OrangeBot.AI Digest — 2026-07-26

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Deep research requires agents to find answers that jointly satisfy multiple constraints. Discovering such answers is costly, whereas verifying a candidate can often be decomposed into tractable constraint-wise checks. This discovery--verification asymmetry suggests that a research agent should do more than simply search longer: it should recursively improve its current answer by verifying intermediate results and using the partially verified state to guide subsequent refinement. We introduce AREX, a family of Recursively Self-Improving (RSI) deep research agents. AREX alternates between an inner research loop that gathers evidence and constructs a provisional answer, and an outer self-improvement loop that audits the answer constraint-wise, identifies unresolved claims, and launches targeted follow-up research. To sustain RSI over long horizons, AREX learns an autonomous context-update tool that compresses growing interaction history into a compact improvement state preserving verified evidence and unresolved constraints, without relying on an external model. We train AREX on verified synthetic tasks and high-quality trajectories through agentic mid-training and long-horizon reinforcement learning. To mitigate sparse final rewards during long horizon learning, we emphasize key steps where decisive evidence is acquired or erroneous research directions are corrected. We instantiate a dense 4B model and a 122B-A10B Mixture-of-Experts model. Across BrowseComp, WideSearch, DeepSearchQA, Humanity's Last Exam (HLE), and other reasoning and tool-use benchmarks, AREX substantially outperforms comparable-scale baselines and remains competitive with models using substantially more activated parameters.

Large language models are increasingly used in K-12 education, but existing benchmarks mainly test exam question answering rather than understanding how curriculum knowledge is structured and visually presented. We call this capability curriculum cognition. It covers prerequisite chains, concept taxonomies, experiment-concept links, pedagogical sequencing, and visual grounding. We introduce K12-KGraph, a curriculum-aligned knowledge graph extracted from official People's Education Press textbooks in mathematics, physics, chemistry, and biology across primary, middle, and high school. It contains nine node types and fourteen relation types covering curriculum structure and visual grounding. From this graph, we derive K12-Bench, a 23,640-question multi-select benchmark with five task families: Ground, Prereq, Neighbor, Evidence, and Locate. We also build K12-Train, a graph-guided supervised fine-tuning corpus of 7,335 samples, including 2,267 text-only QA pairs and 5,068 multimodal VQA pairs. On K12-Bench, Gemini-3-Flash achieves only 57 percent exact match and Gemma-4-31B-IT reaches 46 percent, with Prereq and Neighbor being the hardest tasks. Our training experiments show that domain-specific supervision can reduce this gap. Under a matched 2,300-sample budget, K12-Train-Text consistently outperforms equally sized subsets of eight mainstream instruction-tuning corpora on GaokaoBench and EduEval. For vision-language models, K12-Train-Full achieves the best overall results on Gaokao-MM, MDK12-medium, and K12Vista among all compared training configurations, despite using fewer samples than the full DataFlow and WizardLM baselines. It also surpasses both text-only and multimodal-only variants, showing that textual and visual supervision are complementary. We release the graph, benchmark, training data, and complete construction pipeline.

Embodied visual tracking (EVT) requires a mobile agent to continuously follow a specific target described in natural language using only onboard vision. While recent vision-language-action (VLA) policies unify target identification and trajectory planning, their chain-of-thought (CoT) reasoning often operates in abstract spatial latents that are difficult to supervise and weakly aligned with explicit image-space detections. To address this, we introduce ReferTrack, a referring-then-tracking paradigm that grounds EVT using a single forward-facing camera. Our model first selects the target from an indexed set of bounding boxes, then decodes tracking waypoints conditioned on this image-grounded decision. To preserve target motion cues over time, ReferTrack maintains a sliding-window queue of previously selected bounding boxes, injecting their geometric features into the visual history via temporal-viewpoint-bbox indicator (TVBI) tokens. We further enhance target identification by co-training on a custom Refer-QA dataset. On EVT-Bench, ReferTrack achieves state-of-the-art single-view performance with success rates of 89.4%, 73.3%, and 74.1% on the single-target, distracted, and ambiguity tracking splits, respectively -- matching or even surpassing several multi-camera baselines on identification-heavy tasks. Finally, real-world deployments on legged and humanoid robots validate its robust sim-to-real transfer capabilities. Code is available at https://github.com/MedlarTea/referTrack.

On-policy self-distillation (OPSD) is promising as it removes the external teacher required by on-policy distillation (OPD), yet it still needs asymmetric information between teacher and student to ensure that the self-teacher provides a stronger learning signal than the student. Existing methods create this asymmetry either through privileged answers or visual evidence. We ask whether both can be removed, yielding a simpler form of OPSD driven purely by input conditioning. For this purpose, we propose Visual Contrastive Self-Distillation, namely VCSD, which converts image-content removal into an on-policy self-distillation signal. At each student-generated response prefix, the EMA teacher produces two next-token distributions under the same prompt and prefix -- one conditioned on the original image and the other on a content-erased control. Their token-wise log-probability difference highlights candidates whose likelihood is specifically increased by the instance-level visual content. We use this contrast to sharpen the teacher's original-image distribution within its plausible support, and distill the resulting full-distribution target into the student. Using ViRL39K dataset, VCSD consistently outperforms matched OPSD across Qwen3-VL and Qwen3.5 models. For example, on Qwen3-VL, it improves the seven-benchmark aggregate from 62.27% rightarrow 67.04% at 2B, 71.30% rightarrow 73.16% at 4B, and 72.51% rightarrow 76.26% at 8B. Furthermore, VCSD requires no external teacher, privileged answers, visual evidence signals, reasoning traces, or additional inference-time cost.

Spatial intelligence is essential for agents to move from static semantic understanding toward interacting with the physical world. Many spatial tasks are grounded in continuous visual scenes, where locations, regions, and paths are more naturally expressed by pointing, marking, or drawing than by reporting precise coordinates or discrete textual symbols. Yet existing spatial reasoning benchmarks usually require coordinates, options, or text, creating an answer-interface mismatch for image-generation models. This makes it difficult to evaluate image-generation models under the same task semantics as text-output VLMs, despite their ability to externalize spatial judgments directly in pixel space. We propose ProVisE (Protocolized Visual Evaluation), a benchmark-agnostic framework that elicits protocol-constrained visual answers from image-generation models and parses them into structured predictions compatible with original metrics. ProVisE also includes an Agentic builder that constructs and validates task-specific protocols for new benchmarks. We further introduce SpatialGen-Bench, a curated diagnostic benchmark of 470 samples across 14 spatial subtasks, four capability levels, and diverse answer forms. We evaluate representative text-output VLMs and image-generation models in a unified setting and validate Agentic protocol construction on six external spatial benchmarks. Results show that image-generation models are competitive when spatial answers can be externalized directly in pixel space, while text-output VLMs retain a clear advantage in compositional spatial reasoning. These findings reveal complementary strengths of pixel-space expression and text-based reasoning and establish a metric-compatible testbed for studying spatial cognition in image-generation models.

We introduce SANA-Video 2.0, a hybrid video diffusion transformer instantiated at 5B and 14B scales under a unified architecture. Designed to generate high-quality video up to 720p on a single GPU, SANA-Video 2.0 matches full-softmax video DiTs in quality while retaining the favorable long-sequence scaling of linear attention. To avoid quadratic attention throughout, Hybrid Linear-Softmax Attention combines gated linear attention for O(N)-dominated mixing with periodic gated-softmax anchors at a 3:1 ratio, restoring the full-rank token interactions that pure linear attention lacks. To propagate these refreshed representations across depth, Block Attention Residuals (AttnRes) route completed block summaries into later linear layers, enabling anchor-feature reuse and boosting deep-layer effective rank by ~12%. Through from-scratch training, SANA-Video 2.0 learns the complete hybrid directly rather than linearizing pretrained models, with reduced-resolution proxy studies establishing 25% softmax as the optimal quality-efficiency trade-off. With 40-step sampling, SANA-Video 2.0 achieves a VBench score of 84.30 in 13.2s at 480p on a single H100, remaining competitive with far larger softmax video DiTs at a fraction of the latency. Its compiled DiT forward pass is 3.2x faster than a matched full-softmax baseline at 720p/60s, a gap that expands with video duration. Furthermore, full-stack Sol-Engine optimization (kernel fusion, caching, and sparse attention) accelerates this hardware-friendly backbone by a further 3.58x, bringing the 5B pipeline to 13.06s at 720p/5s and making it 120x faster than Wan 2.2-A14B on one H100. Overall, our hybrid design recovers softmax-level expressiveness at substantially reduced cost, unlocking scalable long, high resolution video generation.

Traditional agent development is split across prompt templates, tool schemas, callback code, and workflow graphs. We present NVIDIA Object-Oriented Agents (NOOA), a model-agnostic Python framework for building reliable AI agents. NOOA takes a simpler approach: an agent is a Python object. Its methods are the actions the model can take, fields are its state, docstrings are its prompts, and its type annotations are contracts. A method whose code body consists of "..." is completed at runtime by an LLM-driven agent loop, while methods with normal bodies remain standard deterministic Python. This gives developers and agents the same interface, so agent behavior can be tested, traced, refactored, and improved just like other software. This paper makes three contributions. (1) We present the agent-as-a-Python-object programming model and the design principles behind it. Where Python has existing abstractions, we adopt them directly. Agent-specific capabilities--context, events, state rendering, long-term memory, and validated LLM loops--are exposed through simple Pythonic APIs, so both developers and agents share one familiar programming model. (2) We identify six model-facing ideas that NOOA is, to our knowledge, the first to combine on a single surface: typed input/output, pass-by-reference over live objects, code as action, programmable loop engineering, explicit object state, and model-callable harness APIs for context and events. We find the community already converging on several of these ideas--often as experimental or partial features--and present the comparison to encourage further adoption. (3) We demonstrate that current models use this interface effectively, both in targeted capability tests and on agentic and reasoning benchmarks such as SWE-bench Verified and Terminal-Bench 2.0 and ARC-AGI-3.

We introduce Tencent WorkBuddy Bench, a multi-domain evaluation suite for coding agents; this report documents its construction methodology, scoring protocol, and a cross-model leaderboard. At its core is a unified evaluation framework for constructing and running distribution-informed coding-agent tasks across four work domains - Code, Web, Office, and Security. Rather than adapting public issue text, every task is reverse-engineered from a real commit, pull request, or business scenario and rewritten as a short, colloquial, role-played request, so that a task's prompt is not recoverable by web-searching the underlying issue, pull request, or commit thread. Because the dataset is released openly - task directories, environment images, evaluation harness, tests, and reference solutions - contamination resistance rests on this construction together with dataset versioning rather than on secrecy. The four subsets - repository-level engineering, front-end development, office and business workflows, and red-/blue-team security - probe complementary facets of real work, each with its own verification style. All are packaged in a uniform task-directory format and run, under a uniform and reproducible protocol, on two agent harnesses (CodeBuddy Code and Claude Code); the full open release makes the benchmark reproducible end to end and directly auditable, since any third party can re-run each task and inspect its content. Because each subset uses a different scoring instrument, scores are not comparable across subsets and the suite reports no suite-wide average. We report a cross-model leaderboard across several model families.

As LLMs become more capable, they are increasingly deployed as collaborative agents, taking on user-delegated tasks through iterative interaction. Yet genuine interaction is inherently dynamic: users rarely specify their intent upfront, instead disclosing, revising, and reshaping it as the conversation unfolds. Despite this, LLMs are still predominantly evaluated or trained in single-turn, fully-specified settings, leaving open a fundamental question: how well do LLMs track and act on user intent as it evolves over the course of a conversation? To study this, we introduce a framework that transforms static, single-turn tasks into dynamic multi-turn conversations in which the user's intent evolves across turns--incrementally revealed, revised, and at times redirected mid-conversation--while preserving each task's original evaluation protocol, enabling existing benchmarks to be reused as controlled testbeds without new annotation. Across multiple tasks, we surface a consistent phenomenon: strong static-setting performance does not transfer to the evolving-intent setting, with substantial drops across model families. Our findings point to a fundamental gap: today's LLMs do not yet faithfully track and act on the user's evolving intent, a capability invisible to static evaluation yet critical for future collaborative agents.

Understanding motion in video is a fundamental challenge for visual learning, as frame-to-frame change entangles two sources of dynamics: camera motion and object motion. This decomposition has remained underexplored in representation learning, partly because these factors are tightly coupled in natural videos and difficult to supervise separately. Yet recovering it is important for learning robust motion representations that separate meaningful object dynamics from camera-induced variation. We study whether such structured motion representations can be recovered from frozen features of a pretrained image vision transformer. We propose the Structured Dynamics Model (SDM), which explicitly separates the dominant source of temporal change from residual dynamics through future-feature prediction, rather than representing video change with a single entangled latent or with unstructured, spatially dense transition tokens. Training combines self-supervised learning on real video with weak supervision of scene dynamics on synthetic Kubric data. We evaluate SDM on ProbeMotion, a new evaluation suite spanning synthetic and real videos with camera motion, object motion, and combined dynamics. SDM outperforms backbone baselines using global CLS or average-pooled features, and compares favorably to strongly supervised representations such as VGGT on several probes, despite using substantially weaker supervision. These results suggest that pretrained image models can be readily repurposed into structured video-dynamics representations, providing a useful inductive bias for learning and analyzing latent video dynamics.

When a real-world scene is captured by a smartphone camera and viewed on its screen, the displayed image often differs noticeably from the original scene in color, brightness, and contrast. This gap persists despite substantial advances in both modern cameras and displays. A key reason is that most pipelines factor the high-dimensional capture-to-display process into two separately calibrated camera and display stages, and then connect them through low-dimensional color transforms, leading to information bottlenecks and inevitable error accumulation. To address this systemic challenge, we propose Color Pass-Through, an end-to-end learned framework that operates directly on captured images. Our key insight is to treat the camera and display as a coupled system rather than calibrating them in isolation. Coupling the camera and display yields two practical advantages: (1) it brings the entire real-world scenes to the display via end-to-end optimization, and (2) it allows efficient one-step calibration for each distinct observer via complete capture-to-display path. We validate Color Pass-Through using both digital and human observers. Compared with representative baselines, our method achieves an average gain of +2.0 points on a 5-point user study and more than 2x improvement on quantitative metrics, demonstrating improved reproduction of the perceived color of the original scene.

Real-world agent learning is often constrained by costly environment interactions, such as running time-consuming experiments or obtaining human feedback. In-context learning offers a highly sample-efficient way for agents to learn from their own interaction histories, but its gains disappear once that experience is removed from the context. Separately, context distillation provides a mechanism for internalizing contextual information into model weights. However, applying it to agents' interaction histories without sacrificing environment sample efficiency remains underexplored. We term this problem Experience Distillation and develop an implementation that requires no further environment interaction beyond the collected experience. Experiments on 749 curated software-engineering tasks and six text-adventure games show that it retains at least 64.8\% of the gains from in-context learning across both domains, whereas direct supervised fine-tuning on the collected experience recovers only 3.8\%. Compared with classical reinforcement-learning baselines, in-context learning from trial-and-error experience followed by Experience Distillation matches their performance with at least \(9.6\times\) fewer environment samples.

Multi-agent interactive world models should not only generate consistent observations, but also maintain world states that persist across agents and evolve across views. Existing autoregressive video diffusion pipelines carry forward observation history as conditioning context, which makes shared state difficult to maintain in multi-agent and multi-view settings. We present WorldWeaver (W^2), a streaming multi-agent video diffusion model that augments rollout with cross-agent world state registers: learnable tokens that store shared world information, track individual agent status, and are dynamically updated after each generated chunk. We ground these registers with supervision signals spanning individual agent status, global state views including bird's-eye views, and scene text. We further improve the architecture with a Mixture-of-Transformers design that uses separate weights for world state modeling and visual frame modeling. Extensive experiments in two-agent Minecraft video generation show that explicit world-state modeling improves logical consistency and generation quality.

We study on-policy distillation (OPD) for agentic tasks, where an LLM agent interacts with an environment over multiple turns and a student imitates a teacher over these multi-turn interaction histories. Fully online OPD is costly because each update requires fresh student rollouts through the environment and teacher queries at visited histories. We propose Replayed-Prefix On-Policy Distillation (ReOPD), an off-environment alternative that reuses pre-collected teacher trajectories as replayed prefixes: the student acts at selected steps, while the teacher provides dense per-step supervision without executing new environment interactions. We show that multi-turn OPD introduces a prefix trap: making histories more student-on-policy improves relevance to the student, but can query the teacher on histories where its target is unreliable. This creates a two-sided distribution shift between student occupancy and teacher reliability. ReOPD addresses this by treating multi-turn OPD as a reliability-aware prefix distribution design and implements it with a simple step-decaying sampling schedule that emphasizes early, lower-shift prefixes. Across mathematical reasoning with Python and search environments over multiple teacher and student model scales, ReOPD preserves or improves OPD-level accuracy, uses zero tool calls during student training, and is at least 4times faster per rollout than OPD. ReOPD therefore turns expensive agent-environment interaction into a reusable offline resource, enabling scalable distillation across tools, tasks, and environments.

Deploying navigation systems at scale requires a recipe that minimizes sensor assumptions, generalizes across robot embodiments, and trains efficiently. Yet, today's best systems depend on depth sensors, multi-camera rigs, or pre-built maps, limiting the hardware they support and increasing deployment cost. We introduce Robostral Navigate, an 8B vision-language model built around this scalability objective. The model consumes only a stream of monocular RGB images - the most ubiquitous sensor across robotic platforms and predicts waypoints by pointing to the next target location in the current camera view. Operating purely in image space, rather than robot-specific coordinates, makes the policy naturally robust to changes in camera intrinsics and scene scale, enabling deployment across wheeled, legged, and aerial robots without recalibration. We generate 2.4 million trajectories across 350k simulated scenes to reduce the reliance on real-world data collection and scale easily. We further introduce a prefix-caching training recipe that packs entire episodes into single training sequences, reducing training tokens by 22x and cutting training time from months to days. A tree-based attention mask prevents conditioning on previous ground-truth actions, encouraging visually grounded action prediction, and reinforcement learning is used to further improve exploration and recovery capabilities. On the Room-to-Room and Room-Across-Room in Continuous Environments (R2R-CE and RxR-CE) benchmarks, Robostral Navigate sets a new state of the art. On R2R-CE, it achieves a 77.4% success rate, surpassing the best monocular method by 10.5 points and the strongest depth- or multi-camera system by 5.3 points despite using only a single RGB camera. On RxR-CE, it reaches 75.1% success rate, outperforming all monocular baselines.

Jamie John / Financial Times : How AI companies are targeting the education market, including making free or cut-price tailored learning tools in partnership with schools and edtech startups —  Anthropic and OpenAI are among groups providing free and cut-price tailored solutions for educators and students

Aditya Kalra / Reuters : India eases foreign investment rules for e-commerce companies, a win for Amazon, which lobbied to buy goods directly from Indian sellers and sell them overseas —  India's government on Thursday eased foreign investment rules to allow e-commerce companies to buy products directly from Indian sellers …

Ashley Stewart / Business Insider : Once hailed as an AI visionary, Satya Nadella faces pressure as a compute crunch forces Microsoft to prioritize its own AI products over Azure cloud customers —  Microsoft went all in on AI.  Is its North Star now a noose?  —  Three years ago, Satya Nadella catapulted Microsoft to the front …

Reuters : Shein's IPO prospectus shows Q1 revenue of $9.05B, up 1% YoY, as it swings from $395M profit in Q1 2025 to $99M loss partly due to US' “de minimis” rule removal —  Shein swung to a $99 million net loss in the first quarter of this year, the draft Hong Kong listing prospectus …

Mark Gurman / Bloomberg : Sources: Apple may have delayed AI glasses launch partly over privacy concerns that Meta's glasses created for the category, as it works to address the issues —  Company debated whether glasses will be able to record video.  —  Apple's upcoming smart glasses have a major challenge in front of them …

William Sandlund / Financial Times : Crypto exchanges like tradeXYZ are giving global investors exposure to Chinese AI-linked stocks like CXMT, bypassing Beijing's control on foreign capital access —  Perpetual futures let investors circumvent restrictions on foreign access to China's equity market

Wall Street Journal : Sources including AI lab staff say users have been persuading chatbots to accurately answer prompts about planning mass-casualty attacks and making bio-weapons —  AI companies play a cat-and-mouse game, trying to boost the capabilities of their creations while scrambling to block answers to dangerous queries

Bloomberg : CXMT, which raised $9.8B in a hugely oversubscribed Shanghai IPO, is poised for a debut pop that could lift its market cap several times above its initial ~$85B —  Investor excitement over the memory chip trade is setting outsize expectations for the Shanghai debut of CXMT Corp. …

Meir Orbach / CTech : Elio, which is developing a new type of image sensor designed for AI rather than human vision, raised a $21M Series A led by Innovation Endeavors and Xora —  Elio's founders, Nadav Grossinger and Nitay Romano, spent seven years developing physical sensing systems at Meta.

Financial Times : A look at China's bid to build an alternative global order in AI by making open models widely available and training people in developing countries to use them —  Beijing makes most ambitious offer yet to build alternative global order  —  China's President Xi Jinping has set …

Reuters : China's market regulator says it had fined and confiscated ~$770M from Trip.com for abusing its dominant position in the domestic online hotel-booking market —  China's market regulator said on Saturday it had fined and confiscated a total of 5.2 billion yuan ($770 million) from Trip.com Group …

Ima Jackson-Obot / Financial Times : Several universities including Yale, Johns Hopkins, and the University of Waterloo have restricted or disabled their use of AI detectors over accuracy concerns —  Some institutions are overhauling assessment and trying to move away from the emphasis on surveillance

Pei Li / Bloomberg : Sources: DeepSeek told investors it is suspending its second funding round after remarks attributed to Liang Wenfeng on US-China AI competition went viral —  DeepSeek has told prospective investors in its second fundraising round that it's suspending the deal for now, people familiar with the matter said …

New York Times : Sources: OpenAI and Anthropic quietly lobby Washington regulators to restrict open-source AI models, even as Sam Altman publicly says he supports open source AI —  Anthropic and OpenAI are clashing with the rest of the tech industry over whether “open-source” models from China should be freely available or restricted.

Ian King / Bloomberg : SK Group Chair Chey Tae Won says Anthropic has asked SK Hynix for supplies to make its own chips, calling it remarkable that an AI developer has chip ambitions —  AI developer Anthropic PBC has asked SK Hynix Inc., one of the world's biggest manufacturers of memory chips …

unbug 写道： Scratch 因可视化编程风靡全球，尤其深受教培机构的喜爱。如今，社区爱好者给 Scratch 引入了 AI 编程助手，帮助没有编程知识的家长也能跟着孩子学习编程和创作。Scratch AI 编程助手的特点是通过语音和文字的自然语言输入，以可视化的方式一步一步教学生编程和数学知识，在十分钟内就能帮助学生从设计到创建角色造型、创建场景、完成算法和测试，最终完成一个个故事、动画和互动小游戏。这打破了以往先学概念再学创作的漫长模式，让学生从创作中学习，将计算思维、数学思维和逻辑思维的教学门槛降到了最低。还在犹豫暑假是否报编程班的家长，花半个小时就能试出孩子是否有兴趣。 Scratch AI 编程助手：https://app.vibelf.com

安全加固 Android 操作系统 GrapheneOS 项目通过其官网介绍了锁定设备的防数据提取功能，警告使用胁迫密码删除手机数据可能会导致人身伤害或法律后果。GrapheneOS 称其安全功能目前是基于 Google Pixel 手机的硬件安全特性，2027 年通过与摩托罗拉和高通合作将会扩大到其它设备。开发者称 GrapheneOS 不依赖于“胁迫密码”保护用户数据，操作系统在 Android 以及 Pixel 基础上提供了大量安全特性，包括：针对暴力破解，10 次失败后设备锁定 4 小时，15 次失败后锁定 41 天，最多允许 20 次尝试；密码支持最长 128 个字符，支持使用高熵的 Diceware 密码短语，可选指纹 PIN 码；设备锁定时会在软件和硬件层面阻止新的 USB 连接；锁定设备间隔 18 小时后会自动重启；Android 为每个次要用户（secondary user）和私密空间使用独立的加密密钥加密数据，等等。

USA Today、Politico、Economist、People 和 Reuters 等知名网络出版商考虑彻底屏蔽 Google，甚至与 Google 签署了每年 6000 万美元合同的 Reddit 也在重新评估与 Google 的关系。原因是 Google 的 AI 摘要功能导致了网站访问量大幅下降，如 USA Today 过去一年来自美国用户的访问量下降了近一半，主要吸引用户评论的 Reddit 也注意到 Google 的 AI 在阻止用户访问其网站。鉴于 Google 在搜索市场占据了最高市场份额，与 Google 完全终止关系可能会导致访问量更大幅度的下降，但继续允许 Google 抓取内容用于其 AI 功能也没有任何好处——从长远看会加速其死亡。

Cloudflare 宣布将从 9 月 15 日起对三类机器人——搜索爬虫、训练 AI 的爬虫以及智能体机器人——设置新的默认值。对于新加入 Cloudflare 的域名，默认将屏蔽训练 AI 的爬虫以及智能体。但有些机器人爬虫兼具了搜索爬虫和 AI 爬虫的双重功能，因此根据新规则 Googlebot、Applebot 和 BingBot 都将被屏蔽。客户如果不想完全屏蔽此类双重功能爬虫，他们可以在设置中进行修改，排除如 Google 的爬虫——毕竟 Google 垄断了整个搜索市场。

苹果在 2024 年宣布将威廉·吉布森（William Gibson）著名的赛博朋克小说《神经漫游者》改编成 10 集电视剧，登陆 Apple TV Plus。在本周举行的圣迭戈漫展 Comic-Con 2026 上，苹果公布了《神经漫游者》电视剧的首个预告片，宣布它将于 2027 年 1 月 22 日上映。《神经漫游者》出版于 1984 年，故事讲述了一名神经受损的顶尖黑客 Case 与指甲下有可伸缩刀片的女刺客 Molly 搭档，对一个有着不为人知的秘密的超级企业实施盗窃活动。

Debian 项目正在讨论如何使用 LLM 以及是否允许使用 LLM。其中一个提议是明确禁止使用 LLM，但只限于 Debian 本身，上游项目以及上游安全补丁不包含在内。另一个提议是允许 AI 辅助贡献，但需要确保 AI 生成代码符合 Debian 的各项规定，贡献者需要承担责任和履行披露义务。开发者指出，LLM 存在版权和许可方面的问题，也不能确保质量和准确性，会增加社区审核者的负担，AI 公司在训练大模型时无视版权和许可抓取数据，伤害了整个自由软件社区，对无数用户依赖的网站发起了一波波拒绝服务攻击，它的训练也消耗大量资源。

美国司法部起诉了一位公民在入境搜查时提供了所谓的“胁迫密码”，输入该密码导致手机上的数据自动删除。这起案件被认为是美国首例“胁迫密码”案例。本案的被告是亚特兰大居民 Samuel Tunick，他的手机运行了安全加固的 Android 操作系统 GrapheneOS，它内置了一项安全功能，允许用户设置“胁迫密码”，如果输入的不是解锁密码而是“胁迫密码”，那么手机上的数据将会自动清除。本案引发了在入境时美国公民可以援引哪些宪法权利的疑问。美国政府长期坚称，在允许入境前边境处不属于美国领土。数字安全专家 Runa Sandvik 建议，为了避免被当局认为是故意销毁数据，公民可以在入境前先清空数据，入境后再重新下载。

在一群初创公司之后，科技巨头如微软、英伟达、思科、戴尔、IBM、甚至 OpenAI 等数十家公司和组织联署发表公开信，呼吁美国政府不要过早限制开放权重模型，认为此举会扼杀竞争或导致本土创新放缓。公开信称，开放权重模型能促进竞争，确保 AI 技术的好处能广泛共享，避免其掌握在少数人手中，而闭源模型并不意味着绝对安全。没有署名的知名 AI 公司包括了 Google、Anthropic 以及 xAI/SpaceX，其中 SpaceX CEO 马斯克通过社交媒体口头表达了对公开信的支持，而 Anthropic 明显是站在支持限制开放权重模型的一边，该公司正投入 4000 万美元游说特朗普政府加强监管 AI 模型。

IRGC 声称使用巡航导弹袭击并摧毁了亚马逊 AWS 位于巴林的数据中心。亚马逊和美国军方未证实这一说法。如果属实，这是亚马逊巴林数据中心第三次遭到袭击，根据 AWS 自己的公告，巴林数据中心自 2026 年 5 月以来就处于离线状态。

印度政府的网络犯罪协调中心命令 GitHub 移除 Jack Dorsey 的去中心化蓝牙聊天应用 Bitchat。前 Twitter CEO Dorsey 在 X/Twitter 上公布了收到的通知，通知的日期是 7 月 23 日，声称该应用能在网络受限的情况下进行通信，能被反国家分子、恐怖组织、有组织犯罪集团和网络犯罪分子滥用，他们试图逃避合法监测，继续在法律限制下进行通信。印度最近爆发了被称为“蟑螂人民党（Cockroach Janta Party)”的抗议活动。

Google 账号加入了可选的人脸识别登录。用户向 Google 提供自拍视频，随后就可通过人脸识别登录。在人脸验证过程中 Google 可能会要求用户以特定方式转动头部，此举旨在防止深度伪造之类的身份冒用，确保用户当前确实在摄像头前。Google 需要保存用户的自拍视频以将其用于未来的登录验证。搜索巨人表示会对视频进行加密，且仅用于登录不会用于其它用途。如果用户改变主意，可以从 Google 帐户中删除相关视频。

当你的计算机接上 LG 显示器，它会通过 Windows Update 自动安装应用 LG Monitor App Installer，安装之后该应用会不时弹出广告，其中最常见的广告是迈克菲杀毒软件。其它还有 LG 自家产品广告如 LG Switch、LG Calibration Studio、LG Dual Controller 和 LG Channels。此举在消费者中间引发了强烈反应，以至于微软都出来做出回应。微软 Windows 和设备执行副总裁 Pavan Davuluri 称，他们联系了 LG 团队，LG 已经同意立即在其应用中禁止迈克菲广告弹窗。除此之外一切照旧。

近两百家美国科技初创公司组成 Little Tech Association 联盟，联署致函美国总统特朗普、商务部长 Howard Lutnick 等政府高官，呼吁政府不要禁止使用来自中国的开放权重模型。此前由于中国 AI 模型在性能上逼近美国的前沿 AI 模型 Anthropic 的 Fable 5 和 OpenAI 的 GPT-5.6 Sol，美国政府官员表示在考虑封禁中国的 AI 模型。Little Tech Association 的联署信指出，此举会削弱美国初创公司，且并不能阻止开放权重模型扩散。而美国要保持其领先地位需要有世界领先的开放权重模型，确保美国公司能访问其它地方的开放权重模型。

根据发表在 BMJ 上的一项研究，GLP-1 减肥药相比其它糖尿病药物有更高的脱发风险。研究人员指出，快速减肥和减少卡路里摄入是公认的脱发诱因。研究人员称，多数 GLP-1相关的脱发病例都是可逆的，因为毛囊保持完整。研究人员对比了 GLP-1 和其它两种糖尿病药物 SGLT-2 和 DPP-4，结果显示 GLP-1 的脱发风险比 SGLT-2 高 37%，比 DPP-4 高 68%。研究人员称，脱发通常不会造成身体伤害，但可能会产生心理社会影响，如影响自尊、生活质量和治疗依从性。研究人员认为需要开展进一步研究去深入理解之间的关联。

根据发表在《The Lancet Public Health》上的一项研究，1990-2023 年全球出生时的预期寿命从 64.6 岁增至 73.8 岁，而预期健康寿命则从 55.9 岁增至 63.1 岁。预期寿命和健康寿命不同步，预期寿命增幅超过了健康寿命的增幅，这意味着人类不健康时间持续更久。2023 年全球平均有 14.5% 的寿命处于不健康状况，高于 1990 年的 13.6%，不健康时间从 1990 年的 8.8 年增至 2023 年的 10.7 年。不健康的负担在各国和地区之间存在显著差异，通常寿命最长的地方不健康时间也最长。2023 年美国不健康寿命最长为 14 年，其次是澳大利亚（13.9年）和加拿大（13.7年）。