Core Insights - The launch of Tenzai Model-v1.1 by Dongfang Materials' Japanese subsidiary marks a significant advancement in the application of AI within the finance and taxation sector, transitioning from "execution automation" to "cognitive intelligence" [1][4] Technology Foundation - Tenzai Model-v1.1 is built on a trillion-parameter architecture, utilizing a Transformer model optimized for finance and taxation scenarios, incorporating over 5 million real tax documents, 1 million high-quality Q&A pairs, a 50-year database of Japanese tax laws, and over 100,000 real business cases [1][2] - The model employs Domain-adaptive Continued Pre-training and Multi-task Fine-tuning to achieve near-human cognitive abilities in semantic understanding, logical reasoning, and judgment suggestions [2] Innovative Architecture - The system integrates Retrieval-Augmented Generation (RAG) technology to address potential inaccuracies in professional content, ensuring that every recommendation is backed by legal references and case studies [2] - Tenzai Model-v1.1 features multi-modal understanding, capable of processing images, text, and tabular data, achieving a recognition accuracy of 99.8% for complex documents [2] System Performance - The model supports a context length of up to 32K tokens, with an average response time of under 2 seconds, processing 1,200 documents per hour, significantly outperforming current market solutions [2] - It includes a continuous learning mechanism for monthly updates on tax laws and supports private deployment and flexible SaaS architecture [2] Application Depth - Tenzai Model-v1.1 represents a leap from traditional automation systems, enabling semantic understanding, contextual reasoning, and proactive risk alerts in tax-related queries [2][3] - The system has been integrated with major Japanese accounting software, supporting cloud, private, and hybrid deployments, with plans for a mobile app and international versions by 2026 [3] Industry Impact - The release of Tenzai Model-v1.1 signifies a maturation of vertical large models in professional services, transforming unstructured tax knowledge into computable, inferable, and interactive AI capabilities [4]