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AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Bindago - AI Message Personalisation for LinkedIn Outreach How It Works Write Once. Personalise for Everyone. You write one base message. AI generates a unique section for each lead using their real LinkedIn profile dat…

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 The COVID-19 pandemic forced a large part of the North American knowledge workforce to work from home. The ch…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.25284v1 Announce Type: new Abstract: Physical human-robot collaboration requires a robot to provide assistance when human intention is clear while remaining compliant when several future motions are plausible. We present an adaptive stiffness framework based on generative action-chunk sampling. Conditioned on an RGB image and external joint-torque estimates, the policy samples multiple future action chunks from an observation-conditioned prior. Variation among the sampled action chunks is used to continuously adapt joint stiffness and damping. Greater variation makes the robot more compliant to facilitate human guidance, whereas lower variation provides firmer assistance. In a real-world collaborative transport task with four possible directions, the proposed method achieved an average success rate of 0.95, compared with 0.83 for a fixed-stiffness ablation and 0.69 for a deterministic baseline. Near direction determination, variation among the sampled action chunks increased and the controller accordingly reduced stiffness. These results suggest that variation among actions sampled by a generative policy can serve as an online control signal for balancing assistance and compliance in physical human-robot interaction.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.24959v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models encode visual observations as flat 2D patch tokens that carry no intrinsic geometric structure, and augmenting them with dense monocular depth injects per-pixel scalar values that encode neither surface orientation nor geometric confidence. This leaves the policy with limited structured spatial reasoning for action prediction. We propose GaussVLA, a Mamba-based VLA that incorporates two custom modules: Gaussian Spatial Tokenizer (GST) to lift frozen semantic and depth features into compact 3D Gaussian tokens, pools geometrically salient regions with learned queries, and \emph{Depth-Aware Chain-of-Thought (DA-CoT)} that performs structured, non-autoregressive geometric reasoning under language and flow-time conditioning. Across both simulation and real-world evaluations, GaussVLA demonstrates strong spatial-manipulation performance while remaining parameter-efficient. On LIBERO, it achieves 93.5% average success and 100.0% success on the Spatial suite with only 200M parameters, improving over SpatialVLA by 19.7% relative average success while remaining significantly more parameter-efficient.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.24954v1 Announce Type: new Abstract: Large language models (LLMs) hallucinate numerical values when generating high-stakes meteorological text, posing risks for weather communication. We present AFDBench, an AI meteorologist that generates professional Area Forecast Discussions (AFDs) by reasoning through structured AI weather forecast data from Google's WeatherNext 2. We introduce AFDBench, the first benchmark for evaluating generative meteorological reasoning, comprising 7,732 expert written discussions from 13 National Weather Service (NWS) offices paired with real AI weather forecast inputs, and three complementary metrics: Met-Align (numerical accuracy), Style-Align (professional dialect adherence), and Input-Grounding (fidelity to source weather data). Zero-shot evaluations reveal that open-source LLMs achieve low Style-Align (~0.33) and moderate Input-Grounding (~0.88), failing to write in the professional NWS register or faithfully use their input data. We apply Group Relative Policy Optimization (GRPO) with domain-specific rewards targeting temperature accuracy, synoptic correctness, and format compliance. On 1,033 held-out samples from two unseen NWS offices, GRPO nearly doubles Style-Align from 0.318 to 0.619 and improves Input-Grounding from 0.881 to 0.940, demonstrating that reinforcement learning teaches a 7B-parameter model to write like a professional meteorologist and faithfully interpret AI weather data.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.24949v1 Announce Type: new Abstract: Reinforcement learning (RL) post-training has emerged as a powerful framework for enhancing the capabilities of large language models (LLMs), enabling impressive reasoning, math, and coding capabilities. Yet for many researchers and practitioners, the principles behind classical RL remain a "black box". In this work, we deconstruct the RL post-training algorithm, investigating each step to clarify what is actually happening beneath the surface. By isolating the mechanics of RL with Verifiable Rewards in a controlled and simplified environment, we examine how RL outcomes are shaped by the base model's prior distribution, the granularity of the reward signal, the diversity of the prompt distribution, and model scale. We use the entropy of the policy's output distribution as a lens to compare the distributions learned through pretraining, SFT, and RL post-training, revealing how each stage shapes model certainty. Our investigation sheds light on how these choices interact to affect post-training success. For example, we show that the effect of so-called 'spurious rewards' depends on the prompt distribution used for post-training. We also provide insight into why the success of RL post-training depends on whether the base model already places sufficient probability mass on the desired behavior, linking it to the classical concept of exploration in RL. Ultimately, we provide this primer as a resource to those in the NLP community wishing to incorporate RL as a tool in their toolbox.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.24947v1 Announce Type: new Abstract: End-to-end training of multimodal neural networks often exhibits unstable neural dynamics characterized by three coupled failure modes that degrade learning: (i) modality imbalance, where one branch dominates gradient-based optimization; (ii) unstable gating, where noisy confidence cues induce erratic modality selection; and (iii) fusion interference, where modality-specific gradients conflict at the shared fusion layer. We propose CAT-GS (Calibrated, Adaptive, Thresholded Gating with Fusion Surgery), a neural dynamics-based optimization controller for intelligent computing applications. CAT-GS operates during backpropagation without modifying model architectures, fusion modules, or task losses. Through calibration of teacher-derived reliability via temperature scaling and EMA smoothing, CAT-GS stabilizes neural dynamics using a margin-thresholded policy to switch between warm-up dropout, weak-modality prioritization, and weak-biased blending, stabilizes gradient magnitudes under aggressive gating via capped gradient-budget renormalization, and applies fusion-only PCGrad to reduce destructive cross-modal interference at the primary shared bottleneck. We evaluate CAT-GS on audio--visual multimodal pattern recognition benchmarks (CREMA-D, AV-MNIST, and VGGSound), a tri-modal setting (UR-FUNNY), controlled synthetic data (CG-MNIST), and additional cross-domain benchmarks (AVE and CMU-MOSI). CAT-GS improves or matches fused multimodal accuracy against strong imbalance-aware baselines (including OGM-GE, G$^2$D, and UMT) across settings, and yields smoother gating behavior with fewer conflicting fusion gradients.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.24937v1 Announce Type: new Abstract: Multi-Modal Anomaly Detection (MMAD) detects rare abnormal events from heterogeneous data sources and is increasingly used in safety- and reliability-critical applications such as industrial inspection and cybersecurity. Yet the literature is fragmented across domains and modality combinations, and existing surveys usually group methods by architecture rather than by how abnormality is defined and separated in multi-modal settings. We survey MMAD from an assumption-driven perspective. We formalize the problem, identify five intrinsic characteristics underlying its core challenges, and organize prior work into two complementary paradigms. The first, normality-assumption methods, models regularity via representation learning, cross-modal alignment, and knowledge enhancement. The second, anomaly-assumption methods, sharpens decision boundaries through coarse-grained, structural, and semantic anomaly injection. We also investigate how foundation models are reshaping MMAD through scalable pretraining, flexible cross-modal transfer, and emerging reasoning capabilities. Finally, we compile representative benchmarks and evaluation protocols across domains and highlight open problems and future directions for robust, adaptive, and interpretable MMAD systems.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：World's first patient to undergo live AI-assisted brain surgery has tumour removed 4 hours ago Smitha MundasadHealth reporter BBC Rhys Hibbert's tumour could have led to blindness The world's first patient to have brain…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Much of the autonomous vehicle (AV) space is uncharted territory. However, at Waymo, with more than 200 million miles driven fully autonomously, we’re one of very few companies that can look to our past to illuminate ou…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Microsoft Corp. co-founder Bill Gates sounded the alarm recently regarding how artificial intelligence will cause profound disruption in the labor market, warning that millions of jobs are at risk and there’s no plan yet to help those who will lose out. Gates, now a philanthropist, said in an almost-6,000-word essay that the transition to the […] The post Bill Gates issues stark warning about AI and the future of humanity appeared first on SiliconANGLE.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：FAA and EASA order compressor blade replacements on V2500 engines - AvioRadar 1 World News Cessna Citation CJ4 reaches 500 deliveries as Gen3 nears certification 27. August 2026. 2 World News Lufthansa receives first Bo…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Since completing its acquisition of VMware in 2023, Broadcom Inc. has reshaped the company around VMware Cloud Foundation or VCF. Over the past three years, Broadcom has positioned VCF as a major on-premises alternative to public cloud, with private cloud becoming a central part of its strategy that will undoubtedly be one of the primary […] The post What to expect during VMware Explore: Join theCUBE Aug. 31-Sept. 2 appeared first on SiliconANGLE.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：- YouTube AboutPressCopyrightContact usCreatorsAdvertiseDevelopersTermsPrivacyPolicy & SafetyHow YouTube worksTest new features

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Can you trust an AI model to do what you intended? This is a central question both for those deploying AI systems and for those seeking to evaluate their capabilities. In deployment, a model that pursues a goal through…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：My lab develops low-cost and easy-to-use tools for identifying fake medicines, but we’re always on the lookout for other types of fakes that we can go after. For example, the cosmetics industry has a massive problem wit…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Health & Bioscience

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：How AI Watermark Mandates Could Unmask Journalists Who Never Touched AI | Techdirt How AI Watermark Mandates Could Unmask Journalists Who Never Touched AI (Mis)Uses of Technology from the everything-is-a-tradeoffs dept…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：AI for kids・Askie Safe AI for children Your child's AI helper and study buddy for bedtime stories, school help, and creative AI stories for kids. Safe AI chat designed for children ages 4-15 with parental controls. AI c…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Notifications You must be signed in to change notification settings Fork 0 Star 2 BranchesTags Open more actions menu Latest commit History 132 Commits 132 Commits Folders and files NameName Last commit message Last com…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Discover Connery: open-source plugin infrastructure for LLM apps. Secure integrations, personalization, and human-in-the-loop control for AI agents.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Notifications You must be signed in to change notification settings Fork 0 Star 1 BranchesTags Open more actions menu Latest commit History 14 Commits 14 Commits Folders and files NameName Last commit message Last commi…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：The EU AI Act: A Strategic Roadmap for CIOs and CTOs August 26, 2026 · 1,572 words Every CIO and CTO must read this, Not because the EU AI Act is another compliance checkbox to file away with GDPR, but because it is abo…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：The rise of generative and agentic AI has dramatically changed the software-building process - and the skills required.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：This post was not written with or by AI. I wanted to explore how AI could help deepen my faith. I enjoyed using Claude to research topics which were on my mind. It does a good job finding and quoting scripture but a poo…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Summary Debian opened a General Resolution to decide whether to allow AI-generated or AI-assisted code. Proposal A seeks to ban all LLM-generated and assisted code, citing copyright, quality, community, and ethics. Prop…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：China’s Moonshot AI is in early talks with Microsoft, Amazon and Google to host Kimi K3 Credit: Bangla press via Shutterstock.com Moonshot AI is in early discussions with Microsoft, Amazon, and Google about hosting Kimi…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Uh oh! There was an error while loading. Please reload this page. Notifications You must be signed in to change notification settings Fork 0 Star 1 BranchesTags Open more actions menu Latest commit History 8 Commits 8 C…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Bill Gates, chair of the Gates Foundation, speaks during a 2024 conference. | Bloomberg via Getty Images Bill Gates has been reflecting a lot on AI lately, and the process has triggered a stark awakening. Once a staunch AI optimist, the Microsoft cofounder is now deeply pessimistic about what AI means for our collective future. Having been conspicuously quiet on AI issues recently, Gates is back with a nearly 6,000-word essay seeking to reclaim a central role in shaping the technology globally. Titled "The turbulent AI era is here. The choices we make now are critical," Gates warns the world is not remotely ready for what is coming. Worse still, he notes, "we are not preparing for it." Gates attempts to chart a path forward, but the essay l … Read the full story at The Verge.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Uber and Wayve had said they would start trips ‘later this summer’ but launch now looks unlikely this year The rollout of robotaxis on the streets of London is unlikely to happen this year, as regulatory and technical hurdles push back the ambitious schedule set out by the UK government. Only weeks ago Uber and London-based Wayve were granted the first minicab licences in the capital to allow them to offer self-driving taxi rides to paying customers – but with a human safety driver in place, for now – and said they would start trips “later this summer” before a full public launch. 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：25 Aug 2026 · Essay Should we let AI govern us? What if AI were in charge of public policy and politics? Perhaps it would shift food subsidies, electrify transport, build cheap power, sign the plastics treaty, restore t…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：‘This is crazy. This is insane’: Bill Gates has changed his mind about AI and jobs Aug 26, 2026, 3:00am EDT Technology PostEmailWhatsapp The News Bill Gates says it’s time to hit the AI panic button. The technology has…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Uh oh! There was an error while loading. Please reload this page. Notifications You must be signed in to change notification settings Fork 0 Star 12 BranchesTags Open more actions menu Latest commit History 6,169 Commit…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：← Back to blog Can AI Music Tools Really Replace Epidemic Sound? An Honest Look MuseGen Team 7/30/2026 #Epidemic Sound alternative#AI music vs stock music#royalty-free AI music#AI music for creators If you make videos,…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.23887v1 Announce Type: new Abstract: Latent-conditioned adaptive policies can control robots across changing dynamics, but their learned latents remain internal representations of the policy rather than physical models that can be inspected, rolled out, or used by other control modules. This limits closed-loop analysis, diagnosis, and further improvement of a fixed policy. A direct mapping from latent to physical parameters is also under-specified, because multiple systems can induce similar closed-loop behavior. We therefore decode each operational latent into a distribution of quadrotor models using conditional flow matching. The decoded distribution enables two downstream uses without modifying the policy: online predictive tuning of a high-level controller around the fixed low-level policy, and robustness analysis under specified disturbances. Under perturbed actuator dynamics, decoded-model predictive tuning reduces position tracking RMSE by $23\%$ and heading RMSE by $45\%$ relative to fixed gains. Under Gaussian force disturbances, decoded-model ensembles closely predict the lateral tracking-error evolution. Together, these results show that control latents can be converted into physical model ensembles for tuning, robustness analysis, and diagnosis of frozen adaptive policies.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.23839v1 Announce Type: new Abstract: Embodied Agents System (EAS) are increasingly deployed in open-world physical domains, where reliability directly dictates deployment quality and human-agent trust. However, existing evaluations rely on outcome-centric metrics as success rate or safety scores that collapse diverse execution trajectories into coarse scores, obscuring the dynamic processes underlying agent behavior. Therefore, they ignore a critical property of EAS -- which we define as the Resilience -- that reflects how EASs recover, stabilize, and extend under perturbations and across iterative updates. The lack of resilience is particularly critical in open-world environments due to continuous unexpected disruptions, thus directly affecting the quality of EAS deployment. To address this problem, we gain insight from the resilience-engineering concepts to EAS groundings and propose a novel resilience evaluation framework that can be flexibly applied to any EAS. Specifically, we define the first comprehensive resilience metrics suite for EASs system that exposes Rebound, Stability, and Graceful Extensibility across embodied tasks execution, providing a practical grounding for EAS resilience analysis. We further implement the resilience evaluation layer that transforms execution process into assessments for diagnosis and optimization. Across 400 household tasks with 10 EAS, we reveal the process-level distinction hidden by outcome metrics, including recovery cost differences among successful episodes ($\Delta C_{rec}=25.2$), increased instability and task-family degradation. Metrics-guided optimizations reduce recovery cost and increase stability, graceful extensibility completion, showing the diagnostic effect of resilience evaluation. Our results reveal a trade-off among resilience characteristics, suggesting that a resilient EAS construction should be configured according to deployment-specific requirements.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.23831v1 Announce Type: new Abstract: While reinforcement learning (RL) allows generalist robot policies to continually improve during deployment, the large model size of modern generalist policies, such as VLAs, poses a fundamental obstacle to effective RL improvement. In particular, their severe inference latency---which can lead to pauses or jerky movements---can alter the effective environment dynamics and, if not correctly accounted for, break the Markov assumption that RL relies on, causing standard RL algorithms to fail completely. In this work, we introduce a latency-aware framework, Asynchronous RL with Intermediate Information (ARLI), that enables RL-based improvement of generalist policies under inference delays. Our framework builds on asynchronous inference approaches, which interleave action generation with execution to hide latency, and addresses its incompatibility with RL by providing a low-latency RL policy design that maximizes reactivity within the inference window through two contributions: state augmentations that restore near-Markovian structure by incorporating committed actions and a mid-inference observation. We evaluate our approach across simulated and real-world manipulation tasks, and find that it enables effective finetuning under inference delays where standard RL fails entirely, even matching or exceeding the performance of standard RL in idealized no-latency settings.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.23664v1 Announce Type: new Abstract: Reward fine-tuning is becoming an important tool for adapting diffusion models to human preferences and task-specific objectives, but existing methods largely inherit policy-gradient machinery from large language models. Unlike autoregressive models, diffusion models do not provide tractable likelihoods for generated samples. As a result, current approaches either construct trajectory likelihoods from stochastic denoising transitions or approximate endpoint likelihoods with evidence lower bound, introducing additional computation and algorithmic complexity. We demonstrate that this likelihood-based machinery is not necessary for effective diffusion reward fine-tuning. We propose reward-based velocity matching (RVM), a simple trajectory-free update that acts directly on the velocity field. RVM reinforces directions associated with high-reward generations, suppresses those with low reward, and involves an optional anchor term controlling drift from a reference velocity. Notably, it provides a general framework that recovers recent fine-tuning methods, including RAM and DiffusionNFT, as special cases. Across various large-scale diffusion models reward fine-tuning tasks, RVM is competitive with or outperforms trajectory-based policy-gradient methods under substantially reduced training cost. We further find that, once the velocity update is simplified, the particular loss variant matters less than reward and anchor design. For video generation, standard preference rewards can favor visually clean but nearly static outputs; introducing a new dynamic-tracking reward that substantially improve motions while improving overall VBench performance. These results suggest that scalable reward fine-tuning for diffusion models is better posed in the native velocity representation than as likelihood-based policy optimization.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Your FinOps lead needs to easily drill-down into Databricks spend and identify what’s driving costs, ...

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：New disclosures show utilities spent a record $16.7 million lobbying Sacramento through the first six quarters of the 2025–2026 legislative session as lawmakers consider a utility bailout in end of session scramble Sacr…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：These are two different products, but consumers end up confused because of misinformation and poor marketing copy.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：DocEndorse AI Agent & Slack Integration | Slack Marketplace You must enable javascript in order to use Slack. You can do this in your browser settings. Browse Apps Sign In To InstallLearn More DocEndorse AI Agent Sign I…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Notifications You must be signed in to change notification settings Fork 0 Star 0 BranchesTags Open more actions menu Latest commit History 2 Commits 2 Commits Folders and files NameName Last commit message Last commit…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：An insurer settles a claim months after the loss. An asset manager marks someone...

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：“Dear Tyler, I enjoyed reading your notes on visiting Anthropic to advise on Claude’s constitution. Framing AI governance around the common law, case law (“Talmud”), and independent adjudication is a much more adaptive…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：One of the stories of the last few weeks has been that AI companies have been scanning books in very large numbers in order to train their models with content guaranteed to have been written before 2002, and thus AI fre…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Will moving dots cure your car nausea? History says yes.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Scaling enterprise AI depends on more than deploying intelligent agents. For Banco BS2, a Brasil-based digital bank, it begins with establishing the infrastructure, governance and operational discipline needed to support AI before expanding its use across the organization. Operating in Brasil’s highly regulated banking environment makes that foundation especially important. The country’s financial institutions must […] The post Banco BS2 takes a foundation-first approach to scaling enterprise AI appeared first on SiliconANGLE.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Artificial intelligence startup Anthropic PBC announced today it’s changing how Claude, its flagship AI product, uses memory by allowing users to see everything it remembers “topic by topic,” and edit or delete any of it. Claude also does not store sensitive subjects by default. This includes topics mentioned by users, including health concerns, race, ethnicity, […] The post Anthropic updates Claude’s memory to enhance customization and protect sensitive topics appeared first on SiliconANGLE.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：On Tuesday, Anthropic launched a major update to how Claude remembers things. The new system combines Claude’s memory in Cowork The post Anthropic gives chat and Cowork one memory appeared first on The New Stack.