OrangeBot.AI Digest — 2026-09-21

Secure the AI agents you're building at runtime

The autonomous computer fleet at your command

DICOM viewing and metadata editing on Mac and Windows

Launch-ready product demos, from a Chrome extension

Social media scheduling platform for humans and agents

Automate your App Store Screenshots and print money...

Turn any brand URL into a ready-to-send insight deck

Search 3M government contracts in 64 countries

Fast, structured AI decisions for software automation

Quickly turn interview footage into a rough cut

connect nodes to build AI pipelines, powered by Hugging Face

Cron jobs that actually run

Keep content across your Mac organized in defined areas

Google's AI creative studio now on mobile

Automate App Store refund responses and track every outcome

Mixture-of-Experts (MoE) scales capacity, but existing designs cannot set three quantities independently. For a single token, participation is how many experts contribute knowledge to its output, execution is how many are actually computed (compute cost), and materialization is how many expert-sized parameter sets must be built and stored (memory cost). Sparse routing keeps execution and materialization low, but shrinks participation: for each token, only a few experts contribute. Dense output-mixing restores full participation, but its execution grows with the number of experts. Parameter-merging keeps execution at one expert, but its materialization grows with the number of routing decisions. We propose IntBMoE, a block-conditioned MoE that decouples all three by pairing dense expert composition with sparse block execution. Its blocks come from a small learned codebook, one per entry. At each internal layer, a lightweight hypernetwork merges all expert bases in that layer's pool into one composed expert. Participation is full, because every composed expert draws on the entire pool. Execution stays sparse, because a router sends each token to only a few blocks. Materialization is bounded, because the codebook, not the input, fixes how many blocks exist. Dual-Path Residual Gating (DPRG) further couples two independently composed paths through multiplicative gating. Experiments on image classification show consistent gains over representative sparse and dense MoE baselines. Additional experiments on language modeling and sequential recommendation validate its generalization beyond vision. IntBMoE is fully deployed in AMap's generative recommendation system, serving hundreds of millions of users under a 60ms latency budget, with a 2.4% relative UVCTR gain in online A/B testing. Our code is available at https://github.com/AMAP-ML/DreamX-Rec/.

Reusable skills give agents transferable procedural knowledge, making scalable acquisition essential for extending agents beyond prior experience. Existing methods face two limitations: trajectory-based synthesis requires interactions with specific environments, while document-derived skills may lack executable evidence and verification. Source code offers a complementary path: it requires no prior agent experience yet provides executable evidence for grounding abstractions. We present Code2Skill, a fully automated pipeline that transforms selected code units into implementation-anchored records of atomic operations, composite workflows, and recurring patterns, then verifies each record through source-body-blind reconstruction and source-aware comparison. Applied to 19,769 popular, actively maintained GitHub repositories, Code2Skill produces CodeSkillBank, a grounded bank of 1,006,822 accepted records with workflow, boundary, provenance, and source-evidence metadata. Across 72 protocol-matched evaluations covering nine model settings and eight benchmarks, models augmented with retrieved CodeSkillBank skills improve by 11.7% on average over matched baselines and outperform them in 57 cases. Under a unified downstream interface, Code2Skill also outperforms trajectory-derived skill banks on all seven shared benchmarks, showing that repository-derived skills can provide useful procedural knowledge before agents accumulate sufficient interaction experience. Skills synthesized from tested AI-generated code achieve a 93.50% pass rate, compared with 93.00% for human-written code, providing initial evidence that the pipeline can expand with the growing volume of AI-generated software. Overall, Code2Skill transforms procedural knowledge embedded in repositories into grounded, verifiable, and transferable agent skills.

Training capable coding agents via reinforcement learning (RL) requires diverse tasks with reliable verifiers. Open-source codebases offer a rich source of such tasks, while existing methods typically rely on development artifacts such as issues and commits, limiting the range of tasks that can be extracted. To better scale RL environments, we present CodeMidas, an agentic pipeline that turns implemented functionality in existing codebases into executable RL environments using source code as its only task-specific input. CodeMidas allocates agentic compute to every stage of environment construction: agents explore implemented functionality to formulate behavioral specifications, construct tests grounded in execution of the original code, and validate and filter candidate tasks through execution checks and repeated solution rollouts. The resulting dataset has 5,545 training tasks from 3,185 open-source codebases spanning 23 programming languages and 15 technical domains. Training MiMo-V2.5 on these tasks with GRPO improves performance on all five diverse benchmarks, covering issue repair (DeepSWE + 11.7%), whole-program construction (ProgramBench +17%), and terminal work (Terminal-Bench v2.1 +8.5%). Ablations show that increasing the number of high-quality training tasks improves performance. Trajectory analysis shows the RL-trained agent demonstrates better behaviors like increasing codebase exploration and more diverse self-verification. These results establish source code as a scalable foundation for constructing RL environments that improve coding agents across diverse software tasks.

Data agents aim to fulfill natural-language instructions over heterogeneous data, including tables, files, and databases. However, data agents face a challenging agent-data gap: heterogeneous data resides outside the agent, while the agent can access it (e.g., column names and file paths) only through generic tools. Existing approaches either let agents directly explore raw data sources or inject manually constructed semantic layers into prompts. However, neither scales well to large heterogeneous data sources nor adapts to different agent behaviors. In this paper, we introduce EvoOntology, a self-evolving ontology layer for data agents. EvoOntology encapsulates the ontology as an MCP server comprising a schema layer, a content layer, and a tool layer, enabling agents to actively query and interact with the ontology at runtime. To this end, we introduce a builder agent for autonomous ontology construction and a self-evolution loop that continuously refines the ontology through attribution-guided typed edits that are accepted only after a backbone-conditional paired evaluation. Experiments on three well-adopted data-agent benchmarks with four LLM backbones demonstrate that EvoOntology consistently outperforms strong baselines and existing semantic-layer approaches, effectively bridging the agent-data gap and enabling more effective interaction with heterogeneous data. Code: https://github.com/ruc-datalab/EvoOntology

Computer-use agents (CUAs) have advanced along two separate lines: graphical interaction and software development through code and the command line. Real digital work requires both, interleaved rather than stacked end to end. We study hybrid CUAs that autonomously decide when to explore an interface, implement software, and run and visually verify their artifacts. We introduce RecreationWorld, a five-platform framework built around recreation: given a running reference, an agent must discover its behavior and build a faithful implementation with no prescribed workflow. RecreationWorld provides reproducible environments on Ubuntu, macOS, Windows, Android, and Web, plus a unified harness with native GUI control and coding tools. The running reference serves as an oracle for hidden behavioral tests, providing execution-grounded rewards. We scale trajectory generation with high-quality open-source applications. Models trained on these trajectories improve across five out-of-distribution coding and hybrid computer-use benchmarks and more frequently verify their rendered outputs, providing evidence of transfer beyond recreation. For held-out evaluation, we introduce RecreationBench, comprising 250 diverse tasks across domains and platforms. Reference-grounded programmatic and visual assertions cover action-conditioned outcomes at multiple interaction depths; each is validated on the reference and by human reviewers before the suite is frozen for automatic scoring. GPT-6 Astra leads at 58.1% overall, but passes all programmatic tests on just 2.8% of tasks. Agents reproduce static interface structure more reliably than interactions and computed outputs, while generated applications remain smaller and more monolithic than their references. We release the benchmark, environments, and test suites.

We define OmniVChat (Omni Video Chat) as the task of native audio-visual dialogue between a user and an omni model. In OmniVChat, omni models directly and simultaneously receive audio and video from a user and return text. The user's query is embedded in the audio and video, without a separate text question, external captioning, or speech recognition. Direct audio-visual input reduces external latency and computation while preserving perceptual cues. However, research on OmniVChat faces two constraints: data availability and evaluation. Recordings of people using their own devices are scarce. Furthermore, a good reply often needs to account for the user's surroundings, facial expressions, and nearby objects, and such responses can be expressed in many different ways, making keyword matching unreliable for evaluating reply quality. Recent progress in agent systems and video generation makes generation for comprehension viable, which means using synthesized dialogues for training and evaluation. Therefore, we present OmniVChat-Studio, a multi-agent data engine for synthesizing single- and multi-turn audio-visual dialogues. We use synthesized dialogues to build OmniVChat-Bench, an evaluation benchmark that evaluates omni models' basic dialogue abilities across five ability categories. We also present OmniVChat-RL, a reinforcement learning reward design that jointly targets reply correctness, efficiency, and style in OmniVChat. Training Qwen3-Omni-Instruct with OmniVChat-RL on synthesized dialogues improves its performance on both OmniVChat-Bench and the human-recorded OmniVChat-Bench-Human. These gains validate the reward design and show transfer to real-world dialogues in training and evaluation.

Professional design requires any-color control: the ability to specify an object's target color with any 24-bit hex value for image generation and editing. Prior work has explored color generation, editing, and colorization, but often relies on dedicated color representations or specialized inference procedures. Advances in large language models offer a simpler starting point: even compact models can associate hex values with color semantics. We present Paint-Anything, which learns a shared hex-prompt interface for generation and editing through object-level color supervision. We develop a data pipeline that constructs Paint-500K from real images through object grounding, perceptual color labeling, and editing-pair synthesis. Since shadows make real-image labels only approximate colors, we complement this supervision with pure-color anchors whose pixels exactly match their paired hex values. These anchors are used only at high-noise timesteps, leaving low-noise training to natural images. We further introduce Any Color Benchmark (ACBench), comprising ACBench-T2I and ACBench-Edit, to measure object-level hex color fidelity across both tasks. On FLUX.2-4B, Paint-Anything improves ACBench-T2I and ACBench-Edit scores by 85.3% and 28.3%, respectively, relative to the base model, with ablations supporting the training recipe. It also achieves the highest average CompColor score among the compared methods.

Professional graphic design is a long-horizon agentic task in which structured, editable artifacts emerge from many interdependent actions, yet outcomes admit no reliable programmatic oracle. We introduce a continual adaptation framework in which a frozen frontier model operates professional design software through more than 230 tools, while an external procedural memory of natural-language skills accumulates and refines reusable design procedures from experience. The memory widens by acquiring procedures for recurring uncovered subtasks and deepens by revising existing procedures against their own successful and failed executions, while a matched replay gate admits only changes that repair failures without regressing observed successes. Five rounds over 1,406 real user briefs and 1,869 automatically graded trajectories, with no weight updates and no human labels, grow the bank from 76 documentation-derived skills to 139 and raise GenEval2 execution success on Claude-Sonnet-4 from 72.7% to 99.3% (+11.99 points in generation quality), with 61.8% and 67.6% win rates against the no-skill agent across four specialized design benchmarks on Claude-Sonnet-4 and Claude-Opus-4.6. We further show the two mechanisms are effective in combination: on 200 held-out briefs from user-traffic benchmark, widening or deepening alone reaches a 49.4% / 48.6% win rate over the no-skill agent, while their combination reaches 58.5% (p = 0.025). Procedural memory offers a practical route to continual adaptation of agents under noisy, unverifiable feedback.

Reference-to-video (R2V) generation is evolving toward increasingly general and versatile reference control, giving rise to the emerging paradigm of omni R2V generation. However, existing benchmarks fall short of these emerging capabilities: their test cases cover limited reference types and compositions, and their evaluation protocols largely assess holistic reference consistency, overlooking whether reference factors are properly preserved, disentangled, and routed. Meanwhile, the high cost of constructing omni R2V training data makes suitable training resources scarce. To address these gaps, we introduce OmniVBench and the Omni-R2V Dataset for evaluating and training omni R2V models. OmniVBench expands R2V evaluation across broader reference types, fine-grained control tasks, and richer reference compositions, covering 7 task families and 18 fine-grained tasks spanning content, motion, style, structure, narrative, and multi-reference settings. We introduce factor-grounded evaluation with 12,172 case-specific checklist items, assessing whether intended reference factors are faithfully preserved, correctly disentangled and bound to their targets, and properly realized according to the instruction. We further introduce the Omni-R2V Dataset, bringing industrial-grade training resources for diverse R2V tasks to the broader research community. Drawing primarily on a large-scale corpus of professional video footage, it comprises 340K processed training samples spanning diverse reference types and multi-reference compositions. We develop task-specific pipelines for reference-target pair construction, offering a practical and scalable recipe for omni R2V data construction. Extensive evaluation of advanced open- and closed-source R2V models reveals clear performance gaps across task families and evaluation dimensions on OmniVBench, highlighting remaining limitations of current R2V models.

Business intelligence (BI) is a cornerstone of enterprise decision-making and is widely used by enterprise users in software such as Power BI and Tableau. In traditional BI workflows, users need to prepare data by (1) identifying relevant tables, (2) performing data transformations, and (3) building join relationships, before they can (4) answer their business questions. These steps can be complex and time-consuming, making BI challenging. Given the strong capabilities of large language models (LLMs) in working with data, we study their ability to answer BI questions end-to-end, without requiring users to manually perform the tedious preparation steps. To do this, we harvest a large collection of real-world BI projects from public sources, and manually extract pairs of (questions, ground-truth answers) from real user dashboards. The resulting benchmark, BI-Bench, is the first benchmark to systematically study LLMs' ability on end-to-end BI. We find that even frontier LLMs perform poorly on BI-Bench, with less than 50% accuracy. To address their limitations, we design a tool-augmented BI-Agent that decomposes BI workflows into subtasks on structured data, such as search, join, and transform, and orchestrates specialized data management methods across BI stages. Furthermore, we develop a post-training framework that synthesizes training trajectories from real BI projects, enabling BI-Agent to be further post-trained using both supervised fine-tuning (SFT) and reinforcement learning (RL). BI-Agent achieves substantial accuracy gains of up to 40 percentage points with vanilla LLMs, and post-trained BI-Agent yields gains of up to 30 points. Our results highlight the importance of combining tool-augmented reasoning with domain-specific post-training in complex BI workflows, and point to promising directions for future research.

We present MintAct, a family of vision-language models that unifies UI grounding, multi-step navigation across mobile, desktop, and web, and visual tool use, trained at 2B, 4B, and 8B scales. Through careful design of our environments, data, and training recipes, MintAct models match the performance of per-domain specialists across all of these capabilities. To enable this, we develop a scalable environment and reinforcement learning (RL) infrastructure. On the environment side, we host hundreds of concurrent instances across heterogeneous per-domain backends, serving both trajectory data collection and online RL. To enable efficient and scalable RL training, an asynchronous framework keeps explicit control over the cross-domain training distribution and remains stable under noisy environment feedback and off-policy drift. Experimental results show that MintAct achieves state-of-the-art performance (48.9 on OSWorld-Verified) across a wide range of benchmarks at comparable model sizes.

Skills can improve the performance of Large Language Model (LLM) agents by providing task-specific procedural guidance, while skill optimization further improves their effectiveness through iterative refinement. However, existing skill optimization methods typically represent skills as unstructured natural-language instructions, creating two key challenges: 1) Unstructured skills often lack explicit workflow-level guidance and contain substantial redundancy, making them difficult for LLMs to execute; 2) the vast search space of unconstrained natural-language skills makes skill optimization ineffective. To address these challenges, we propose representing skills as graph-structured natural-language artifacts. In graph-structured skills, each node represents an execution step together with its operational guidance, while directed edges encode context-dependent transitions between steps. Compared to unstructured skills, graph-structured skills can provide clear workflow-level guidance. Moreover, the proposed graph-structured skill can also facilitate skill optimization. Building on this structured representation, we introduce GraphSkillEvo, a population-based evolutionary optimization framework with mutation and crossover operators for graph-structured skills. By maintaining multiple candidate skills and combining effective components, GraphSkillEvo enables broader and more comprehensive exploration of the structured skill space than purely LLM-based iterative self-refinement. Extensive experiments across five agent benchmarks demonstrate that GraphSkillEvo consistently outperforms the strong skill optimization baseline SkillOpt, improving average accuracy by 4.01% on GPT-5.4-nano and 1.76% on GPT-5.4. Our code is available at https://github.com/ruisun7/GraphSkillEvo.

Scaling large language models efficiently has motivated sparse capacity mechanisms such as Mixture-of-Experts and, more recently, conditional memory: token-indexed embedding tables that augment the backbone with cheap parametric lookups. Existing memory-embedding methods retrieve via a deterministic function of the surface form, which collapses different contextual senses of the same token (e.g., python the language vs. the animal) into a single fixed entry. We introduce Mixture of Memory Embeddings (MoME), a context-aware memory mechanism that replaces each token's single memory row with a mixture of M slots and uses a learned gate over the hidden state to choose which slots to read at each position. In controlled pretraining experiments across nanochat, Llama-3/MobileLLM, and Qwen3 backbones, MoME improves over Value Embedding, Bigram, and STEM baselines in iso-parameter and iso-training-FLOP settings, shows a more promising memory-size scaling trend at sub-billion scale, and remains efficient in training and inference. Qualitative routing analyses on polysemous tokens further suggest that the learned mixture exhibits a degree of semantic interpretability, dispatching the same surface token to distinct memory slots under different senses.

On-policy distillation (OPD) improves reasoning models by learning the token-level discrepancy between a stronger teacher and an on-policy student. However, this discrepancy does not purely reflect the capability gap between the teacher and the student: it also contains deviations arising from the teacher itself, which are consequently mixed into the observed teacher--student discrepancy and indiscriminately learned by standard OPD during training. This issue is further exacerbated by privileged OPD, where privileged information induces larger teacher-side likelihood shifts, thereby encouraging the student to learn more of the teacher's own deviation. We introduce Calibrated On-Policy Distillation (Cal-OPD), which estimates the teacher's self-deviation region through positive and negative privileged interventions and calibrates the original teacher--student discrepancy by retaining only the component that lies beyond this region. Experiments on mathematical reasoning benchmarks show that, while retaining only about 52--65\% of the original teacher--student discrepancy as the optimization signal, Cal-OPD consistently outperforms standard OPD and its variants across model scales.

We need studies on conversational AI (CAI) at scale to understand human behavior and shape CAI design. However, fragmented reporting of systems and study configurations hinders replication, extension, and knowledge accumulation. We present Gricea, an open-science platform representing studies as configurable, deployable research artifacts that researchers can run, inspect, share, and reuse. Informed by a formative analysis of prior CAI research, Gricea couples study procedures, participant-facing systems, and conversational task behavior in. In a replication study using Gricea, we replicated configurations 93% of eligible CUI 2026 papers; while also flagging missing information in 96% of papers that hinder faithful replication --- further motivating Gricea's need. In a user study, researchers and practitioners from diverse backgrounds successfully constructed runnable studies addressing various open-ended research questions. Together, these findings demonstrate Gricea's support for constructing, reproducing, and extending CAI studies through shared research artifacts, enabling cumulative knowledge building through open science.

Xiaomi : Xiaomi debuts open-weight omnimodal models MiMo-V2.6 Pro and Flash; Pro allegedly performs “on par with Opus 5 and GPT-5.6 Sol across most agent benchmarks” —  Frontier intelligence, all the modalities, built in public.  Today, we are releasing and open-sourcing the MiMo-V2.6 series.

Bloomberg : Some startups, like Harvey, Abridge, Ramp, and Rogo, are embracing open-weight models or training their own models to reduce expensive reliance on frontier labs —  The $15.6 billion legal startup Harvey built its business around training AI models like OpenAI's GPT-4 to do specialized work for lawyers.

Maureen Farrell / New York Times : Sources: SoftBank's SB Energy has delayed its IPO, originally planned for this month, as investors question the company's sought-after valuation of $50B+ —  Several companies tied to the data center industry have delayed their initial public offerings amid the increasing public backlash to these energy guzzling facilities.

Nichola Groom / Reuters : California Gov. Gavin Newsom signs seven bills aimed at regulating data centers, imposing new requirements on electricity costs, water use, and local oversight —  California Governor Gavin Newsom on Monday signed seven bills aimed at regulating the fast-growing data center industry …

Bloomberg : Sensor Tower: Muse was downloaded 902K+ times in the six days after its launch on Sept. 8, vs. Meta AI's 773K in the same post-launch period; META jumps 12%+ —  Meta Platforms Inc.'s new artificial intelligence agent, Muse, has quickly risen to the top of mobile app charts …

OpenAI : OpenAI says it is working with an independent advisory group of mathematicians to responsibly share math-related AI advances —  Loading...  On August 28, we began training a new internal model.  In addition to resolving the Navier-Stokes Millennium Prize problem, this model has now resolved …

The Information : Source: before the Hugging Face incident, OpenAI was negotiating a legally binding deal with Anthropic for the companies to stress-test each other's models —  OpenAI is rethinking a range of safety strategies as it responds to fears from employees and others about the dangers its AI poses.

OpenAI : OpenAI says automated research could improve alignment, but “fully autonomous RSI is not happening today” and shouldn't be pursued unless it can be done safely —  Our mission is to ensure that artificial general intelligence benefits all of humanity.

Maria Curi / Axios : Ahead of Sam Altman's UN address, OpenAI urges the US to lead an effort to develop global safety and security standards for building frontier systems —  OpenAI on Monday released international AI safety standards as world leaders, namely the U.S. and China, weigh how to mitigate risk.

Adam Clark / Barron's Online : Intel closes up 12%, AMD 10%, and Arm 17% on Monday amid investor optimism that Meta's Muse could boost CPU demand; AMD's market cap tops $1T for the first time —  Advanced Micro Devices' market valuation surpasses $1 trillion for the first time in intraday trading.

Alexander Martin / The Record : ShinyHunters hijacked the dark web site of cybercrime gang Cl0p, set an eight-figure extortion demand, describing the amount as “2.333%” of Cl0p's net worth —  The ShinyHunters extortion group hijacked the dark web leak site of the prolific Cl0p ransomware gang, according to material posted on the site over the weekend.

Kate Abnett / Reuters : The EU proposes rules requiring data centers with a capacity of 500+ kW to disclose their energy and water efficiency using an EU-designed labeling system —  The European Commission proposed rules on Monday requiring data centres in Europe to disclose how efficiently they use energy and water …

xAI : SpaceXAI releases Grok 4.7, which it says is better at verifying its own work and managing longer context, available for $2/1M input and $6/1M output tokens —  SpaceXAI's most powerful model for coding and knowledge work.  Twice as fast, at half the price of comparable models.

Federico Viticci / MacStories : Mac Studio (M5 Ultra) with 256 GB of RAM review: a dream machine to run local AI agents and a massive leap over M3 Ultra for prompt processing and generation —  For the past few days, I've been testing the (currently) top-of-the-line M5 Ultra Mac Studio with 256 GB of RAM.

Brock E.W. Turner / Axios : NYC-based Corridor, which uses AI to offer health benefits for small businesses, raised a $16M seed led by Bain Capital Ventures, following a $9M pre-seed —  Corridor, an AI-native health benefits broker, raised $16 million in seed funding led by Bain Capital Ventures, CEO Nikhil Aggarwal tells Axios Pro exclusively.

Google 因地理位置数据处理被爱尔兰数据保护委员会（DPC）罚款 4.03 亿欧元。DPC 对 Google 的调查持续了六年，涉及 Google 在 2018 年 5 月 25 日至 2020 年 2 月 4 日间 Web & App Activity、Location History 和 Location Accuracy 三项功能的位置数据处理。DPC 的报告认为 Google 的位置数据处理违反了 2018 年生效的数据保护法律 GDPR，可能导致用户未意识到自己的位置信息正被用于投放定向广告或推断其兴趣偏好，丧失对自己个人数据的控制权。Google 发表声明，表示它从 2019 年起就调整了位置数据管理。引入了位置数据自动删除功能。

深度集成 Gemini、运行 Android 的笔记本电脑 Googlebooks 将于 10 月 4 日上市。Google 硬件合作伙伴中除了宏碁推出一款起售价 899 美元的型号外，其余厂商的产品都超过 1000 美元。Googlebook 不同于 Chromebook 面向低端市场，它面向的是中端笔记本电脑市场。Googlebooks 的 Continue On 功能允许用户在手机或 Googlebook 之间无缝切换，但需要应用开发者支持；Cast My Apps 可以直接在 Googlebook 上使用 Android 手机已安装应用；Play Store 是 Googlebook 获取应用的主要渠道，侧载受到了限制，只能安装运行已通过 Google 验证身份的开发者的应用；通过深度集成 Gemini Intelligence，用户仅仅移动光标就能激活被称为“Magic Pointer”的 AI 功能，AI 会分析屏幕上的内容，根据上下文提供建议，能从多个应用中提取数据。比如将光标指向电邮中的日期即可创建日历预约。

Saturn 的一项研究显示，ChatGPT、Claude、Copilot、Grok 和 Gemini 等主流 AI 模型在回答财务相关问题时，平均有 57% 会给出错误答案。研究使用了逾百个财务相关问题，分别测试了 ChatGPT、Gemini、Claude 和 Copilot 提供的免费及付费 AI 模型。每个问题最多重复提问五次，向 18 种 AI 模型共提出了逾 10,000 个问题。研究发现，AI 模型的回答中包含计算错误、遗漏即将实施的税收政策变更，或是凭空捏造规则（即幻觉）。在最严重的情况下，依赖 AI 对税务问题的回答可能导致严重的经济损失。研究发现，付费模型的回答比免费模型更准确，较新的模型表现优于较旧的模型。表现最好的是推理模式的 Claude Opus 5，但仍然有 39% 的答案存在错误。

青少年有社交网络成瘾问题，年龄超过 60 岁的婴儿潮一代也存在类似问题。YouGov 在 2026 年进行的一项民调发现，55 岁以上人群有超过五成每周至少使用一次社交媒体。62 岁的 Tracie 女士说，拿起手机就忘记放下了，称很多同龄人都沉迷于不断向下滑动手机屏幕浏览信息。Surrey 大学副教授 Brigitte Stangl 认为，社交媒体在缓解老年人孤独感方面非常有效，“上网可消除行动不便或交通出行困难带来的问题”——这些问题对老年群体影响最为显著。61 岁的演员 Kerri Hedley-Cheney 称，社交媒体让她能加入表演组织，建立人脉和互相支持。她同时也承认可能会在社媒上浪费几小时时间。她指出，使用社交媒体也缩短了她的注意力持续时间，类似年轻人，她现在的注意力间隔只有大约 10 秒钟。

位于埃及帝王谷的图坦卡蒙陵墓一直令考古学家困惑不已，怀疑墙壁后面有隐藏密室。借助于雷达，研究人员认为他们找到了密室存在的证据。但是否发掘密室还需要等待埃及最高文物委员会的批准。如果批准，发掘工作最快将于 11 月启动。图坦卡蒙陵墓建于公元前 14 世纪，于 1922 年被发现。英国埃及学家 Nicholas Reeves 认为，隐藏密室可能是图坦卡蒙前任统治者娜芙蒂蒂（Nefertiti）的安息地—— 娜芙蒂蒂是法老阿肯纳顿的王后，她的女儿安克姗海娜曼嫁给了同父异母的弟弟图坦卡蒙，她在阿肯纳顿死后曾短暂统治埃及。Reeves 认为图坦卡蒙陵墓最初是一座规模庞大的皇家陵墓，当年轻的图坦卡蒙意外去世时，陵墓入口被扩建并改造以供其安葬，更深处的区域则被封闭起来。探地雷达显示陵墓后有一条宽两米、填满碎石的通道。

根据发表在《Sociology of Religion: A Quarterly Review》上的一项研究，互联网普及与宗教信仰下降相关。研究人员汇集了一个 81 个国家的数据集，时间跨度从 1990-2022 年。研究团队构建了一个包含六项指标的指数去衡量各国的平均宗教水平。这些指标涵盖了宗教仪式参与率、神（God）在个人生活中的重要性、宗教的总体重要性、自我认定的宗教身份、对教会的信任度以及是否正式隶属于某个教派。结果显示，一个国家的互联网普及度越高，其宗教信仰程度就会呈下降趋势。在考虑了其它因素之后这种负关联仍然存在，且独立于整体经济或教育水平的提升。

日本总务省公布的人口推算数据显示，截至 9 月 15 日，日本 65 岁以上老年人口较上年增加 2 万人，达到 3624 万人。老年人口时隔两年再次增加，在总人口中的占比为 29.6%，刷新历史最高纪录。截至 2025 年，日本 65 岁以上就业人数达到 943 万人，连续 22 年增加，创历史新高。按性别来看，日本的男性老年人口为 1571 万人，女性为 2054 万人。2025 年死亡老年人口多于新满 65 岁的人数，日本的老年人口总数有所减少，但 2026 年再次转为增加。

OpenAI 在 bzr.openai.com 上的广告收集器会设置名为 __obi、作用域为 .openai.com 的 Cookie。该 Cookie 的值在用户访问 ChatGPT 期间生效，并与用户的 ChatGPT 账户绑定。随后，__obi 会从用户访问的其它网站发送回 OpenAI。任何在 ChatGPT 上购买广告的公司，都会在其自己的网站上添加一小段 OpenAI 代码，其方式与添加 Meta 和 Google 跟踪代码的方式相同。加载该代码会将 __obi 以及有关用户浏览页面的数据（包括搜索的产品、正在阅读的文章以及购买行为）发送给 OpenAI。OpenAI 可将用户在这些网站上的行为与其 ChatGPT 账户关联起来。通过广告收集器 ChatGPT 知道你在其它网站的活动。

霸王龙究竟是“冷血动物”还是“温血动物”，一直是古生物学界争论的问题。冷血动物体温随环境变化，温血动物则能靠自身代谢维持稳定体温。越来越多证据表明霸王龙可能是温血动物，但它究竟有多“热”，此前始终没有一个确切答案。霸王龙已经灭绝了6600多万年，还有方法测量它的体温吗？UCLA 的科学家给出了肯定的答案。他们从霸王龙保存至今的牙齿化石中找到了线索，并首次测出了它的具体体温。这项研究的关键，是牙釉质中的碳、氧同位素。霸王龙形成牙齿时，这些同位素会参与形成一些特殊的化学键，而化学键的数量与形成时的温度有关。温度较低时，形成的化学键更多，温度较高时则更少。测量这些化学键的数量，就能反推出牙齿形成时的温度。结果显示，霸王龙的体温约为 36℃。这个温度与现代大象类似，与人类接近，处于现代爬行动物和鸟类之间。如今大多数冷血爬行动物的体温大约为 28℃-30℃，而许多鸟类的体温可以达到 40℃-43℃。霸王龙虽然依然是长着鳞片、通过产卵繁殖的爬行动物，但它的体温已经明显高于现代冷血爬行动物。较高的体温通常与较高的代谢水平有关。科学家认为，这一结果支持了霸王龙是一种活跃动物的观点。它可能需要通过捕食或寻找腐肉获取足够能量，以维持较高的代谢水平，而不是像一些现代爬行动物那样依靠晒太阳来提高体温。这个结果还有助于解释另一个问题：霸王龙为什么能生活在寒冷地区。

在开发者发现 ZCode 会将用户整个工作区打包上传后，AI 公司智谱推出数据不留存功能。智谱星期天晚在微信公众号宣布，其“模型即服务”（Model as a Service，MaaS）平台将于近期正式推出“数据内容不留存”功能，将不对用户的模型调用输入、输出内容进行任何形式的静态存储，数据仅在当次调用过程中临时使用。企业和开发者用户可通过智谱 Maa S控制台提交开通申请。零数据留存即用户提交给模型的提示词（Prompt）、模型返回的生成结果，仅在服务器内存中参与当次推理计算；一旦请求处理完成，相关数据不会存储，也不会被用于模型迭代训练。智谱同时明确，数据不留存机制存在两种例外情况。一是由产品形态决定的功能型存储，包括 Batch API（批处理接口）、Files API（文件管理接口），其存储逻辑由产品特性需求决定。另一类是合规性，根据国家法律法规要求，或为核查涉嫌违规、滥用服务的行为，平台可能留存对应数据 30 天及以上。

新加坡国家图书馆管理局启动了名为 ReadSG 的全国阅读运动，旨在在数字时代恢复公众的阅读习惯，它推出的鼓励阅读措施包括每天打卡看书 15 分钟即可换取“金币”和积分的奖励游戏。公众在政府科技局的 CrowdTaskSG 平台上记录到至少 15 分钟的阅读时长就能获得 20 金币。兑换比例是 1,000 金币相当于 1 新加坡元，这意味着 15 分钟阅读的价值约 0.02 新加坡元。图管局表示虽然奖励非常少，它旨在鼓励阅读而不是让民众把它当成收入来源。图管局 2024 年全国阅读习惯调查显示，89% 成人每周阅读一次以上，但阅读书籍类的只占当中 28%。

英伟达在 2024 年称当年它的 GPU 产品共使用了逾 10 亿 RISC-V 核心。这些 RISC-V 核心没有被用于图形渲染，而是充当了微控制器，执行各类辅助任务，根据型号不同，每个 GPU 包含了 10-40 个 RISC-V 核心。英伟达在 RISC-V 之前使用的是私有微控制器 FAst Logic CONtroller（Falcon）。Falcon 最早于 2005 年随 G98 引入，到了 2016 年单个 GPU 芯片集成了超过 15 个不同的 Falcon 引擎，十年间使用的 Falcon 核心总数约 30 亿个，这些核心被用于视频编解码、电源管理、安全引擎等不同任务。Falcon 为 32 位核心，没有数据缓存，不再满足英伟达的需求，它开始寻找替代，在评估了 Arm、MIPS 等架构之后，它最终选择了开源指令集架构的 RISC-V。

根据美国俄勒冈州的一份报告，该州有 111 个运营中的数据中心，建筑面积 2210 万平方英尺，2025 年用电量占到该州总用电量的 23%。该州还有 32 个数据中心处于建设中或规划中，将新增 690 万平方英尺的建筑面积。报告预测，到 2030 年该州数据中心的能耗将上升至近 25TWh，约占该州总电力需求的 31%-32%，相当于 250 万户家庭的用电量。报告称，这些数据中心直接提供了 2630 个就业岗位，占到了该州总人口的 0.06%。

尼泊尔正面临从致命泥石流灾难后的艰巨重建任务。它寻求从气候赔偿基金 Fund for Responding to Loss and Damage 获得 2000 万美元的赔偿——即要求主要排放国承担责任，尼泊尔认为这场灾难是由气候变化引发的。尼泊尔外交部长 Shisir Khanal 表示，这不仅仅是寻求援助或慈善，更是一项关于正义以及“法律与道义责任”的诉求。他表示，该国的温室气体排放量几乎可忽略不计，但却要为一场并非由其引发的全球危机付出惨痛代价。相比数十亿美元的损失，2000 万美元的赔偿金虽然少，但却是检验气候赔偿基金公信力的一次重要考验。该基金设立于 2022 年的联合国气候大会期间，旨在应对气候变化对发展中国家的影响。迄今为止主要温室气体排放国承诺捐赠的资金中只有极少部分到位，而至少已有 119 个国家提出了应对气候问题的资金申请，它还没有发放任何资金。这些国家申请的总金额达到了 28 亿美元，而该基金实际到位资金仅 2.5 亿-3 亿美元，其中美国已于 2025 年撤出。根据最新数据，这次尼泊尔西藏泥石流灾害，尼泊尔至少 1410 人死亡，6145 人失踪；西藏有 43 人死亡，519 人失踪。

微软利用使用 GPT-5.6 Sol 和 Claude Opus 4.8 的 AI 智能体、历时 14.5 周，花费 12 万美元 token 将 Copilot 运行时从 TypeScript 语言移植到 Rust 语言。该项目采用逐个更新运行时模块的方式执行，共进行了 135 次发布，平均每天提交约 1.3 个 Pull Request，最终将 43 万行 TypeScript 代码转换为 80 万行可用于生产的 Rust 代码。测试显示，原 TypeScript 代码每秒能完成 7.55 个生命周期（one-turn session lifecycles），而 Rust 代码每秒 120 个——意味着在特定工作负载下速度提升了 15.9 倍。包含 10 个客户端的智能体在 TypeScript 语言下消耗了 1383 MB 内存，而 Rust 语言版本仅消耗了 126 MB。Rust 版本将任务保持在进程内执行，无需像 TypeScript 版本那样启动外部后台进程完成任务。