KernelMosaic用優化軌跡增強CUDA內核生成
Computer Science > Machine Learning arXiv:2611.
14604 (cs) [Submitted on 10 Nov 2026] Title:KernelMosaic: An Open Dataset and Agentic Framework for CUDA Kernel Generation Authors:Ningyuan Xi, Yuhang Lai, Ning Miao View a PDF of the paper titled KernelMosaic: An Open Dataset and Agentic Framework for CUDA Kernel Generation, by Ningyuan Xi and 2 other authors View PDF HTML (experimental) Abstract:Training large language models to generate efficient CUDA kernels requires supervision spanning diverse computational requirements and iterative code optimization.
Constructing this supervision is challenging because of heterogeneous specifications, and useful implementations emerge through iterative complicated testing and refinement.We introduce KernelMosaic, an open dataset pairing diverse CUDA tasks with verified solutions and trajectories.
Human-guided review and diversity-aware selection curate tasks expressed as natural-language descriptions, reference programs, mathematical specifications, and their intersections.
To build the solutions and trajectories, our agentic framework guides a strong LLM through implementation, correctness repair, and performance optimization with reusable CUDA guidance and execution feedback.
Supervised fine-tuning of a small open-source model on KernelMosaic raises correctness from 4%, 0%, and 0% to 88%, 82%, and 16% on KernelBench L1, L2, and L3, respectively, while increasing the fraction of tasks with kernels faster than PyTorch eager at every level.
Further training experiments show that supervision from the curated trajectories substantially outperforms direct responses and promotes sustained testing and revision.These results demonstrate that KernelMosaic enables a general-purpose model to achieve substantial gains in CUDA kernel generation.
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2611.14604 [cs.LG] (or arXiv:2611.14604v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2611.
14604 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Ningyuan Xi [view email] [v1] Tue, 10 Nov 2026 17:45:06 UTC (679 KB) Full-text links: Access Paper: View a PDF of the paper titled KernelMosaic: An Open Dataset and Agentic Framework for CUDA Kernel Generation, by Ningyuan Xi and 2 other authorsView PDFHTML (experimental)TeX Source view license Additional Features Audio Summary Current browse context: cs.
LG < prev | next > new | recent | 2026-11 Change to browse by: cs cs.AI References & Citations SciXGoogle Scholar Semantic Scholar export BibTeX citation Loading...BibTeX formatted citation × loading...
Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.
ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?
) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.
AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) IArxiv recommender toggle IArxiv Recommender (What is IArxiv?
) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy.arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community?Learn more about arXivLabs.Which authors of this paper are endorsers?| Disable MathJax (What is MathJax?)
Related
相關文章
DecArena檢驗十萬工具規模的去中心化協作
Computer Science > Multiagent Systems arXiv:2611.15521 (cs) [Submitted on 11 Nov 2026] Title:DecArena: Evaluating Large-Scale Decentralized Multi-Agent Collaboration Authors:Jiali Cheng, Oleg Rokhlenko, Joo-Kyung Kim, Ha。
Build a Robot Pick-and-Place Simulation with SimReady Assets
Back to Articles Build a Robot Pick-and-Place Simulation with SimReady Assets Enterprise + Article Published November 13, 2026 Upvote - Dillon Bailey dillonb-nv Follow nvidia Pomi Lee pomilee Follow nvidia Rishabh Chadha rchadha-nv Follow nvidia This walkthrough showcases a beverage can pick-and-place task that uses the UR17G arm, Robotiq 2F-140 gripper, and beverage can to compose a scene in Isaac Sim, check their behavior, and run a pick-and-place example with agent-generated controls.
蹲守北京黑客松現場:機器人主持婚禮、變臉,60支極客小隊連夜攢局手搓Demo
作者 | 程茜 編輯 | 漠影 60支隊伍、40+小時,一場充滿想象力的極客盛宴在中關村上演。 現場圍觀機器人主持婚禮、表演變臉;戴上眼鏡能實時識別飛鳥,也可以用AI眼鏡輔助聽障群體教學實操;各類Agent即將奔赴外貿、教育場景落地試用。 這些腦洞十足又緊貼真實場景需求的作品,都來自剛剛收官的Junction China 2026國際極客嘉年華。 作為歐洲極具影響力的老牌國際黑客松品牌,Junction此番首次落地北京,共設置了AI、機器人和可穿戴設備三條賽道。
為Agent全生命週期量身定製!海光掏出全家桶方案,打通Agent落地每一層
作者 | 程茜 編輯 | 漠影 一場由智能體驅動的應用變革,正在撬動AI底層算力體系重塑。 今年9月,IDC發佈的《2026年中國人工智能計算力發展評估報告》給出一組關鍵預判:全球AI算力需求滿足率自2024年79%持續下行,2027年跌至71%的谷底,直至2030年才回升至77%。 有一個簡單邏輯可以概括算力消耗的變化:Token消耗量≈多智能體協同×單智能體資源消耗×單任務執行次數。智能體的持續運行模式,正在持續放大AI基礎硬件的負載壓力。
靈睿智芯完成數億元Pre-A+輪融資,產業資本密集押注
本文作者: 陳悅琳 2026-11-13 09:52 導語:目前,基於P100的智能體CPU研發正緊鑼密鼓推進,並與國內頭部系統廠商達成合作意向。近日,上海靈睿智芯計算技術有限公司(以下簡稱“靈睿智芯”)宣佈完成數億元Pre-A+輪融資。本輪融資由美團戰投領投,京東關聯基金、乾杯基金、力合算芯基金(東方算芯CVC)、鈞犀資本、商湯國香資本、尚頎資本(上汽CVC)、深信服科技的產業投資基金琥珀資本等頭部產業資本和眾多老股東跟投。
CopilotKit Releases OpenBot 1.0: An MIT-Licensed, Self-Hostable AI Coworker With Built-In Governance
Most AI coworker products are vendor locked. They pick the model, host the data, and write the rules. CopilotKit is taking the opposite route. OpenBot 1.0, was released November 9th, an MIT-licensed, self-hostable agent workspace.