Alibaba’s Qwen Team Launches Qwen3.7-Plus, Adding Vision, Deep Reasoning, Tool Invocation, and Autonomous — 2026-06-02

## Short Segments JetBrains introduces Mellum2, a 12-billion parameter model designed for fast, specialized tasks in AI pipelines. We'll explore how this model enhances software engineering workflows. Also, NVIDIA Apex offers a new way to speed up Transformer training with fused optimizers and native torch.amp. Plus, learn how to build a secure auth code flow using AgentCore Gateway with MCP clients. Later, Alibaba's Qwen team launches Qwen3.7-Plus, a multimodal model with advanced capabilities on the Bailian platform. JetBrains releases Mellum2, a 12B MoE model for fast, specialized tasks in multi-model AI pipelines. JetBrains has unveiled Mellum2, a 12-billion parameter model that promises to enhance software engineering tasks within AI systems. Unlike its predecessor, Mellum2 is open-sourced under the Apache 2.0 license, making it accessible for broader use. This model is designed as a "focal model," meaning it serves as a specialized component within larger AI systems rather than a standalone solution. Mellum2's architecture employs a Mixture-of-Experts (MoE) approach, activating only a subset of its parameters per token, which reduces inference time significantly. With 64 experts and 8 activated per token, it maintains the computational efficiency of a 2.5-billion parameter dense model while offering higher specialization capacity. This makes Mellum2 particularly suited for tasks like code generation, debugging, and multi-step reasoning. By integrating Mellum2, developers can expect faster and more efficient AI-driven software engineering processes, enhancing productivity and innovation in AI development environments. How to speed up Transformer training using NVIDIA Apex and native torch.amp. NVIDIA Apex is streamlining Transformer training with its latest enhancements. By focusing on components like FusedAdam and FusedLayerNorm, Apex optimizes GPU training workflows. The tutorial highlights the importance of correctly setting up Apex to ensure high-performance kernels are utilized. It compares the performance of FusedAdam against PyTorch's AdamW and evaluates FusedLayerNorm with standard normalization layers. Additionally, the integration of legacy apex.amp with modern torch.amp is tested in a Transformer training experiment. The results show that using a fused Apex-plus-AMP path significantly boosts throughput compared to a vanilla FP32 PyTorch path. This approach not only accelerates training but also maximizes the efficiency of GPU resources, making it a valuable tool for developers looking to enhance their AI model training processes. Building a secure auth code flow setup using AgentCore Gateway with MCP clients. In the realm of AI development, securing communications between AI agents and enterprise servers is crucial. Amazon's Bedrock AgentCore Gateway offers a solution by providing a centralized entry point for secure agent-to-tool communications. This setup involves implementing an OAuth Code flow for inbound authorization, ensuring that only verified users and agents can access MCP servers. Organizations typically use identity providers like Okta or Amazon Cognito to manage user identities and issue security tokens. By following this guide, developers can establish a robust authentication mechanism that protects sensitive data and maintains secure interactions between AI agents and enterprise tools. This setup not only enhances security but also streamlines the integration of AI agents into existing workflows, facilitating more efficient and secure AI deployments. ## Feature Story Alibaba's Qwen team launches Qwen3.7-Plus, adding vision, deep reasoning, and autonomous iteration on the Bailian platform. Alibaba's Qwen team has unveiled Qwen3.7-Plus, a multimodal large language model now available on the Bailian platform. This model marks a significant advancement in AI capabilities, integrating visual understanding with deep reasoning and autonomous iteration. Unlike its text-only sibling, Qwen3.7-Max, Qwen3.7-Plus can interpret images and videos, enhancing its ability to interact with real-world data. The model's new features include self-programming, tool invocation, and verification, allowing it to autonomously complete tasks by writing and revising its own code, calling external APIs, and testing outputs. This positions Qwen3.7-Plus as a hybrid agent capable of planning and executing complex workflows. The release follows Alibaba's May unveiling of the Qwen3.7 generation, further solidifying its role in advancing multimodal AI technology. For developers and enterprises, this means access to a powerful tool that can automate and optimize a wide range of tasks, from app development to data analysis. As AI continues to evolve, Qwen3.7-Plus represents a step towards more integrated and autonomous AI systems, offering new possibilities for innovation and efficiency in various industries.