Decision model factory builds fast specialists with improved accuracy
Decision Model Factory: Building Specialized Sovereign Decision Models
Artificial IntelligenceSoftware Engineering
Summary
Different tasks and languages need different decision models to work well. The authors created a system called the Decision Model Factory that quickly makes specialized models tailored to specific tasks and contexts. These models perform better than the original standard ones, especially on Japanese language tasks. They also made a runner that speeds up answering multiple questions without losing accuracy. Together, these tools make it easier and faster to build and use decision models that fit exactly what’s needed.
What this means in practice
- •For natural language processing developers: Create specialized decision models for Japanese language tasks that outperform standard models on public benchmarks.
- •For software engineers deploying ai models: Deploy fast decision models that reuse context efficiently for multiple questions, doubling serving speed while keeping accuracy.
Authors
Yuma Ichikawa
Abstract
Decision needs vary with each application, language, and deployment environment. The Decision Model Factory turns those needs into specialized, sovereign models on demand. A brief recipe guides the training, calibration, verification, and evaluation of a selected backbone, delivering a deployable decision model under the operator's control. The same workflow creates a new specialist whenever the requirements change. Across Japanese decision tasks, compact deployment, and long-context passage ranking, five factory-built models improve upon their backbones; three surpass the strongest results in a fixed public Japanese benchmark comparison. A dedicated runner computes only permitted-answer probabilities and reuses shared context, more than doubling serving speed on matched multi-question workloads with essentially unchanged decision scores. Together, the factory and runner make fast, task-specific decision models practical to build and deploy whenever needed.