The Tongyi Lab has officially released the new generation AI Agent foundation model
In extreme pressure tests targeting the unknown hardware platform ZW-M890L PPU, the model demonstrated strong long-term strategy coherence and In-Context generalization capabilities through runtime feedback, achieving continuous operation for 35 hours and 1158 tool calls without interruption, successfully completing deep autonomous reasoning. It also achieved a geometric mean acceleration of 10.0 times on multiple workloads.
To completely solve the industry chronic problem of AI models being "overfit" to specific development frameworks, Qwen3.7-Max introduced a unique orthogonal decoupling design of "task - runtime framework - validator" in its training architecture. By moving reinforcement learning training from synthetic data to real distributions, it achieved true universal agent strategies and cross-framework generalization capabilities. This fundamental breakthrough enables it to deliver complex applications such as real-time interactive 3D particle system web pages end-to-end in professional workflows from front-end prototypes to complex software engineering.
At the same time, the model integrates office productivity tools such as office-cli by relying on the Model Context Protocol (MCP) and supports multi-agent orchestration and embodied intelligence control extensions. The newly upgraded
This marks that AI Agents are accelerating from "theoretically feasible" to "engineering reality," laying a highly reliable technical foundation for the automation deployment of complex distributed industrial workflows.
