OrangeBot.AI Digest — 2025-10-17

🚀🚀 「大模型」2小时完全从0训练26M的小参数GPT！🌏 Train a 26M-parameter GPT from scratch in just 2h!

Node Version Manager - POSIX-compliant bash script to manage multiple active node.js versions

Interactive roadmaps, guides and other educational content to help developers grow in their careers.

Windows inside a Docker container.

🍀 A cross-platform instant messaging desktop application with exceptional performance built on Rust + Vue3, compatible with Windows, macOS, Linux, Android, and iOS（一款基于Rust+Vue3极致性能的跨平台即时通讯桌面应用，兼容Windows、MacOS、Linux、Android、IOS）🎉 10月20号 3.0版本重磅发布，敬请期待🎉

🕸️ Web apps in pure Python 🐍

An Open Source implementation of Notebook LM with more flexibility and features

Malicious traffic detection system

leaked prompts of GPTs

Open Source Identity and Access Management For Modern Applications and Services

WAHA - WhatsApp HTTP API (REST API) that you can configure in a click! 3 engines: WEBJS (browser based), NOWEB (websocket nodejs), GOWS (websocket go)

Turn any PDF or image document into structured data for your AI. A powerful, lightweight OCR toolkit that bridges the gap between images/PDFs and LLMs. Supports 100+ languages.

The simplest, fastest repository for training/finetuning medium-sized GPTs.

The official Java SDK for Model Context Protocol servers and clients. Maintained in collaboration with Spring AI

Kronos: A Foundation Model for the Language of Financial Markets

Hallucination detection remains a fundamental challenge for the safe and reliable deployment of large language models (LLMs), especially in applications requiring factual accuracy. Existing hallucination benchmarks often operate at the sequence level and are limited to English, lacking the fine-grained, multilingual supervision needed for a comprehensive evaluation. In this work, we introduce PsiloQA, a large-scale, multilingual dataset annotated with span-level hallucinations across 14 languages. PsiloQA is constructed through an automated three-stage pipeline: generating question-answer pairs from Wikipedia using GPT-4o, eliciting potentially hallucinated answers from diverse LLMs in a no-context setting, and automatically annotating hallucinated spans using GPT-4o by comparing against golden answers and retrieved context. We evaluate a wide range of hallucination detection methods -- including uncertainty quantification, LLM-based tagging, and fine-tuned encoder models -- and show that encoder-based models achieve the strongest performance across languages. Furthermore, PsiloQA demonstrates effective cross-lingual generalization and supports robust knowledge transfer to other benchmarks, all while being significantly more cost-efficient than human-annotated datasets. Our dataset and results advance the development of scalable, fine-grained hallucination detection in multilingual settings.

Recently, Agentic Reinforcement Learning (Agentic RL) has made significant progress in incentivizing the multi-turn, long-horizon tool-use capabilities of web agents. While mainstream agentic RL algorithms autonomously explore high-uncertainty tool-call steps under the guidance of entropy, excessive reliance on entropy signals can impose further constraints, leading to the training collapse. In this paper, we delve into the challenges caused by entropy and propose the Agentic Entropy-Balanced Policy Optimization (AEPO), an agentic RL algorithm designed to balance entropy in both the rollout and policy update phases. AEPO comprises two core components: (1) a dynamic entropy-balanced rollout mechanism that adaptively allocate global and branch sampling budget through entropy pre-monitoring, while imposing a branch penalty on consecutive high-entropy tool-call steps to prevent over-branching issues; and (2) Entropy-Balanced Policy Optimization that inserts a stop-gradient operation into the high-entropy clipping term to preserve and properly rescale gradients on high-entropy tokens, while incorporating entropy-aware advantage estimation to prioritize learning on high-uncertainty tokens. Results across 14 challenging datasets show that AEPO consistently outperforms 7 mainstream RL algorithms. With just 1K RL samples, Qwen3-14B with AEPO achieves impressive results: 47.6% on GAIA, 11.2% on Humanity's Last Exam, and 43.0% on WebWalker for Pass@1; 65.0% on GAIA, 26.0% on Humanity's Last Exam, and 70.0% on WebWalker for Pass@5. Further analysis reveals that AEPO improves rollout sampling diversity while maintaining stable policy entropy, facilitating scalable web agent training.

Identity-consistent generation has become an important focus in text-to-image research, with recent models achieving notable success in producing images aligned with a reference identity. Yet, the scarcity of large-scale paired datasets containing multiple images of the same individual forces most approaches to adopt reconstruction-based training. This reliance often leads to a failure mode we term copy-paste, where the model directly replicates the reference face rather than preserving identity across natural variations in pose, expression, or lighting. Such over-similarity undermines controllability and limits the expressive power of generation. To address these limitations, we (1) construct a large-scale paired dataset MultiID-2M, tailored for multi-person scenarios, providing diverse references for each identity; (2) introduce a benchmark that quantifies both copy-paste artifacts and the trade-off between identity fidelity and variation; and (3) propose a novel training paradigm with a contrastive identity loss that leverages paired data to balance fidelity with diversity. These contributions culminate in WithAnyone, a diffusion-based model that effectively mitigates copy-paste while preserving high identity similarity. Extensive qualitative and quantitative experiments demonstrate that WithAnyone significantly reduces copy-paste artifacts, improves controllability over pose and expression, and maintains strong perceptual quality. User studies further validate that our method achieves high identity fidelity while enabling expressive controllable generation.

In an era where AI is evolving from a passive tool into an active and adaptive companion, we introduce AI for Service (AI4Service), a new paradigm that enables proactive and real-time assistance in daily life. Existing AI services remain largely reactive, responding only to explicit user commands. We argue that a truly intelligent and helpful assistant should be capable of anticipating user needs and taking actions proactively when appropriate. To realize this vision, we propose Alpha-Service, a unified framework that addresses two fundamental challenges: Know When to intervene by detecting service opportunities from egocentric video streams, and Know How to provide both generalized and personalized services. Inspired by the von Neumann computer architecture and based on AI glasses, Alpha-Service consists of five key components: an Input Unit for perception, a Central Processing Unit for task scheduling, an Arithmetic Logic Unit for tool utilization, a Memory Unit for long-term personalization, and an Output Unit for natural human interaction. As an initial exploration, we implement Alpha-Service through a multi-agent system deployed on AI glasses. Case studies, including a real-time Blackjack advisor, a museum tour guide, and a shopping fit assistant, demonstrate its ability to seamlessly perceive the environment, infer user intent, and provide timely and useful assistance without explicit prompts.

The edifice of native Vision-Language Models (VLMs) has emerged as a rising contender to typical modular VLMs, shaped by evolving model architectures and training paradigms. Yet, two lingering clouds cast shadows over its widespread exploration and promotion: (-) What fundamental constraints set native VLMs apart from modular ones, and to what extent can these barriers be overcome? (-) How to make research in native VLMs more accessible and democratized, thereby accelerating progress in the field. In this paper, we clarify these challenges and outline guiding principles for constructing native VLMs. Specifically, one native VLM primitive should: (i) effectively align pixel and word representations within a shared semantic space; (ii) seamlessly integrate the strengths of formerly separate vision and language modules; (iii) inherently embody various cross-modal properties that support unified vision-language encoding, aligning, and reasoning. Hence, we launch NEO, a novel family of native VLMs built from first principles, capable of rivaling top-tier modular counterparts across diverse real-world scenarios. With only 390M image-text examples, NEO efficiently develops visual perception from scratch while mitigating vision-language conflicts inside a dense and monolithic model crafted from our elaborate primitives. We position NEO as a cornerstone for scalable and powerful native VLMs, paired with a rich set of reusable components that foster a cost-effective and extensible ecosystem. Our code and models are publicly available at: https://github.com/EvolvingLMMs-Lab/NEO.

Video generation models have achieved remarkable progress, particularly excelling in realistic scenarios; however, their performance degrades notably in imaginative scenarios. These prompts often involve rarely co-occurring concepts with long-distance semantic relationships, falling outside training distributions. Existing methods typically apply test-time scaling for improving video quality, but their fixed search spaces and static reward designs limit adaptability to imaginative scenarios. To fill this gap, we propose ImagerySearch, a prompt-guided adaptive test-time search strategy that dynamically adjusts both the inference search space and reward function according to semantic relationships in the prompt. This enables more coherent and visually plausible videos in challenging imaginative settings. To evaluate progress in this direction, we introduce LDT-Bench, the first dedicated benchmark for long-distance semantic prompts, consisting of 2,839 diverse concept pairs and an automated protocol for assessing creative generation capabilities. Extensive experiments show that ImagerySearch consistently outperforms strong video generation baselines and existing test-time scaling approaches on LDT-Bench, and achieves competitive improvements on VBench, demonstrating its effectiveness across diverse prompt types. We will release LDT-Bench and code to facilitate future research on imaginative video generation.

Reinforcement Learning with Verifiable Rewards (RLVR) has recently emerged as a core paradigm for enhancing the reasoning capabilities of Large Language Models (LLMs). To address the lack of verification signals at test time, prior studies incorporate the training of model's self-verification capability into the standard RLVR process, thereby unifying reasoning and verification capabilities within a single LLM. However, previous practice requires the LLM to sequentially generate solutions and self-verifications using two separate prompt templates, which significantly reduces efficiency. In this work, we theoretically reveal that the closed-form solution to the RL objective of self-verification can be reduced to a remarkably simple form: the true reasoning reward of a solution is equal to its last-token self-rewarding score, which is computed as the difference between the policy model's next-token log-probability assigned to any pre-specified token at the solution's last token and a pre-calculated constant, scaled by the KL coefficient. Based on this insight, we propose LaSeR (Reinforcement Learning with Last-Token Self-Rewarding), an algorithm that simply augments the original RLVR loss with a MSE loss that aligns the last-token self-rewarding scores with verifier-based reasoning rewards, jointly optimizing the reasoning and self-rewarding capabilities of LLMs. The optimized self-rewarding scores can be utilized in both training and testing to enhance model performance. Notably, our algorithm derives these scores from the predicted next-token probability distribution of the last token immediately after generation, incurring only the minimal extra cost of one additional token inference. Experiments show that our method not only improves the model's reasoning performance but also equips it with remarkable self-rewarding capability, thereby boosting its inference-time scaling performance.

Large language model (LLM)-based agents are increasingly trained with reinforcement learning (RL) to enhance their ability to interact with external environments through tool use, particularly in search-based settings that require multi-turn reasoning and knowledge acquisition. However, existing approaches typically rely on outcome-based rewards that are only provided at the final answer. This reward sparsity becomes particularly problematic in multi-turn settings, where long trajectories exacerbate two critical issues: (i) advantage collapse, where all rollouts receive identical rewards and provide no useful learning signals, and (ii) lack of fine-grained credit assignment, where dependencies between turns are obscured, especially in long-horizon tasks. In this paper, we propose Information Gain-based Policy Optimization (IGPO), a simple yet effective RL framework that provides dense and intrinsic supervision for multi-turn agent training. IGPO models each interaction turn as an incremental process of acquiring information about the ground truth, and defines turn-level rewards as the marginal increase in the policy's probability of producing the correct answer. Unlike prior process-level reward approaches that depend on external reward models or costly Monte Carlo estimation, IGPO derives intrinsic rewards directly from the model's own belief updates. These intrinsic turn-level rewards are combined with outcome-level supervision to form dense reward trajectories. Extensive experiments on both in-domain and out-of-domain benchmarks demonstrate that IGPO consistently outperforms strong baselines in multi-turn scenarios, achieving higher accuracy and improved sample efficiency.

Large language models (LLMs) for code rely on subword tokenizers, such as byte-pair encoding (BPE), learned from mixed natural language text and programming language code but driven by statistics rather than grammar. As a result, semantically identical code snippets can be tokenized differently depending on superficial factors such as whitespace or identifier naming. To measure the impact of this misalignment, we introduce TokDrift, a framework that applies semantic-preserving rewrite rules to create code variants differing only in tokenization. Across nine code LLMs, including large ones with over 30B parameters, even minor formatting changes can cause substantial shifts in model behavior. Layer-wise analysis shows that the issue originates in early embeddings, where subword segmentation fails to capture grammar token boundaries. Our findings identify misaligned tokenization as a hidden obstacle to reliable code understanding and generation, highlighting the need for grammar-aware tokenization for future code LLMs.

This work studies how to adaptively recompute key-value (KV) caches for diffusion large language models (DLMs) to maximize prediction accuracy while minimizing decoding latency. Prior methods' decoders recompute QKV for all tokens at every denoising step and layer, despite KV states changing little across most steps, especially in shallow layers, leading to substantial redundancy. We make three observations: (1) distant {bf MASK} tokens primarily act as a length-bias and can be cached block-wise beyond the active prediction window; (2) KV dynamics increase with depth, suggesting that selective refresh starting from deeper layers is sufficient; and (3) the most-attended token exhibits the smallest KV drift, providing a conservative lower bound on cache change for other tokens. Building on these, we propose {bf Elastic-Cache}, a training-free, architecture-agnostic strategy that jointly decides {when} to refresh (via an attention-aware drift test on the most-attended token) and {where} to refresh (via a depth-aware schedule that recomputes from a chosen layer onward while reusing shallow-layer caches and off-window MASK caches). Unlike fixed-period schemes, Elastic-Cache performs adaptive, layer-aware cache updates for diffusion LLMs, reducing redundant computation and accelerating decoding with negligible loss in generation quality. Experiments on LLaDA-Instruct, LLaDA-1.5, and LLaDA-V across mathematical reasoning and code generation tasks demonstrate consistent speedups: 8.7times on GSM8K (256 tokens), 45.1times on longer sequences, and 4.8times on HumanEval, while consistently maintaining higher accuracy than the baseline. Our method achieves significantly higher throughput (6.8times on GSM8K) than existing confidence-based approaches while preserving generation quality, enabling practical deployment of diffusion LLMs.

In this paper, we present BitNet Distillation (BitDistill), a lightweight pipeline that fine-tunes off-the-shelf full-precision LLMs (e.g., Qwen) into 1.58-bit precision (i.e., ternary weights {-1, 0, 1}) for specific downstream tasks, achieving strong task-specific performance with minimal computational cost. Specifically, BitDistill incorporates three key techniques: the SubLN module, as introduced in BitNet; multi-head attention distillation, based on MiniLM; and continual pre-training, which serves as a crucial warm-up step to mitigate the scalability issue of the performance gap between finetuned full-precision and 1.58-bit LLMs on specific tasks. Experimental results show that BitDistill achieves performance comparable to the full-precision counterpart models across model size, while enabling up to 10x memory savings and 2.65x faster inference on CPUs. Code is available at https://github.com/microsoft/BitNet.

In this report, we propose PaddleOCR-VL, a SOTA and resource-efficient model tailored for document parsing. Its core component is PaddleOCR-VL-0.9B, a compact yet powerful vision-language model (VLM) that integrates a NaViT-style dynamic resolution visual encoder with the ERNIE-4.5-0.3B language model to enable accurate element recognition. This innovative model efficiently supports 109 languages and excels in recognizing complex elements (e.g., text, tables, formulas, and charts), while maintaining minimal resource consumption. Through comprehensive evaluations on widely used public benchmarks and in-house benchmarks, PaddleOCR-VL achieves SOTA performance in both page-level document parsing and element-level recognition. It significantly outperforms existing solutions, exhibits strong competitiveness against top-tier VLMs, and delivers fast inference speeds. These strengths make it highly suitable for practical deployment in real-world scenarios.

Recent advancements in multimodal reward models (RMs) have substantially improved post-training for visual generative models. However, current RMs face inherent limitations: (1) visual inputs consume large context budgets, forcing fewer frames and causing loss of fine-grained details; and (2) all visual information is packed into the initial prompt, exacerbating hallucination and forgetting during chain-of-thought reasoning. To overcome these issues, we introduce VideoReward Thinker (VR-Thinker), a thinking-with-image framework that equips the RM with visual reasoning operations (e.g., select frame) and a configurable visual memory window. This allows the RM to actively acquire and update visual evidence within context limits, improving reasoning fidelity and reliability. We activate visual reasoning via a reinforcement fine-tuning pipeline: (i) Cold Start with curated visual chain-of-thought data to distill basic reasoning skills and operation formatting; (ii) select samples whose per-dimension and overall judgments are all correct, then conduct Rejection sampling Fine-Tuning on these high-quality traces to further enhance reasoning; and (iii) apply Group Relative Policy Optimization (GRPO) to strengthen reasoning. Our approach delivers state-of-the-art accuracy among open-source models on video preference benchmarks, especially for longer videos: a 7B VR-Thinker achieves 80.5% on VideoGen Reward, 82.3% on GenAI-Bench, and 75.6% on MJ-Bench-Video. These results validate the effectiveness and promise of thinking-with-image multimodal reward modeling.

Recent work suggests that large language models (LLMs) encode factuality signals in their internal representations, such as hidden states, attention weights, or token probabilities, implying that LLMs may "know what they don't know". However, LLMs can also produce factual errors by relying on shortcuts or spurious associations. These error are driven by the same training objective that encourage correct predictions, raising the question of whether internal computations can reliably distinguish between factual and hallucinated outputs. In this work, we conduct a mechanistic analysis of how LLMs internally process factual queries by comparing two types of hallucinations based on their reliance on subject information. We find that when hallucinations are associated with subject knowledge, LLMs employ the same internal recall process as for correct responses, leading to overlapping and indistinguishable hidden-state geometries. In contrast, hallucinations detached from subject knowledge produce distinct, clustered representations that make them detectable. These findings reveal a fundamental limitation: LLMs do not encode truthfulness in their internal states but only patterns of knowledge recall, demonstrating that "LLMs don't really know what they don't know".

While Large Language Models (LLMs) have excelled in textual reasoning, they struggle with mathematical domains like geometry that intrinsically rely on visual aids. Existing approaches to Visual Chain-of-Thought (VCoT) are often limited by rigid external tools or fail to generate the high-fidelity, strategically-timed diagrams necessary for complex problem-solving. To bridge this gap, we introduce MathCanvas, a comprehensive framework designed to endow unified Large Multimodal Models (LMMs) with intrinsic VCoT capabilities for mathematics. Our approach consists of two phases. First, a Visual Manipulation stage pre-trains the model on a novel 15.2M-pair corpus, comprising 10M caption-to-diagram pairs (MathCanvas-Imagen) and 5.2M step-by-step editing trajectories (MathCanvas-Edit), to master diagram generation and editing. Second, a Strategic Visual-Aided Reasoning stage fine-tunes the model on MathCanvas-Instruct, a new 219K-example dataset of interleaved visual-textual reasoning paths, teaching it when and how to leverage visual aids. To facilitate rigorous evaluation, we introduce MathCanvas-Bench, a challenging benchmark with 3K problems that require models to produce interleaved visual-textual solutions. Our model, BAGEL-Canvas, trained under this framework, achieves an 86% relative improvement over strong LMM baselines on MathCanvas-Bench, demonstrating excellent generalization to other public math benchmarks. Our work provides a complete toolkit-framework, datasets, and benchmark-to unlock complex, human-like visual-aided reasoning in LMMs. Project Page: https://mathcanvas.github.io/

《Doom》游戏在 1997 年 12 月公开了引擎源代码，之后在官方源代码基础上出现了很多衍生项目，包括 1998 年发布的 ZDoom 以及今天的 GZDoom。但 GZDoom 背后的开源社区因创始人兼维护者 Cristoph Oelckers（aka Graf Zahl）承认在代码库中加入了未经测试的 AI 生成代码而发生分裂，抗议者创建了分支 UZDoom。Graf Zahl 为自己辩护，称自己只是用 AI 生成了对游戏底层功能并不重要的样板代码（Boilerplate Code）。但抗议者坚称 AI 代码在整个开源项目中无容身之地，一大原因是 AI 代码的许可证未知，可能与 GZDoom 使用的 GPL 许可证不兼容。

韩国在四个月试用后剥夺了 AI 教科书的正式教材地位，将其归类为“补充材料”。政府在该项目上投入了逾 1.2 万亿韩元（约 8.5 亿美元），教科书出版商投入了约 8000 亿韩元（5.67 亿美元）。一度对 AI 教科书抱有好奇心的学生在使用后表示非常失望，学生、教师和家长抱怨 AI 教材存在事实错误、数据隐私风险、增加了学生的屏幕时间，加重了师生的负担。是否使用 AI 教材现在由学校自行决定，其普及率从上学期的 37% 下降到了秋季学期的 19%，目前有 2095 所学校使用。

1990 年代格陵兰冰盖和北极其它地区因全球暖化而加剧融化，但南极洲冰盖没有受到什么影响。今天的情况发生了改变。研究发现，南极洲的冰盖、冰川、浮冰架以及海冰与北极一样易受气候暖化影响。卫星数据和实地观测发现，南极洲出现了类似格陵兰岛的加剧融化迹象：冰原表面融化加剧，冰川移动加快，海冰减少。科学家警告，南极洲快速格陵兰化将会带来严重后果，包括海平面加速上升以及降雨和干旱模式的显著变化。南极冰盖面积约 1400 万平方公里，平均厚度超过 1.6 公里，蕴藏着地球 61% 的淡水，如果全部融化，足以使全球平均海平面上升约 58 米。冰盖面积较小的西部尤其脆弱，其冰量足以使海平面上升逾 3 米。

根据世界气象组织（WMO）的最新报告《WMO温室气体公报》，2024年大气中二氧化碳（CO2）水平再创新高，加剧地球气温长期上升。源自人类活动的持续CO2排放和野火激增是造成这种情况的原因，加上陆地生态系统和海洋等“碳汇”对CO2的吸收减少，有可能导致气候恶性循环。自 1960 年代以来，CO2 增速已达三倍，2011-2020 年的十年间，年均增幅从每年 0.8ppm 加速到每年 2.4ppm。2023 年到 2024 年，全球 CO2 平均浓度增幅飙升到了 3.5ppm，这是自 1957 年开始现代测量以来的最大增幅。甲烷和一氧化二氮（与人类活动相关的第二和第三重要的长寿命温室气体）的浓度也已升至创纪录水平。

微软计划最快从明年开始，将大部分新产品生产由中国以外的地点生产，而亚马逊云服务（AWS）也在将其供应链下的零部件生产逐步调整。这些举措反映出美国科技企业在持续的中美紧张局势中，试图加快与中国供应链的分裂。微软已要求多家供应商协助准备“非中国”生产方案，用于其 Surface 笔记本电脑和数据中心服务器的制造，包括关键零部件和组装环节，预计最早从 2026 年开始实现全部在中国以外生产。微软游戏机 Xbox 虽然也有寻求提高中国以外的生产比例，但尚未要求完全脱离中国。

Mozilla 正在测试 Firefox 浏览器内置的 VPN 服务。Firefox VPN 与 Mozilla VPN 不同，Mozilla VPN 是一项独立的付费服务，可同时在五台设备上使用；而 Firefox VPN 只限于浏览器本身，设计通过 Mozilla 管理的服务器路由流量，隐藏用户的真实 IP 地址，同时为其通信添加一层加密，可免费使用。Firefox VPN 仍处于早期开发阶段，将在未来几个月选择随机用户进行测试。

PayPal 的加密货币合作伙伴 Paxos 周三在一次内部转账中误铸了价值 300 万亿美元的 PayPal 稳定币 PYUSD。该公司在数分钟内就发现了错误随即销毁了多余的代币。这笔交易没有造成任何资金转移或用户损失。Paxos 表示，没有安全事故，客户资金也都安全无虞。这笔代币总价值超过了所有流通美元的总和，也超过了整个加密货币市场的总和。不同于不可逆的比特币转账，稳定币发行者拥有即时创造或销毁数十亿美元稳定币的权力。Tether 曾在 2019 年误铸并销毁了价值 50 亿美元的 USDT 稳定币。

Tor 浏览器项目释出了 v15.0a4，移除了 Mozilla 过去一年整合在 Firefox 浏览器中的 AI 功能。Tor browser 是在 Firefox ESR 版本基础上开发的。开发者称，Mozilla 整合的这些 AI 功能从安全和隐私的角度都不具有可审计性，他们不想在 Tor 浏览器中推荐或宣传此类系统，因此他们将其全部移除了。

挪威是电动汽车普及的典范，在免征购置税、增值税以及免费使用收费公路等一系列激励政策的推动下，电动汽车几乎占到了全部汽车销售的 100%。挪威每月的燃油汽车销量仅仅为数百辆，电动汽车约占 95-97%。财长 Jens Stoltenberg 表示任务已经完成。该国正在调整政策，将电动汽车的增值税豁免从最高 50 万挪威克朗（约 4.9 万美元）降至最高 30 万挪威克朗（约 3 万美元）。该政策将持续到 2026 年，2027 年废除。政府将继续加大对汽油和柴油汽车的征税，确保电动汽车的普及率不会下降。

日本政府正式要求 OpenAI 停止侵权。OpenAI 本月初发布了 Sora 2，它能以 1080p 的分辨率生成 20 秒长度的视频。之后网络上充斥着 Sora 2 视频，其中很多使用了来自日本流行动漫和游戏中的版权角色，包括来自《海贼王》、《鬼灭之刃》、《宝可梦》和《马力欧》的角色。日本 IP 和 AI 战略大臣 Minoru Kiuchi 称动漫是日本向世界展示的无可替代的瑰宝。数字大臣 Masaaki Taira 希望 OpenAI 能自愿遵守相关版权法。如果问题得不到解决，可能会根据日本 AI Promotion Act 中的相关措施采取行动。

苹果发布了采用 M5 芯片的新 MacBook Pro，10 月 22 日发售，起售价 12,999 元。M5 芯片由 10 核 CPU——其中 4 个性能核心和 6 个能效核心，以及 10 核 GPU、神经网络加速器、硬件加速光线追踪 和16 核神经网络引擎构成，AI 性能比上一代的 M4 提升最高 3.5 倍，图像处理能力提升最高 1.6 倍，CPU 多线程性能提升最高 20%。苹果声称，MacBook Pro 的电池续航时间最长达到了 24 小时。

英国气候变化委员会 Climate Change Committee (CCC)警告政府需要为 2050 年全球气候上升 2 摄氏度做准备。准备不足可能会在未来产生严重的经济和健康后果。气候委员会称，全球变暖 2 摄氏度将对英国的天气产生重大影响，极端事件将变得更频繁和普遍。英国可能会遭遇更多热浪、干旱和洪水，野火季节可能会持续到秋季。由于气候变化，英国天气模式已经在发生变化，2025 年英国气象局确认了四次官方认可的热浪，这是有记录以来最热的夏季。由于人为气候暖化，未来出现与 2025 年夏季相似炎热或更炎热夏季的可能性要高得多。

肥胖的传统定义是基于 BMI 指数，即 BMI 指数达到或超过 30 的人属于肥胖。但这一定义长期以来一直受到争议，因为它没有区分脂肪和肌肉。为解决争议，医学专家在今年 1 月呼吁采用新的定义。新肥胖的定义包括：BMI 指数超过 40；BMI 指数较高同时腰围、腰臀或腰高比中至少一项指标偏高；不管 BMI 但腰围、腰臀或腰高比中有两项偏高；通过扫描直接测量体内脂肪过多。医学专家认为，肥胖应分为两类：临床肥胖（有疾病迹象）和临床前肥胖（没有疾病迹象）。研究表明，修改后的定义会导致美国成年人肥胖率急剧上升。研究人员分析了 301,026 名年龄在 18 岁至 80 岁之间的美国人数据，根据传统定义其中 44% 的人肥胖，而根据新的定义肥胖率达到了 69%，其中 70 岁以上人群肥胖率达到 78%。

Reddit 联合创始人 Alexis Ohanian 对互联网的现状表达了不满，称互联网的很大一部分已死。他指出大部分互联网内容是 AI 或机器人生成的。他援引了“死亡互联网理论”——即认为互联网的机器人数量超过了活跃的人类数量。他认为需要真实的人才能避免互联网的死亡，认为下一代的社交媒体将会是在群聊基础上发展而来。群组聊天的成员都是由真实的人组成，虽然一部分群聊者也开始使用 AI 技术去帮助生成和编辑信息。Ohanian 称，群聊是黄金标准，但不是新技术，肯定会有下一次迭代，群聊是今天我们所有人获取最佳信息的地方。

对许多糖尿病患者来说，控糖不仅是一场长期战斗，更是一场与生活质量的平衡。注射治疗的不便、药物依从性的困扰、体重和血脂的双重负担，让“控糖”成为一个难以轻松面对的命题。这一切或许正在发生改变。礼来公布了其在研口服 GLP-1 类药物 orforglipron 在两项关键的三期临床试验A CHIEVE-2 与 ACHIEVE-5 中取得积极成果。研究显示，该药物不仅显著降低血糖，还在体重、血脂等多项代谢指标上表现出色，为全球糖尿病患者带来了新的希望。GLP-1 类药物近年来成为糖尿病治疗领域的“明星”，但绝大多数需要注射使用。orforglipron是一种口服小分子药物，每日仅需一次服用，无需严格的饮食或饮水限制。这一转变，极大地降低了治疗的心理门槛，让长期规范治疗变得更加容易和自然。礼来计划于 2026 年向全球监管机构提交 orforglipron 用于治疗2型糖尿病的申请，而肥胖治疗适应症的申报预计将在今年底完成。