Instinctive, multilingual decisions in one pass. Naluri (Indonesian for instinct; Neural Answering via Label Understanding and Rapid Inference) is a small System One decision model: about 280M parameters, built on XLM-RoBERTa base. You give it a text or JSON state and a typed question with its options written out. It returns calibrated probabilities over those options in a single forward pass, taking about 5 ms per question on a laptop GPU and about 50–60 ms on a CPU. It never generates text. It supports the same three question types as TypeSafe Jev and Laya: Code, training pipeline and full report: github.com/jerichosiahaya/naluri. - Option descriptions matter. Naluri reads them, so clear…