Impact Statement），批准了核设施的建造许可证。TerraPower 的非核设施已从 2024 年 6 月开始建造。TerraPower 计划建造的凯默勒一号机组（Kemmerer Unit 1）将是美国首座使用液态钠冷却而不是水冷却的商业核反应堆。凯默勒是燃煤 Naughton 发电厂的所在地，该发电厂将于 2026 年停止使用燃煤，十年后停止燃烧天然气。TerraPower 项目将用一个 345 兆瓦的反应堆取代它，TerraPower 反应堆将开创性地使用许多以前未在商业上部署过的技术。其中包括需要最少换料的反应堆设计、液态钠冷却以及熔盐蓄热系统，该系统将为发电厂提供更好地与可再生能源整合必需的灵活性。

主要通过赌博网站传播的寰宇浏览器（Universe Browser）号称是“全网权威安全认证，值得信赖的浏览器”，安全公司 Infoblox 发现该浏览器会将流量理由经过中国境内的服务器，会秘密安装多个在后台静默运行的程序，它们的功能类似恶意程序，包括键盘记录、秘密连接和更改设备网络连接。研究人员表示，寰宇浏览器与赌博网站 BBIN（aka 寶盈集團）相关，研究人员将该组织命名为 Vault Viper，他们是在研究柬埔寨一赌场的系统时发现了 Vault Viper 相关的独特 DNS 指纹。研究人员逆向工程了寰宇浏览器，发现了很多类似恶意软件的功能，它还会试图逃避杀毒软件的检测。在浏览器启动时，它会立即检查用户的位置、语言以及是否在虚拟机中运行。它安装了两个扩展：其中一个允许上传屏幕截图。Infoblox 认为寰宇浏览器是识别富有玩家并获取其设备访问权限的完美工具。

目前还没有证据表明 AI 能显著提高生产力，然而今天企业大幅裁员时通常会把 AI 作为借口。Oxford Internet Institute 的 AI 和工作助理教授 Fabian Stephany 怀疑裁员与新技术带来的效率提升相关，企业只是将 AI 作为裁员的借口。Stephany 表示，企业以此将自己定位在 AI 技术的前沿，展现创新性和竞争力，同时掩盖裁员的真实原因。很多企业在新冠疫情期间招聘了太多员工，近期的裁员可能只是一种“市场清理”。Jean-Christophe Bouglé 在一篇热门的 LinkedIn 帖子中表示，AI 普及速度比宣称的慢得多，大型企业中 AI 进展缓慢，甚至会由于成本或安全问题而推迟部署 AI 项目。在很多国家经济放缓的背景下，企业以 AI 为借口推行大规模裁员。

微软宣布，文件资源管理器（File Explorer，前称 Windows Explorer）自动禁用互联网下载文件的预览功能，此举旨在阻止利用恶意文件窃取凭证的攻击。该攻击不需要任何用户互动，也不需要诱骗用户打开或执行文件，只需要选定恶意文件预览。大部分用户不需要采取任何行动，本月释出的例行安全更新已经自动启用了最新的保护措施。但这一保护生效需要登出重新登陆。

10 月 26 日上午 9 点，日本宇宙航空研究开发机构（JAXA）从鹿儿岛县的种子岛宇宙中心用 H3 火箭 7 号机发射了向国际空间站（ISS）运送食物和实验装置的新型无人补给飞船“HTV-X”1号机。HTV-X 在大约 14 分钟后与火箭分离，发射取得成功。HTV-X 是 2009-2020 年 9 次运送全部成功的“鹳”的后续飞船。物资运载量由 4 吨增至近 6 吨，此外新增对运载物资的供电功能，能运送需要在冷冻柜低温保存的实验样品。

天文学家在距离地球约 190 光年的双星系统 TOI-2267 中，发现了三颗地球大小的行星。TOI-2267 拥有独特的行星配置：两颗行星绕其中一颗恒星运行，另一颗行星则绕伴星运行。这使得 TOI-2267 成为第一个已知在两颗恒星周围都观测到凌日现象的双星系统。 TOI-2267 是一个紧密双星系统，两颗恒星以极近距离互绕，形成一个对行星形成而言极不稳定的重力环境，然而却在其中发现了三颗短周期、地球大小的行星。在如此紧密的双星系统中发现三颗地球大小行星，是一次极为罕见的机会，能让科学家在复杂重力环境下测试行星形成理论的极限，并更深入理解银河系中行星系统结构的多样性。这个系统堪称是研究岩质行星如何在极端动力学条件下形成与存续的天然实验室，在此之前普遍认为这样的环境无法维持稳定的行星轨道。

华盛顿邮报报道了硅谷和纽约初创公司推广 996 工作制——即每周工作六天，从早上九点一直到晚上九点。这些企业将 996 宣扬为美德，认为是一种磨练，目的是在市场的激烈竞争中取得优势。因为 AI 领域的机会窗口只有 2-3 年，谁能率先获得优势，就能占领市场。风险投资公司 LifeX Ventures 管理合伙人 Inaki Berenguer 说，你最好跑得比其他任何人都快。旧金山 AI 初创公司 Sonatic CEO Kinjal Nandy 表示虽然工作时间长，但他们仍然提供了各种福利，留出了就餐和锻炼健身的时间，甚至还提供约会服务 Raya 的免费订阅。许多初创公司要求员工到办公室工作，不允许远程办公。AI 初创公司 StarSling 要求每周六天到办公室工作；Rilla 要求每周去办公室工作 70 小时；Google 联合创始人 Sergey Brin 此前也建议 AI 工程师每周工作 60 小时。WHO 的数据显示，相比标准的 35-40 小时工作时间，每周工作逾 55 小时会导致中风风险增加 35%，心脏病死亡风险增加 17%。长时间工作也影响生产力。一项英国研究表明，每周工作逾 60 小时会降低整体产出，降低认知能力。

陈立武就任英特尔 CEO 后宣布的第一件事就是大裁员。英特尔公司在约三个月内裁掉了多达 20,500 名员工，加上上一任裁掉的 15,000 个职位，芯片巨人在不到两年内裁掉了 35,500 名员工。截至 2024 年 12 月 28 日，英特尔拥有 108,900 名员工，其中包括数千名 Altera 员工——如今 Altera 已经成为独立公司。根据英特尔递交到 SEC 的最新文件，截至 2025 年 9 月 27 日，该公司共有 88,400 名员工，其中英特尔 83,300 名，Mobileye 等子公司 5,100 名。这意味着在陈立武的领导下，英特尔解雇了 20,50 0名员工，裁员主要集中在第二季度。

蓝箭航天本周完成了朱雀三号遥一运载火箭首飞任务的第一阶段工作——加注合练及静态点火试验，为今年晚些时候第二阶段的试飞和第一级回收做准备。朱雀三号一二级箭体直径 4.5 米，整流罩直径 5.2 米，全箭长 66.1 米，起飞质量约 570 吨，起飞推力超过 750 吨，采用不锈钢作为箭体主结构材料，一子级配备九台天鹊-12A液氧甲烷发动机，设计可在执行轨道发射任务后自主高精度返回，在回收场实现软着陆并重复使用。火箭如果是一次性使用其有效载荷为 11,800 公斤，如果尝试回收第一级则有效载荷为 8,000 公斤。相比下 SpaceX 的 Falcon 9 能将 22,800 公斤负荷发射到低地球轨道。

美国保守派不喜欢现在的维基百科：他们打造了保守派自己的在线百科全书 Conservapedia，但根本没人看；共和党人坚称维基百科存在自由主义偏见，要求运营维基百科的非盈利组织维基媒体基金会给出解释，称要对潜在的平台操纵展开调查；马斯克（Elon Musk）表示将会推出 AI 驱动的百科全书替代 Grokipedia。参与创建维基百科的联合创始人 Larry Sanger 很早就离开了该平台，他如今皈依基督教，2024 年投票给了特朗普，拥抱 MAGA，目前正致力于招募保守派人士去积极编辑维基百科条目。