Hugging Face Daily PapersHaoyang Su, Weiran Huang1 min readpaperadvanced
JevSpawn: Adaptive Agentic Inference through Compositional Action Spaces
Summary
JevSpawn introduces a compositional policy that maps natural‑language task specs to finite‑field probabilistic action spaces, enabling parallel action spawning and feedback‑driven branch selection. Benchmarks show it outperforms several LLM agent baselines while reducing inference latency.
- LLM agents generate actions token‑by‑token, which is slow; JevSpawn replaces this with fast finite‑field predictions.
- The method composes natural‑language specifications into a finite action space without pre‑defining fields.
- Parallel spawning of actions plus feedback‑driven branch selection cuts repeated generation and context computation.
- Evaluated on eight tasks against seven baselines and a TypeSafe Jev variant, showing higher success rates and faster navigation.
Engineers building LLM‑driven agents need faster, structured inference to handle complex tasks efficiently.
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