Qwen has launched Qwen-AgentWorld-35B-A3B, a 35B-parameter MoE model with only about 3B active parameters per token. It is trained to simulate responses from MCP, terminal, software engineering, Android, web, and OS GUI environments by predicting next observations after agent actions, enabling efficient agent training and environment simulation without real tool execution.
Qwen releases 35B-parameter MoE for agent environment simulation
Qwen releases Qwen3.8-Omni-Flash, a native multimodal agentic model with 1M token context
Alibaba Cloud has introduced Qwen3.8-Omni-Flash, a natively multimodal agentic model designed for real-world productivity tasks that substantially improves multimodal understanding and reasoning compared to previous omni models.
Alibaba Qwen releases Qwen3.8-Omni-Flash, an agentic omni-modal model with 1M context
Alibaba's Qwen team has released Qwen3.8-Omni-Flash, its first omni-modal model designed around agentic capabilities for audio-video understanding and tool use. The model accepts text, images, audio, and video inputs to return text outputs, featuring a 1M-token context window and native function calling.
ExecCritic uses role-specific RL to improve coding agents via test-guided repair
ExecCritic introduces a framework that separates test construction from source-code repair to prevent false confidence in coding agents. The system employs a Test agent and a Repair agent, both backed by Qwen-3.5-35B-A3B, trained separately using reinforcement learning.
Alibaba releases Qwen3.8-Flash-Next 176B preview weights for agentic coding
Alibaba has released the model weights for Qwen3.8-Flash-Next, serving as a preview of the upcoming Qwen4 architecture for developers to experiment with and evaluate.
Qwen 3.8 Max release highlights planning, simplification, and experimental design
The full release of Qwen 3.8 Max confirms early impressions that the model is exceptionally fast, highly capable at planning, and skilled at identifying unnecessary complexity in problems.