SPEM framework improves skill use in LLM agents
SPEM is a lightweight structure‑aware procedural memory framework designed to make skill reuse in large‑language‑model agents more efficient. The system stores individual skills in an Atomic Skill Memory and builds reusable skill compositions in a Skill‑DAG Memory. When a new task arrives, SPEM first looks for high‑confidence Skill‑DAG templates; if none are found, it retrieves atomic skills and…
Key points
- SPEM uses Atomic Skill Memory and Skill‑DAG Memory to reduce token and runtime costs
- SPEM reaches 87.1 % success on ALFWorld and 38.38 % reward on SkillsBench
- It reuses high‑confidence Skill‑DAG templates and writes back new DAGs after successful runs
Experiments on the ALFWorld and SkillsBench benchmarks show that SPEM achieves the best overall effectiveness‑efficiency trade‑off, reaching 87.1 % success on ALFWorld and 38.38 % reward on SkillsBench while cutting token and runtime costs compared to strong baselines.
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