JevSpawn: compositional action spaces speed up agentic inference on 8 benchmarks
Haoyang Su · hf · 2026-10-02
JevSpawn is a compositional policy that maps natural-language task specs into finite probabilistic action spaces. Parallel action spawning with feedback-driven branch selection, representation revision, and recovery from retained alternatives lets agents derive actions dynamically—unlike prior Jev-style models that need predefined fields. Shared action structure and model prefixes cut repeated generation without extra training. On 8 benchmarks against 7 agent baselines and a TypeSafe Jev variant, it improves task performance and navigation speed.
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