搜索“RAG”

VLM endpoint (e.g. vLLM or SGLang serving a vision-language model) for instruction generation and evaluation; a local FLUX checkpoint loadable by 🤗 diffusers; (optional) object-storage credentials if your source images live in object storage; local file input is fully supported as well Running the pipeline All commands are run from the repo root (so that config.py is on the Python path). Single-concept Output of each step lives next to its input: Multi-concept Identical commands with the multi p

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into a single, higher-dimensional tensor Merging Experts Convert a model with separate expert weights into the consolidated "merged" format. By merging expert weights, we can leverage highly efficient batched matrix multiplication on GPUs, significantly speeding up computation. How it Works: The script iterates through each MoE layer and stacks the weights of all experts (e.g., gate proj, up proj, down proj) into a single tensor. Usage: Splitting Experts This process performs the reverse operat

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action. A typical response has the following form: Coordinates are integer-normalized to [0, 999]. The action vocabulary is platform aware: Mobile: Click, DoubleClick, LongPress, Drag, Swipe, Type, LaunchApp, Wait, CallUser, GetScreenshot, device navigation, Answer, and Finished; Desktop: pointing actions, RightClick, Drag, Swipe, Type, Hotkey, Wait, CallUser, and Finished; Web: pointing actions, Drag, directional Scroll, Hover, Type, URL Launch, Hotkey, browser navigation, CallUser, and Finishe

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ource-reported action scaffolds may still differ. MobileWorld uses GUI-only success rate on 117 tasks with 50 steps, WebVoyager uses the refreshed 595-task split, Odysseys uses average rubric score over 200 tasks, VenusBench-CAPTCHA uses micro Pass@1 over all 219 examples, and VenusBench-GD uses English-instruction micro-average accuracy. “ ” denotes results reproduced by us. [2026/09] We release VenusBench-CAPTCHA, a comprehensive benchmark for evaluating GUI agents on diverse CAPTCHA tasks. Se

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mfyUI Support We provide native ComfyUI custom nodes for visual, node-based workflows. All three capabilities (text-to-image, image understanding, image editing) are available as drag-and-drop nodes. Installation Or use the one-line installer: Available Nodes Workflow Examples For full documentation, see apps/comfyui/README.md. 🚀 SGLang Support We now support SGLang for high-throughput serving and optimized inference. For detailed configuration and examples, please refer to cookbook. ⚠️ License

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supports in-place GPU memory updates on inference, avoiding costly allocation and copying; Multi-Mode Transfer Support: Support NCCL, RDMA, and shared memory transfer mode to leverage NVLink/NVSwitch/RDMA bandwidth and reduce long-tail latency; Heterogeneous Deployment Compatibility: Fully supports co-location and separation modes, make RL sync/async algorithms runs seamlessly; Extensibility: Easily extends to support new training and inference engines Architecture The Awex weight exchange fram

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e warp-group roles, WGMMA dataflow, pipeline stages, recurrent-state placement, and fixed/packed scheduling modes. Just change the import: Notes: safe gate=True is required to leverage TensorCore acceleration; beta supports both float32 and bfloat16; initial state must be float32; cu seqlens (for variable-length sequences) must be int32 Usage See USAGE.md for detailed usage examples and notes. Benchmarks Benchmarks run on a single NVIDIA GB200/H200 GPU with PyTorch 2.9.1, Triton 3.5.1. FLA basel

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t, OCRBench v2.1 denotes our revision of OCRBench v2: audited and corrected reference annotations together with task-aligned scoring procedures. It preserves the original task coverage. These scores should be compared only with results using the same revised annotations and protocol; original OCRBench v2 scores are not directly comparable. The report appendix documents the changes. Evaluation OCRBench evaluation is one component of the PolyOCR project. The implementation is organized under evalu

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ngzhuang Xu, Cunyin Peng, etc; Exploring Superior Function Calls via Reinforcement Learning. arxiv, 2025. paper, code Bingguang Hao, Maolin Wang, Zengzhuang Xu, Yicheng Chen, etc; RAG-R1 : Incentivize the Search and Reasoning Capabilities of LLMs through Multi-query Parallelism. arxiv, 2025. paper, code, model Zhiwen Tan, Jiaming Huang, Qintong Wu, Hongxuan Zhang, Chenyi Zhuang, Jinjie Gu; V2P: From Background Suppression to Center Peaking for Robust GUI Grounding Task. arxiv, 2025. paper, code

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ACL uses native netfilter and ipset with the iptables backend and TC eBPF with the bpfnat backend. Both support prioritized stateful IPv4 CIDR, domain, protocol, and port rules, fragments, and managed DNS policies. See the network ACL guide for its API, host requirements, limitations, and recovery behavior. Build and test networkacl-test runs one backend-neutral conformance suite against native iptables and TC eBPF enforcement in isolated network namespaces. It covers allow and deny precedence,

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ernel solves this with five key advantages: Datacenter Use: Unified Programming Interface A single Python SDK (akernel sdk) to programmatically control compute, networking, and storage — no YAML, no manual orchestration. Multi-language support is planned. One all-in-one image, multiple deployment targets — deploy in under 10 minutes: Secure Isolation with Extreme Performance 40 ms cold start : Fork-based launch with lazy loading for near-zero startup latency; Sandbox isolation: gVisor by default

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Pack version recoverable (docs) avernet.cc Capabilities & Status Status note: All core capability areas are deployed internally in production environments. Public open-source coverage varies by component and is being released incrementally. Legend: Available = usable in the public repo now · Partial = partially public · In progress = being opened or integrated · Planned = intended but not yet public Trusted core Identity, auth, permissions, security, audit, and lifecycle management for agents a

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hareRobot achieves a 30.2% improvement in task decomposition accuracy (BLEU-4 reached 55.05%), significantly better than existing methods; Affordance perception capability: The average accuracy (AP) of object affordance area recognition is 27.1%, which is 14.6% higher than the baseline model. Trajectory prediction capability: End-effector trajectory prediction error reduced by 42.9% (DFD index decreased from 0.191 to 0.109); General capability: In the OpenEQA benchmark, the scene understanding s

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avigation NotificationsYou must be signed in to change notification settings; Fork4 (4); Star9 (9)You must be signed in to star a repository Repository files navigation README Leverage MCP's standardization capabilities to provide a unified vendor integration specification, creating an embodied robot universal skill store that enables one-click integration of robot skills with robot frameworks. Robot skills are registered according to the MCP specification; Organized in a hierarchical structure

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to introduce RoboBrain-X0, a groundbreaking cross-ontology foundation model designed to overcome the limitations of single-robot systems in heterogeneous ontology transfer. By leveraging End-Effector pose representation in SE(3) task space, coupled with a Unified Action Vocabulary (UAV) and action tokenizer, RoboBrain-X0 achieves efficient zero-shot generalization and complex task decomposition. Its Grouped Residual Quantizer (GRVQ) maps continuous control sequences from diverse degrees of freed

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al examples of the three inference modes (Incremental-Mode, Forward-Mode, and Backward-Mode). In practice, to predict the task state reward more accurately, we highly recommend averaging the inference reward results from all three modes to use as the final reward in RL. ✨ More Cases for Testing Many thanks to Robometer for providing more interesting test examples 🤗. To better demonstrate the usage of 'single-view without goal image' with our latest Robo-Dopamine-GRM-2.0-8B-Preview model, we also

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age generation tasks without the need for additional plugins like ControlNet, IP-Adapter, or auxiliary 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 embeddin

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first-party and third-party use (API and Enterprise) unless customers meet strict criteria for removal. To prevent CSAM generation, we have built a dedicated safety stack that leverages system mitigations used across our other products as well as additional safeguards developed specifically for Sora. For full details on our specific CSAM safety stack can be found on our Sora System Card1. OpenAI Sora System Card: https://openai.com/index/sora-system-card/↩︎; Teen Safety: Sora 2 has a number of a

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eroffensive challenges: Evasion: Covers the skills needed to avoid detection by security controls and monitoring systems; Network Attack Simulation: Assesses understanding and leveraging of commonly used attack flows, reconnaissance methods, and network protocols and components (such as firewalls and file servers); Vulnerability Discovery and Exploitation: Tests capabilities in reverse engineering, code analysis, cryptography, and exploitation Compared to o3-mini, o3 showed improved cyberoffensi

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mentally trained from GPT-5.5 with training data related to beneficial life sciences capability. Unlike GPT-5.5, it is trained not to refuse sophisticated biology queries, and leverages a trusted access and responsible deployment structure as the primary safeguard. 3. Beneficial Capability Evaluations The Beneficial Capability Evaluations below measure how well GPT-Rosalind-5.5 performs on the intended research-support tasks it was designed to improve. Preparedness evaluations, in the following

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