🚀 Introducing FlashQLA: high-performance linear attention kernels built on TileLang. ⚡ 2–3× forwar... - 惯性聚合

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Qwen(@Alibaba_Qwen)

We open-source Qwen-AgentWorld-35B-A3B (MoE, 35B/3B active, 256K context) and AgentWorldBench. Two ... 🧠 Paradigm II — Agent Foundation Model: world modeling as agent capability. Single-turn, non-agent... 📣📣 Meet Qwen-AgentWorld — a native language world model that simulates 7 agent environments (MCP,... Qwen-Robot Suite，enable AI from chatbot to physical action in the real world. More demos, please vis... By treating natural language as a universal action interface，Qwen-RobotWorld bridges the gap betwee... 📣 Introducing the Qwen-Robot Suite — Qwen-RobotNav, Qwen-RobotManip, Qwen-RobotWorld, three foundat... Demo3：Browser Agent Demo2: Multimodal Interactive Hybrid Agent Performance1：Text Benchmarks Qwen3.7-Plus delivers competitive text performance that approaches Max... 👏👏 Introducing Qwen3.7-Plus — a multimodal agent model that unifies vision and language into one v... 📢Qwen3.7-Max just hit #3 on ITbench-AA — a fresh benchmark testing how well models handle real-worl... Fast, faster, Qwen. 🚀 Thrilled to see Qwen3.5 reaching a record-breaking 580 tps for agentic workl... 🚀🚀 🙌🙌 🚀🚀 Qwen3.7-Max just hit #4 on Code Arena, on par with Claude Opus 4.6 ，top-ranked Chinese lab on t... ✅Implicit caching is now live on Qwen3.7-Max — kicks in automatically, no setup needed. ⚡️Faster + ... ⚡️⚡️ 👀👀 🚀Qwen3.7-Max just landed at 56.6 on the Artificial Analysis Intelligence Index — a solid 4.8pt jump... Cowork Productivity Assistant：Qwen3.7-Max serves as your advanced coworker for real-world productivi... Self-Evolving in the Wild：Over the course of ~35 hours of continuous autonomous execution, the model... Cross-Harness Generalization：Across QwenClawBench and CoWorkBench, Qwen3.7-Max delivers strong, cons... Agent Scaling：Building on Qwen3.5's environment scaling approach, we've aggressively expanded the qu... Performance：Qwen3.7-Max performs strongly across benchmarks in coding agents , and improves massivel... 📣Meet Qwen3.7-Max — our latest flagship, made for the Agent Era. A versatile foundation for agents... 🚀🚀 🚀🚀Qwen3.7 Preview lands on Arena ！ Here come Qwen3.7-Max-Preview & Qwen3.7-Plus-Preview. Ali... 🚀Qwen3.6-Plus is on Nous Portal now and FREE for a limited time. Hermes Agent, here we go!! ⚡️ @Nou... 📣We're calling for ambassadors! Whether you're a developer with great technical taste or a local c... 📢 Official Announcement: Qwen Partners with Fireworks AI to Accelerate Access to Qwen Family Models... Today we’re releasing Qwen-Scope 🔭, an open suite of sparse autoencoders for the Qwen model family.... Forward and backward benchmark results across common configurations. 🚀 Introducing FlashQLA: high-performance linear attention kernels built on TileLang. ⚡ 2–3× forwar... Balanced Performance Across Artistic Styles： More uniform quality across diverse aesthetic domains, ... Comprehensive Multilingual Text Rendering： Better glyph accuracy, more consistent typography, and cl... Qwen-Image-2.0-Pro is now live 🚀🚀 We’ve pushed image quality, multilingual text rendering, and in... 👀👀 🐦🐦‍⬛🐤 Available on @ollama ! 🤝🤝 ⚡️⚡️Run Qwen3.6-27B locally! @UnslothAI Day 0 vLLM support for Qwen3.6-27B! @vllm_project ♥️❤️ Thanks to @lmsysorg ！ Try it on SGLang now!🚀🚀 VLM Performance：Qwen3.6-27B is natively multimodal, supporting both vision-language thinking and non... LM Performance：With only 27B parameters, Qwen3.6-27B outperforms the Qwen3.5-397B-A17B (397B total /... 🚀 Meet Qwen3.6-27B, our latest dense, open-source model, packing flagship-level coding power! Yes,... Keep improving 🚀🚀 @arena 🚀 Introducing Qwen3.6-Max-Preview, an early preview of our next flagship model Highlights： ⚡️ Impr... VLM Performance：Qwen3.6 is natively multimodal, and Qwen3.6-35B-A3B showcases perception and multimo... LM Performance：Qwen3.6-35B-A3B outperforms the dense 27B-param Qwen3.5-27B on several key coding ben...

🚀 Introducing FlashQLA: high-performance linear attention kernels built on TileLang. ⚡ 2–3× forwar...

Qwen (@Aliba · 2026-04-29 · via Qwen(@Alibaba_Qwen)

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