OrangeBot.AI Digest — 2025-09-18

Python tool for converting files and office documents to Markdown.

All Algorithms implemented in Python

A command line tool and library for transferring data with URL syntax, supporting DICT, FILE, FTP, FTPS, GOPHER, GOPHERS, HTTP, HTTPS, IMAP, IMAPS, LDAP, LDAPS, MQTT, POP3, POP3S, RTMP, RTMPS, RTSP, SCP, SFTP, SMB, SMBS, SMTP, SMTPS, TELNET, TFTP, WS and WSS. libcurl offers a myriad of powerful features

Flutter makes it easy and fast to build beautiful apps for mobile and beyond

Tongyi DeepResearch, the Leading Open-source DeepResearch Agent

Open-source framework for conversational voice AI agents.

A complete computer science study plan to become a software engineer.

AI wearables. Put it on, speak, transcribe, automatically

Main repository for the Linera protocol

基于大模型和 RAG 的智能问数系统。Text-to-SQL Generation via LLMs using RAG.

3D Reconstruction for all

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 80+ languages.

Fine-tuning & Reinforcement Learning for LLMs. 🦥 Train OpenAI gpt-oss, Qwen3, Llama 4, DeepSeek-R1, Gemma 3, TTS 2x faster with 70% less VRAM.

Lightweight coding agent that runs in your terminal

We present Hala, a family of Arabic-centric instruction and translation models built with our translate-and-tune pipeline. We first compress a strong ARleftrightarrowEN teacher to FP8 (yielding sim2times higher throughput with no quality loss) and use it to create high-fidelity bilingual supervision. A lightweight language model LFM2-1.2B is then fine-tuned on this data and used to translate high-quality English instruction sets into Arabic, producing a million-scale corpus tailored to instruction following. We train Hala models at 350M, 700M, 1.2B, and 9B parameters, and apply slerp merging to balance Arabic specialization with base-model strengths. On Arabic-centric benchmarks, Hala achieves state-of-the-art results within both the "nano" (leq2B) and "small" (7-9B) categories, outperforming their bases. We release models, data, evaluation, and recipes to accelerate research in Arabic NLP.

We introduce SAIL-VL2, an open-suite vision-language foundation model (LVM) for comprehensive multimodal understanding and reasoning. As the successor to SAIL-VL, SAIL-VL2 achieves state-of-the-art performance at the 2B and 8B parameter scales across diverse image and video benchmarks, demonstrating strong capabilities from fine-grained perception to complex reasoning. Three core innovations drive its effectiveness. First, a large-scale data curation pipeline with scoring and filtering strategies enhances both quality and distribution across captioning, OCR, QA, and video data, improving training efficiency. Second, a progressive training framework begins with a powerful pre-trained vision encoder (SAIL-ViT), advances through multimodal pre-training, and culminates in a thinking-fusion SFT-RL hybrid paradigm that systematically strengthens model capabilities. Third, architectural advances extend beyond dense LLMs to efficient sparse Mixture-of-Experts (MoE) designs. With these contributions, SAIL-VL2 demonstrates competitive performance across 106 datasets and achieves state-of-the-art results on challenging reasoning benchmarks such as MMMU and MathVista. Furthermore, on the OpenCompass leaderboard, SAIL-VL2-2B ranks first among officially released open-source models under the 4B parameter scale, while serving as an efficient and extensible foundation for the open-source multimodal community.

Omnidirectional vision, using 360-degree vision to understand the environment, has become increasingly critical across domains like robotics, industrial inspection, and environmental monitoring. Compared to traditional pinhole vision, omnidirectional vision provides holistic environmental awareness, significantly enhancing the completeness of scene perception and the reliability of decision-making. However, foundational research in this area has historically lagged behind traditional pinhole vision. This talk presents an emerging trend in the embodied AI era: the rapid development of omnidirectional vision, driven by growing industrial demand and academic interest. We highlight recent breakthroughs in omnidirectional generation, omnidirectional perception, omnidirectional understanding, and related datasets. Drawing on insights from both academia and industry, we propose an ideal panoramic system architecture in the embodied AI era, PANORAMA, which consists of four key subsystems. Moreover, we offer in-depth opinions related to emerging trends and cross-community impacts at the intersection of panoramic vision and embodied AI, along with the future roadmap and open challenges. This overview synthesizes state-of-the-art advancements and outlines challenges and opportunities for future research in building robust, general-purpose omnidirectional AI systems in the embodied AI era.

Exams are a fundamental test of expert-level intelligence and require integrated understanding, reasoning, and generation. Existing exam-style benchmarks mainly focus on understanding and reasoning tasks, and current generation benchmarks emphasize the illustration of world knowledge and visual concepts, neglecting the evaluation of rigorous drawing exams. We introduce GenExam, the first benchmark for multidisciplinary text-to-image exams, featuring 1,000 samples across 10 subjects with exam-style prompts organized under a four-level taxonomy. Each problem is equipped with ground-truth images and fine-grained scoring points to enable a precise evaluation of semantic correctness and visual plausibility. Experiments show that even state-of-the-art models such as GPT-Image-1 and Gemini-2.5-Flash-Image achieve less than 15% strict scores, and most models yield almost 0%, suggesting the great challenge of our benchmark. By framing image generation as an exam, GenExam offers a rigorous assessment of models' ability to integrate knowledge, reasoning, and generation, providing insights on the path to general AGI.

While Code Language Models (CLMs) have demonstrated superior performance in software engineering tasks such as code generation and summarization, recent empirical studies reveal a critical privacy vulnerability: these models exhibit unintended memorization of sensitive training data, enabling verbatim reproduction of confidential information when specifically prompted. To address this issue, several approaches, including training data de-duplication and differential privacy augmentation, have been proposed. However, these methods require full-model retraining for deployed CLMs, which incurs substantial computational costs. In this paper, we aim to answer the following research question: Can sensitive information memorized by CLMs be erased effectively and efficiently? We conduct a pioneering investigation into erasing sensitive memorization in CLMs through machine unlearning - a post-hoc modification method that removes specific information from trained models without requiring full retraining. Specifically, we first quantify the memorization risks of sensitive data within CLM training datasets and curate a high-risk dataset of 50,000 sensitive memorized samples as unlearning targets. We study two widely used gradient ascent-based unlearning approaches: the vanilla and constraint-based methods, and introduce CodeEraser, an advanced variant that selectively unlearns sensitive memorized segments in code while preserving the structural integrity and functional correctness of the surrounding code. Extensive experiments on three families of CLMs, i.e., CodeParrot, CodeGen-Mono, and Qwen2.5-Coder, validate the effectiveness and efficiency of CodeEraser in erasing targeted sensitive memorization while maintaining model utility.

Recent developments in Large Language Model (LLM)-based agents have shown impressive capabilities spanning multiple domains, exemplified by deep research systems that demonstrate superior performance on complex information-seeking and synthesis tasks. While general-purpose deep research agents have shown impressive capabilities, they struggle significantly with medical domain challenges, as evidenced by leading proprietary systems achieving limited accuracy on complex medical benchmarks. The key limitations are: (1) the model lacks sufficient dense medical knowledge for clinical reasoning, and (2) the framework is constrained by the absence of specialized retrieval tools tailored for medical contexts.We present a medical deep research agent that addresses these challenges through two core innovations. First, we develop a novel data synthesis framework using medical knowledge graphs, extracting the longest chains from subgraphs around rare medical entities to generate complex multi-hop question-answer pairs. Second, we integrate a custom-built private medical retrieval engine alongside general-purpose tools, enabling accurate medical information synthesis. Our approach generates 2100+ diverse trajectories across 12 medical specialties, each averaging 4.2 tool interactions.Through a two-stage training paradigm combining supervised fine-tuning and online reinforcement learning with composite rewards, our MedResearcher-R1-32B model demonstrates exceptional performance, establishing new state-of-the-art results on medical benchmarks while maintaining competitive performance on general deep research tasks. Our work demonstrates that strategic domain-specific innovations in architecture, tool design, and training data construction can enable smaller open-source models to outperform much larger proprietary systems in specialized domains.

Large Language Models (LLMs) have made remarkable progress in mathematical reasoning, but still continue to struggle with high-precision tasks like numerical computation and formal symbolic manipulation. Integrating external tools has emerged as a promising approach to bridge this gap. Despite recent advances, existing methods struggle with three key challenges: constructing tool-integrated reasoning data, performing fine-grained optimization, and enhancing inference. To overcome these limitations, we propose THOR (Tool-Integrated Hierarchical Optimization via RL). First, we introduce TIRGen, a multi-agent actor-critic-based pipeline for constructing high-quality datasets of tool-integrated reasoning paths, aligning with the policy and generalizing well across diverse models. Second, to perform fine-grained hierarchical optimization, we introduce an RL strategy that jointly optimizes for both trajectory-level problem solving and step-level code generation. This is motivated by our key insight that the success of an intermediate tool call is a strong predictor of the final answer's correctness. Finally, THOR incorporates a self-correction mechanism that leverages immediate tool feedback to dynamically revise erroneous reasoning paths during inference. Our approach demonstrates strong generalization across diverse models, performing effectively in both reasoning and non-reasoning models. It further achieves state-of-the-art performance for models of a similar scale on multiple mathematical benchmarks, while also delivering consistent improvements on code benchmarks. Our code will be publicly available at https://github.com/JingMog/THOR.

Generative machine learning offers new opportunities to better understand complex Earth system dynamics. Recent diffusion-based methods address spectral biases and improve ensemble calibration in weather forecasting compared to deterministic methods, yet have so far proven difficult to scale stably at high resolutions. We introduce AERIS, a 1.3 to 80B parameter pixel-level Swin diffusion transformer to address this gap, and SWiPe, a generalizable technique that composes window parallelism with sequence and pipeline parallelism to shard window-based transformers without added communication cost or increased global batch size. On Aurora (10,080 nodes), AERIS sustains 10.21 ExaFLOPS (mixed precision) and a peak performance of 11.21 ExaFLOPS with 1 times 1 patch size on the 0.25{\deg} ERA5 dataset, achieving 95.5% weak scaling efficiency, and 81.6% strong scaling efficiency. AERIS outperforms the IFS ENS and remains stable on seasonal scales to 90 days, highlighting the potential of billion-parameter diffusion models for weather and climate prediction.

Large language models (LLMs) often struggle with context fidelity, producing inconsistent answers when responding to questions based on provided information. Existing approaches either rely on expensive supervised fine-tuning to generate evidence post-answer or train models to perform web searches without necessarily improving utilization of the given context. We propose CARE, a novel native retrieval-augmented reasoning framework that teaches LLMs to explicitly integrate in-context evidence within their reasoning process with the model's own retrieval capabilities. Our method requires limited labeled evidence data while significantly enhancing both retrieval accuracy and answer generation performance through strategically retrieved in-context tokens in the reasoning chain. Extensive experiments on multiple real-world and counterfactual QA benchmarks demonstrate that our approach substantially outperforms supervised fine-tuning, traditional retrieval-augmented generation methods, and external retrieval solutions. This work represents a fundamental advancement in making LLMs more accurate, reliable, and efficient for knowledge-intensive tasks.

We introduce Wan-Animate, a unified framework for character animation and replacement. Given a character image and a reference video, Wan-Animate can animate the character by precisely replicating the expressions and movements of the character in the video to generate high-fidelity character videos. Alternatively, it can integrate the animated character into the reference video to replace the original character, replicating the scene's lighting and color tone to achieve seamless environmental integration. Wan-Animate is built upon the Wan model. To adapt it for character animation tasks, we employ a modified input paradigm to differentiate between reference conditions and regions for generation. This design unifies multiple tasks into a common symbolic representation. We use spatially-aligned skeleton signals to replicate body motion and implicit facial features extracted from source images to reenact expressions, enabling the generation of character videos with high controllability and expressiveness. Furthermore, to enhance environmental integration during character replacement, we develop an auxiliary Relighting LoRA. This module preserves the character's appearance consistency while applying the appropriate environmental lighting and color tone. Experimental results demonstrate that Wan-Animate achieves state-of-the-art performance. We are committed to open-sourcing the model weights and its source code.

This paper reviews the MARS2 2025 Challenge on Multimodal Reasoning. We aim to bring together different approaches in multimodal machine learning and LLMs via a large benchmark. We hope it better allows researchers to follow the state-of-the-art in this very dynamic area. Meanwhile, a growing number of testbeds have boosted the evolution of general-purpose large language models. Thus, this year's MARS2 focuses on real-world and specialized scenarios to broaden the multimodal reasoning applications of MLLMs. Our organizing team released two tailored datasets Lens and AdsQA as test sets, which support general reasoning in 12 daily scenarios and domain-specific reasoning in advertisement videos, respectively. We evaluated 40+ baselines that include both generalist MLLMs and task-specific models, and opened up three competition tracks, i.e., Visual Grounding in Real-world Scenarios (VG-RS), Visual Question Answering with Spatial Awareness (VQA-SA), and Visual Reasoning in Creative Advertisement Videos (VR-Ads). Finally, 76 teams from the renowned academic and industrial institutions have registered and 40+ valid submissions (out of 1200+) have been included in our ranking lists. Our datasets, code sets (40+ baselines and 15+ participants' methods), and rankings are publicly available on the MARS2 workshop website and our GitHub organization page https://github.com/mars2workshop/, where our updates and announcements of upcoming events will be continuously provided.

We introduce SteeringControl, a benchmark for evaluating representation steering methods across core alignment objectives--bias, harmful generation, and hallucination--and their effects on secondary behaviors such as sycophancy and commonsense morality. While prior alignment work often highlights truthfulness or reasoning ability to demonstrate the side effects of representation steering, we find there are many unexplored tradeoffs not yet understood in a systematic way. We collect a dataset of safety-relevant primary and secondary behaviors to evaluate steering effectiveness and behavioral entanglement centered around five popular steering methods. To enable this, we craft a modular steering framework based on unique components that serve as the building blocks of many existing methods. Our results on Qwen-2.5-7B and Llama-3.1-8B find that strong steering performance is dependent on the specific combination of steering method, model, and targeted behavior, and that severe concept entanglement can result from poor combinations of these three as well. We release our code here: https://github.com/wang-research-lab/SteeringControl.git.

Variational quantum circuits (VQCs) are central to quantum machine learning, while recent progress in Kolmogorov-Arnold networks (KANs) highlights the power of learnable activation functions. We unify these directions by introducing quantum variational activation functions (QVAFs), realized through single-qubit data re-uploading circuits called DatA Re-Uploading ActivatioNs (DARUANs). We show that DARUAN with trainable weights in data pre-processing possesses an exponentially growing frequency spectrum with data repetitions, enabling an exponential reduction in parameter size compared with Fourier-based activations without loss of expressivity. Embedding DARUAN into KANs yields quantum-inspired KANs (QKANs), which retain the interpretability of KANs while improving their parameter efficiency, expressivity, and generalization. We further introduce two novel techniques to enhance scalability, feasibility and computational efficiency, such as layer extension and hybrid QKANs (HQKANs) as drop-in replacements of multi-layer perceptrons (MLPs) for feed-forward networks in large-scale models. We provide theoretical analysis and extensive experiments on function regression, image classification, and autoregressive generative language modeling, demonstrating the efficiency and scalability of QKANs. DARUANs and QKANs offer a promising direction for advancing quantum machine learning on both noisy intermediate-scale quantum (NISQ) hardware and classical quantum simulators.

Personalized financial advice requires consideration of user goals, constraints, risk tolerance, and jurisdiction. Prior LLM work has focused on support systems for investors and financial planners. Simultaneously, numerous recent studies examine broader personal finance tasks, including budgeting, debt management, retirement, and estate planning, through agentic pipelines that incur high maintenance costs, yielding less than 25% of their expected financial returns. In this study, we introduce a novel and reproducible framework that integrates relevant financial context with behavioral finance studies to construct supervision data for end-to-end advisors. Using this framework, we create a 19k sample reasoning dataset and conduct a comprehensive fine-tuning of the Qwen-3-8B model on the dataset. Through a held-out test split and a blind LLM-jury study, we demonstrate that through careful data curation and behavioral integration, our 8B model achieves performance comparable to significantly larger baselines (14-32B parameters) across factual accuracy, fluency, and personalization metrics while incurring 80% lower costs than the larger counterparts.

We propose LLM-Interleaved (LLM-I), a flexible and dynamic framework that reframes interleaved image-text generation as a tool-use problem. LLM-I is designed to overcome the "one-tool" bottleneck of current unified models, which are limited to synthetic imagery and struggle with tasks requiring factual grounding or programmatic precision. Our framework empowers a central LLM or MLLM agent to intelligently orchestrate a diverse toolkit of specialized visual tools, including online image search, diffusion-based generation, code execution, and image editing. The agent is trained to select and apply these tools proficiently via a Reinforcement Learning (RL) framework that features a hybrid reward system combining rule-based logic with judgments from LLM and MLLM evaluators. Trained on a diverse new dataset using four different model backbones, LLM-I demonstrates state-of-the-art performance, outperforming existing methods by a large margin across four benchmarks. We also introduce a novel test-time scaling strategy that provides further performance gains. Project Page: https://github.com/ByteDance-BandAI/LLM-I.

根据发表在《自然》期刊上的一项研究，如果全球气温继续上升，到本世纪末大西洋几乎所有珊瑚都将停止生长。英国埃克塞特大学等研究机构的研究人员分析了大西洋 400 多个珊瑚礁，他们估计即使在乐观的气候暖化情景下，到 2040 年该地区逾七成的珊瑚礁也将开始死亡。如果到本世纪地球气温比工业化前水平升高超过 2 摄氏度，该地区 99% 的珊瑚礁将面临同样命运。地球气温目前已比工业化前水平高约 1.3 摄氏度。珊瑚的死亡具有深远影响。珊瑚组成的礁为鱼类等海洋生物提供栖息地，是抵御海浪的屏障，帮助保护海岸线应对海岸海平面上升的冲击。而四分之一的海洋生物依赖于珊瑚礁，逾十亿人受益珊瑚礁。

DeepSeek 的研究人员在《自然》期刊上发表了 R1 模型论文《DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning》。研究人员披露 R1 的训练成本仅 29.4 万美元，但其基础模型花了约 600 万美元；R1 主要使用英伟达的 H800 AI 芯片训练，该芯片自 2023 年起被禁止出口到中国。DeepSeek 的主要创新是使用名叫纯强化学习（pure reinforcement learning）的方法自动化试错，对模型得出正确答案进行奖励，而不是教它遵循人类选择的推理示例。模型还使用名叫 group relative policy optimization 的方法给自己打分。对于今年早些使用 OpenAI 指责 DeepSeek 使用其模型的输出进行训练，研究人员予以否认。DeepSeek-R1 是 Hugging Face 上最受欢迎的模型之一，下载量达到 1090 万次，2025 年使用强化学习的大模型几乎都受到了 R1 的启发。

美国气候研究机构气候中心（Climate Central）公布分析结果称，受全球变暖影响，今年 6-8 月日本观测到的“危险性高温日”达到 62 天，比未发生气候变暖的情况增加 22 天。该机构指出“若不及时减少温室气体排放，各地的生态系统和经济将遭受更多损害。”研究团队将各地在 1991-2020 年观测到的气温中，排名前 10% 的高温或超过该数值的日子定义为“危险性高温日”。这类高温超出人们日常适应范围，是中暑和死亡风险上升的参考指标。分析结果显示，在全球范围内约 9.5 亿人因气候变暖多经历了 30 天以上的“危险性高温日”。在日本以外的国家和地区中，增加天数最多是 59 天的牙买加（共 74 天）和开曼群岛（共 66 天），56 天的海地（共 66 天）等紧随其后。

根据 NASA Exoplanet Archive 的数据，NASA 确认的系外行星数量达到了 6007 颗。最早的系外行星是在 1990 年代初确认的，2019 年确认的系外行星数量突破了 4000 颗，2022 年突破 5000 颗，3 年后的 2025 年 9 月突破了 6000 颗，而 NASA 的数据库里还有 TESS (Transiting Exoplanet Survey Satellite)太空望远镜探测到的 7668 颗候选行星等待确认。最新确认的一颗系外行星是 KMT-2023-BLG-1896L b，属于类海王星行星，质量约 16.35 倍于地球。

天空亮度有一个分类法叫 Bortle scale，以纽约业余天文学家 John E. Bortle 的名字命名，这套分类法共分 9 级，无人造光的最黑暗地区为 1 级，市中心为 9 级。大部分人一生中生活在亮度为 5 级以上的环境中，而今天越来越多的人生活在 7 级、8 级和 9 级亮度的地区。更明亮的 LED 灯的普及也让光污染愈发严重。近期的一项研究估计，从 2011-2022 年，全球光污染每年增长 10%，约每八年翻一番。尽管如此，Bortle 1 级的黑暗区仍然存在，比如澳大利亚内陆和智利北部的 Atacama 沙漠。但智利也面临在天文台附近建造采矿项目的压力。此外互联网卫星也给天文观测带来了额外的挑战：地球轨道上运行的卫星数量已从几百颗增加到 1.2 万颗，天文学家预测十年内卫星数量将达到 10 万颗以上。

FX 的研究部门自 2009 年以来一直跟踪英语剧本类电视剧的制作数量。根据其统计，剧本类电视剧制作数量在 2022 年达到峰值的 599 部，此后呈下降趋势。大受好评的新剧数量急剧下降，而流媒体平台则转向优先制作引起轰动的剧集的续集。这些获得续订的剧集也扩大了规模，大幅增加了制作预算。《Severance》第二季的制作费用达到了 2 亿美元，而《怪奇物语》第四季则高达 2.7 亿美元。Netflix 从 2018 年起大量制作非剧本类内容如纪录片和真人秀。与此同时，主要靠广告支持的免费视频平台 YouTube 则成为了一个巨头，不断扩大市场份额。YouTube 在流媒体观众数量方面领先于 Netflix、Paramount+ 和 Hulu。

根据世卫组织和世界气象组织的报告，全球有超过 24 亿工人暴露在极端高温下，每年造成超过 2285 万起职业伤害，有 1.9 万人死于与高温相关的工伤，世界各国政府正在实施保护工人免受日益加剧的热应激影响的法律。日本对未能在湿球温度达到 28 摄氏度时提供降温措施的雇主处以 3400 美元的罚款。新加坡要求大型户外场所安装时间分辨率为小时的温度传感器，要求在湿球温度达到 33 摄氏度时每小时休息 15分 钟。今年夏天希腊、意大利和西班牙的气温达到了 47 摄氏度，这些南欧国家下令下午停工。

美国国会众议院监督和政府改革委员（House Oversight and Government Reform Committee）致函 Valve 总裁 Gabe Newell、Discord CEO Humam Sakhnini、Twitch CEO Dan Clancy 以及 Reddit CEO Steve Huffman，要求他们于 10 月 8 日前来国会就其平台上的用户激进化问题作证。该委员会的主席、肯塔基州共和党人 James Comer 在一份声明中表示：“国会有责任监督激进分子推进政治暴力的网络平台。为防止未来出现激进化和暴力事件，Discord、Steam、Twitch 和 Reddit 的 CEO 必须到监督委员会面前，解释将采取哪些措施以确保其平台不被用于邪恶目的。”保守派们可能要打击游戏了：Valve 拥有最大的 PC 游戏平台 Steam、Discord 是最大的玩家讨论游戏的聊天应用，Twitch 是游戏直播平台，而 Reddit 上有大量的游戏讨论 subreddit。

9 月 16 日下午，在长春航空展上两架小鹏汇天 eVTOL（电动垂直起降器）在飞行过程中发生碰撞，导致其中一架坠毁。小鹏汇天官方称，当事发时广东汇天通航已完成航展预演，正在进行双机编队训练，两架飞行器因间距控制不当发生接触。其中一架顺利降落，另一架则在着陆过程中因机身损伤引发起火。小鹏汇天强调，现场人员均安全，相关部门已迅速完成应急处置，事件具体原因仍在进一步调查中。但 CNN 报道称有一人受伤。小鹏汇天旗下“陆地航母”飞行汽车前不久宣布获得 5000 台订单及 15 亿元预收款。

GNOME 桌面环境项目释出了代号为 Brescia 的 GNOME 49。主要新特性包括：快速设置新增“请勿打扰”开关、登录屏幕专门的可访问性（Accessibility）菜单、快速设置菜单支持处理未知电源配置文件、支持 YUV422 和 YUV444 (HDR) 色彩空间、支持被动投屏（screen casts）和支持异步键盘映射设置。其它变化包括媒体控件、支持锁屏重启和关机操作，GNOME Display Manager(GDM)支持动态用户欢迎会话，以及多显示器设置中快速设置支持每个显示器亮度滑块。

根据发表在《Science Advances》期刊上的一项研究，野生黑猩猩每天通过食用成熟水果摄入的酒精量相当于一瓶啤酒。科学家认为这是人类对酒精的嗜好可能源自灵长类祖先的证据。与其它动物一样，黑猩猩也被发现会食用掉落在森林地面上的成熟水果。最新研究首次测量了它们通过食用成熟水果摄入的酒精量。研究人员测量了科特迪瓦和乌干达野生黑猩猩所大量食用的无花果和李子等水果中乙醇的含量，然后根据黑猩猩每天食用的水量量计算出它们大约摄入了 14 克的乙醇，相当于一瓶 330 毫升啤酒的酒精量。黑猩猩最常用食用的水果是酒精含量最高的水果。

在发生多起与 ChatGPT 相关的青少年自杀案件之后，OpenAI 正引入更严格的安全措施。ChatGPT 将估计用户的年龄，如果认为用户未满 18 岁它可能会要求用户出示身份证件确认是否成年。本月初 OpenAI 已经为 ChatGPT 引入了家长控制功能。除了尝试估计或验证用户年龄，ChatGPT 还将接受训练，对青少年用户应用不同的规则，比如不会进行自杀或自残相关的讨论。如果未成年用户有自杀念头，OpenAI 将会尝试联系其父母或相关部门。

Disney（包括漫威、卢卡斯影业和 20 世纪福克斯）、Warner Bros. Discovery（包括 DC 漫画） 和 NBCUniversal （包括梦工厂）起诉中国 AI 公司上海稀宇科技有限公司（MiniMax）蓄意且肆无忌惮的侵犯版权。在递交到加州中区联邦地区法院的诉状中，好莱坞巨头指控 MiniMax 无视美国版权法，将它们的版权角色作为自己的角色使用。MiniMax 运营着名为海螺（Hailuo）的图像和视频生成服务，大规模盗版和掠夺原告们的版权作品。MiniMax 宣传海螺服务是口袋里的好莱坞工作室，但其业务是建立在窃取好莱坞工作室知识产权的基础之上。起诉书列举了侵权案例——使用迪士尼的版权角色达斯维达生成图像和视频。好莱坞工作室寻求赔偿以及禁止 MiniMax 继续侵犯其版权作品。

一项田野实验结果显示，气候变暖可能增加热带森林的土壤呼吸速率。研究表明，未来升温或使热带土壤的碳损失超过此前预计，影响全球气候预测。研究人员追踪了碳如何在波多黎各的热带森林土壤中迁移。研究人员将分别处于下、中、上坡位的 3 个具有林下植被和土壤的 12 平方米地块人为升温至比环境温度高4摄氏度。他们在一年时间里以半小时间隔记录了这些地块及类似位置对照样本的土壤呼吸速率，共收集了 574500 个测量数据。研究表明，加温地块的土壤呼吸速率比对照地块高 42%-204%，达到陆地生态系统报告中最高的土壤呼吸速率。此外，加温地块每年额外释放的碳为每公顷 6.5-81.7 吨，具体数量取决于坡位，且上坡地块释放的碳最多。研究人员认为，这些增长可能因为加温土壤中的微生物群落发生了改变。

FT 报道，主要科技公司已被要求停止购买英伟达 AI 芯片。字节跳动和阿里巴巴等公司被要求停止测试和订购英伟达专为中国市场设计的的R TX Pro 6000D。多家企业此前表示将订购上万块 RTX Pro 6000D 芯片，并已开始与英伟达的服务器供应商一道进行相关测试和查验工作。在收到命令后，他们要求供应商停止测试和验证工作。