TL;DR A decision-native model called Jeva arrives, purpose-built for "pick the right one" tasks like tool selection and retrieval. Reordering candidates never changes its answer, and its cache-friendly design cuts latency by up to 27.98x. Title: Jeva: System One Models from First Principles URL: Points 🎯 Purpose-built for typed decisions like tool selection, intent classification, and document retrieval, with no-answer (Null) detection built in from the start 🔀 Fixes the classic problem of candidate reordering flipping the decision: Jeva's flip rate across reorderings is a flat 0.00% 📊 Hits 92.05% on BANKING77 and 98.66% AUROC on null detection, both well ahead of comparison models ⚡ Caches and reuses per-candidate computation, cutting latency by up to 27.98x when candidates are fully reused (2,102ms to 75.1ms) 📈 Holds 69.88% accuracy as candidate sets scale from 32 to 2048, running 2.3x faster than a recursive selection baseline 🧩 Achieves set-dependent decisions with just a single attention block, without needing a K-by-K interaction matrix For the unglamorous but frequent job of picking among tools or RAG candidates, a model designed around caching from the ground up looks genuinely useful. #LLM# #DecisionMaking#