# Parallel Action Spawning

JevSpawn introduces a training-free compositional policy that turns natural-language task specifications into executable finite action fields, exploring them through parallel action spawning, feedback-driven branch selection, representation revision, and recovery from retained branches; across eight benchmark tasks against seven agent baselines and a TypeSafe Jev variant it achieves the highest scores on five of eight tasks and, on Maze and Grid, raises success rates to 0.96 and 0.95 while reducing end-to-end latency from the fastest baselines' 47.91 and 61.44 seconds to 40.91 and 40.58 seconds.

JevSpawn introduces a training-free compositional policy that turns natural-language task specifications into executable finite action fields, exploring them through parallel action spawning, feedback-driven branch selection, representation revision, and recovery from retained branches; across eight benchmark tasks against seven agent baselines and a TypeSafe Jev variant it achieves the highest scores on five of eight tasks and, on Maze and Grid, raises success rates to 0.96 and 0.95 while reducing end-to-end latency from the fastest baselines' 47.91 and 61.44 seconds to 40.91 and 40.58 seconds.