The QwenLM team has released FlashQLA, a new kernel library that dramatically accelerates the forward and backward passes of Gated Delta Network (GDN) Chunked Prefill, targeting both large-scale pretraining and edge-side agentic inference scenarios.
The post Qwen Team Releases FlashQLA: a High-Performance Linear Attention Kernel Library That Achieves Up to 3× Speedup on NVIDIA Hopper GPUs appeared first on MarkTechPost.
We break down Qwen-RobotSuite, the Qwen team's three new embodied AI models. We cover RobotManip, a Vision-Language-Action model built on Qwen3.5-4B for manipulation. We cover RobotWorld, a language-conditioned video world model with a 60-layer MMDiT. We cover RobotNav, a navigation model built on Qwen3-VL across 2B, 4B, and 8B sizes. We walk through the architecture, data pipelines, and benchmark results for each.
The post Meet Qwen-RobotSuite: Three Embodied AI Models for VLA Manipulation, Video World Modeling, and Navigation appeared first on MarkTechPost.
Qwen3.7-Plus is Alibaba's multimodal agent model on Bailian, understanding images and video while adding self-programming and tool invocation.
The post Alibaba’s Qwen Team Launches Qwen3.7-Plus, Adding Vision, Deep Reasoning, Tool Invocation, and Autonomous Iteration on the Bailian Platform appeared first on MarkTechPost.
Alibaba’s Qwen team has unveiled Qwen3.7-Max, a flagship model built for the agent era. Unlike conventional chatbot-focused LLMs, it is designed as a foundation for autonomous AI agents that can code, debug, use tools, manage workflows, and execute long-running enterprise tasks. Alibaba claims the model can operate autonomously for up to 35 hours without performance […]
The post Qwen3.7-Max: Alibaba’s New Agent-First LLM for Coding, Reasoning, and Long-Horizon AI Workflows appeared first on Analytics Vidhya.
Alibaba's Qwen team introduced Qwen3.7-Max at the 2026 Alibaba Cloud Summit, describing it as its most advanced and comprehensive agent model to date. The model features a 1M-token context window, extended-thinking mode, and is designed for long-horizon tasks including coding, debugging, and multi-step workflow automation. It scored 56.6 on the Artificial Analysis Intelligence Index, ranking fifth overall among proprietary models.
The post Qwen Introduces Qwen3.7-Max: A Reasoning Agent Model With a 1M-Token Context Window appeared first on MarkTechPost.
Alibaba’s Qwen Team has released Qwen3.6-27B, the first dense open-weight model in the Qwen3.6 family — and arguably the most capable 27-billion-parameter model available today for coding agents. It brings substantial improvements in agentic coding, a novel Thinking Preservation mechanism, and a hybrid architecture that blends Gated DeltaNet linear attention with traditional self-attention — all […]
The post Alibaba Qwen Team Releases Qwen3.6-27B: A Dense Open-Weight Model Outperforming 397B MoE on Agentic Coding Benchmarks appeared first on MarkTechPost.
Qwen Team Open-Sources Qwen3.6-35B-A3B: A Sparse MoE Vision-Language Model with 3B Active Parameters and Agentic Coding Capabilities
The post Qwen Team Open-Sources Qwen3.6-35B-A3B: A Sparse MoE Vision-Language Model with 3B Active Parameters and Agentic Coding Capabilities appeared first on MarkTechPost.
