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达成，这将成为DeepSeek于2023 年创立以来，规模最大的初创企业投资之一。企查查信息显示，在投资Winverse之前，DeepSeek已投资宇树科技，其持股比例为0.2308%，持股数为933,399股，以合作开发具身智能模型。 据悉，Winverse AI创立于2026年，专注于机器人AI模型研发，目前尚未对外披露太多信息。 根据该公司创始人胡瀚的GitHub个人主页显示，他于2008年获得清华大学学士学位，并于2014年获得清华大学博士学位，师从周杰教授。 胡瀚在创立Winverse AI前是腾讯杰出科学家，负责混元视觉语言模型（VLM）和具身基础模型团队，曾担任混元语言模型技术负责人，并创立了混元语音团队。 在加入腾讯混元之前，胡瀚还曾任职于微软亚洲研究院和百度，在微软亚洲研究院（MSRA）任职期间担任首席研究员和研究经理。 ▲胡瀚的GitHub个人主页 此外，胡瀚参与的论文《Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows》获得了 ICCV 2021 最佳论文奖（Marr Pr

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tSAM-Ti，并新增照片转3D、单目照片生成世界坐标对齐深度信息等演示。 在空间交互方面，此次更新新增可滚动视图、可编辑文本框及支持调整尺寸的卡片组件，同时优化图标渲染方式，以减少绘制调用次数。开发者还可以通过新增的生成式AI示例，将AI生成的虚拟物体放置在真实环境表面，并利用ArUco标记追踪功能实现虚实物体定位。 此外，桌面模拟器新增昼夜光照模式及独立性能分析工具，Roomcraft示例加入跨设备协作功能。底层工具方面，XR Blocks将Gemini模型调用迁移至Interactions API，默认Flash模型更新为Gemini 3.8 Flash，并将three.js升级至r186版本。 不过，Google尚未公布上述端侧AI功能在具体XR设备上的运行性能、延迟或兼容性数据，相关功能目前主要面向开发者测试与原型验证。 来源：Github 投稿/爆料：tougao@youxituoluo.com 稿件/商务合作： 林南（微信 19250561593） 加入行业交流群：林南（微信 19250561593） 元宇宙数字产业服务平台 下载「陀螺科技」APP，获取前沿深度元宇宙讯息

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nfirmed 5,674 of the 6,123 findings they reviewed as valid, but only 516 vulnerabilities had been patched upstream. The company sent 6,157 findings to maintainers, and 584 CVE and GitHub Security Advisory identifiers were issued, though some findings received both. Roughly 23,000 candidates remain unreviewed. Claude is finding potential vulnerabilities faster than human researchers can verify them. How the fast track works Instead of making maintainers wait for its researchers to work through th

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at is a Google Calendar, a cryptocurrency wallet, or a web browser, and makes it available to any compatible AI agent. Because these servers are published on public platforms like GitHub, tracking them provides visibility into what tools developers are building for agents and how widely those tools are being adopted. Key findings The scale of AI agent tooling is rising quickly: Over the last year, the number of publicly released MCP tools rose from around 5,000 to 177,000, accompanied by a subst

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us Tasks’ Benton et. al. 2024, 'Sabotage Evaluations for Frontier Models' METR Example Task Suite (Public) Jimenez et. al. 2024, 'SWE-Bench: Can Language Models Resolve Real-World Github Issues' Chan et. al. 2024, 'MLE-Bench: Evaluating Machine Learning Agents on Machine Learning Engineering' The following resources provide useful guidance on agent scaffolds: Wang et. al. 2023, 'A Survey on Large Language Model based Autonomous Agents' Masterman et. al. 2024, 'The Landscape of Emergent AI Agent

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ark is implemented as capture-the-flag-style challenges using Inspect, AISI’s open-source framework for AI evaluations, and the SandboxEscapeBench open-source code is available on GitHub. What we found Advanced models can reliably escape common misconfigurations when prompted to do so. In evaluation environments, the models we tested were frequently able to break out of sandbox environments in scenarios involving misconfigurations that are common in the real world, such as exposed Docker sockets

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zon tasks were run 5 times per variant (none fully completed, some models reached initial milestones) References AutoGen Team. (2024). Multi-Agent Experiment V 0.1. Retrieved from GitHub: github.com/microsoft/../tools/autogenbench/scenarios/GAIA/Templates/Orchestrator; Carnegie Mellon University. (2024). picoCTF. Retrieved from picoCTF: https://picoctf.org/; Chan, A., Salganik, R., Markelius, A., Pang, C., Rajkumar, N., Krasheninnikov, D., . . . Krueger, D. (2023). Harms from Increasingly Agenti

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s that NVLink-Sharp in-switch reductions are deterministic on Blackwell as well as Hopper with CUDA 12.8+. As is the case with many things, this information can be found on NCCL’s github issues Conveniently, these are also ordered in ascending levels of difficulty. Each one requires some additional considerations to achieve batch invariance with reasonable performance. Let’s talk about RMSNorm first. Batch-invariant RMSNorm RMSNorm can be implemented as: The requirement for batch invariance is t

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Organization blocking; Mutual TLS; Workload identity federation Federation rules; X.509 certificates; Kubernetes; AWS; Microsoft Azure; Google Cloud; Oracle Cloud Infrastructure; GitHub Actions; SPIFFE; IP egress ranges; Amazon Bedrock Operations Rate limits; Spend limits; Admin APIs; Error codes Home; Quickstart Core concepts Plugin architecture; Skills; MCP server Plan Brainstorm use cases; Define tools Build Build an MCP server; Add UI to your MCP server (optional); Add events to your MCP se

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Organization blocking; Mutual TLS; Workload identity federation Federation rules; X.509 certificates; Kubernetes; AWS; Microsoft Azure; Google Cloud; Oracle Cloud Infrastructure; GitHub Actions; SPIFFE; IP egress ranges; Amazon Bedrock Operations Rate limits; Spend limits; Admin APIs; Error codes Home; Quickstart Core concepts Plugin architecture; Skills; MCP server Plan Brainstorm use cases; Define tools Build Build an MCP server; Add UI to your MCP server (optional); Add events to your MCP se

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Organization blocking; Mutual TLS; Workload identity federation Federation rules; X.509 certificates; Kubernetes; AWS; Microsoft Azure; Google Cloud; Oracle Cloud Infrastructure; GitHub Actions; SPIFFE; IP egress ranges; Amazon Bedrock Operations Rate limits; Spend limits; Admin APIs; Error codes Home; Quickstart Core concepts Plugin architecture; Skills; MCP server Plan Brainstorm use cases; Define tools Build Build an MCP server; Add UI to your MCP server (optional); Add events to your MCP se

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Organization blocking; Mutual TLS; Workload identity federation Federation rules; X.509 certificates; Kubernetes; AWS; Microsoft Azure; Google Cloud; Oracle Cloud Infrastructure; GitHub Actions; SPIFFE; IP egress ranges; Amazon Bedrock Operations Rate limits; Spend limits; Admin APIs; Error codes Home; Quickstart Core concepts Plugin architecture; Skills; MCP server Plan Brainstorm use cases; Define tools Build Build an MCP server; Add UI to your MCP server (optional); Add events to your MCP se

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Organization blocking; Mutual TLS; Workload identity federation Federation rules; X.509 certificates; Kubernetes; AWS; Microsoft Azure; Google Cloud; Oracle Cloud Infrastructure; GitHub Actions; SPIFFE; IP egress ranges; Amazon Bedrock Operations Rate limits; Spend limits; Admin APIs; Error codes Home; Quickstart Core concepts Plugin architecture; Skills; MCP server Plan Brainstorm use cases; Define tools Build Build an MCP server; Add UI to your MCP server (optional); Add events to your MCP se

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Organization blocking; Mutual TLS; Workload identity federation Federation rules; X.509 certificates; Kubernetes; AWS; Microsoft Azure; Google Cloud; Oracle Cloud Infrastructure; GitHub Actions; SPIFFE; IP egress ranges; Amazon Bedrock Operations Rate limits; Spend limits; Admin APIs; Error codes Home; Quickstart Core concepts Plugin architecture; Skills; MCP server Plan Brainstorm use cases; Define tools Build Build an MCP server; Add UI to your MCP server (optional); Add events to your MCP se

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ttach skills directly to your agents. Define your agents using folders and files. Record and replay your agent's browser sessions. Your agents can now react to webhook events from GitHub, Slack, Stripe, or your own API. Build and control long-running agent experiences with sessions, modes, permissions, subagents. Introducing Durable Agents Survive client disconnects, browser refreshes, or network blips with cacheable resumable streams. Send or receive events with Redis Streams and Google Cloud P

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tutorial on building a movie recommendation agent with vector search and Observational Memory — all on a single MongoDB Atlas cluster. Automating multi-language PR descriptions in GitHub Actions Build a GitHub Action that uses Mastra to read pull request diffs and generate multi-language PR descriptions. Build a research coordinator with supervisor agents Build a research coordinator that orchestrates specialized agents to research topics and produce detailed reports. Building a code review bot

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Notebooks provides managed Python notebooks for shared evaluations and custom analytics. CoreWeave ARIA analyzes runs, proposes experiments, and recommends code changes stored in GitHub. Its integration with Weights & Biases Models supports autoresearch based on detected signals. Keep the evaluation evidence alongside any proposed change so the team can judge the result. CoreWeave Sandboxes provides isolated central processing unit (CPU) or graphics processing unit (GPU) environments for agents

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Team. (2026). Kimi K2.5: Visual agentic intelligence. arXiv. https://doi.org/10.48550/arXiv.2602.02276; Kimi K2 Thinking. Moonshot AI. (2025). Kimi K2 Thinking. https://moonshotai.github.io/Kimi-K2/thinking.html; Qwen3. Yang, A., Yang, B., Hui, B., et al. (2025). Qwen3 technical report. arXiv. https://doi.org/10.48550/arXiv.2505.09388; OLMo 3. Team OLMo. (2025). OLMo 3. arXiv. https://doi.org/10.48550/arXiv.2512.13961; GPT-5. OpenAI. (2025). OpenAI GPT-5 system card. arXiv. https://doi.org/10.48

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run for most independent researchers, we've also uploaded SQL databases containing the max activating examples for each feature. Usage instructions are available on the project's GitHub. Our SAEs have learned many features which reconstruct behaviors core to reasoning models, such as backtracking. Here are a few curated features from our general reasoning SAE: Reasoning models and mechanistic interpretability At Goodfire, we build tools for mechanistic interpretability: the scientific effort to

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s well as the max-activating example visualizations in later sections using Anthropics open-source circuit visualisation tools[5]Attribution Graphs Frontend[link] Anthropic, 2025. GitHub.. The graph itself is generated by our own reimplementation of the graph construction and pruning algorithms from Ameisen et al. [6]Circuit Tracing: Revealing Computational Graphs in Language Models[link] Ameisen, Emmanuel, Lindsey, Jack, Pearce, Adam, Gurnee, Wes, Turner, Nicholas L., Chen, Brian, Citro, Craig,

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