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0系列显卡就能运行DLSS 5。这样的升级，已经超出了一次常规的性能优化。 要知道，过去几十年，游戏画质的提升很大程度上依赖GPU不断增强传统图形计算能力，从光栅化到光线追踪，都是如此。但从DLSS 4到DLSS 5，神经（网络）渲染技术毫无疑问正在成为一个关键变量，这次就引入了最新的3D引导神经渲染技术。 不止英伟达，不止PC。苹果早在去年WWDC上就通过Metal 4提供了将机器学习整合到实时渲染管线中的能力，并从去年的A19系列和M5芯片开始，在GPU中加入了神经网络加速器；而高通、联发科，也都在新一代旗舰芯片上首次引入了神经渲染技术。 图片来源：苹果 对于已经发展了几十年的计算机图形技术，这或许意味着一场比单纯提升GPU性能更深刻的变化。半年提速10倍，神经渲染究竟改变了什么？ 需要先说明，DLSS 5最新升级的重点在效率。 按照英伟达的说法，通过神经网络模型与渲染管线的共同优化，新模型的参数量已经缩减至约1.18亿，同时将GPU运行开销降低了一半，基本保持了原有的视觉效果。按照测试结果，新模型可以让《NBA 2K27》的帧率最高提升40%。 图片来源：英伟达 DLSS 5也正

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ies in competing bacteria, from cell envelopes to essential intracellular machinery, making it difficult for pathogens to develop resistance. The exponential growth of genomic and metagenomic sequencing has populated global databases with billions of natural amino acid sequences. This pool of natural data has already produced globally used bacteriocins like Nisin, alongside recent genome-mined breakthroughs such as Darobactin, Dynobactin, and Lariocidin. Many, however, remain locked within the u

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widespread file formats for each of its parts, namely: hf dataset stored using Hugging Face datasets library serialization to parquet videos are stored in mp4 format to save space metadata are stored in plain json/jsonl files Dataset can be uploaded/downloaded from the HuggingFace hub seamlessly. To work on a local dataset, you can specify its location with the root argument if it's not in the default /.cache/huggingface/lerobot location. Evaluate a pretrained policy Check out example 2 that ill

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frames. Code layers is rolling out in open beta starting today, and will use AI credits. On paid plans, creating and editing within code layers is free through November 4, 2026. Sean Blanton is a product designer at Figma, where he builds agentic tools at the intersection of design and code. Previously, he worked at DoorDash and Meta. Build by design: New workflows for new ways of building Introducing Specs: Markdown as a medium Create, ship, and manage data-backed websites with Payload in Figma

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在线的智能体、自动化任务。 Git那些适合分布式离线工作的特性，反倒成了仓库扩容的阻碍。那如果从头设计一套源码控制系统，原生就考虑智能体永远在线这个前提会是什么样？ 02.六大设计原则，部分思路与Git截然不同 基于上述现状，他们在构建Scion时在几个核心原则上采取了与Git截然不同的方法，同时其借鉴了大型单体仓库成熟的源代码控制技术，如谷歌的Piper和Meta的Sapling。 其构建Scion的几个核心原则是： 客户端始终在线：Git默认客户端离线工作并且需要仓库的完整副本。由于Anthropic几乎所有的客户端都是智能体和自动化作业，他们围绕一个始终可访问的服务器设计了Scion的客户端和协议，让客户端在需要时获取数据。 客户端-服务器不对称：在Git中，每个客户端都能够充当功能齐全的服务器。在Scion中，服务器负责回答有关文件历史记录和blame的问题，创建提交，执行合并和变基等操作。 客户端维护一个有界本地对象缓存，在磁盘上布局工作副本文件，监控文件系统变更，并为用户提供符合人体工程学的服务器交互方式。客户端自身计算能力有限，但作为交换，它所需的本地数据量也少得多。 一

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ack office applications are being infused with AI routines, and this is driving usage up the hockey stick handle at a rapid pace. That is why Microsoft is putting the pedal to the metal with its Maia family of custom, homegrown XPU accelerators, and letting customers know Microsoft is working on the problem is why the company has rolled out the feeds and speeds on its future “Clea” Maia 300 accelerator as the Open Compute Project Summit 2026 kicks off this week. The Maia 300 chip is a much-impro

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ps smooth out the visible steps that can show up in gradients like skies when SDR footage is stretched to a wider range. The output is a standard ComfyUI image with no color-space metadata, so downstream Create Video nodes need to be told whether the data is HDR or HDR PQ. The color management and EXR support in this release also handle that step. Nvidia VFX Nodes Workflow Better EXR support for VFX pipelines In addition to Nvidia’s VFX nodes, we’re improving support for EXR files format. EXR is

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for everyday tasks. Tech giants are preparing for a potential future in which people get things done by speaking or typing to AI agent rather than by operating apps and websites. Meta recently launched its Muse personal assistant, along with a new slate of smart glasses designed to give users easier access to its new AI helper. OpenAI is also developing several hardware gadgets. Apple is betting that its focus on privacy and the fact that consumers already run their digital lives on its iPhones

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ion.json. It does not optimize benchmark ECE or select checkpoints. NLL-optimal temperature need not minimize ECE on another dataset. Calibration artifacts are bound to checkpoint metadata hashes; regenerate one after changing weights. If relocating a checkpoint, update its artifact's checkpoint path only after checking all stored file hashes. Browser demo and HTTP API Open http://127.0.0.1:7860. The demo supports typed questions, multiple image uploads, probabilities, and calibrated confidence.

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goals were that it should be simple to encode/decode, extensible, have selectable encoding precision, and allow random access. RAD file structure, consisting of an extensible JSON metadata header followed by randomly-seekable chunks. Each chunk is a RADC file with JSON header and the properties of a 64K chunk of splats, encoded and compressed in column-order. The file structure is simple: the header RAD0, the byte size of the header metadata, the metadata JSON, followed by one or more chunks of

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ts own tools through skills: Tool creation: Agents can write new tools (Python modules, Bash scripts, etc.) and register them into a hierarchical, file-system-based structure with metadata describing interfaces and dependencies; Tool management: Tool-specific container-based execution isolation supports heterogeneous tools with conflicting dependencies, while a shared workspace enables data sharing across containers; Asynchronous execution: Agents can decide to run selected tools in the backgrou

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ent. The judge model reviews and scores each transcript for the target behavior plus secondary qualities that help contextualize the score. These scores and summaries then go to a meta-judge, which produces a report with an overall suite summary, a breakdown of different scenarios, elicitation strategies and their success, and any other details you request Seed Configuration Bloom's configuration system is highly adaptable—you can tailor evaluations to elicit a wide range of failure modes. Confi

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puting our monitorability metric, g-mean2, along with a scaffold that simulates running evaluations. The scaffold is a high-level orchestration layer that loads dataset and prompt metadata, samples and grades rows using mocked model and monitor outputs, computes intervention, process, and outcome metrics, and writes samples and summaries to disk. We will not be supporting this scaffold because it is only meant to be an illustration of how to run our evaluations rather than a full evaluation fram

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ning and inference engines Architecture The Awex weight exchange framework consists primarily of three components: WeightWriter: Runs within each training process, responsible for metadata collection and reporting of weight shards for the current training process, weight convert, resharding transfer plan construction, weight transmission, and other functions; WeightReader: Runs on the control process of each inference instance, which starts a WorkerWeightsReader on each GPU managed by the infere

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enyi Zhuang, Jinjie Gu; Don’t Just Fine-tune the Agent, Tune the Environment arxiv, 2025. paper Siyuan Lu, Zechuan Wang, Hongxuan Zhang, Qintong Wu, Leilei Gan, Chenyi Zhuang, etc Meta Learning Profile-Aware Maneuvering: A Dynamic Multi-Agent System for Robust GAIA Problem Solving by AWorld. arxiv, 2025. paper, code Zhitian Xie, Qintong Wu, Chengyue Yu, Chenyi Zhuang, Jinjie Gu; Recon-Act: A Self-Evolving Multi-Agent Browser-Use System via Web Reconnaissance, Tool Generation, and Task Execution.

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such as scheduling, splitting bills, gifting, and sharing content. “Now, we will use AI technology to build the next 10 years of connections,” she said. Kanana Space is similar to Meta's AI agent, Muse. Swiping the KakaoTalk screen with two fingers brings up Ryan, Kakao's AI character. Users can ask Ryan to send messages, manage schedules, search the web, make reservations, and process payments. They can also engage in everyday conversations with the assistant. Kakao plans to develop Kanana Spac

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rkflows for your genomic studies Sequencing analysis often requires many connected decisions and datasets before a biological result can be interpreted. Files must be matched with metadata, quality must be assessed, the correct biological replicate must be identified, and an appropriate statistical design must be selected. Rosalind NGS Workbench helps connect those steps in one reviewable process. From sequencing data to a research decision From FASTQ inputs to quality control, bulk RNA-seq, or

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《科创板日报》10月10日讯 Meta Muse掀起的Agent热潮尚未褪去，不到一个月，谷歌便携全新Gemini Agent框架登场，推动智能体赛道竞争再度升级。 当地时间10月8日，谷歌在Gemini at Work 2026大会上正式推出Gemini agent。其定位企业级产品，可独立完成多类知识工作：解答业务问题、处理文档知识、生成图像媒体、编写并运行代码。 这些能力对智能体而言已不足为奇，真正吸引眼球的，是谷歌首次提出的“Coworker Agent（同事智能体）”概念。 谷歌表示，Gemini agent既可以作为个人私人助理，也能够作为团队成员，充当项目经理、财务分析师等组织内特定岗位角色，代表团队与组织执行工作。其可以拥有独立企业身份，配备专属邮箱、日历与企业账号，成为一名“AI同事”。 作为同事智能体，Gemini agent不仅拥有更完整的记忆机制，还支持在谷歌自有模型以及Anthropic Claude等第三方模型中进行选择。谷歌云首席执行官托马斯・库里安（Thomas Kurian）强调：“基于内置成本管控能力，它会根据任务自动匹配最优模型。” 更值得玩味的，

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lumn names are intelligently extracted from header rows; Extra data: Data in your spreadsheet that doesn't explicitly belong in a structured data table (notes, titles, etc.); Cell metadata: 40+ features per cell including formatting (font bold , background color rgb ), position (row number , coordinate ), data types (is date like , is percentage ), and layout (is merged cell , horizontal alignment ); Sheet context: Optional LLM-generated titles and descriptions for each worksheet and extracted t

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is the first frontier AI provider to release an agent that connects to enterprise workflows, illustrating how the cloud provider is differentiating itself in the AI race. Related:Meta joins enterprise agent race with Muse Wonder, facing a trust gap Digital workspace “This is a play on digital workspace,” said Mark Beccue, an analyst at Omdia, a division of Informa TechTarget. “It’s an everyday use of a personal assistant in your everyday work.” While Google is the first frontier AI developer wi

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