Jev-LCT: Free Calibrated Confidence from Looped Transformer Trajectories

Small decision models have two bad options today: generate text token-by-token (slow, fragile to parse), or "introspect" their confidence verbally (systematically miscalibrated). Jev-LCT is an open-source System-One decision engine that takes a third route — extracting calibrated probabilities directly from a transformer's internal recurrent dynamics. Parallel Looped Prefill: recurrently recomputes only the top k=2 layers of a causal transformer, preserving full representation fidelity. Endogenous Trajectory Confidence: reads calibrated probabilities from hidden-state convergence dynamics (cos