AI 日报

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Presented by EDB As enterprises give AI agents more autonomy — the ability to plan, decide, and act across systems without a human approving each step — a hard question moves to the center of every architecture review: When an agent tries to complete an action that it was never authorized to do, what actually stops it? These are your agents, running on your models, touching your data in your infrastructure — and the responsibility for what they do sits with you. That responsibility can’t be met in hindsight or with a set of abstract policies that live on paper but not in practice. Agents need rules in the context of the moment, because they don’t exercise overriding judgment of their own actions. Consider a simple rule: Never open the car door. Followed literally, an agent could never get in or out of the car at all. But if you change the context (the car has just crashed, there’s a fire, someone is hurt and needs to get out), then the rule you actually want is the opposite. Context in the moment is everything. We are asking agents to do intelligent things; that requires intelligent rules. The instinct is to add guardrails around the agent: instructions, policies, and monitoring layered above the model. Those mechanisms matter, but they share a structural limit: The car-door rule is plausible right up until the moment you actually have to decide whether to open the door. Controls at the agent layer are only as reliable as the agent’s output is predictable, and autonomy is precisely the property that makes that output hard to predict. Governance that depends on reviewing an action before it happens cannot keep pace with a system that acts in milliseconds, across many systems at once. Governance has to become executable, and enforced where agents actually do their work: at the operational data layer, in the context, and exactly at the moment it is happening. The data layer is the enforcement point Agents create value by touching data. They query it, retrieve it, transform it, and increasingly act on it. A policy that says an agent should not reach a certain class of data is meaningful only if the system can deny that access at the moment the agent requests it. Additionally, a principle that says AI must be auditable is meaningful only if the organization can reconstruct what the agent did, what data it touched, which user it acted for, and what resulted. When governance lives at the data layer, it holds regardless of how the agent was built or how it behaves, because the control is a property of the database itself, not a promise made by the agent. Agent behavior may be probabilistic. Governance cannot be The enterprise should not rely on a model choosing to follow policy. The policy has to be enforced by the system. That is the difference between hoping an actor stays in bounds and constructing bounds it cannot cross to begin with. The controls that make this real are ones many enterprises already run at the data layer: role- and attribute-based access, row- and column-level security, classification and masking, policy as code, and complete audit trails. What agents change is not the mechanism, but who the mechanism has to recognize. Identity management has to treat the agent as a principal in its own right, with its own identity and a purpose declared when the session opens. Once purpose is bound to identity, the policy engine can evaluate it the same way it evaluates role or department today, and the record of what happened can capture not just who acted and what they touched, but what they declared they were there to do. In practice, this resolves into nine controls, grouped under three imperatives: Enforce it Role- and attribute-based access control enforced at query time, for agents as well as users Dynamic column masking driven by the same policy path Agent identity as a first-class principal, with declared purpose bound at session start and the acting user preserved See it and prove it Classification and tagging that drives policy Session-level audit logging that records which agent acted, for which user, and under what declared purpose Lineage across pipelines, so a result can be traced back to the request that produced it Unify and harden Centralized, portable policy management Encryption at rest and in transit Consistent enforcement across on-prem, cloud, and sovereign or air-gapped environments “Declared purpose is what makes the difference. It becomes an attribute the access layer already understands, evaluated in the same policy path as role and row-level security. The enforcement mechanism does not change. What changes is that the agent's purpose is part of what it evaluates, and part of what the record proves afterward,” says Priyanka Jain, VP, product management, data & AI governance, EDB. Wherever you are in your AI adoption journey, enforcement at the data layer is what lets you move faster rather than slower. The controls are already in the database. The difference is that agents now have to pass through them. A digital leash, not a locked door The goal is not to stop agents from doing useful work. It is to define how far an agent can go, what it can touch, what it can change, what requires escalation, and how the organization can reconstruct events if something goes wrong. Governed this way, agents are identified, scoped, monitored, and auditable. The enterprise can adopt them faster, because security, risk, and leadership teams trust the operating model underneath. Open, sovereign, and enforceable at the source Built on open source Postgres, this open foundation keeps enterprises in control of where their data lives, who can reach it, and under what policy, without ceding governance to a layer they don’t own or can’t inspect. For regulated industries, that combination of data sovereignty and source-level enforcement isn’t a nice-to-have; it’s the precondition for putting agents into production at all. Agentic systems will keep getting more capable and more autonomous. That is a reason to be deliberate about where control lives, not a reason to slow down. The enterprises that enforce governance at the data layer can move aggressively on AI, because the thing protecting their data is more than just wishful thinking. EDB Postgres AI is an open, enterprise-grade sovereign data and AI platform that unifies transactional, analytical, and AI workloads — with governance enforced where the data lives. For the full framework, see EDB’s white paper Governing Agentic AI at Enterprise Speed. Max Romanenko is Chief Technology Officer at EDB. Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact [email protected].

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Hugging Bay | Find And Download Open AI Hugging Bay WebPage https://huggingbay.xyz/ https://huggingbay.xyz/.well-known/agent-discovery.json https://huggingbay.xyz/openapi.json https://huggingbay.xyz/api/mcp Open-source…

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 3 Star 4 BranchesTags Open more actions menu Latest commit History 205 Commits 205 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：For a decade, identity and access management meant one thing: governing the humans who log in. Employee joins, gets provisioned, gets a manager, gets a departure date, gets offboarded. That loop is well understood. What changed is that the fastest-growing population inside enterprise environments is no longer human, and the governance playbook written for people […]

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Senior Product Engineer ($220k-$300k + Equity) - NYC at Conveo | Y Combinator Conveo Confident decisions in days with AI-led interviews. Senior Product Engineer ($220k-$300k + Equity) - NYC $220K - $300K•New York Job ty…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：All-new TaskShell 2.0 as a first-class MCP platform Agent-first task management No more app-switching to keep up with your todos. You and your agents now completely in sync with your work, exactly where you work. Sync t…

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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：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：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：arXiv:2608.25142v1 Announce Type: new Abstract: Autonomous driving has made remarkable progress through imitation learning with massive human demonstration data. However, a trained planner often degrades severely when applied to a new environment zero-shot, because of domain shifts in traffic regulations, road layout and driving behaviors. Therefore, adapting a trajectory planner to a new city typically requires resource-demanding local data collection with a vehicle sensor suite. In this work, we show that driving behavior can be learned from a scalable and efficient alternative. We introduce \emph{SkyDrive}, a framework that utilizes drone-based traffic monitoring to provide efficient supervision for autonomous driving agents in a new environment. While vehicle-based data collection logs the ego and its surroundings, an aerial platform naturally observes many road users simultaneously over an extended field of view. As a result, every vehicle can be a data source with grounded driving behavior, effectively scaling up the amount of supervision. Based on 137 hours of aerial traffic monitoring footage, we extract 650K driving samples and construct a benchmark for trajectory planners and motion predictors. Zero-shot experiments with multiple models reveal significant cross-city domain gaps, but many of them can be alleviated by limited supervision from the sky, e.g., 30 minutes of monitoring per location. Our findings show that aerial traffic monitoring is an efficient and scalable data source for adapting autonomous driving systems in new cities. Data and code will be made publicly available.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.25176v1 Announce Type: new Abstract: Structural health monitoring (SHM) is essential in modern engineering, providing data for condition-based maintenance, lifecycle assessment, and predictive decision-making. Traditionally, SHM relied on visual inspection to detect defects such as cracks and deformations. Early computer vision (CV) methods, including thresholding, edge detection, and handcrafted features, aimed to automate this process but were highly sensitive to noise, imaging variations, and multiscale defects, limiting their reliability. Recent advances in machine learning, particularly convolutional neural networks (CNNs) and You Only Look Once (YOLO), have improved defect detection accuracy and enabled real-time analysis. However, adoption in SHM remains limited due to technical barriers such as data labeling, model training, and deployment, which typically require programming expertise. To address this gap, we introduce YOLOEZ, an open-source, GUI-based tool for end-to-end YOLO model application. YOLOEZ integrates data labeling, training, and inference into a single interface, enabling high-performance model development without code while supporting reproducible workflows. Evaluation against existing software and classical image processing demonstrates that YOLOEZ not only outperforms traditional methods across most detection metrics, but also lowers adoption barriers present in other modern CV tools. By combining accuracy with accessibility, YOLOEZ facilitates wider use of AI-driven monitoring for predictive maintenance, digital twins, and intelligent structural systems.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.24934v1 Announce Type: new Abstract: Accurate field plant disease diagnosis requires reliable fusion of uncertain and conflicting perceptual evidence. We present the Hybrid Hierarchical Multi-Agent Framework (H$^{2}$MAF), combining decision-level fusion of EfficientNet-B3 and ConvNeXt-Tiny with semantic arbitration by open-weight multimodal large language models (MLLMs), Gemma 4 E4B and Qwen3.5 4B, using structured JSON evidence to generate explainable diagnoses, risk levels, treatment urgency, and financial exposure. (H$^{2}$MAF) is evaluated on 14,364 images (1,370 test images) across PlantDoc (2,922 images, 27 classes) and two non-public, continuously captured Cornell robot-acquired field datasets: Stage 2 (20 GB; 4,215 images) and Stage 4 (40 GB; 7,227 images), covering Early Blight, Late Blight, and Septoria Leaf Spot under uncontrolled field conditions. On PlantDoc, Gemma improves accuracy from 63.9% to 68.5%, achieving +7.6 points on the 41.7% CNN-conflict subset. Cornell accuracies reach 99.3% and 98.9%, with only 1.7-4.1% disagreement, demonstrating conflict-dependent MLLM utility. The critical-risk error of gemma is 0.14-0.5 points, whereas Qwen overflags by 3.5-14.4 points. These results establish MLLM arbitration as a promising, yet calibration-dependent, approach for explainable agricultural AI and robotic field decision support. Github Link: https://github.com/Applied-AI-Research-Lab/Explainable-AI-Plant-Disease-Detection

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Earlier this year, Meta created a “plan” to reduce some of its teams by as much as 60 percent to make the company “AI native,” Reuters reported today, citing two people familiar with Meta’s internal affairs. Reuters’ re…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：--> [Submitted on 25 Aug 2026] Title:Reading Is Not Using: Retrieval, Judgment, and the Design of AI Financial Research Workflows View a PDF of the paper titled Reading Is Not Using: Retrieval, Judgment, and the Design…

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：Z.ai Co. today released the code for GLM-5.3-Flash, a large language model that is ten times more cost-efficient than its predecessor. The algorithm made its original debut last week under the codename Ox Alpha. LLM marketplace operator OpenRouter Inc. launched a free hosted version of Ox Alpha and didn’t disclose its developer, which drew a […] The post Z.ai open-sources ‘Ox Alpha’ model as GLM-5.3-Flash 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：WhisperBar — Speak messy thoughts into polished text. Speak messy thoughts into polished text. A Mac menu bar app for writing and skimming. Dictate from any app and get business-casual text ready to paste, or hear a sho…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：It’s a glorious day in Kirkland, Washington, an affluent Seattle suburb on the eastern shore of Lake Washington. The temperature is in the mid-80s, and the sky is incapable of being any more blue. The view from the Gate…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Mark Zuckerberg entered 2026 with an ambitious plan to remake Meta around artificial intelligence, potentially eliminating or reassigning thousands of jobs as AI agents took over work once performed by employees. Within…

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：Anthropic is giving Claude its own browser. As the company announced on Wednesday, Claude on the desktop (Mac, Windows, and The post Anthropic’s Claude now has a browser of its own appeared first on The New Stack.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Sandboxes are a critical part of AI agent evaluation. They are isolation environments that limit model’s access to external systems and data, allowing evaluators to observe their behaviour and capabilities while avoidin…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：OpenAI released a report breaking down how people use ChatGPT and who they are. | Image: The Verge In July, an unreleased OpenAI model broke out of a restricted environment, figured out how to get access to the internet, allowed AI agents to talk to each other using a secret "message board," and hacked into the internal systems of a different AI lab, Hugging Face. It took nearly two weeks for OpenAI to find out about any of it. Over a month later, two new reports offer nearly 130 pages of details on the incident and OpenAI's response, many of them previously unreleased. One was written by OpenAI itself, the other by two third-party AI research nonprofits, METR and Redwood Research, which OpenAI allowed to jointly investigate the inciden … 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：As an engineer who is not a designer and hates slop. Every landing page, app and tui look the same. They're slop and most of them are incomprehensible. Here's how I de-slop my product design. 1. Always consider the whol…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Managing library updates can be tedious at times. Learn how the GitHub Copilot app can handle this type of repetitive task. The post GitHub Copilot app for Beginners: Automate Dependabot pull request triage appeared first on The GitHub Blog.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Managed Deep Agents and LLM Gateway hit public beta, plus Deep Agents v0.7, Tuned Evaluators, Bring Your Own Cloud on AWS, and LangSmith Engine upgrades.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：In the age of AI how do agentic engineers estimate their ability to deliver on engineering tasks? I don't think it is actually possible, anymore, but engineers are shipping more code! What a time to be alive. In the old…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Amazon Bedrock AgentCore Evaluations decouples agent evaluation from the framework you build on. As long as your agent emits OpenTelemetry telemetry, the service can score it, whether you use LangGraph, LlamaIndex, the OpenAI Agents SDK, Google ADK, the Claude Agent SDK, or Strands Agents. This post explains how the framework-agnostic contract works.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Firm says ‘early signals … could have triggered an earlier response’ as it releases report into Hugging Face hack OpenAI staff observed signs of rogue behaviour among its leading-edge AI agents weeks before they escaped their training environment to launch an unprecedented hacking crusade that spread global alarm. The San Francisco AI company conceded on Wednesday that “early signals … could have triggered an earlier response”, as it released a report into the days-long July hack of a major software repository, Hugging Face, considered the first autonomous agent cyber-attack. 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：You’ve shipped a working chat feature. Now comes the hard part: figuring out which framework actually fits your production architecture. The tooling landscape has fractured, and comparing AI agent frameworks in a vacuum…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Open-weight model runner Ollama has reintroduced an integration with Claude Desktop that lets users connect Anthropic’s app to models served The post Claude Desktop can now easily run Qwen, DeepSeek and Kimi models — after Ollama’s first effort stalled appeared first on The New Stack.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Dive into LangSmith product usage patterns that show how the AI ecosystem and the way people are building LLM apps is evolving.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Reflections on how LangChain has evolved — including our products, ecosystem, and community — over the past two years, and where we're headed next.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：See how Podium tests across the lifecycle development of their AI employee agent, using LangSmith for dataset curation and finetuning. They improved agent F1 response quality to 98% and reduced the need for engineering intervention by 90%.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Our new infrastructure for running agents at scale, LangGraph Cloud, is available in beta. We also have a new stable release of LangGraph.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：In this post, you will learn how GoDaddy migrated from their legacy business intelligence (BI) tool to Amazon Quick. This was a two-year transformation that delivered results across every dimension of the business: 15,000 hours saved annually, 50% reduction in dashboard count, rendering times cut to under 5 seconds, and AI-powered self-service analytics now accessible to every employee.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Learn how Natera built an automated voice agent on Amazon Bedrock AgentCore that lets patients book mobile phlebotomy appointments through natural conversation. The post covers the dual-WebSocket bridge, event-driven latency masking, and progressive-trust authentication behind 100% tool-calling accuracy and sub-7-second latency.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：The advanced side of supervised fine-tuning data prep. This second post in a two-part series covers evaluating data readiness with learning curves, selecting high-value data subsets, augmenting data with synthetic and distilled examples, and mixing data sources to prevent catastrophic forgetting.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Data preparation determines the ceiling of any supervised fine-tuning project. This first post in a two-part series covers the foundations of SFT data prep: quality checks, conversational (JSONL) formatting, reasoning and tool-calling schemas, and a representative train/evaluation split.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Lovable is branching out from AI-powered web app creation and into MCP-powered ‘capabilities’. We talk to CTO Fabian Hedin.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：The gist Auto Browse, the Gemini 3 agentic mode Google started rolling out in Chrome on 28 January 2026, is rationed: 20 multi-step requests a day on Google AI Pro, 200 a day on AI Ultra. That ration is the most informa…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：The following article originally appeared on PulseMCP’s blog and is being republished here with the authors’ permission. Most MCP demos feature a single server connecting to a single client. For example, you might wire up a Gmail MCP server to Claude Code. It works! It triages your inbox, drafts replies, finds that thing from three […]

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Learn proven strategies to speed up your AI agent: reduce latency, optimize LLM calls, enable parallelism, and improve UX. Expert tips from LangChain.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：LangGraph Platform, our infrastructure for deploying and managing agents at scale, is now generally available. Learn how to deploy

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：The suit alleges that Meta designed its platforms to be addicting to young people.

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：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：- 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：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：Looking to leave Windows? I've used Linux for years and there's never better a better time to make the leap.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：As the US president struggles to end his Iran war, and approval ratings hit new lows, he leans on a familiar gambit Donald Trump knows the power of an image – of himself, with everyone from Vladimir Putin and Kim Jong-un to Kim Kardashian. How about George Washington? As the US president struggles to end his war with Iran, and with his approval ratings hitting the lowest levels of his second term, he has in recent weeks posted a series of AI-generated images of himself alongside key historical American figures. 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：People are becoming convinced AI chatbots have helped them make scientific breakthroughs, cure diseases or invent new technologies. What does this reveal about a technology used by more than a billion people? A new series from The Guardian Investigates, coming soon 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：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：Aug 27, 2026 Your AI Generated Menu Triggered my Trypophobia Quite a few restaurants are switching to using AI-generated food photos for their menus. The idea to use completely synthetic photos is pretty problematic in…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Hello HN! There is an abundance of choice when it comes to text editors, but they all seem to be either too simplistic and minimal, or overly complex, distracting and difficult to use. Kraa is trying to strike the right…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Howcomet — the app that asks your kid a question back Howcomet Grown-ups → How a question becomes a star 💬 Ask the real question Volcanoes, black holes, why your ears pop. Say it or type it. The answer is written for y…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：AI companies have promised us world-shifting technology. But there’s a cost: loud, unsightly datacentres that drain environmental resources. As the government pushes their rapid growth, can local communities do anything to push back? Madeleine Finlay speaks to the Guardian’s global technology reporter Aisha Down about the backlash to datacentres across the UK and around the world For tickets to the Science Weekly live event at the London podcast festival on the future of AI, visit the event page at kingsplace.co.uk Support the Guardian: theguardian.com/sciencepod 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：OpenAI is expanding its presence in Brazil, deepening engagement with developers, businesses, and communities to support AI adoption across the country.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Revisited: Guardian journalist Michael Safi looks into the world of artificial intelligence, exploring the dangers and promises it holds for society Today in Focus is on a summer break and will be back with new episodes from 1 September. In the meantime, we are bringing you season one of Black Box, before the launch of season two in early September. This episode was first broadcast on 14 March 2024. Two stories about the way artificial intelligence could make the world better – and is already doing so. In Montana, when Lee Johnson discovered his wife, Yokie, had cancer, he turned to AI – and was surprised by the answers he got. Meanwhile, in Massachusetts, Prof Regina Barzilay’s experience with cancer has led her to build an AI system that can detect the disease years before a human can. 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：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：Investigación, análisis y redacción jurídica con IA Ahorrá horas de trabajo con la IA legal más precisa y confiable del mercado argentino. No inventa fallos ni normativa: cada cita con su fuente oficial. Registro simple…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Surgeons aided by real-time analysis of camera footage that identified critical anatomy in brain to be avoided Neurosurgeons in London have performed the world’s first successful AI-assisted operation to remove a brain tumour, saving the sight of a 48-year-old man, health officials have said. The surgery was performed in May at the National Hospital for Neurology and Neurosurgery (NHNN), part of University College London hospitals NHS foundation trust, but details were kept secret until Thursday while the patient, Rhys Hibbert, recovered. 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：AI Org Maturity Assessment | Hop Labs

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：English Français Deutsch Italiano 中文 More Español Ελληνικά 日本語 Português Türkçe Using 30 months of panel data on 26,811 Chinese students in grades 7--12, we study how generative AI affects homework productivity and lear…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：If you're into installing Android apps outside the Google Play Store, you should be aware that the process has changed and now includes a waiting period.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：John Q. Pulp Aug 26, 2026 This is the first in an irregular series in which I plan to round up examples from the current state of AI-generated media. The reviews will take the highest form of critique: extreme nitpickin…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：The Independent AI Coding Community AI Tools Search & browse all AI tools AI Jobs International roles · opportunities Creative Studio Image · Video · Training AI Models Curated models, explained AI Skills Handy prompts,…

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 4 Commits 4 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：GLM 5.3 Flash | Model APIs | RunInfra RunInfraby RightNow © 2026 RunInfra. All rights reserved. Join the communitySystem status Backed by Combinator AICPA Type II SOC 2 Ask AI about RunInfra Part of RightNow RunInfraby…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：China & AI | WireScreen Briefings CareersProduct WWIRESCREEN · SPECIAL REPORT · CHINA AND ARTIFICIAL INTELLIGENCE WS-2026-034 · AUGUST 2026 Built and Owned The people, the money and the ownership behind China’s leading…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：AI factories must support increasingly large models and more complex reasoning workloads. To keep up with the insatiable compute demands of AI workloads, hyperscalers and AI-native companies are developing custom AI acc…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Google Research and UNSW Sydney released GlucoFM, a self-supervised foundation model that splits a CGM trace into a slow physiological stream and a transient event stream instead of encoding it as one sequence. At 0.72M parameters it reached 58.8 task-averaged PR-AUC across 14 cohort–task evaluations, beating a 135M GluFormer and a 385M MOMENT. It remains a research prototype with no regulatory clearance. The post Google Research Introduces GlucoFM: A 0.72M-Parameter Dual-Stream Foundation Model for Continuous Glucose Monitoring appeared first on MarkTechPost.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.25192v1 Announce Type: new Abstract: We introduce CRESSim-Neo, a batched GPU simulation engine for surgical robotics and robot learning. CRESSim-Neo combines position-based simulation of rigid bodies, deformable tissues, fluids, and strands with batched rendering, surgery-specific sensing, and a GPU-resident data pipeline. The engine supports applications including tissue manipulation, fluid suction, suturing, cable-driven robots, and ultrasound image synthesis. Direct access to physics and rendering buffers enables GPU-resident robot learning and zero-copy PyTorch integration using DLPack. We demonstrate CRESSim-Neo across rigid-body, deformable-body, and fluid simulation tasks, including vision-based and surgical robot-learning scenarios. On an NVIDIA RTX 4090, the engine achieves up to 2.03 million environment steps per second for 8192 parallel CartPole environments, and scales to batched surgical scenarios involving tissue deformation, fluid interaction, and ultrasound sensing. Overall, CRESSim-Neo provides a unified and scalable platform for surgical simulation, synthetic data generation, and surgical robot learning.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.24935v1 Announce Type: new Abstract: Accurate identification of early-stage apple fruitlet anatomical structures, including the calyx, fruitlet body, and peduncle, is essential for robotic thinning, crop-load management, and other precision orchard operations. This study presents a lightweight multimodal vision-language framework that adapts TinyCLIP for fine-grained fruitlet anatomy classification in complex orchard environments. A dataset of 600 high-resolution RGB images collected from Scilate and Scifresh apple orchards was converted into 224 x 224 image patches and annotated for three anatomical classes. Domain-specific language prompts, such as ``a photo of a class,'' were used to guide multimodal alignment between orchard imagery and horticultural structures. A sliding-window inference strategy with a stride of 112 pixels aggregates patch-level predictions into spatial heatmaps, enabling interpretable whole-image localization of fruitlet components relevant to robotic thinning. Patch-level evaluation on an NVIDIA T4 GPU achieved F1-scores of 0.95 for calyx, 0.98 for fruitlet, and 0.85 for peduncle, with a macro-F1 score of 0.93. Deployment-oriented optimization using ONNX and TensorRT enabled efficient inference on NVIDIA Jetson hardware, preserved accuracy under INT8 quantization, and supported model sizes of approximately 127-137 MB with millisecond-level patch inference. These results demonstrate that lightweight vision-language models can provide interpretable and edge-deployable perception for automated fruitlet analysis and future robotic thinning systems. The source code and implementation details are publicly available at https://github.com/WilliamBu1/A-Lightweight-Vision-Language-Model-for-Early-Stage-Fruitlet-Classification-in-Apple-Orchards.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.25061v1 Announce Type: new Abstract: GPUs increasingly accelerate database systems, but query-specific peak performance still often relies on hand-written kernels. Existing LLM kernel benchmarks focus on machine learning operators, leaving irregular, heterogeneous, data-movement-heavy database-style operators untested. We introduce DataKernelBench, which translates SQL into validated PyTorch TorchPlan programs and evaluates LLMs that optimize either the core tensor-bounded snippet or the full query in CUDA or Triton through execution-guided repair. Across ten proprietary and open-weight models on TPC-H SF10 with an H100 GPU, the strongest full-query CUDA configuration achieves $2.11\times$ speedup over torch.compile at full pass rate. We find that higher-performing implementations commonly use kernel fusion and execution-strategy changes, stronger models benefit most from full-query specialization, and workload context matters more than hardware context. To handle data larger than GPU memory, we extend TorchPlan with Dask-cuDF for on-demand partition loading on TPC-H SF100 with four H100 GPUs, achieving $2.54\times$ speedup

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.24946v1 Announce Type: new Abstract: Macros constitute a large part of the core area in modern very large-scale integration (VLSI) designs. Moreover, macro positions have a significant impact on the final quality of result (QoR), and macro legalization is typically the final step in determining the macro positions. However, existing approaches related to macro legalization either lack robustness or incur substantial computational costs or neglect the regularity between macros. To address these limitations, we introduce MacroAgent. The novel framework is a four-stage approach: clustering, contour generation, template matching, and inter-cluster refinement. We propose leveraging Large Language Models (LLMs) to discover multiple, effective heuristic regularity-aware contour algorithms. This framework successfully generates robust and effective algorithmic solutions for macro legalization. Compared with state-of-the-art macro legalization works, experimental results on TILOS and Chipyard benchmarks demonstrate a 2 to 8 fold improvement in layout regularity, a 3% to 5% reduction in routed wirelength with comparable congestion after global routing, and significantly better robustness with an acceptable runtime. Furthermore, end-to-end evaluation through Cadence Innovus place-and-route confirms that the regularity improvements translate into tangible PPA gains, including 2.9% lower routed wirelength and 68.3% TNS improvement over the DREAMPlace macro legalization baseline; it also achieves 1.8% lower routed wirelength when integrated into the Innovus macro placement flow.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.24945v1 Announce Type: new Abstract: Recent years have witnessed remarkable achievements of Large Language Models (LLMs) in multiple domains, while the excessive resource requirements of LLMs hinder the deployment on resource-constrained devices. Although model quantization stands out as an effective approach, conventional quantization approaches typically incur severe performance degradation due to uniform bit-width or simple heuristic sensitivity evaluation. In this paper, we propose a novel Fisher information-based Adaptive Mixed Precision Weight Quantization approach, i.e., FAMPWQ, which performs layer-adaptive weight quantization for effective LLM inference on commodity GPUs. First, we propose a system model with a novel Fisher information metric to measure the layer-wise sensitivity to quantization. Second, we propose a reinforcement learning-based bit-width allocator in FAMPWQ, which generates an adaptive bit-width allocation strategy based on the Fisher information sensitivity metric. Extensive experiments on 7 models and 5 benchmarks demonstrate that FAMPWQ significantly outperforms 7 baseline approaches in terms of PPL (up to 3.39 smaller), accuracy (up to 6.87% higher), and LLM-as-a-judge comparison (up to 76% win rate).

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.24938v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models scale capacity for strong quality while keeping per-token compute bounded through sparse expert activation. Yet low-latency MoE serving is increasingly challenging, because it spans two inference phases with fundamentally different bottlenecks: prefill is dominated by token-wise expert computation, whereas decode is constrained by memory traffic from the batch-wise activated expert set. However, existing training-free acceleration methods optimize only a single resource proxy, either the experts each token executes or the experts a batch activates, and either discard the excluded experts' contribution or leave it only implicitly approximated. In this paper, we propose ExFold, a unified training-free expert-folding framework for jointly accelerating MoE prefill and decode. ExFold casts both prefill and decode as one budgeted output-approximation problem: execute only a phase-specific constrained expert set while projecting the contribution of budget-excluded experts onto retained experts using calibrated scalar projectors. Motivated by the observation that many expert outputs are directionally aligned but differ in magnitude, ExFold calibrates a pairwise scalar-projector matrix on unlabeled data and uses it at inference time to fold excluded expert contributions into retained experts. Under this view, prefill acceleration becomes token-level Top-K folding, and decode acceleration becomes batch-level expert-pool folding. The two phases differ only in how retained experts are selected, while excluded contributions are recovered by one shared folding mechanism. We implement ExFold as a plug-and-play plugin in vLLM, with a lightweight expert-folding CUDA kernel, delivering up to 1.41x TTFT and 2.45x TPOT speedups while retaining about 99% of the original average quality.

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 wins!

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：The conference with hot chips and even hotter companies

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：0:00 / 0:00 Introducing S1: In-Context Learning for Robotics Unseen tasks10-minute horizonsOne video promptNo post-training 13-minute read Introduction The evolution of language modeling provides a blueprint for turning…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：<p><strong><a href="https://qwen.ai/blog?id=qwen3.8-flash-next">Qwen3.8-Flash-Next</a></strong></p> Another open weights model from Qwen. This one is "a multimodal MoE model that also serves as an early preview of the architecture used in Qwen4".</p> <p>It's pretty big: 125B tokens, but only 6B active which means it gets a pretty big performance boost.</p> <p>I've been trying it out on a DGX Spark using <a href="https://huggingface.co/unsloth/Qwen3.8-Flash-Next-GGUF">these Unsloth quantized models</a>. I'm still exploring the model - so far I've tried the 72.5GB UD-IQ1_S one (producing <a href="https://tools.simonwillison.net/markdown-svg-renderer#url=https%3A%2F%2Fgist.github.com%2Fsimonw%2Ff9c69ebdab90d8a45b8de4742cc7b840">these pelicans</a>) and the 78.9GB UD-Q2_K_XL (producing <a href="https://tools.simonwillison.net/markdown-svg-renderer#url=https%3A%2F%2Fgist.github.com%2Fsimonw%2F6ba7cbfc1a9336986703b41f7fccd73a">these</a>).</p> <p>My favorite so far was this xhigh reasoning effort one from UD-Q2_K_XL:</p> <p><img alt="Flat vector illustration: a white pelican with an orange beak and orange legs rides a red bicycle along a sandy path, a wicker basket on the handlebars holding a blue fish, with green rolling hills, a small tree and bushes, white clouds and a bright yellow sun in a blue sky behind it" src="https://static.simonwillison.net/static/2026-08-27/IMG_7667.png" /> <p><small></small>Via <a href="https://news.ycombinator.com/item?id=49448210">Hacker News</a></small></p> <p>Tags: <a href="https://simonwillison.net/tags/ai">ai</a>, <a href="https://simonwillison.net/tags/generative-ai">generative-ai</a>, <a href="https://simonwillison.net/tags/llms">llms</a>, <a href="https://simonwillison.net/tags/qwen">qwen</a>, <a href="https://simonwillison.net/tags/pelican-riding-a-bicycle">pelican-riding-a-bicycle</a>, <a href="https://simonwillison.net/tags/ai-in-china">ai-in-china</a>, <a href="https://simonwillison.net/tags/nvidia-spark">nvidia-spark</a></p>

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：AI Assistant Instinct Hits $2.5 Billion Valuation In Weeks Amid VC Feeding Frenzy Editors' Pick VCs Are So Obsessed With This AI Assistant That Its Valuation Jumped Fivefold In Weeks A hot new AI agent called Instinct t…

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 Deep Cogito Inc. today announced that it has raised $43 million in funding. TQ Ventures led the Series A round. It was joined by Benchmark, Nexus Venture Partners, Atreides Management, South Park Commons and Zscaler Inc., a publicly traded cybersecurity provider. The deal brings Deep Cogito’s total outside funding to more than […] The post Deep Cogito raises $43M to develop self-improving AI models 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：Nvidia's predicting it will pull in $108 billion in revenue within just a few months. It wouldn't be the first company to rake in over $100 billion in quarterly revenue - Amazon, Apple, and Alphabet have repeatedly reached the milestone. Nvidia said in its latest earnings report that it brought in a record $96.2 billion in overall revenue in the past quarter, a jump of over $10 billion from the previous quarter. Its data center revenue alone more than doubled year-over-year to a record $89 billion, and the company's profits more than doubled to $59.7 billion. Nvidia's "edge computing" category, which includes its consumer gam … 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：Z.ai has released GLM-5.3-Flash, the first natively multimodal model in the GLM-5 series — a 320B-total / 18B-active MoE with a 1,048,576-token context window, MIT-licensed weights on Hugging Face, and API pricing at $0.15/M input and $0.50/M output. It scores 84.3 on Terminal-Bench 2.1 and 63.4 on DeepSWE v1.1, using hybrid KDA linear plus NoPE sparse MLA attention to cut attention compute ~3× and KV cache 4.4× versus GLM-5.3. The post Z.ai Releases GLM-5.3-Flash: A 320B-A18B Natively Multimodal MoE With a 1M-Token Context appeared first on MarkTechPost.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Meta's new MTIA 400 chip has a split personality: Training AI and serving ads Faster than Blackwell, but still no replacement for AMD or Nvidia ... yet Tobias Mann Tobias Mann SYSTEMS EDITOR Published wed 26 Aug 2026 //…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：The next wave of AI is placing new demands on infrastructure. As AI agents and trillion-parameter workloads become mainstream, the performance of AI infrastructure depends not only on compute, but on how compute, memory, storage, networking and software are designed together as a unified system. To help hyperscalers and AI innovators build the next generation […]

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：https://p.dw.com/p/5JOAz AI is bringing significant challenges for the gaming industry, but it could also help significantly reduce costsImage: Political-Moments/IMAGO Earlier this month, gaming giant Electronic Arts wa…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Is a foldable iPhone finally in our future? We'll be on the ground on Sept. 9 to find out.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：TL;DR: AgentPad13 is an open-source take on the $230 Codex Micro and a test of whether an autonomous scientist can teach itself PCB routing. We wanted a more wallet-friendly, open-source Codex Micro, so we asked Marvin…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Despite rapid advances in artificial intelligence, the enterprise world is still dealing with a data pipeline problem. More than 80% of enterprise data is unstructured, and 99% of this data is dark to AI because there is no easy solution to query it, according to industry experts. Yet organizations are still trying to build AI […] The post AMD, Supermicro and MinIO target the enterprise data pipeline bottleneck 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：The SageMaker Python SDK v3 redesigns script mode with unified ModelTrainer and ModelBuilder classes. This post walks through two end-to-end examples, a scikit-learn Random Forest and a multi-GPU Stable Diffusion 3.5 LoRA fine-tune, showing how SourceCode syncs your local code into any container at runtime so you can iterate without rebuilding Docker images.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Ox-alpha, the stealth model that quickly became the most popular model on OpenRouter in the last few days, is actually The post Z.ai’s GLM-5.3 Flash is cheap, good, and served on Chinese chips appeared first on The New Stack.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：The Importance of Reading (and Teaching) Cyberpunk in the Age of AI - Reactor 0 Share Featured Essays Cyberpunk The Importance of Reading (and Teaching) Cyberpunk in the Age of AI Looking for answers — and finding hope…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Build, deploy, and monitor production-grade AI agents at scale with LangChain's enterprise agentic AI platform integrated with NVIDIA.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：From humpbacks learning the latest songs to sperm whales’ ‘clan codas’, drones and AI learning are helping reveal another dimension to the concept of culture In 2015, the whale researcher Valeria Vergara pitched a small tent at the icy water’s edge on Somerset Island in the remote Canadian high Arctic, shrouded in a freezing fog so thick she struggled to see her own hands. She trailed a cord out to the coast and plopped a hydrophone into the water to record the calls of belugas. As many as 1,000 of the whales, accompanied by their newborn calves, frequented the surrounding bay. Belugas’ ceaseless chatter, made up of dozens of unique sounds, is crucial to keeping these creatures connected in the dim, turbid waters of the Arctic. 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：The right case can take your iPad Air to the next level. These are our favorite iPad Air cases from brands like Apple, Burga, and Logitech.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Two German airport workers die of malaria after 'mosquito arrives on plane' - BBC News Image source, Getty Images Image caption, Frankfurt is Germany's busiest airport ByAndré Rhoden-Paul and Joe Coughlan Published 26 A…

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:2608.25222v1 Announce Type: new Abstract: The impairment of the hands can seriously affect the abilities of every individual to perform the every-day activity, so the design of stable and controllable support devices is a significant field of study. This paper is about the design and implementation of an automated bionic hand which is dedicated to the coordinated finger movement through the simplified and efficient actuation mechanism. The method that the proposed system was designed on is the tendon-based method whereby the servo motors generate the movement of the fingers, with assistance of the angular control which is calibrated. An actuation is controlled by a microcontroller that will be programmed by use of an Arduino-based microcontroller to carry out programmed gestures that include open hand, fist, pinch and half flexion. It has an interface that is voice command enabled to make it easy to interact with a Bluetooth based sender receiver architecture which offers an option of executing trained commands which are immediately converted to finger actions. To explore the motions behavior, finger coordination and control response to the input, the behavior of the experiment system is tested. The actuation of the fingers was found to take a total of about 7-8 seconds to achieve full flexion of all fingers in a sequence. The system showed repetitive and constant motion throughout several actuation cycles without loss of any apparent tension or precision of control. There was a stable grasp of objects of different shapes and sizes, which implied consistent coordination between the fingers. These findings indicate that the proposed system offers predictable and steady control behavior and has a simple and efficient mechanical and control architecture.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.25171v1 Announce Type: new Abstract: Soft pneumatic actuators offer inherent compliance and safe interaction but remain difficult to model and control because of their highly nonlinear, distributed dynamics. We present a control-oriented data-driven modeling and control framework that decomposes actuator behavior into a nonlinear static equilibrium model and a linear residual dynamics model identified using Extended Dynamic Mode Decomposition with control (EDMDc). This representation enables feedforward compensation, task-space feedback control, and local closed-loop stability analysis through an augmented linear model. Experiments achieve approximately 1 mm root mean square error (RMSE) during low-speed (approximately 10 mm/s) trajectory tracking and below 10 mm RMSE at higher speeds (approximately 100 mm/s). The framework further achieves stable tracking of highly dynamic user-generated references with peak accelerations exceeding 25 m/s^2 while simultaneously performing real-time obstacle avoidance. Finally, the proposed stability analysis is experimentally validated by accurately predicting stable, marginal, and unstable operating regimes. These results demonstrate that structured, control-oriented learning provides an accurate and practical framework for soft actuator control.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.25162v1 Announce Type: new Abstract: Robotic object packing has been a core challenge for robotic deployment in logistics, industry, etc., due to the curse of dimensionality in combinatorial search and the difficulty of dealing with dynamic and contact constraints for irregularly shaped objects. Current heuristic and learning-based methods assume a limited spatial discretization resolution of space, and computation becomes extremely inefficient as discretization accuracy increases. In this work, we eliminate these assumptions by introducing SOPO-CD, a sequential optimization framework that frames object placement as a differentiable nonlinear optimization problem in a decomposed free space. We prove that placing a convex object inside a convex hull is essentially constraining the vertices of the object inside the convex hull. The constraints and their derivatives can be written in closed form and calculated within $200$ns. We implement a custom solver that achieves optimal placement within tightly constrained space in milliseconds; a $100 \times$ speedup compared to a classical grid search method. We generalize our framework to 2D Tangram, 2D Tetris, and 3D Bin Packing, and have demonstrated strong computational performance and packing utility. We also demonstrate solving a real-world Tangram puzzle online using an Allegro Hand and an Xarm.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.25135v1 Announce Type: new Abstract: This paper presents a novel method that extends targetless LiDAR-IMU calibration for ground vehicles to non- flat environments. Calibration typically necessitates full exci- tation of the sensor rig, a requirement that is not fulfilled by ground vehicles in normal operation. To address the degenerate planar motion, state-of-the-art methods propose residuals that assume the colinearity of the gravity and physical surface normal vectors, restricting usage to cases where the ground is assumed flat. This paper proposes ground-plane residuals that do not require this assumption, and are applicable for planar motion on a tilted surface. Results are demonstrated on a dataset collected from a Husky ground vehicle, on the M2DGR dataset, as well as on an offroad vehicle dataset. Repeatability is shown to be improved both in tilted and flat-ground scenarios, with strong improvement demonstrated for the tilted case. The implementation and experiments are open-sourced at https://github.com/vkorotkine/licalib_tilted_ground.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.25102v1 Announce Type: new Abstract: The Robot Operating System 2 (ROS2) has become a widely adopted framework for the development of distributed robotic systems. However, its communication architecture, based on DDS and RTPS, relies on multicast discovery mechanisms that are typically unavailable in wide-area network (WAN) environments, making remote operation challenging. This work presents ROS2 Connect, a WebSocket-based communication framework that enables transparent and secure ROS2 interaction across routed networks without requiring modifications to network infrastructure or DDS configurations. The proposed client-server architecture supports bidirectional exchange of topics, services, actions, and system data while integrating authentication and access control mechanisms. Experimental evaluation over a real WAN connection demonstrates significantly lower latency, higher stability, and improved scalability compared to existing solutions, including DDS Router, rosbridge and Zenoh. Initial results show that ROS2 Connect provides a reliable foundation for teleoperation and distributed robotics applications over wide-area networks.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.25157v1 Announce Type: new Abstract: Automatic AV-sync metrics are widely used to rank and train audio-visual generators, but they are rarely audited as measurement instruments. We jointly audit AV-Align, ImageBind AV-relevance, JavisScore, and Synchformer/DeSync under a common reliability protocol: controlled-distortion monotonicity, preprocessing sensitivity, rank uncertainty, cross-metric agreement, PEAVS-proxy agreement, and learned fusion. The result is an axis split, not a single winner: Synchformer/DeSync is the strongest temporal-offset tracker ($\tau=0.84$), ImageBind/JavisScore better match the PEAVS human-aligned proxy ($\tau=0.20$) and content-disruption families, and AV-Align is the weakest standalone metric. The metrics mutually disagree (Krippendorff $\alpha=0.066$), and neither linear nor simple $k$-NN fusion improves PEAVS agreement over the best individual metric. We recommend reporting AV-sync as a Reliability Card (metric-family breakdowns with confidence intervals) rather than a single bare synchronization score.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.24956v1 Announce Type: new Abstract: Data-driven methods are widely used in landslide susceptibility mapping (LSM) because they can effectively model the complex relationships between landslides and geo-environmental conditions. Existing data-driven approaches generally follow two types of data representations. Pixel-based models focus solely on the geo-environmental characteristics of a specific landslide but neglect the influence of its surrounding environment. Patch-based models incorporate surrounding spatial context but may include pixels with weak or no spatial relevance to the target landslide location. To address this limitation, this study proposes a Local-Geo and Spatial Context Fusion (LGSCF) strategy, which synergises the geo-environmental characteristics of landslide points with their corresponding spatial context through a feature-wise modulation mechanism. We tested the LGSCF strategy by integrating it into several representative convolutional neural network (CNN) architectures, creating nine different LGSCF-based models. The study area covers approximately 2644 km2 across Jenai and Sinyi Townships in Nantou County, Taiwan, and the dataset comprises 5332 landslide samples and an equal number of non-landslide samples. The results show that LGSCF-based models consistently outperform their original versions, achieving F1-scores up to 87.09% and AUC values up to 0.9472. Furthermore, the susceptibility maps produced by LGSCF-based models show that known landslides are more accurately concentrated in "very high" susceptibility zones with fewer misclassifications. These findings demonstrate that our fusion strategy can significantly improve the accuracy of landslide susceptibility mapping.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.24904v1 Announce Type: new Abstract: Inertial sensors at multiple body locations can improve activity recognition, but requiring every sensor at inference increases the deployment burden. We study whether four synchronized IMUs available during training can improve a student that uses only the right-arm IMU during fitting and inference. A frozen four-IMU teacher provides logit and feature targets. Fixed-weight knowledge distillation applies each target with the same strength to every fitting sample, although the student may not benefit equally from them. We introduce dynamic influence weighting (DIW), which tests a one-step candidate update on separate fold-internal training participants. DIW then assigns separate sample-wise gates to the logit and feature losses. On WEAR, we evaluate 19 labels and 68,298 complete windows from 22 participants using subject-disjoint five-fold cross-validation. Pooled out-of-fold macro-F1 is 0.561820 for Supervised and 0.571623 for Fixed-weight KD. DIW reaches 0.638451, gains of 7.66 and 6.68 percentage points, respectively. It exceeds Supervised for 18 of 19 labels and 21 of 22 held-out participants. All three routes retain the same 80,915-parameter right-arm student at inference. Under this protocol, DIW converts training-only multi-position information into a stronger single-IMU model without changing deployed sensing or the student forward graph.

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：Image and video models have gotten a lot better over the last few years, even though the internals of these models haven't changed much since Stable Diffusion 3.Of course, there have been small variants like the auto-re…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：--> [Submitted on 21 Aug 2026] Title:Assessing the Impact of High-Resolution Imaging on Statistical Validation of TESS Planet Candidates View a PDF of the paper titled Assessing the Impact of High-Resolution Imaging on…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：--> [Submitted on 25 Aug 2026] Title:Maia 200: A Software Defined Dataflow System for Large-scale AI Acceleration View a PDF of the paper titled Maia 200: A Software Defined Dataflow System for Large-scale AI Accelerati…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：--> [Submitted on 12 Apr 2026 (v1), last revised 9 Aug 2026 (this version, v3)] Title:Toward Measuring AI's Effects on Skill Formation: The Stock-Formation Gap View a PDF of the paper titled Toward Measuring AI's Effect…

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：The TCL Nxtpaper 11 Plus is an 11.5-inch Android tablet that can be all you need, especially with this early Labor Day deal.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：A few months ago Anthropic launched Claude Design, which was like Claude Code, but for, well, design. Honestly I thought it was amazing. As a developer with fairly strong opinions on what good and bad UX and app design…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Apache DataFusion vs DuckDB Apache DataFusion and DuckDB are both fast, in-process analytical query engines. DataFusion is an embeddable Rust library designed to be extended. DuckDB is a self-contained database designed…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：A deep dive into a living memory's forward pass - Sapience Labs Sapience Labs &middot; technical deep dive &middot; August 2026 A deep dive into a living memory's forward pass What actually happens when one AI session w…

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：Notch | Substack Home Subscriptions Chat Activity Explore Profile Notch Notch @notch321 Persistent AI research experiment. See subscribers

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.25196v1 Announce Type: new Abstract: Learning from demonstration (LfD) methods enable non-expert end users to teach robots novel skills without explicit programming. However most evaluations of the usability of LfD with non-experts has been conducted in controlled laboratory environments with a robotics experimenter present. In this work we identify non-expert end users' key barriers when teaching robots via demonstration without live robotics expert feedback in a home environment. In our human subjects experiment we support the non-expert end users through two forms of demonstrator guidance developed in prior work: pre-training and adaptive feedback. Towards the ecological validity of the evaluation, we conduct this experimentation over multiple visits, with a population of care providers. Finally, we propose to open source the resulting LfD dataset of care providers teaching a robot assistive tasks over multiple visits to a home environment.

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:2608.25168v1 Announce Type: new Abstract: In multi-view anomaly detection, more cross-view information can actually hurt. When multiple inspection views are naively fused in a reconstruction-based pipeline, normal cues from intact views propagate to the decoder, which faithfully reconstructs anomalous regions, collapsing the reconstruction gap the detector depends on. We call this failure mode \emph{cross-view information leakage} and show that effective multi-view fusion must explicitly restrict the information reaching the decoder. Building on this insight, we present GLAD(Global-Local Attention Driven framework), the first framework combining vision foundation model features with local and global cross-view fusion for multi-view anomaly detection. The Multi-view Merging Attention (MMA) module performs local cross-view fusion at linear complexity with learnable view importance weighting and token-wise gating, letting each view selectively incorporate fine-grained evidence from other views at $\mathcal{O}(N)$ cost. The Object-Guided Attention (OGA) module captures global context by aggregating class tokens from all views into a single object-level representation and broadcasting it back to patch tokens via temperature-scaled sigmoid gating, replacing the original patch representations rather than adding a residual to preserve the reconstruction gap. Experiments on Real-IAD and MANTA-Tiny show that GLAD outperforms state-of-the-art methods across sample-, image-, and pixel-level metrics, confirming that principled information restriction is key to multi-view anomaly reasoning.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.25148v1 Announce Type: new Abstract: Frozen hematology foundation-model (FM) embeddings reach near-saturated in-domain white-blood-cell (WBC) accuracy, but clinical deployment demands reliability across scanners, sites, stains and preparation pipelines. We audit 15 frozen encoders (hematology, pathology, and general vision) across four public single-cell acquisition domains along two axes: accuracy robustness and calibration. In-domain linear-probe macro-F1 is saturated (0.98-0.997), yet cross-dataset macro-F1 drops 34-72% and rankings re-order: DinoBloom-L, the in-domain best, falls to 10th of 15 on the most-shifted target (MLL23) at the benchmark's shared 224-px input, behind RedDino and several general and pathology encoders. Rank transfer is probe-dependent: 1-NN retrieval is more stable on average than a source-fitted linear head (median $\rho$ 0.65 vs 0.45), but neither probe universally predicts target robustness. Calibration also collapses: source-trained probes are nearly calibrated in-domain (expected calibration error, ECE, 0.004) but confidently wrong off-domain (ECE 0.35), and source-fitted temperature scaling transfers poorly. We further audit pretraining exposure and identify MLL23 as DinoBloom's internal cohort; because DinoBloom's only held-out dataset is also our source domain, this benchmark cannot isolate exposure from scanner-associated shift. Label-free adaptation and marginal-entropy-based model selection appear safe under balanced evaluation but fail under realistic WBC class-prior shift. Class-Balanced Re-standardization (CBR), a training-free pseudo-label-balanced feature normalization, improves all evaluated target-prior scenario means and partially improves calibration, although encoder-level exceptions and residual miscalibration remain. Hematology FM benchmarks must therefore jointly audit accuracy, calibration, exposure, and class-prior robustness.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.25140v1 Announce Type: new Abstract: Existing approaches to building line-level Arabic handwritten-text-recognition (HTR) training data either rely on fully manual annotation, which does not scale, or on automatic OCR-to-reference alignment methods not yet extended to multi-script, two-zone (main-plus-margin) manuscript layouts with a provable correctness guarantee. We present RefLAM (Reference-grounded Line Annotation for Manuscripts), a pipeline converting manuscript page images and clean transcriptions into validated, line-level ground truth without sacrificing human oversight. RefLAM couples a deep-learning page-segmentation model with a multimodal large language model (MLLM) for structured OCR and a diacritic-agnostic fuzzy alignment engine that grounds each OCR line in a contiguous span of the reference text, with a character-level confidence score in $[0,100]$. A perfect score is provably equivalent to character-for-character identity of the normalised strings (the Confidence-100 rule), verified with no counterexample across the released corpus. A reviewer can thus trust a perfect score, confirming most lines at a glance rather than retyping them, so annotation becomes triaged, with attention concentrated on uncertain alignments. Across 7 fully page-validated books we measured a 75$\times$ throughput gain over manual annotation (3,000 vs. 40 lines/hr); applying the same guarantee to 7 further books, we retained 16,533 confidence-100 main-text lines within one week, excluding sub-100 lines rather than manually correcting them. Using RefLAM, we release AraMS-28k: 14 historical Arabic manuscript books, 3,043 pages, and 27,971 main-text and 629 margin-line annotations with bounding boxes, layout labels, and insertion anchors for 191 margin entries (30.4%). We also finetune Muharaf-pretrained baselines (including HATFormer) on AraMS-28k and report CER results confirming its practical utility for downstream HTR training.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.25068v1 Announce Type: new Abstract: Depth pruning removes entire Transformer blocks to reduce the inference cost of large language models, but disrupts the hidden-state distributions expected by downstream layers, leading to significant accuracy loss. We introduce SHIFT-LLM, a training-free post-pruning correction framework that inserts a Linear Residual Adapter (LRA) at each pruning site. Each LRA preserves the identity pathway of the original residual block and adds a lightweight affine residual correction. This correction is calibrated via closed-form least-squares regression on a small held-out set, without gradient computation, to approximate the missing residual update produced by the pruned block. Together with the preserved identity pathway, the resulting LRA output approximates the hidden state produced by the original block, thereby mitigating the distributional mismatch introduced by layer removal while avoiding the expensive attention and feed-forward computations of the removed blocks. The resulting LRAs support low-rank factorization and exact merging across consecutive pruned layers for additional compression, and combine naturally with parameter-efficient fine-tuning for further recovery beyond fine-tuning the pruned model alone. Experiments on five model families, six layer-selection criteria, and seven zero-shot benchmarks show that SHIFT-LLM consistently recovers accuracy lost to depth pruning across most configurations, achieving gains up to +15.7 points on Llama-3.1-8B-Instruct while requiring only a few hundred calibration samples and no gradient computation.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.24966v1 Announce Type: new Abstract: Vision-language models such as LLaVA-1.5-7B often hallucinate objects absent from the image when generating captions. We ask whether an interpretability diagnosis of this failure can guide a targeted fix, and we measure what that fix actually changes. We rank attention heads by how much their image attention drops around hallucinated object words, then screen the shortlist by ablating candidate heads and measuring the change in hallucination-token log probability, yielding a 32-head set. We restrict two interventions to these heads: a head-sliced LoRA adapter and an inference-time grounding controller. On 400 held-out COCO images, the combined method lowers CHAIRs (the fraction of captions with a hallucinated object) from 0.370 to 0.230 and CHAIRi (the fraction of hallucinated object mentions) from 0.156 to 0.096 (p < 0.001, paired sign-flip tests). Two controls sharpen attribution. A random-head LoRA control, matched layer-for-layer and trained identically, performs no better than the matched baseline on a separate 200-image control split, supporting the role of head selection rather than LoRA capacity. Under fixed decoding budgets, the CHAIR reduction persists and grows with budget (23% at 64 tokens to 58% at 128), arguing against a pure max-token or truncation artifact, although the method remains shorter and more conservative. The resulting behavior reduces unsupported object mentions while also lowering object recall (0.78 to 0.70). We present a diagnosis-to-intervention pipeline for object hallucination, and, more importantly, a controlled account of what acting on the diagnostic signal actually does: it localizes intervention sites with real, non-random leverage, reported as a behavioral profile rather than a single score.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.25038v1 Announce Type: new Abstract: We introduce grounded glossary generation, a structured task requiring models to recover semantically meaningful Sanskrit phrases and produce translation-grounded meanings from a sloka-translation pair, formalizing the traditional patha commentary practice as an evaluable NLP objective. We construct a benchmark of 31,316 sloka-translation-glossary triples from the Valmiki Ramayana and Srimad Bhagavatam, paired with two metrics: Jaccard for phrase recovery and Meaning Faithfulness for semantic consistency. Across zero-shot, few-shot, and instruction fine-tuned variants of Gemma-3n-E4B, Gemma-3-12B, Phi-4, and Qwen3.5-9B, instruction fine-tuning substantially outperforms prompting, while explicit segmentation yields gains. Error analysis identifies over-segmentation of sandhi and samasa compounds as the dominant failure mode, pointing to morphological modeling as the key bottleneck for faithful Sanskrit lexical decomposition.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.25028v1 Announce Type: new Abstract: Spoken-language analysis via prompt-based domain-adaptive models is a promising direction for low-resource, non-invasive dementia screening, but such models remain internally opaque. We study the interpretability of the Domain-Adapted models via Prompt-based Fine-tuning (DAPF) framework, which casts dementia detection as diagnosis-related masked-token prediction. We interpret DAPF and strong baselines using a variety of probing and analysis techniques, finding that DAPF achieved the best overall performance (accuracy=0.83 and macro-F1=0.83) with diagnosis most recoverable from its [MASK] representation. However, this representational advantage did not extend to token-level explanation faithfulness. DAPF attributions primarily reflected language task vocabulary, discourse markers, and transcription artifacts, with perturbation tests showing weak or negative effects. This suggests that its masked-token interface determines diagnosis information without producing faithful token-level explanations.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.25022v1 Announce Type: new Abstract: As people adopt transformer-based language models (e.g., ChatGPT and Gemini) for an increasing number of use-cases, it is important to know how such models learn and represent the meaning of the language, and to be more informed about what language is. This document is an attempt to help the reader understand how linguistic meaning (i.e., semantics) is approached from different fields of scientific and philosophical examination. I also explain three primary semantic theories: formal semantics, grounded semantics, and distributional semantics then compare how transformer-based language models differ from how humans learn language.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.25005v1 Announce Type: new Abstract: The imperfective paradox provides a useful test of compositional semantic analysis. Recent work constructs an NLI benchmark and reports that models frequently infer completed telic events from progressive descriptions, attributing this behavior to a Teleological Bias. It further argues that prompting interventions cause a Calibration Crisis. We reexamine the benchmark and conclusions and show that it is substantially affected by conceptual and evaluation mis-specifications. We identify three conceptual mis-specifications. In particular, Aspectual Reduction affects the benchmark construction, analysis, experiments, and conclusions. Under a strict NLI standard, 76% of Group A instances do not explicitly rule out culmination. In our native-speaker annotation, 38% of Group A examples and 29% of the Group C examples were judged to permit an alternative interpretation. To control these issues and lexical variation, we construct Lexically Matched Minimal Pairs. At the evaluation level, we formulate event-semantic NLI as a Multi-step Reasoning Problem and assess both intermediate semantic decisions and final predictions. Our results show that models often do not affirm culmination but nevertheless accept the corresponding simple-past hypothesis, a pattern we characterize as Sufficiency Bias. We further show that prompting interventions produce a Decision Shift among labels without reliably improving the underlying semantic understanding and reasoning. Intermediate and oracle-guided analyses identify two additional failure modes: errors in compositional aspectual classification and Surface-form Attraction toward surface-associated answers. Our experiments on Qwen-7B with suitable prompts, GPT-5.4, and Qwen-72B provide initial evidence for the context sensitivity of aspectual classification and suggest that these models can achieve performance comparable to that of human annotators.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.24988v1 Announce Type: new Abstract: Activation steering can be embedded directly into a language model's weights, shaping behaviour without inference-time intervention and offering a way to encode alignment prior to release. However, models are routinely fine-tuned after deployment, and it is unknown whether embedded interventions survive this. We study the stability of embedded steering for refusal suppression and brevity induction across five instruction-tuned models (3B-14B) under non-adversarial SFT and RLHF. Behaviourally, preservation tracks the training data: steering degrades when optimisation pressure contradicts the targeted behaviour and persists otherwise, with refusal ablation losing 64% of its effect on average under SFT. Mechanistically, however, the weight edit survives almost untouched even where behaviour reverts: mean vector recovery is $\rho = 0.004$, and the fine-tuning update along the steering direction is near-orthogonal to its pre-edit weight pattern (mean $\cos\theta = 0.074$). When steered behaviour degrades, fine-tuning does not achieve it by dismantling or reversing the steering mechanism itself. Embedded steering is therefore mechanistically durable but functionally vulnerable, and requires behavioural re-validation after downstream training.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.24982v1 Announce Type: new Abstract: Foundation-model post-training usually relies on human labels, preference data, stronger teachers, or executable verifiers. We study Unsupervised Post-Training (UPT): update-bearing adaptation on unlabeled inputs whose learning signal is derived from same-lineage model artifacts rather than an external oracle. We catalog 80 strict UPT methods and organize them by the object that supplies the update signal: a prediction statistic, a sample relation, a self-generated target, or an internal evaluator. Beyond inventory, we show how the choice of internal signal and task structure determines whether post-training improves the model or recursively amplifies error. An orthogonal Input Visibility $\times$ Update Persistence view maps deployment regimes and defines a unified framework for UPT selection and evaluation.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.24952v1 Announce Type: new Abstract: Systematic dialectal performance gaps in language models (LMs) are well documented, but the source of these disparities within the modern language modeling pipeline remains unclear. Our study traces this "dialect tax" across the natural language processing pipeline. Using parallel English dialect corpora that hold meaning fixed while varying surface form, we first confirm that LMs recognize matched Standard American English (SAE) and dialectal texts as semantically equivalent. However, we discover further representational gaps corresponding to downstream performance gaps. Across model families and generations, modern LMs still encode dialectal texts unequally during tokenization, pre-training, post-training, and inference. Strikingly, bypassing traditional subword segmentation via a character-level counterfactual tokenizer removes neither input and output asymmetries nor dialectal accuracy gaps. During pre-training, dialect pairs induce more divergent gradient updates than pairs of entirely unrelated SAE documents, indicating that models find semantically equivalent dialectal content harder to learn from than unrelated SAE documents. During post-training, reward models show contextual, unstable dialect preferences, assigning higher values to isolated AAVE-exclusive tokens than to SAE-exclusive tokens, while full reasoning contexts receive task- and model-dependent dialect penalties. Overall, our findings suggest that the dialect tax is encoded and accumulated not by any one step in isolation, but at every step of the language modeling process.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.24920v1 Announce Type: new Abstract: This study examines whether LLM-generated replies remain semantically consistent when the underlying LLM changes. Using messages from real collaborative conversations, we compared the semantic similarity of generated replies across LLMs under two conditions: with and without preceding chat history. Results show that model choice and conversational context both affect response similarity and alignment with human replies. These findings indicate that prompting and conversational context alone may not be sufficient to preserve response consistency across LLMs, highlighting the need for infrastructure and design strategies that can maintain stable and comparable responses amid the rapid and continuous evolution of LLMs.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.24901v1 Announce Type: new Abstract: A decodable "empathy" direction is routinely read as a causal lever, conflating decodability, automated-metric control, and human-perceived change. We test this for two EPITOME-derived facets -- Recognition (cognitive) and Resonance (affective) -- in three instruction-tuned LLMs, scoring every intervention with two LLM judges and a discriminative EPITOME classifier, each gated by an emotional-vs-neutral positive control. The control passes for the affective facet across all automated instruments, but cognitive range is inconsistent across them. Both facets remain decodable after residualizing against a sentence-embedding-derived surface score, and steering can substantially rewrite the text. Yet adding the Resonance direction raises the affective score only partially -- in Qwen by +0.29 (approximately 26% of the natural gap). A direct between-direction contrast confirms the shift is facet-specific in Qwen and Llama (not Gemma); we do not, however, establish a matching human-perceived change. Additive cognitive steering produces no measurable change, but a within-domain control shows the cognitive instrument is too coarse to resolve the differences such steering would produce -- unmeasurable, not a clean null. By contrast, Gemma Recognition ablation lowers the classifier's cognitive score even after adjusting for response length. Detection does not imply reliable control under global interventions, and cognitive-empathy claims warrant an explicit measurement-sensitivity check.

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:2608.24940v1 Announce Type: new Abstract: Partial differential equations (PDEs) often have high-frequency and multi-scale features that neural networks struggle to approximate. Physics-Informed Neural Networks (PINNs) build the governing equations directly into training, but suffer from spectral bias: they learn low-frequency components faster than high-frequency ones. Techniques such as Fourier feature embeddings and sinusoidal activations address this, but most studies assume they help across the board without checking which spectral regimes actually benefit. We introduce a dual-branch, spectrally-gated architecture (DBSG-PINN) that splits low- and high-frequency components into separate subnetworks joined by an adaptive gate, and use it to run a partially controlled ablation of frequency decomposition and spectral routing. We test this on five one-dimensional benchmark PDEs, ranging from smooth, single-scale problems to oscillatory, multi-scale ones. Frequency decomposition helps most on the spectrally complex benchmarks, cutting relative $L_2$ error by up to $59.2\%$ on a multimodal wave problem, but gives little benefit on smoother PDEs. On one benchmark (1D Wave), it performs substantially worse than a simpler fixed-combination variant. The gate's benefit scales with how spectrally rich the target solution is: the full model's advantage over the ablations is largest on multi-scale benchmarks and smallest (or negative) on single-scale ones, consistent with the gate exploiting frequency structure rather than acting as noise,though we do not directly visualize or quantify its spatial activations in this study. All results come from a single training seed across five 1D benchmarks, so we present this as an exploratory study meant to raise questions rather than answer them, and outline the additional seeds and benchmarks needed to test whether the pattern holds.

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：arXiv:2608.24936v1 Announce Type: new Abstract: We present GreenLeaf Law Embed Tiny, a 0.6B parameter embedding model for legal domain retrieval. GreenLeaf-Tiny achieves 75.11% on the Massive Legal Embedding Benchmark (MLEB) and 64.38% on MTEB(Law, v1),demonstrating competitive performance among models under 1B parameters. Our approach combines a two-stage training pipeline that first distills knowledge from a larger teacher model into a compact student architecture, then applies domain-specific fine-tuning with hard negative mining; a carefully curated dataset of 3.4 million query-passage pairs, including 150,000 human-curated samples across diverse legal jurisdictions; and an efficient inference architecture supporting multiple quantization levels (BF16, INT8, binary) enabling deployment in resource-constrained environments. We provide detailed analysis of our training methodology, architectural choices, and comprehensive evaluation across legal retrieval tasks. Our results demonstrate that domain-specific training with high-quality data can improve performance for specialized domain applications

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：While Alibaba has kept inference and token price low, enterprises need to consider other metrics to determine if this is the right model for them.

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：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：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：Google has updated Gemini Audio with some new Gemini 3.5 models, introducing new transcription capabilities that automatically detect specialized jargon and more than 85 languages. Gemini 3.5 Live, 3.5 Live Experimental, and 3.5 Transcribe are designed to provide better precision for Google's voice-controlled AI features, without struggling with background noise or when your speech is interrupted. Gemini 3.5 Transcribe is a completely new addition to the Gemini family, and its introduction comes as we're still waiting for Google to release the Gemini 3.5 Pro model that it promised to roll out in June. Google says that 3.5 Transcribe "repres … 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：Microsoft co-founder Bill Gates argued on Wednesday that artificial intelligence needs significant limits or else the harm to humans will outweigh any potential good. “AI will either be the greatest equalizer ever inven…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：A new report from OpenMind argues the race for dominance in humanoid robotics will be decided by magnets, gearboxes and electricians, not AI.