AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Make an original song in minutes AI Music Generator Describe the sound in your head and create original music. Start free, no music production experience needed. Describe your song Need an idea? Listen before you create…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Open table formats are metadata layers that sit on top of data files in object storage, adding ACID transactions...

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Artificial Analysis DeepSeek • DeepSeek V4 Flash 0731 • Proprietary model • Released August 2026 DeepSeek V4 Flash Vision (Reasoning, Max Effort) Intelligence, Performance & Price Analysis API Provider Benchmarks Model…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Plus: both parties are turning against AI data centers ahead of the midterms. This is today's edition of The Download, our weekday newsletter that provides a daily dose of what's going on in the world of technology. Kid…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Hi I made a game with Three.js and AI. This is inspired by omikuji(Japanese fortune) — the paper fortunes you draw at Japanese shrines. It supports multiplayer, and you can draw a new fortune every day. If you get a bad…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：3 Join the conversation Follow us Add us as a preferred source on Google This Tom's Hardware Premium article is free to read with a Tom's Hardware account; no payment necessary. We're offering free access from August 23…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：OpenAI CFO Sarah Friar explains how advances across chips, compute, models, and products compound to deliver more useful intelligence at greater scale and lower cost.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Although mobiles blared in public are nothing new, this crop of headphone-dodging irritants are truly maddening. It’s time to take them on ‘Interesting cities can’t be quiet”, runs one of grime DJ and music industry thinker Elijah’s epigrams – and in its essential spirit, he is right. Silent, tidy, prim cities should be treated with suspicion. Any such atmosphere denotes that something has gone wrong at a macro level: either you’ve wound up in an authoritarian regime or, worse, Switzerland. But not all urban sounds are created equal, and some are more unwelcome than others. Take the tide of (Instagram, TikTok, YouTube) reels crashing on the shores of our collective attention on public transport, thanks to the astonishing number of grown adults who think it’s OK to surf the slopwave without headphones. Dan Hancox is a writer and editor covering music, politics and cities. His latest book is Multitudes: How Crowds Made the Modern World Continue reading...

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Jalapeño is a custom inference chip from OpenAI that delivers faster, more power-efficient AI inference, with higher throughput and lower latency for modern models.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Northern Ireland artist and business backlash against AI slop - BBC News Image source, Getty Images Image caption, AI slop is rapidly produced, low-quality digital content made with generative artificial intelligence By…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：As people grow used to AI chatbots, we’d like to hear your views about Google’s search engine Google has put artificial intelligence at the front and center of its search bar. The most-visited site on the internet still shows the same list of links to users, but they have to scroll past a summarized response from an AI chatbot, a feature Google calls AI Overviews. The change to what was once the gateway to the rest of the internet has been profound, and the browsing habits of billions of people are shifting. “AI is driving the most significant upgrade of the Google Search experience ever,” Liz Reid, Google’s vice-president of search, wrote last August. In May, the company said more AI is coming. Continue reading...

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：CookWing: AI Recipe Chef - Apps on Google Play CookWing: AI Recipe Chef RedWing Inc. In-app purchases Everyone info 1+ Downloads Everyone Learn more Ever followed an AI-generated recipe exactly, only to wonder halfway t…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Open-source LLM deployment platform Deploy LLMs on any GPU, anywhere Pick a model, pick a GPU, hit deploy. LLMPanel provisions the container, exposes an OpenAI-compatible endpoint, and streams every GPU metric back to o…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Discussion | Link

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Turn a feature into a demo video Describe a product or feature and Mirenta generates a looping, animated walkthrough — browser chrome, a moving cursor, clicks, and callouts included.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Holyrood ‘inching towards moratorium’ amid concerns over massive proposed developments in Auchtertool and beyond In a village 21 miles north of Edinburgh, a real estate consultancy plans to build a datacentre larger than the village itself – 35 metres high, with an area larger than 100 football pitches. Billed as the second-biggest datacentre in the world, the development in Auchertool, Fife, has attracted 1,600 objections. Continue reading...

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Disclosure: These views are my own and do not represent my current or any former employers. Executive summary Email, calendar, meeting, and notification assistants are increasingly presented as a way to begin the day wi…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：AI is making software cheaper to produce. The harder problem is establishing that the software is correct, secure, and safe to deploy. For years, improving developer productivity largely meant reducing the time required…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：## Summary Darkbloom makes strong privacy and security claims -- \"plaintext exposure occurs only within hardware-encrypted memory,\" per-request NaCl Box encryption, Secure Enclave attestation chains, and a threat mode…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Cloudflare recently open-sourced Cloudflare OS on GitHub. Cloudflare OS allows enterprise teams to output work artifacts grounded in enterprise knowledge, know-how, and provisioned connectors, automate repetitive workfl…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Code maintainability plummets in the AI coding era AI code is rotting codebases. July 07, 2026 You have 1 article left to read this month before you need to register a free LeadDev.com account. Estimated reading time: 4…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.21410v1 Announce Type: new Abstract: Robots are increasingly entering everyday environments where privacy is shaped not only by data practices, but also by spatial, bodily, social, and relational boundaries. Their embodied capabilities allow them to reshape these boundaries through situated action, challenging privacy framings centered on data flows, interface settings, or one-time consent. Prior work has examined robot privacy through sensing, data collection, telepresence, transparency, consent, bystander awareness, and multi-stakeholder governance. Building on this work, we propose embodied boundary privacy as a capability-by-context framing for examining how physically present robots may reshape privacy boundaries in situated interaction. Specifically, this framing organizes privacy risks across seven robot capabilities and five deployment contexts, asking how embodied capabilities enable boundary crossings and how situated contexts shape who is affected, how these crossings are interpreted, and when they become contested. We use this perspective to outline design and research implications for embodied privacy mechanisms, including boundary checkpoints, viewpoint-aware sensing control, remote-presence disclosure, object- and body-level access rules, constraints on socially persuasive privacy influence, and local interruption rights. We encourage HRI research, design, and governance to treat robot movement, orientation, proximity, object access, remote presence, and social expression as privacy-relevant actions whose meaning depends on context.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.21407v1 Announce Type: new Abstract: Vision-language-action (VLA) models face a crucial tradeoff between their task success rate and the policy-call frequency. Executing a single action per inference ($N=1$) enables accurate robot control but comes at the cost of huge compute time overheads, making real-time implementation infeasible. On the other hand, executing longer action horizons before replanning ($N\gg1$) reduces compute complexity, but inevitably degrades the system's success rate. In order to improve the VLA accuracy-complexity tradeoff, this paper investigates Mamba's selective state-space modeling as an alternative to causal self-attention within the action expert of the popular SmolVLA model, widely used as a reference model for its highly accurate yet low complexity nature. We evaluate both the Mamba- and Transformer-based experts on the widely-adopted LIBERO benchmark suites across three execution horizons $N\!\in\!\{1,25,50\}$, respectively corresponding to high, moderate and low compute complexities. Our results remarkably show that the advantage of the Mamba expert increases with the execution horizon, indicating significant success retention under long execution horizons $N = 50$ and $N = 25$. When $N = 50$ actions are executed before replanning (i.e., corresponding to feasible real-time deployment), the Mamba expert outperforms the Transformer baseline by $7.8\%$. In addition, when $N = 25$ actions are executed before replanning, our Mamba expert outperforms the Transformer baseline by $3.7\%$. Finally, under per-action replanning ($N=1$), our Mamba variant matches the Transformer-based mean success rate while significantly reducing the overall model parameter complexity by $24\%$ thanks to Mamba's compute-efficient nature.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.21404v1 Announce Type: new Abstract: Fixed coordinate measuring machines (CMMs) and portable articulated-arm CMMs are often assigned to the same inspection task, but their nominal accuracy specifications do not show whether a change of instrument will preserve the disposition of a part. The question is not simply how far the two results differ, but whether that difference crosses the tolerance boundary. We examined this issue with recorded measurements of cylindrical, cubic, and spherical features under nominal 20 {\deg}C and 30 {\deg}C conditions. Repeated records and two roughness profiles without sufficient acquisition information were removed, leaving six dimensional and four form profiles. For each dimensional feature, the distances of the two system means from nominal define the exact tolerance interval in which the systems receive opposite direct labels. The fixed-CMM stream was approximately 11.2 {\mu}m higher than the articulated-arm stream at both conditions. All four form profiles fell on opposite sides of the recorded 10 {\mu}m upper limit. The dimensional disagreement intervals also overlapped strongly; their mean widths were 6.573 {\mu}m at 20 {\deg}C and 4.995 {\mu}m at 30 {\deg}C. The results clarify why an average difference between instruments is not, by itself, a measure of substitution risk. The proposed tolerance map identifies the feature-tolerance combinations for which instrument choice can change the recorded inspection label and, therefore, where a controlled equivalence study and a task-specific uncertainty budget are needed before substitution.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.21402v1 Announce Type: new Abstract: World action models (WAMs) jointly denoise future video frames and robot actions, and the video prior is expected to generalize their control. Camera viewpoint change remains one of their hardest perturbation axes. We study a question specific to this model class: when training with same-state cross-view image pairs, on which output coordinates should a consistency loss be imposed? The WAM denoising target mixes view-covariant coordinates, namely the predicted future scene, with view-invariant coordinates, namely the action chunk, future proprioception, and value. We show that consistency applied to the covariant block is provably harmful, shrinking legitimate view-specific content to a fraction $1/(1+4\lambda)$ of its true value, and we verify this shrinkage law in controlled experiments. Selective cross-view consistency (SCVC) therefore constrains only the invariant block, requires no camera labels, extrinsics, depth, or view synthesis at training or test time, and leaves the deployment interface unchanged. We introduce a carve-and-hold-out evaluation protocol on the LIBERO-Plus camera track that separates a distribution-matched ceiling from genuine interpolation and extrapolation to held-out viewpoints, with a matched pair-trained control isolating the effect of the consistency term from pair exposure. On held-out orbital viewpoints beyond the training envelope, SCVC improves closed-loop success over the matched control by 12.2 points (95% CI [7.4, 17.0]; +15.5, CI [11.7, 19.4], under an independent second seed) -- an effect two further camera axes replicate -- while interpolation within the envelope shows no gain in either seed (-1.2 and -4.3 points) and in-distribution competence is preserved (-0.6, -0.2). We also report a cross-backbone audit showing that published camera-robustness numbers are confounded by wrist-camera pose stability.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.21400v1 Announce Type: new Abstract: Dense traffic is inherently interactive. The ego vehicle and surrounding agents continuously influence each other's reactions, making "what-if" reasoning essential for safe and efficient driving. To enable such an active interaction-aware behavior, we propose a planning framework that integrates an ego-conditioned generative autoregressive prediction model within Model Predictive Path Integral (MPPI) control. The generative prediction model outputs stochastic, multi-modal predictions of surrounding agents conditioned on each of the ego's considered future actions. A nested sampling scheme enables tractable evaluation of expected cost and collision risk under the induced distribution. This formulation allows the ego to actively probe how different candidate actions shape the interaction outcomes and to identify actions that reduce ambiguity in uncertain interactions. Closed-loop simulations demonstrate improved safety and efficiency compared to conventional predict-then-plan and passive interaction-aware approaches.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.21395v1 Announce Type: new Abstract: NoMaD [31] is a learned vision-navigation policy that unifies goal-conditioned navigation and exploration in a single goal-masked diffusion policy. In an unseen environment, however - where neither a goal image nor a topological map is available - it can only explore undirectedly, wandering without global awareness. We present ODG-NoMaD, which gives NoMaD's exploration mode a global sense of where to proceed, without retraining the policy. An overhead depth camera is used once on deployment to build an occupancy map and plan a global path, which is segmented to yield a desired heading; a per-frame traversability map from the robot's onboard depth then refines this into a collision-free direction. The gradient of a cosine direction cost is injected into the final denoising steps, rotating sampled trajectories toward this direction while preserving the multimodality of exploration. In simulated office environments with and without random obstacles, ODG-NoMaD reduces the residual distance to the target by up to an order of magnitude over unguided exploration, outperforms the point-goal cost guidance of NaviDiffusor [37], and is the only configuration that remains collision-free on every trial.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.21390v1 Announce Type: new Abstract: Inertial navigation systems are specialized navigation apparatuses that equip almost all autonomous underwater and surface vehicles. They require precise initial alignment, i.e., determination of their initial attitude, which is typically achieved: (a) in quasi-static conditions (whenever possible); and (b) in two stages: Coarse Alignment (CA), using methods like TRI-axis Attitude Determination (TRIAD), and Fine Alignment (FA), via Zero Velocity Update (ZVU)-based Extended Kalman Filtering (EKF). However, conventional methods suffer from slow convergence and limited bias estimability. In response, this paper introduces: (a) an optimized version of a recently proposed CA method, namely, TRIAD with Coarse Bias Estimation (TRIAD-CBE); and (b) a novel FA EKF observation model that incorporates Non-Orthonormality (NON) error constraints derived from TRIAD, directly linking these errors to the inertial sensor biases. As validated through extensive Monte Carlo (MC) simulations, as well as real-world experiments using two Inertial Measurement Units (IMUs) of different grades, our approaches substantially accelerate the convergence of misalignment and bias estimates (from minutes to seconds), while maintaining accuracy/precision comparable to traditional techniques.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.21388v1 Announce Type: new Abstract: Reliable and continuous human tracking is essential for natural human-robot interaction, particularly for companion robots. However, many existing approaches rely on wearable tags or fixed cameras with limited fields of view, which reduces system flexibility and often causes tracking failures when the target moves outside the sensing range. In this paper, we present a human tracking approach based on a gimbal-mounted camera integrated into a mobile robot. By actively controlling the gimbal mechanism, the camera can dynamically adjust its viewing direction to maintain the target within the field of view, even under substantial relative motion between the robot and the human. Furthermore, a continual learning strategy is applied to the person re-identification (ReID) task to adapt to changes in appearance and environmental conditions during long-term tracking. Experimental results demonstrate that the proposed system significantly improves the stability and continuity of human tracking, enables real-time re-identification, and provides responsive feedback for reliable tracking of human motion from walking to running. User studies further indicate that the proposed approach enhances user comfort by eliminating the need for wearable tags.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.21387v1 Announce Type: new Abstract: Service robots are increasingly deployed in elderly-care facilities to alleviate caregiver workload and enhance the quality of daily care. However, most existing studies focus on isolated service functions and lack integrated capabilities for continuous companionship, natural interaction, and safety monitoring. In this paper, we present an intelligent companion robot system that unifies active visual human-following, real-time LLM-driven speech interaction for intent understanding and task execution, and VLM-based safety monitoring for fall detection and abnormal posture assessment. The perception layer ensures robust human tracking and uses an active gimbal to maintain the user in view during occlusions or abrupt movements. At the interaction layer, a Large Language Model interprets spoken requests and maps them to robot actions, enabling escorting and semantic navigation. Simultaneously, a VLM-based safety agent continuously analyzes visual observations to detect fall-related or abnormal postures and triggers emergency responses when necessary. Experimental results demonstrate the system's ability to reliably follow and interact with humans, while effectively detecting potential falls to ensure user safety.