MSCPM: Multi-level Skill Composition for Procedural Memory in LLM Agents

Currently, considerable research on LLM-based agents distills procedural memory from the past trajectories of agents to avoid re-exploring recurring tasks and enhance decision-making, yet existing methods remain limited in skill composition. Inspired by human procedural memory, we propose Multi-level Skill Composition for Procedural Memory (MSCPM), a framework that organizes memory through asymmetric inter-skill relations and explicitly models multi-level skill composition. Specifically, we extract operation, subgoal, and task-domain skills bottom-up through clustering and chunking, forming a hierarchical skill graph. On this graph, a cascade of coarse-to-fine subgoal selection, typed plan orchestration, and dynamic operation assembly composes skills into execution strategies tailored to each new task. Across AppWorld and BFCL-V3, MSCPM surpasses the strongest baseline by 1.94–2.58/1.99–3.97 points in Avg@4/Pass@4, and improves over the no-memory baseline by 26.1%/18.0% and 14.7%/13.7% with Qwen3-32B, rising to 35.8%/26.4% and 22.5%/23.7% with Qwen3-8B. Smaller agents equipped with MSCPM even surpass larger memory-free ones.