国产游戏理解模型刷新SOTA，对话逗逗AI CEO：开源模型+行业数据是突破关键

Core Insights - The article highlights the significant impact of domestic open-source models in the AI industry, particularly in the gaming sector, as evidenced by the performance of LynkSoul VLM v1 at the Tokyo Game Show [1][2]. Group 1: Model Performance - LynkSoul VLM v1 outperformed leading closed-source models like GPT-4o, Claude 4 Sonnet, and Gemini 2.5 Flash in game understanding, achieving higher accuracy in visual understanding, game context comprehension, and natural language expression [10][11]. - In a test scenario within "League of Legends," LynkSoul VLM v1 scored 3.44 in vision understanding accuracy, 3.29 in game understanding, and 2.91 in natural expression, significantly surpassing the scores of its competitors [11]. - The model demonstrated robust generalization capabilities across various games, maintaining superior performance in the same three core metrics [12]. Group 2: User Engagement and Data Accumulation - The success of LynkSoul VLM v1 is attributed to the accumulation of over 8 million game player interactions, which provided valuable data for model training and refinement [18][19]. - The model's ability to understand and respond to real-time game scenarios is enhanced by user participation and data collection, which are critical for its development [19]. Group 3: Technical Innovations - The model's latency for game interactions is currently between 1.5 to 2 seconds, with ongoing efforts to reduce this through local processing and smaller model implementations [20][21]. - Long-term memory capabilities are achieved through a combination of thematic indexing and vector retrieval, allowing the AI to recall past interactions and provide personalized responses [23][24]. Group 4: Market Positioning and Future Outlook - The company aims for global expansion, having already launched its product in overseas markets with positive user engagement, particularly in English and Japanese-speaking regions [43][44]. - The future strategy includes integrating hardware with software solutions, ensuring that the AI companion can operate across various platforms and devices [36][37].