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Access mode which removes friction at the expense of oversight. Auto-review offers an alternative path. It replaces user approval at the sandbox boundary with review by a separate agent. Internally, Auto-review increases our confidence in running long agentic tasks without synchronous human oversight. In Auto-review mode, Codex sessions stop for human approval roughly 200x less often than in manual approval mode, while still catching many of the actions humans would want stopped. Most actions ru

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in to change notification settings; Fork0 (0); Star134 (134)You must be signed in to star a repository Self-hosted multi-model deep research system that fuses independent research agents into evidence-grounded reports. Repository files navigation README; Contributing; Apache-2.0 license; Security English · 中文 Fuse the complementary strengths of multiple models into one stronger result. PanelWise starts with a simple question: if every model sees something worth keeping, can their complementary j

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und a bug? Open an issue; Suggest Features: Have an idea? Start a discussion; Improve Docs: Documentation improvements are always welcome; Submit Code: See our Contributing Guide; Agent Workflows: Read the Repository Guidelines for structure, testing, and PR expectations Development Setup See DEVELOPMENT.md for detailed build and CPU/integration test instructions. 📄 License Apache License 2.0. See LICENSE for details. Awex - high-performance RL training-inference weight synchronization framework

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change notification settings; Fork5 (5); Star910 (910)You must be signed in to star a repository Repository files navigation README; Contributing; Apache-2.0 license; Security An agent workspace for Codex, Claude Code, and Muse that asynchronously turns user interaction traces into personalized benchmarks to adapt harnesses, reduce recurring errors, and support selective substitution with smaller provider models you configure. Native trace learning currently supports Codex and Claude Code. Muse

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n README; MIT license 中文版 | Automation | Evolution | Contributing | General AI often hits a "wall of context"—the nuanced data, workflows, and intuition that define your world. An agent's true power lies not in the model alone, but in its Agent Harness: the framework orchestrating its tools, memory, context, and execution. This is the AWorld Thesis: A powerful harness is not enough. True AI scaling is unlocked only when experts like you embed the invaluable knowledge, effectively building the ga

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13 at its if(kakao)26 event, held at the Kakao AI Campus in Yongin, Gyeonggi Province, that it is transforming KakaoTalk from a messaging app into an artificial intelligence (AI) agent platform. The company plans to unveil Kanana Space, an AI assistant that users can call up anytime within KakaoTalk, and Canvas, a service that lets users build their own AI agents, by the end of this year. Kakao aims to expand KakaoTalk into an integrated platform connecting a range of AI agents in the first hal

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ntral processing units (CPUs). Unlike its existing Helios platform, which combines large numbers of graphics processing units (GPUs), Helion is designed to run large numbers of AI agents simultaneously. AMD said the spread of AI agents is expanding computing demand in AI data centers beyond GPUs to CPUs. AI agents do more than call large language models (LLMs); they repeatedly execute programs and access databases. These tasks require substantial CPU resources, the company said. Helion configura

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tural upgrade introduces a shared background service connecting its terminal, web and desktop interfaces, alongside a rebuilt API, a new embeddable SDK, and support for running subagents and shell commands in the background. In a blog post published on Monday, Anomaly , the company behind the open-source coding agent, calls the release a “major overhaul,” adding that it drew on the experiences of “millions of daily users” to build a more adaptable system capable of supporting an expanding range

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人机交互难题。 l 适配嘈杂场馆环境的感知交互：面对广播、背景音乐、人群喧闹，通过多模态定向拾音（声音 + 唇动）、多模态降噪、人声增强，结合大模型语义理解，过滤无关杂音，捕捉游客表达，支撑问答、游戏、点播等体验。支持免唤醒主动交互，游客走近机器人即可主动打招呼，不需要喊唤醒词，还原自然社交状态。 l 长程多轮交互，从单次问答走向完整游戏体验：依托长程任务 Agent，承接对话上下文。游客挥手即可开启答题、猜谜闯关，机器人记住对话脉络，持续推进游戏进程；支持猜拳、音乐表演等手势语音动作联动，游客可以参与共创表演内容，而不是被动观看演示。 l 五维 IP 定制，让机器人拥有统一角色人格：通过音色、表情、人设、动作、服装造型五个维度定制，结合场景知识库、检索增强生成技术，机器人可以支持多国语言、方言科普讲解，把长隆 IP 融入打招呼、讲解、互动反馈，实现外观、声音、性格、动作完全统一。 l 空间语义理解：打通语言到真实行动听懂游客的“目的地”诉求，关联园区三维地图，完成位置咨询、路线指引、带路、巡回讲解，将自然语言指令转化为真实空间里的导航行动，实现 “听得懂、答得出、带得去” 的完整

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拥有100万个token的上下文窗口，最多可以一次性输出131072个token，这和GLM-5.3的信息一致。 这不是智谱首次推出高速旗舰模型。今年5月，GLM-5.1就曾推出一个面向部分企业用户的高速版API“GLM-5.1-highspeed”，模型输出速度达到400 tokens/s。 当时，智谱展示了高速模型在实际生产场景的价值。一个Coding Agent任务往往需要经历数十轮模型调用，单轮响应只要慢上几秒，整体耗时就可能拉长十几分钟。 更快速的模型能给写代码的过程“开10倍速”，模型能够一边理解工程上下文，一边持续生成代码与修改方案，用户刚输入需求，函数、接口与调用链已经同步展开，迭代速度明显提升。 这种输出速度主要来自于软硬件基础设施的优化。此前，GLM 5.1 Highspeed模型API是由智谱GLM团队与TileRT团队联合打造的，这一API在推理引擎、调度系统与底层基础设施三个层面进行了系统级优化： 在推理引擎层，研究团队针对GLM-5.1的架构特点，重写了核心推理路径，有效提升了单卡吞吐能力； 在调度系统层，他们通过动态批处理、请求合并和KV缓存调度优化，显著

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as also able to transfer its skills to Red Dead Redemption 2 without any extra training, moving a character on horseback. Odyssey also plans to use its world model for training AI agents. In one demo, an AI agent received a task in natural language and tried to complete it through its own actions inside an Odyssey-3-generated environment. The goal is for agents to learn from the outcomes of their own actions, a potentially new training paradigm for AI . The public research preview offers interac

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样，ds bot基于DeepSeek Harness做了一套多Bot团队界面。 每个Bot都可以拥有自己的名字、角色、模型，更重要的是「持久化」——能够积累长期记忆。 另外，为了节省token，你还可以给Bot们安排一个主管，有事先找主管，再让它去协同Bot们就行。 作者FeiZ还给划了重点： 你可以为ds bot接入任何你想接入的模型。未来甚至可以接入本地Agent。 ds bot继承了dsh的「一切皆插件」理念，以保持可拓展性。 虽然非官方，不过似乎这才是蓝色大肥鱼的正统风味（bushi）？ 上线不到一天，大肥鱼爱好者们已经给这个项目点了250+星。 Grok Bot我养不起，ds bot那不得高低尝尝咸淡？ 在ds bot里，每个Bot都有独立身份，可以设置名称、职责说明、指令、形象，以及背后调用的模型。 Bot们具有持久化的运行状态，拥有长期记忆。 还可以设置Main Bot。这个Bot主管拥有高权限，能帮你管理、配置其他Bot和群聊，既能降低管理多个Agent的复杂度，也能在群聊这种狂烧token的环境里，降低token消耗。 这方面，ds bot是专门做了优化的：在一个群聊

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frontier reasoning with specialized tool orchestration across medicinal chemistry, genomics, wet-lab assistance, and other scientific applications. Over time, we envision teams of agents working together across these domains, giving researchers access to broader expertise and the ability to pursue more ambitious scientific questions. Exploring scientific workflows Different disciplines use different data and methods, but the underlying need is often the same: connect a biological question with t

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knowledge, operate on continuous pixel values rather than discrete tokens, and provide outputs that can be easily and quickly inspected.. However, our interpreter model, analysis agent, and white-box model interface techniques are not unique to image models and scale nicely to any neural network architecture. For example, extrapolating the functionality of a tool like Paint With Ember to scientific models, one can imagine identifying key features and concepts across genomes instead of images, o

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g model parameters. ▶ 𝗥𝗼𝗯𝗼𝗗𝗼𝗷𝗼: 56.83 avg. score, 50.83% success rate, +𝟮𝟳.𝟴𝟲 𝗽𝗼𝗶𝗻𝘁𝘀 𝘃𝘀. 𝘁𝗵𝗲 𝗚𝗣𝗧-𝟲 𝗔𝘀𝘁𝗿𝗮 𝗯𝗮𝘀𝗲𝗹𝗶𝗻𝗲 (𝟮𝟴.𝟵𝟳) ▶ 𝗥𝗦𝗜 𝗹𝗼𝗼𝗽: Execute → Diagnose → Improve → Validate → Inherit ▶ 𝗘𝘅𝗽𝗹𝗼𝗿𝗲𝗱 𝗮𝗻𝗱 𝘃𝗮𝗹𝗶𝗱𝗮𝘁𝗲𝗱: Multi-point EEF prediction and Planning + Feedback Subagents This achievement reflects the collective efforts of our joint team. RoboRSI will be 𝗼𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲𝗱 𝘀𝗼𝗼𝗻 at https://t.co/iTVQ1YDFcn. A comprehensive 𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗿𝗲𝗽𝗼𝗿𝘁 detailing our methods and findings is also on the way. Stay tuned!

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ires you to approve everything, which is prone to decision fatigue unless you spend a lot of time configuring specific rules. It allows you to run long tasks while having a second agent review all actions taken by the primary agent. Its only goal is to prevent high-risk actions from being taken and to protect against unwanted actions that are not aligned with the original user intent. This Auto-review feature is now free and does not draw usage from your plan.— Tibo (@thsottiaux) October 6, 2026

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rd to make exactly what you want. Start editing video by editing text 1 media hour / month; 100 AI credits (one time); Export 720p, with a watermark; Limited use of Underlord, our agentic video co-editor and AI tools; Limited trial of AI Speech Elevate your projects, watermark-free 10 media hours / month; 400 AI credits / month; Export 1080p, watermark-free; Access to Underlord, our AI video co-editor; AI tools including Studio Sound, Remove Filler Words, Create Clips, and more; AI Speech with c

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Another brilliant release showing models need better harnesses and environments. Pine has launched cloud computer built for AI agents: developers create one through an SDK, give it a job in plain language, and it plans and executes the work across websites, files and apps instead of driving a computer designed for humans. Running GPT-5.6 Luna, Pine reports 1/20 the model-token cost of GPT-5.6 Sol + Codex on SaaS-Bench v1.1. Other results on SaaS-Bench v1.1: - 78.3% checkpoint score, highest in t

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flows; What’s New in version 1.0 Follow us on We’re excited to introduce the official release of LlamaIndex Workflows 1.0, a lightweight framework for building complex, multi-step agentic AI applications in Python and Typescript. We first introduced Workflows showcasing how to build reusable, event-driven applications powered by llama index . Since then, we’ve seen a growing need for developers to define complex AI application logic for agents without losing control of the execution flow. This f

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zing the core primitives of RAG: chunking, embedding, indexing, and retrieval. For basic question-answering workloads, that foundational blueprint worked perfectly. But enterprise agents have completely outgrown it. Traditional RAG treats data access as a static, one-shot preprocessing step. It pulls a handful of context fragments, bundles them into a prompt window, and blindly hopes for the best. Autonomous agents cannot navigate an unstructured corpus through a fuzzy semantic search bar. They

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