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Tongyi Lab Unveils Qwen3.7-Max with Orthogonal Decoupling Tech, Tops Multiple Domestic Benchmarks
Tongyi Lab has officially launched the next-generation AI Agent foundation model, Qwen3.7-Max , which tops multiple authoritative benchmarks and addresses key industry challenges like the fragility and instability of intelligent agents in long-chain production environments.
In rigorous stress tests on the unknown hardware platform ZW-M890L PPU, the model demonstrated robust long-term strategic coherence and In-Context generalization via runtime feedback. It sustained uninterrupted operation for 35 hours with 1,158 tool calls, successfully executing deep autonomous reasoning while achieving a geometric mean acceleration of 10.0x across various workloads.
To resolve the industry-wide issue of AI models being "overfitted" to specific development frameworks, Qwen3.7-Max introduces a unique orthogonal decoupling design of "task-runtime framework-validator" in its training architecture. By shifting reinforcement learning from synthetic data to real-world distributions, it achieves true universal agent strategies and cross-framework generalization. This breakthrough enables end-to-end delivery of complex applications, such as real-time interactive 3D particle system web pages, within professional workflows from front-end prototypes to sophisticated software engineering.
Additionally, the model integrates office productivity tools like office-cli via the Model Context Protocol (MCP), supporting multi-agent orchestration and embodied intelligence control extensions. The newly upgraded Qwen3.7-Max will be officially released through the Alibaba Cloud BaiLian platform, fully compatible with OpenAI and Anthropic API protocols, ensuring seamless plug-and-play integration with mainstream agent frameworks including Claude Code, OpenClaw, and Qwen Code.
This milestone signals that AI Agents are rapidly transitioning from "theoretically feasible" to "engineering reality," establishing a highly reliable technical foundation for automating complex distributed industrial workflows.
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Tongyi Lab has officially launched the next-generation AI Agent foundation model,
In rigorous stress tests on the unknown hardware platform ZW-M890L PPU, the model demonstrated robust long-term strategic coherence and In-Context generalization via runtime feedback. It sustained uninterrupted operation for 35 hours with 1,158 tool calls, successfully executing deep autonomous reasoning while achieving a geometric mean acceleration of 10.0x across various workloads.
To resolve the industry-wide issue of AI models being "overfitted" to specific development frameworks, Qwen3.7-Max introduces a unique orthogonal decoupling design of "task-runtime framework-validator" in its training architecture. By shifting reinforcement learning from synthetic data to real-world distributions, it achieves true universal agent strategies and cross-framework generalization. This breakthrough enables end-to-end delivery of complex applications, such as real-time interactive 3D particle system web pages, within professional workflows from front-end prototypes to sophisticated software engineering.
Additionally, the model integrates office productivity tools like office-cli via the Model Context Protocol (MCP), supporting multi-agent orchestration and embodied intelligence control extensions. The newly upgraded
This milestone signals that AI Agents are rapidly transitioning from "theoretically feasible" to "engineering reality," establishing a highly reliable technical foundation for automating complex distributed industrial workflows.
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As global interest in robotics accelerates, Genesis AI has introduced GENE-26.5, its inaugural robot foundation model. This launch represents a major leap forward in general-purpose robotics, particularly in managing intricate and unstructured tasks.
Microsoft Unveils First Self-Developed Full-Duplex AI Speech Model Capable of Simultaneous Listening and Speaking Across 16 Languages
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