@Alibaba_Qwen
Open foundation models for AGI.
📣📣 Meet Qwen-AgentWorld — a native language world model that simulates 7 agent environments (MCP, Search, Terminal, SWE, Web, OS, Android) within a single model. Environment modeling is the training objective from day one, not a post-hoc adaptation. 🤔 LLMs are trained to be better agents — better at acting in environments. But nobody has trained them to model the environments themselves. 🗺️ Our roadmap: investigate how language world modeling can push the boundaries of general agent capabilities, along two routes: 1️⃣ Build a foundation model for environment simulation — outperforming Claude Opus 4.8 and GPT-5.4 on AgentWorldBench 2️⃣ Investigate how world modeling enhances agent training: 🔬 Controllable Sim RL (agentic RL with LWM as environments) surpasses training in real environments 🧠 Learning to predict environments (LWM warm-up) makes agents stronger — remarkably, even without any agent-specific training, this predictive knowledge transfers to agentic tasks with zero fine-tuning 📑 Paper: t.co/Jx2l5RKq71 📖 Blog: t.co/7tVcKyhsx2 💻 GitHub: t.co/B5Lvb1UZCn 🤗 HuggingFace: t.co/Kw3QBL1TM5 🧩 ModelScope: t.co/YBnGYgMWWI
Part I: Building the Foundation Model for Environment Simulation Faithful environment simulation requires multi-step causal reasoning, stateful tracking, and domain-specific knowledge. Frontier LLMs have some simulation ability from pretraining — but it's incidental, not an objective. So we train from scratch: CPT injects environment knowledge → SFT activates next-state prediction as a thinking pattern → RL sharpens simulation fidelity with hybrid rewards.
📣 Introducing the Qwen-Robot Suite — Qwen-RobotNav, Qwen-RobotManip, Qwen-RobotWorld, three foundation models, a full stack for embodied intelligence. 🧭 Qwen-RobotNav — the gateway to mobility. • Unifies 5 navigation tasks in one model: instruction following, point-goal, object-goal, target tracking, autonomous driving • Controllable observation protocol • Tool interface for agentic systems 🤖 Qwen-RobotManip — the foundation of interaction. • Unified state-action space across heterogeneous robots • Camera-frame delta poses for coherent cross-embodiment training • Pretrained on a 38,100+ hour open-source corpus 🌍 Qwen-RobotWorld — infinite worlds for physical agents. • Single world model, 20+ embodiments • Natural-language action interface • Predicts physically grounded futures across manipulation, driving, and navigation Each model is independently useful, and could be composed as physical-world tools.Together, they form the low-level toolkit for general-purpose agentic systems that don't just see the world, but act in it. 📷 Blog: t.co/ytLcbYET26 📖 Report： Qwen-RobotNav: t.co/uPmSwDYGxg Qwen-RobotManip: t.co/GeyIzJSpU8 Qwen-RobotWorld： t.co/SXPH1qzDFy
Qwen-RobotNav：a scalable navigation model built on Qwen3-VL that addresses this through a parameterised interface with two complementary dimensions: task modes that select the navigation behaviour, and controllable observation parameters (token budget, temporal decay, per-camera weights) that govern how visual history is encoded. Trained on 15.6 million samples with training-time randomization over all parameters, Qwen-RobotNav generalizes to any inference-time configuration without architectural modification, unifying five task families under a single set of weights and serving as a natural building block for agentic systems. Here's the blog link to know more about Qwen-RobotNav: t.co/h4GjQxSYuJ
👏👏 Introducing Qwen3.7-Plus — a multimodal agent model that unifies vision and language into one versatile agent foundation. ✅ Multimodal interactive hybrid agent: unified GUI & CLI operation across visual and text tasks ✅ Versatile coding agent & productivity assistant with
Performance1：Text Benchmarks Qwen3.7-Plus delivers competitive text performance that approaches Max-tier models across the board.
📢Qwen3.7-Max just hit #3 on ITbench-AA — a fresh benchmark testing how well models handle real-world enterprise IT tasks, agentic-style. 🔧Agentic era, go with Qwen.🏃🏃 x.com/ArtificialAnly…
Fast, faster, Qwen. 🚀 Thrilled to see Qwen3.5 reaching a record-breaking 580 tps for agentic workloads on the TokenSpeed engine! This milestone wouldn't be possible without our incredible partners. Huge thanks to @lightseekorg, @NVIDIAAI, the Mooncake team, and @tri_dao for x.com/PyTorch/status…
🚀🚀 Qwen3.7-Max just hit #4 on Code Arena, on par with Claude Opus 4.6 ，top-ranked Chinese lab on the board! @arena More to ship. Stay tuned. 🕶️ x.com/arena/status/2…
✅Implicit caching is now live on Qwen3.7-Max — kicks in automatically, no setup needed. ⚡️Faster + cheaper out of the box. Need higher, more deterministic hit rates? Try explicit caching instead. 🙌 🔗Best practices 🔗 ：alibabacloud.com/help/en/model-…
🚀Qwen3.7-Max just landed at 56.6 on the Artificial Analysis Intelligence Index — a solid 4.8pt jump over Qwen3.6-Max-Preview. @ArtificialAnlys ⚡️Sharper sci reasoning, stronger agentic chops, better coding, and it hallucinates less. x.com/ArtificialAnly…
📣Meet Qwen3.7-Max — our latest flagship, made for the Agent Era. A versatile foundation for agents that actually get things done: 🧑💻 Coding agent, end to end. Frontend prototypes, multi-file refactors, real debugging — nails it. 🗂️ A reliable office and productivity assistant.
Performance：Qwen3.7-Max performs strongly across benchmarks in coding agents , and improves massively in general-purpose agents. Qwen3.7-Max also demonstrates exceptional strength on the hardest reasoning benchmarks, and stands out in general capabilities and multilingualism.
