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Decision Model Factory: Building Specialized Sovereign Decision Models

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. Fujitsu Artificial Intelligence Laboratory Generative AI CPJ

LLM-as-an-Improver: Turning Verification into Better Candidates

Akiyoshi Tomihari, and Yuma Ichikawa arXiv:2609.19515 (2026)

Toward Unified Robot Learning: Bridging Representation, Vision-Language-Action, and World Models

Shaunak A. Mehta, Ananya Hazarika, Haochen Zhang, Fan Yang, Ryo Moriyama, Wenkai Li, Yash Patel, and Kanata Suzuki arXiv:2609.03927 (2026)

Stochastic geometry analysis of periodic handover skipping in two-tier networks with cluster-deployed small cells

Kiichi Tokuyama, Yifan Xu, and Yuichiro Wada IEICE Transactions on Communications, Page(s): 1 - 18 (2026)

Confidence-aware pseudo-label selection and verifier training for semi-supervised LLM reasoning with minimal labels

Keizo Kato, Chenhui Chu, Yugo Murawaki, and Sadao Kurohashi Front. Artif. Intell., Sec. Machine Learning, Volume 9 (2026)

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