SPEM: Structure-Aware Procedural Memory for Efficient Skill Use in LLM Agents
Source: ArXiv AI ↗
SPEM, a structure‑aware procedural memory framework, has been introduced to improve how large‑language‑model (LLM) agents employ reusable skills. By maintaining an Atomic Skill Memory for individual actions and a Skill‑DAG Memory that stores reusable compositions derived from successful executions, SPEM enables agents to select and execute skills with awareness of dependencies, execution order, state transitions, and data‑flow constraints. When faced with a new task, the system first attempts to reuse high‑confidence Skill‑DAG templates; if none are available, it retrieves atomic skills and employs a Skill‑GNN to choose the appropriate actions. Successful runs are written back as new Skill‑DAGs, allowing the procedural memory to grow over time.
In benchmark evaluations, SPEM demonstrated superior effectiveness‑efficiency trade‑offs compared with strong baselines. On the ALFWorld environment, it achieved an 87.1 % success rate, while on the SkillsBench suite it attained a 38.38 % reward score. These gains were realized alongside reductions in token usage and runtime costs, indicating that the framework can scale skill libraries without the overhead typically associated with loading full skill sets or relying on flat semantic retrieval that ignores structural relationships.
The results suggest that SPEM’s ability to accumulate and reuse structured skill compositions could reshape the development of LLM‑based agents, making them more capable of handling complex, multi‑step tasks efficiently. By continuously enriching its Skill‑DAG Memory, the system promises ongoing performance improvements as more executions are recorded, potentially benefiting a wide range of applications that depend on autonomous agents with extensive skill repertoires.
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The U.S.'s central bank rate currently stands at 3.75%, so a global rates move like this could feed through to local borrowing costs. The U.S.'s economy is growing at 2.3% with inflation at 3.2%, the backdrop this story's effects play out against locally. Housing-market effects like this are usually felt most where mortgage rates track the U.S.'s own central bank rate closely. With the U.S.'s unemployment at 4.4%, personal-finance effects like this land on a labour market that is currently fairly stable. Health-system effects are harder to localise from economic indicators alone — the real impact in the U.S. depends more on local healthcare capacity than the figures above.
The UK's central bank rate currently stands at 3.75%, so a global rates move like this could feed through to local borrowing costs. The UK's economy is growing at 0.8% with inflation at 3.2%, the backdrop this story's effects play out against locally. Housing-market effects like this are usually felt most where mortgage rates track the UK's own central bank rate closely. With the UK's unemployment at 5.6%, personal-finance effects like this land on a labour market that is currently fairly stable. Health-system effects are harder to localise from economic indicators alone — the real impact in the UK depends more on local healthcare capacity than the figures above.
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