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Tencent officially released the H混元 Game Visual Generation Platform, which is an AI content engine built on the H混元 large model, specifically designed for industrial-level content production in the gaming industry. The launch of this platform marks the entry of the game art design industry into a new era of efficient creation, expected to increase creation efficiency by tens of times. In the past, game art designers often had to jump between multiple software when creating character images, from finding reference images to drafting sketches, making three-view drawings, and creating dynamic demonstrations, the entire process was scattered and cumbersome, with files requiring repeated imports and exports.

Zhipu AI today launched its latest Agent product, AutoGLM-Thinking, at the 2025 Zhongguancun Forum. This is the industry's first Agent product to integrate deep research capabilities with operational capabilities, its key feature being the ability to think and act concurrently. AutoGLM-Thinking breaks the limitations of traditional AI models, capable of simultaneously performing complex reasoning and executing actions. It can, like a human, open and browse web pages, completing entire tasks from data retrieval and analysis to report generation, achieving a tight integration of thought and action. Zhipu AI stated...

Tencent has recently launched a smart workbench product called ima.copilot (shortened to ima), which is powered by Tencent's Hunyuan large model technology. This product aims to provide users with a new experience in searching, reading, and writing through its thinking knowledge base. The core features of ima.copilot include knowledge acquisition, the creation of a personalized knowledge base, and intelligent writing assistance.

Recently, significant progress has been made in the research and application of multimodal large models. Foreign companies such as OpenAI, Google, and Microsoft have introduced a series of advanced models, and domestic institutions such as Zhipu AI and Jietu Xingchen have also made breakthroughs in this field. These models typically rely on visual encoders to extract visual features and integrate them with large language models, but they face issues such as visual inductive bias due to training