JevSpawn: Adaptive Agentic Inference through Compositional Action Spaces
JevSpawn turns language instructions into finite action spaces for faster agentic inference without extra training.
JevSpawn is a compositional policy that connects natural-language task specifications to finite probabilistic exploration for LLM agents. Parallel action spawning is combined with feedback-driven branch selection, representation revision, and recovery from retained alternatives. Shared action structure and model prefixes reduce repeated generation and context computation without additional training. Evaluations on eight benchmark tasks against seven agent baselines and a TypeSafe Jev variant report improved task performance and faster navigation.
- JevSpawn maps natural-language tasks to finite probabilistic action spaces.
- Parallel spawning, branch selection, and recovery reuse shared prefixes without extra training.
- Evaluated on eight tasks against seven agent baselines and a TypeSafe Jev variant.
- Authors report better task performance and faster navigation.
From the original article
1 min read at huggingface.co
LLM agents generate intermediate reasoning and actions token by token, making extended interactions slow and computationally expensive. Jev-style models offer fast probabilistic predictions over finite fields, but require those fields to be specified in advance.
By Haoyang Su, Weiran Huang · huggingface.co
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