OrangeBot.AI Digest — 2025-11-19

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An open-source, code-first Go toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control.

所有小初高、大学PDF教材。

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

The Cloud Native Application Proxy

[EMNLP2025] "LightRAG: Simple and Fast Retrieval-Augmented Generation"

A list of open source games.

verl: Volcano Engine Reinforcement Learning for LLMs

Open-Source Memory Engine for LLMs, AI Agents & Multi-Agent Systems

Curated coding interview preparation materials for busy software engineers

Send a phone call from AI agent, in an API call. Or, directly call the bot from the configured phone number!

Run Windows Subsystem For Android on your Windows 10 and Windows 11 PC using prebuilt binaries with Google Play Store (MindTheGapps) and/or Magisk or KernelSU (root solutions) built in.

Powerful web graphics runtime built on WebGL, WebGPU, WebXR and glTF

Collection of publicly available IPTV channels from all over the world

We present AraLingBench: a fully human annotated benchmark for evaluating the Arabic linguistic competence of large language models (LLMs). The benchmark spans five core categories: grammar, morphology, spelling, reading comprehension, and syntax, through 150 expert-designed multiple choice questions that directly assess structural language understanding. Evaluating 35 Arabic and bilingual LLMs reveals that current models demonstrate strong surface level proficiency but struggle with deeper grammatical and syntactic reasoning. AraLingBench highlights a persistent gap between high scores on knowledge-based benchmarks and true linguistic mastery, showing that many models succeed through memorization or pattern recognition rather than authentic comprehension. By isolating and measuring fundamental linguistic skills, AraLingBench provides a diagnostic framework for developing Arabic LLMs. The full evaluation code is publicly available on GitHub.

Improving reasoning capabilities of Large Language Models (LLMs), especially under parameter constraints, is crucial for real-world applications. Prior work proposes recurrent transformers, which allocate a fixed number of extra iterations per token to improve generation quality. After the first, standard forward pass, instead of verbalization, last-layer hidden states are fed back as inputs for additional iterations to refine token predictions. Yet we identify a latent overthinking phenomenon: easy token predictions that are already correct after the first pass are sometimes revised into errors in additional iterations. To address this, we propose Think-at-Hard (TaH), a dynamic latent thinking method that iterates deeper only at hard tokens. It employs a lightweight neural decider to trigger latent iterations only at tokens that are likely incorrect after the standard forward pass. During latent iterations, Low-Rank Adaptation (LoRA) modules shift the LLM objective from general next-token prediction to focused hard-token refinement. We further introduce a duo-causal attention mechanism that extends attention from the token sequence dimension to an additional iteration depth dimension. This enables cross-iteration information flow while maintaining full sequential parallelism. Experiments show that TaH boosts LLM reasoning performance across five challenging benchmarks while maintaining the same parameter count. Compared with baselines that iterate twice for all output tokens, TaH delivers 8.1-11.3% accuracy gains while exempting 94% of tokens from the second iteration. Against strong single-iteration Qwen3 models finetuned with the same data, it also delivers 4.0-5.0% accuracy gains. When allowing less than 3% additional parameters from LoRA and the iteration decider, the gains increase to 8.5-12.6% and 5.3-5.4%, respectively. Our code is available at https://github.com/thu-nics/TaH.

Innovative visual stylization is a cornerstone of artistic creation, yet generating novel and consistent visual styles remains a significant challenge. Existing generative approaches typically rely on lengthy textual prompts, reference images, or parameter-efficient fine-tuning to guide style-aware image generation, but often struggle with style consistency, limited creativity, and complex style representations. In this paper, we affirm that a style is worth one numerical code by introducing the novel task, code-to-style image generation, which produces images with novel, consistent visual styles conditioned solely on a numerical style code. To date, this field has only been primarily explored by the industry (e.g., Midjourney), with no open-source research from the academic community. To fill this gap, we propose CoTyle, the first open-source method for this task. Specifically, we first train a discrete style codebook from a collection of images to extract style embeddings. These embeddings serve as conditions for a text-to-image diffusion model (T2I-DM) to generate stylistic images. Subsequently, we train an autoregressive style generator on the discrete style embeddings to model their distribution, allowing the synthesis of novel style embeddings. During inference, a numerical style code is mapped to a unique style embedding by the style generator, and this embedding guides the T2I-DM to generate images in the corresponding style. Unlike existing methods, our method offers unparalleled simplicity and diversity, unlocking a vast space of reproducible styles from minimal input. Extensive experiments validate that CoTyle effectively turns a numerical code into a style controller, demonstrating a style is worth one code.

While Chain-of-Thought (CoT) prompting enables sophisticated symbolic reasoning in LLMs, it remains confined to discrete text and cannot simulate the continuous, physics-governed dynamics of the real world. Recent video generation models have emerged as potential world simulators through Chain-of-Frames (CoF) reasoning -- materializing thought as frame-by-frame visual sequences, with each frame representing a physically-grounded reasoning step. Despite compelling demonstrations, a challenge persists: existing benchmarks, focusing on fidelity or alignment, do not assess CoF reasoning and thus cannot measure core cognitive abilities in multi-step planning, algorithmic logic, or abstract pattern extrapolation. This evaluation void prevents systematic understanding of model capabilities and principled guidance for improvement. We introduce Gen-ViRe (Generative Visual Reasoning Benchmark), a framework grounded in cognitive science and real-world AI applications, which decomposes CoF reasoning into six cognitive dimensions -- from perceptual logic to abstract planning -- and 24 subtasks. Through multi-source data curation, minimal prompting protocols, and hybrid VLM-assisted evaluation with detailed criteria, Gen-ViRe delivers the first quantitative assessment of video models as reasoners. Our experiments on SOTA systems reveal substantial discrepancies between impressive visual quality and actual reasoning depth, establishing baselines and diagnostic tools to advance genuine world simulators.

Evaluating the robustness of Large Vision-Language Models (LVLMs) is essential for their continued development and responsible deployment in real-world applications. However, existing robustness benchmarks typically focus on hallucination or misleading textual inputs, while largely overlooking the equally critical challenge posed by misleading visual inputs in assessing visual understanding. To fill this important gap, we introduce MVI-Bench, the first comprehensive benchmark specially designed for evaluating how Misleading Visual Inputs undermine the robustness of LVLMs. Grounded in fundamental visual primitives, the design of MVI-Bench centers on three hierarchical levels of misleading visual inputs: Visual Concept, Visual Attribute, and Visual Relationship. Using this taxonomy, we curate six representative categories and compile 1,248 expertly annotated VQA instances. To facilitate fine-grained robustness evaluation, we further introduce MVI-Sensitivity, a novel metric that characterizes LVLM robustness at a granular level. Empirical results across 18 state-of-the-art LVLMs uncover pronounced vulnerabilities to misleading visual inputs, and our in-depth analyses on MVI-Bench provide actionable insights that can guide the development of more reliable and robust LVLMs. The benchmark and codebase can be accessed at https://github.com/chenyil6/MVI-Bench.

Self-reflection mechanisms that rely on purely text-based rethinking processes perform well in most multimodal tasks. However, when directly applied to long-form video understanding scenarios, they exhibit clear limitations. The fundamental reasons for this lie in two points: (1)long-form video understanding involves richer and more dynamic visual input, meaning rethinking only the text information is insufficient and necessitates a further rethinking process specifically targeting visual information; (2) purely text-based reflection mechanisms lack cross-modal interaction capabilities, preventing them from fully integrating visual information during reflection. Motivated by these insights, we propose REVISOR (REflective VIsual Segment Oriented Reasoning), a novel framework for tool-augmented multimodal reflection. REVISOR enables MLLMs to collaboratively construct introspective reflection processes across textual and visual modalities, significantly enhancing their reasoning capability for long-form video understanding. To ensure that REVISOR can learn to accurately review video segments highly relevant to the question during reinforcement learning, we designed the Dual Attribution Decoupled Reward (DADR) mechanism. Integrated into the GRPO training strategy, this mechanism enforces causal alignment between the model's reasoning and the selected video evidence. Notably, the REVISOR framework significantly enhances long-form video understanding capability of MLLMs without requiring supplementary supervised fine-tuning or external models, achieving impressive results on four benchmarks including VideoMME, LongVideoBench, MLVU, and LVBench.

The rapid advancement of Large Language Models (LLMs) has led to performance saturation on many established benchmarks, questioning their ability to distinguish frontier models. Concurrently, existing high-difficulty benchmarks often suffer from narrow disciplinary focus, oversimplified answer formats, and vulnerability to data contamination, creating a fidelity gap with real-world scientific inquiry. To address these challenges, we introduce ATLAS (AGI-Oriented Testbed for Logical Application in Science), a large-scale, high-difficulty, and cross-disciplinary evaluation suite composed of approximately 800 original problems. Developed by domain experts (PhD-level and above), ATLAS spans seven core scientific fields: mathematics, physics, chemistry, biology, computer science, earth science, and materials science. Its key features include: (1) High Originality and Contamination Resistance, with all questions newly created or substantially adapted to prevent test data leakage; (2) Cross-Disciplinary Focus, designed to assess models' ability to integrate knowledge and reason across scientific domains; (3) High-Fidelity Answers, prioritizing complex, open-ended answers involving multi-step reasoning and LaTeX-formatted expressions over simple multiple-choice questions; and (4) Rigorous Quality Control, employing a multi-stage process of expert peer review and adversarial testing to ensure question difficulty, scientific value, and correctness. We also propose a robust evaluation paradigm using a panel of LLM judges for automated, nuanced assessment of complex answers. Preliminary results on leading models demonstrate ATLAS's effectiveness in differentiating their advanced scientific reasoning capabilities. We plan to develop ATLAS into a long-term, open, community-driven platform to provide a reliable "ruler" for progress toward Artificial General Intelligence.

Omnimodal large language models (OmniLLMs) have attracted increasing research attention of late towards unified audio-video understanding, wherein processing audio-video token sequences creates a significant computational bottleneck, however. Existing token compression methods have yet to accommodate this emerging need of jointly compressing multimodal tokens. To bridge this gap, we present OmniZip, a training-free, audio-guided audio-visual token-compression framework that optimizes multimodal token representation and accelerates inference. Specifically, OmniZip first identifies salient audio tokens, then computes an audio retention score for each time group to capture information density, thereby dynamically guiding video token pruning and preserving cues from audio anchors enhanced by cross-modal similarity. For each time window, OmniZip compresses the video tokens using an interleaved spatio-temporal scheme. Extensive empirical results demonstrate the merits of OmniZip - it achieves 3.42X inference speedup and 1.4X memory reduction over other top-performing counterparts, while maintaining performance with no training.

Foundation models have revolutionized artificial intelligence across numerous domains, yet their transformative potential remains largely untapped in Extreme Multi-label Classification (XMC). Queries in XMC are associated with relevant labels from extremely large label spaces, where it is critical to strike a balance between efficiency and performance. Therefore, many recent approaches efficiently pose XMC as a maximum inner product search between embeddings learned from small encoder-only transformer architectures. In this paper, we address two important aspects in XMC: how to effectively harness larger decoder-only models, and how to exploit visual information while maintaining computational efficiency. We demonstrate that both play a critical role in XMC separately and can be combined for improved performance. We show that a few billion-size decoder can deliver substantial improvements while keeping computational overhead manageable. Furthermore, our Vision-enhanced eXtreme Multi-label Learning framework (ViXML) efficiently integrates foundation vision models by pooling a single embedding per image. This limits computational growth while unlocking multi-modal capabilities. Remarkably, ViXML with small encoders outperforms text-only decoder in most cases, showing that an image is worth billions of parameters. Finally, we present an extension of existing text-only datasets to exploit visual metadata and make them available for future benchmarking. Comprehensive experiments across four public text-only datasets and their corresponding image enhanced versions validate our proposals' effectiveness, surpassing previous state-of-the-art by up to +8.21\% in P@1 on the largest dataset. ViXML's code is available at https://github.com/DiegoOrtego/vixml.

Large Language Models (LLMs) are increasingly being explored for building Agents capable of active environmental interaction (e.g., via tool use) to solve complex problems. Reinforcement Learning (RL) is considered a key technology with significant potential for training such Agents; however, the effective application of RL to LLM Agents is still in its nascent stages and faces considerable challenges. Currently, this emerging field lacks in-depth exploration into RL approaches specifically tailored for the LLM Agent context, alongside a scarcity of flexible and easily extensible training frameworks designed for this purpose. To help advance this area, this paper first revisits and clarifies Reinforcement Learning methodologies for LLM Agents by systematically extending the Markov Decision Process (MDP) framework to comprehensively define the key components of an LLM Agent. Secondly, we introduce Agent-R1, a modular, flexible, and user-friendly training framework for RL-based LLM Agents, designed for straightforward adaptation across diverse task scenarios and interactive environments. We conducted experiments on Multihop QA benchmark tasks, providing initial validation for the effectiveness of our proposed methods and framework.

We introduce Orion, a visual agent framework that can take in any modality and generate any modality. Using an agentic framework with multiple tool-calling capabilities, Orion is designed for visual AI tasks and achieves state-of-the-art results. Unlike traditional vision-language models that produce descriptive outputs, Orion orchestrates a suite of specialized computer vision tools, including object detection, keypoint localization, panoptic segmentation, Optical Character Recognition, and geometric analysis, to execute complex multi-step visual workflows. The system achieves competitive performance on MMMU, MMBench, DocVQA, and MMLongBench while extending monolithic vision-language models to production-grade visual intelligence. By combining neural perception with symbolic execution, Orion enables autonomous visual reasoning, marking a transition from passive visual understanding to active, tool-driven visual intelligence.

Foundation models have emerged as effective backbones for many vision tasks. However, current self-supervised features entangle high-level semantics with low-level physical factors, such as geometry and illumination, hindering their use in tasks requiring explicit physical reasoning. In this paper, we introduce Φeat, a novel physically-grounded visual backbone that encourages a representation sensitive to material identity, including reflectance cues and geometric mesostructure. Our key idea is to employ a pretraining strategy that contrasts spatial crops and physical augmentations of the same material under varying shapes and lighting conditions. While similar data have been used in high-end supervised tasks such as intrinsic decomposition or material estimation, we demonstrate that a pure self-supervised training strategy, without explicit labels, already provides a strong prior for tasks requiring robust features invariant to external physical factors. We evaluate the learned representations through feature similarity analysis and material selection, showing that Φeat captures physically-grounded structure beyond semantic grouping. These findings highlight the promise of unsupervised physical feature learning as a foundation for physics-aware perception in vision and graphics. These findings highlight the promise of unsupervised physical feature learning as a foundation for physics-aware perception in vision and graphics.

Large language models (LLMs) are powerful zero- and few-shot learners. However, when predicting over a set of candidate options, LLMs suffer from label biases, and existing calibration methods overlook biases arising from multi-token class labels. We tackle an issue we call label length bias, where labels of different lengths are treated inconsistently, even after standard length normalization. To mitigate it, we propose normalized contextual calibration (NCC), an effective method that normalizes and calibrates predictions at the full-label level. NCC achieves statistically significant improvements over prior approaches across multiple datasets and models, with gains of up to 10% F1. Moreover, NCC extends bias mitigation to broader tasks such as multiple-choice question answering. Our analysis shows that, when combined with in-context learning, NCC is less sensitive to few-shot example selection, requires fewer examples for competitive performance, and produces more reliable confidence estimates. These findings highlight the importance of mitigating full-label biases to improve the performance and robustness of LLM-based methods, particularly in real-world applications where class labels naturally consist of multiple tokens.

Agentic coding tools receive goals written in natural language as input, break them down into specific tasks, and write or execute the actual code with minimal human intervention. Central to this process are agent context files ("READMEs for agents") that provide persistent, project-level instructions. In this paper, we conduct the first large-scale empirical study of 2,303 agent context files from 1,925 repositories to characterize their structure, maintenance, and content. We find that these files are not static documentation but complex, difficult-to-read artifacts that evolve like configuration code, maintained through frequent, small additions. Our content analysis of 16 instruction types shows that developers prioritize functional context, such as build and run commands (62.3%), implementation details (69.9%), and architecture (67.7%). We also identify a significant gap: non-functional requirements like security (14.5%) and performance (14.5%) are rarely specified. These findings indicate that while developers use context files to make agents functional, they provide few guardrails to ensure that agent-written code is secure or performant, highlighting the need for improved tooling and practices.

We introduce proactive hearing assistants that automatically identify and separate the wearer's conversation partners, without requiring explicit prompts. Our system operates on egocentric binaural audio and uses the wearer's self-speech as an anchor, leveraging turn-taking behavior and dialogue dynamics to infer conversational partners and suppress others. To enable real-time, on-device operation, we propose a dual-model architecture: a lightweight streaming model runs every 12.5 ms for low-latency extraction of the conversation partners, while a slower model runs less frequently to capture longer-range conversational dynamics. Results on real-world 2- and 3-speaker conversation test sets, collected with binaural egocentric hardware from 11 participants totaling 6.8 hours, show generalization in identifying and isolating conversational partners in multi-conversation settings. Our work marks a step toward hearing assistants that adapt proactively to conversational dynamics and engagement. More information can be found on our website: https://proactivehearing.cs.washington.edu/

Google 发布了其最先进的 Gemini 3 模型，模型的 LMArena Leaderboard 得分达到了 1501 Elo，在多项基准测试中表现出色，其中 GPQA Diamond 博士级推理能力测试得分 91.9%，不使用任何工具的情况下在 Humanity's Last Exam 测试中得分 37.5%。Gemini 3 即日起可在 Gemini 应用、AI Mode in Search for Google AI Pro、Google AI Studio、Vertex AI 和 Google Antigravity 中使用。第三方平台如 Cursor、GitHub、JetBrains、Manus 和 Replit 也可访问该模型。Google 还表示，AI Overviews 月活用户已达 20 亿，Gemini 应用月活用户逾 6.5 亿。

开源 3D 图形设计软件 Blender 释出了 v5.0。主要变化包括：通过 Wayland/Vulkan 在 Linux 上支持 HDR 和广色域色彩，显著改进主题和 UI，新的色彩空间工具，改进曲线和几何体功能，工作色彩空间（working color space），AgX HDR 视图，Convert to Display 合成器节点，Jump Time by Delta 运算符，等等。

脑深部电刺激（DBS）——即在脑内植入电极，类似“大脑起搏器”——可使一半对治疗耐受的重度抑郁患者的症状明显改善。重度抑郁障碍是全球最常见且致残性极高的心理健康问题之一。虽然抗抑郁药和心理治疗可帮助许多患者，但治疗抵抗率依然较高，大约有 30%–50% 的患者对现有治疗反应不佳。过去几十年脑深部电刺激（DBS）逐渐被用于治疗帕金森病等神经系统疾病。DBS 通过将微型电极植入大脑深部，释放低强度电刺激以调节异常脑网络活动。上海交大医学院附属瑞金医院的 26 名对治疗耐受的抑郁患者参与了这项研究。临床研究表明，26 名患者中有 13 人（50%）症状显著改善，其中 9 人（35%）达到临床缓解（临床治愈标准）。

根据发表在《营养与代谢》期刊上的一项研究，超加工食品增加年轻人的糖尿病风险。研究发现，较高的超加工食品摄入量增加了患前驱糖尿病的可能性。前驱糖尿病指血糖升高的早期阶段，可能进一步发展为糖尿病。摄入较多超加工食品的年轻人还表现出胰岛素抵抗的迹象，表明身体利用胰岛素控制血糖的效率下降了。成年早期是身体发育成熟并形成可能持续数十年的生活习惯的关键时期。在该阶段，用水果、蔬菜和全谷物等天然食品代替超加工食品，可降低未来患Ⅱ型糖尿病的风险。研究对 85 名 17～22 岁的年轻人进行了为期 4 年的跟踪调查。参与者在每次随访时列出他们最近一个工作日及周末吃过的所有食物。研究人员将这些食物分为两类：超加工食品（如糖果、汽水、麦片、包装食品、调味酸奶和餐厅食物）和非超加工食物，并计算了每个人每天摄入总热量中的超加工食品占比。结果显示，在基线与随访期间，超加工食品摄入量每增加 10%，患前驱糖尿病的风险就会增加 64%、血糖调节受损增加 56%。

美国聚变能源技术公司 Zap Energy 宣布，其最新一代“聚变Z箍缩实验3”（FuZE-3）在实验中获得高达 830MPa 的电子压力，对应等离子体总压力约 1.6GPa。该成果刷新了迄今在剪切流稳定Z箍缩装置中实现的压力纪录，是迈向聚变能量增益道路上的重要一步。实现可控核聚变需要在极短时间内获得高温高密度等离子体，其压力是综合反映温度与密度的关键指标。压力越高，发生的聚变反应就越频繁，从而更接近能源输出大于输入的目标。与寻求极高压力或极长约束时间的其他路线不同，Zap Energy 的剪切流稳定Z箍缩技术试图在压缩效率与等离子体稳定性之间寻找平衡。FuZE-3 的设计目标是在“三重乘积”（密度×温度×约束时间）上达到新的高度，这是聚变性能的重要指标。

研究团队首次成功完成一个前所未有的银河系模拟：在电脑中一颗一颗地追踪超过 1,000 亿颗恒星，并演化 1 万年。这个结果靠的是 AI 与高效能数值模拟的结合，不只模拟的单颗恒星数量比以往最先进的模型多了约 100 倍，整体运算速度也快了 100 倍以上。这次的银河模拟总粒子数约 3,000 亿颗，恒星与气体每粒子约 0.75 个太阳质量，暗物质每粒子约 6 个太阳质量。对于恒星与气体来说，模拟中的一颗粒子几乎就可以当作单颗恒星或一小团云气来看，而不是一整团星团的平均。而要让 3,000 亿颗粒子互相感应重力与流体互动，自然需要超级电脑。计算使用了多套系统，其中主力是日本的超算富岳（Fugaku），最多使用超过 15 万个节点（Arm A64FX CPU），总 CPU 核心数量达数百万级。在富岳上，每 100 万年演化需要 2.78 小时，要模拟完整的 10 亿年银河演化只需要约 115 天，比传统方法估计的 36 年少了两个数量级以上。

在意大利 Lugano 湖畔举行的加密货币大会，投资者们在最后一天学习的是如何逃脱绑架：在双手被塑料扎带绑住的情况下如何咬断塑料。在欺骗、网络攻击和市场波动外，加密货币投资者如今面临一个更切身的严重威胁：绑架以及折磨。根据公开报道统计，今年至今袭击加密货币投资者或其家人的案件超过了 60 起。在法国，一位加密货币网红的父亲被发现藏在攻击者汽车后备箱——他被捆绑、殴打，还被泼满了汽油。在美国明尼苏达州，窃贼持枪挟持一家人 9 小时，索要价值 800 万美元的加密货币。在曼哈顿，检方指控两名男子在一栋有 17 个房间的豪华联排别墅内绑架虐待了一名加密货币交易员。Lugano 的投资者们因此参加了为期一天、收费 1000 欧元的反绑架研讨会。保持自身安全最佳方法是低调和减少炫富，做好自卫准备。

根据递交到 SEC 的文件，哈佛大学持有价值逾 4.42 亿美元的加密货币，持有形式是贝莱德（BlackRock）发行的 iShares Bitcoin Trust(IBIT) ETF。布朗大学也披露持有约 1400 万美元的加密货币 ETF。哈佛大学在比特币 ETF 上的投资超过了其持有的任何股票，包括其持有的英伟达、微软和亚马逊等主流公司的股份。4.42 亿美元仅占哈佛大学近 570 亿美元捐赠基金的不到 1%。尽管比特币币值近期下跌，但 IBIT 的总市值超过了 700 亿美元。哈佛的投资再次表明加密货币正日益被机构接受。

自 2022 年以来 Debian Live Images 都包含了非自由固件，Debian Libre Live Images 项目旨在让用户在不安装任何非自由软件的情况下运行和安装 Debian 操作系统。项目目前提供了 64 位 x86 CPU (amd64) 的 Live ISO 镜像。开发者表示，作为首个公开版本，镜像可能存在问题，建议用户在使用前查阅已知问题列表。

在亚马逊 AWS 和微软 Azure 之后，互联网再次体验到单点故障对整个互联网基础设施的影响：Cloudflare 宕机事故影响了整个互联网。Cloudflare 提供了多种服务，包括 DDoS 保护、网页应用防火墙、公共 DNS 解析器、反向代理和 CDN 等，它的服务和 AWS 以及 Azure 一样被广泛使用，它的故障也波及了整个互联网。Cloudflare 的状态页面显示它已经知道问题，并且处于“我们正在继续调查问题”之中。专家表示此类宕机事件凸显了现代互联网的脆弱性，突出了支持互联网的少数几家公司出现问题可能会造成严重破坏。

根据发表在《Frontiers in Zoology》期刊上的一项研究，生活在城市的浣熊显示出驯化的早期迹象。城里的残羹剩饭为动物提供了取之不尽的美食，但对动物而言城市和它们生活的野外环境有很大区别，为适应城市生活它们面临巨大的选择适应压力。驯化并非只是人类捕捉野生动物然后选择性繁殖，野生动物适应人类环境也是一种驯化。驯化的动物相比野生动物有些显著差异的生物特征，如脸短、头小、耳朵下垂以及白色皮毛斑块，这些特征被称为驯化综合征。生物学家 Raffaela Lesch 和同事分析了 iNaturalist 上的近两万张浣熊照片，发现城市浣熊的吻部比农村同类短 3.5%。研究人员计划接下来捕捉城市浣熊，观察它们是否比农村浣熊更友善。

你可能从儿时起一直被告诫要“坚持下去”，仿佛命悬一线时松手就会死亡。根据发表在《Nature Human Behaviour》期刊上的元分析研究，很多时候放弃其实是最明智的选择。研究人员分析了 235 项研究，涉及在遇到成功障碍后人们如何调整目标。研究作者 Hugh Riddell 称，坚持不可能实现的目标会造成严重影响，增加压力、幸福感下降，甚至引发健康问题。放弃旧目标重新转向新目标能恢复目标感和幸福感。研究还发现，放弃目标与压力、焦虑和抑郁的显著降低相关。

Alphabet CEO Sundar Pichai 在接受采访时称如果 AI 泡沫破裂没有公司能免受影响。他承认目前的 AI 热存在非理性因素。当被问及 Google 能否免受 AI 泡沫破裂的影响，Pichai 表示能承受但不可能免受影响。Alphabet 的股价在七个月内翻了一番达到 3.5 万亿美元。Pichai 表示 Google 独特的“全栈”技术模式——从芯片到YouTube 数据到模型和前沿科学——意味着它更有能力应对 AI 市场的任何动荡。他称 AI 是人类迄今创造的“最深刻的技术”，“我们将不得不应对社会变革，”也将“创造新的机遇”。

微软正进一步在 Windows 11 中整合 AI 功能。Windows 11 Build 26220.7262 的设置 > 系统 > AI 组件下新增了“实验性智能体”功能选项。该功能会启用智能体工作空间（Agent Workspace），但目前还无法使用。微软正在将 Windows 打造成一个 AI 原生操作系统。新功能允许 AI 智能体访问用户目录最常用的文件夹如桌面、音乐、图片和视频。如果启用该功能，AI 智能体还将拥有自己的运行时环境、桌面、用户帐户，并始终在后台运行。

Take-Two Interactive CEO Strauss Zelnick 在接受采访时表示，游戏行业正转向 PC 从封闭转向开放，但游戏机作为一种体验并不会消失。Zelnick 表示，游戏机和手游的市场份额产不多，但手游增长速度比游戏机更快。索尼 PS 和任天堂 Switch 的游戏机业务取得了成功，而竞争对手拥有 Xbox 的微软则暗示下一代硬件将更面向 PC 游戏。Valve 最近宣布的 Steam Machin 就是游戏机和 PC 的混合设备。