JevSpawn: Adaptive Agentic Inference through Compositional Action Spaces
JevSpawn tries to make agent inference faster by turning open-ended instructions into structured, parallel action choices.
The paper says current LLM agents are slowed by token-by-token reasoning and action generation. JevSpawn derives finite action spaces from natural-language tasks, spawns action branches in parallel, and uses feedback to select, revise, or recover from alternatives. The authors report tests on eight benchmarks against seven agent baselines and a TypeSafe Jev variant, with better task performance and faster navigation. Source: HF Daily Papers' note.
The paper says current LLM agents are slowed by token-by-token reasoning and action generation. JevSpawn derives finite action spaces from natural-language tasks, spawns action branches in parallel, and uses feedback to select, revise, or recover from alternatives. The authors report tests on eight benchmarks against seven agent baselines and a TypeSafe Jev variant, with better task performance and faster navigation. Source: HF Daily Papers' note.
score 4