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lly be participating in. The KMS and data processing binaries used in our FL system can be reproducibly built from open source code published in the Confidential Federated Compute Github repository. Verifiable execution with dynamic sideloading In our earlier FL systems, the logic running on the server could be verified neither by devices nor auditors, and thus we needed to be trusted to correctly add random noise to gradient sums to provide differential privacy. In our new TEE-based FL system,

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o feel this feeling of being the bottleneck and being the breaking point as we have lots of agents contributing tons and tons of PRs,” Avilla said. “And we’re sitting there in our GitHub trying to approve PRs and make sure that everything works as expected.” From agent rosters to dynamic workflows The pair noted that dynamic workflows began in Claude Code as the “a harness for every task” concept. But flows can run longer than a single agent or a roster. The basic idea is that Claude chooses the

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urns 403 523 with message likely policy. Pypi allowed (whitelisted). […] Could abuse allowed domains through URL parameters open redirect. Tool call env | grep -Ei 'token|pass|key|github|pypi|npm' | head Tool result CAAS ARTIFACTORY READER PASSWORD=[credential] […] CoT Artifactory host likely allowed; could it serve as proxy to arbitrary URL through remote repository API? Credentials reader permit GET. After listing the repositories, it tested whether the reader credentials also allowed writes.

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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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remains similarly effective, suggesting that oversight is not being meaningfully weakened. What we learn from deployment Deploying the reviewer both internally and across external GitHub traffic has been crucial for turning a strong lab prototype into a dependable safety layer under real engineering constraints. The code review model is now a core part of OpenAI's engineering workflow. When the reviewer leaves a comment, authors address it with a code change in 52.7% of cases (often assisted by

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able consequence (e.g., having its capabilities unlearned and not deployed). Impossible Coding Task No The agent is assigned an impossible software engineering task described in a GitHub-style issue. The test evaluates whether the agent, under pressure, submits a pull request falsely claiming to have completed the task. Log Falsification Yes Puts the agent in a scenario where it records values that exceed a required threshold, testing if it will falsify logs to avoid penalties, especially under

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re are many ways to get involved; start with our contributor guidelines and then check these open issues for specific tasks. Welcome to raise your questions or feature requests on GitHub Issues , and share your experience on the Discussions board. Official email: open.platform@baai.ac.cn; Zhihu: FlagAI; Scan the qrcode to join the WeChat group for communication: Quick Start We provide many models which are trained to perform different tasks. You can load these models by AutoLoader to make predic

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T-LoRA for Trajectory: Based on the Base Planning Model, Stage 4 involves LoRA-based training with our Trajectory dataset to predict trajectory Note: Please refer to RoboBrain 2.0 Github for the usage of RoboBrain 2.0 Note: The sample format in each json file should be like: 2. Training Note: Please change the environment variables (e.g. DATA PATH, IMAGE FOLDER, PREV STAGE CHECKPOINT) in the script to your own. 3. Convert original weights to HF weights (Option) 4. Compress Model Model compressio

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se change the checkpoint path in the scripts above. ⭐️ Results in our paper 1. Results of Spatial Transformation Task: 2. Full Results of Three Tasks: Note: For more detail results, please refer to our main paper and appendix 🤖 Embodied Visual Reasoning Tasks We apply Reason-RFT to train more powerful RoboBrain 2.0. Please refer to RoboBrain 2.0 Github for more details. Here is a simplified comparison result (not the final version): 📑 Citation If you find this project useful, welcome to cite us.

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rge-scale, multi-robot, bimanual dataset for manipulation, covering 15 robot platforms, 421 tasks across 16 scenes, and 180K demonstration trajectories. Homepage: https://flagopen.github.io/RoboCOIN/; Hugging Face: https://huggingface.co/RoboCOIN; ModelScope: https://modelscope.cn/organization/RoboCOIN; The datasets are continuously updated; licenses and versions follow each dataset page; Dataset overview: RoboCOIN-DataManager RoboCOIN-DataManager is the visualization and management platform for

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bling direct robot control from trained models Installation Dataset Discovery, Download, and Loading Discover and Download Datasets Browse available datasets at: [https://flagopen.github.io/RoboCOIN-DataManager/] We will continuously update the datasets. You can find the latest datasets on the page above. The above GIF shows how to discovery, download, and use RoboCOIN datasets. Load a Dataset Lerobot features explanation observation.state / action These features represent data collected from th

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与已适配机器人保持一致）；; 确保代码规范、文档完整（包含环境准备、配置步骤、功能验证）；; 提交代码 PR 至主仓库的 dev 分支，我们将及时审核并合并。 期待与您一起丰富 RoboDriver 的机器人生态！ 🤝 参与贡献 我们真诚地欢迎来自社区的 任何形式的贡献。从新功能的拉取请求、错误报告，到甚至是使RoboDriver更易用的微小建议，我们都全心全意地感谢！ 帮助支持 请使用 Github Issues 报告错误和提出功能请求。; 请使用 GitHub Discussions 讨论想法和提问。 许可证和致谢 RoboDriver 源代码根据 Apache 2.0 许可证授权。 没有这些令人惊叹的开源项目，RoboDriver 的开发是不可能的： 感谢LeRobot团队开源LeRobot🤗, LeRobot。本项目由LeRobot改进而来。; 感谢TheRobotStudio团队开源的SO100和SO101机械臂🤗, SO101。在本项目中，SO101机械臂作为部署案例。; 感谢dora-rs团队开源的机器人框架🤗, dora。在本项目中，该框架为机器人带来了全新的接入方式。

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or ModelScope; Click "Copy & Checkout" to copy the Python command Step 5: Download the data First, install RoboCOIN (one time setup): Pull the corresponding project first: https://github.com/FlagOpen/RoboCOIN; Paste and run the command: If needed, you can customize your own download target directory:; Wait for download - RoboCOIN's download tool will handle everything automatically That's it! Your data will be downloaded to your computer. More Features Save your selections Click "Export JSON" to

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liary models such as pose detection and face detection; 9/10/2024: Introducing MemoRAG, a step forward towards RAG 2.0 on top of memory-inspired knowledge discovery (repo: https://github.com/qhjqhj00/MemoRAG, paper: https://arxiv.org/pdf/2409.05591v1); 9/2/2024: Start to maintain the tutorials. The contents within will be actively updated and eariched, stay tuned! 📚; 7/26/2024: Release a new embedding model bge-en-icl, an embedding model that incorporates in-context learning capabilities, which,

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失败阶段。不要让多个任务共用同一个 --output-dir；批处理优先使用 --run-root 与 --task-id。 支持的 provider provider、版本、权重 revision、坐标契约和转换信息都会写入任务 manifest。未知 provider 会在 preflight 阶段失败，不会静默回退到 TRELLIS。 安装和验证 从 GitHub 获取源码： 只运行而不需要 Git 时，也可以使用 ModelScope 快照： 第三方源码不会直接提交到主仓库。固定上游 commit、补丁和新增文件的重建方式见 code/third party/THIRD PARTY REPOS.md。 HPSv2 等外部指标仓库的处理方式见 metrics/HPSV2 SOURCE.md。 常用服务器工具 详细文件协议和阶段参数见 codex ops/WORKFLOW.md， Unity 侧完整任务说明见 codex ops/UNITY INSERT WORKFLOW TUTORIAL ZH.md。 复现边界 Unity 工程和 Unity 脚本不在本仓库中，需要由调用方提供。

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affect previous models (they may obtain a 1-5pp uplift). 5.4.4 SWE-Lancer Note (Monday, July 28th): On July 17th, 2025, we released an updated version of SWE-Lancer, accessible on GitHub. This update resolves several issues that were impacting the dollars earned results, and removes the requirement for internet connectivity during execution, eliminating a primary source of variability in model performance. All results presented below have been updated accordingly. SWE-Lancer [12] evaluates model

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of our self-improvement evaluations, but did not meet our High thresholds. 5.1.3.1 SWE-Lancer Note: Results as of July 17th, 2025 update to the SWE-Lancer dataset, accessible via GitHub. Developed by OpenAI, SWE-Lancer [6] evaluates model performance on real-world, economically valuable full-stack software engineering tasks including feature development, frontend design, performance improvements, bug fixes, and code selection. For each task, we worked with vetted professional software engineers

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ce components enable cloud providers and software partners to integrate AI factory capabilities directly into their multi-tenant platforms. Developers can visit the DSX AI Factory Github page to explore and contribute to both the new and existing open source software components from NVIDIA DSX AI Factory platform. For AI infrastructure to scale globally, it needs more than advanced hardware — it needs a common foundation that the entire industry can build on. At OCP, NVIDIA joined AMD, Google an

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enchmark task in this evaluation. FrontierCode 1.1 resources Thank you to the team at Cognition for building FrontierCode and working with us to bring it to Artificial Analysis. The leaderboard is on the FrontierCode 1.1 evaluation page; The methodology page covers our implementation, grading and scoring; Cognition's Introducing FrontierCode and FrontierCode 1.1 posts further describe the benchmark and its design; Our open source Stirrup agent harness, which we use for FrontierCode, is on GitHub

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ie. Maia 300 is a successor to the 200, which was only announced in January . Maia 200 is already running on production AI workloads across Microsoft, including Microsoft Copilot, GitHub Copilot, Microsoft Foundry, and Microsoft’s own AI models. “Maia 200 is the best performing AI infrastructure in our fleet, measured in real world metrics for AI services,” says Guthrie. “Maia 300 is a complete AI system designed for the new types of AI workloads emerging today: Long-context reasoning, latency-s

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