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arXiv AI By Ran Zhou, Zehui Kong, Fanyu Lin, Jiaheng Liu, Zhen Zhang, Yuxiang Ren

SPEM: Structure-Aware Procedural Memory for Efficient Skill Use in LLM Agents

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SPEM is a lightweight, structure‑aware procedural memory framework designed to improve skill use in large language model agents. It stores individual skills in an Atomic Skill Memory and reusable skill compositions in a Skill‑DAG Memory, reusing high‑confidence templates when possible and falling back to atomic skills with a Skill‑GNN for state‑aware selection. The system writes successful executions back as new Skill‑DAGs, enabling continuous accumulation of procedural knowledge and achieving higher effectiveness‑efficiency trade‑offs on ALFWorld and SkillsBench benchmarks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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