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.
arXiv:2606. 06087v1 Announce Type: cross Abstract: Agent systems increasingly use textual skills to encode reusable task procedures, but injecting these skills into the prompt at every step incurs substantial context overhead and exposes skill content as plaintext.
arXiv:2608. 09168v1 Announce Type: new Abstract: Agent skills are increasingly used to equip large language model (LLM) agents with reusable procedural knowledge.
Agent skills are increasingly used to equip large language model (LLM) agents with reusable procedural knowledge. Although recent work has substantially improved skill retrieval due to the increasing skill libraries, retrieving a plausible skill bundle does not guarantee that executing it is worthwhile.
arXiv:2606. 06787v1 Announce Type: new Abstract: Large Language Models (LLMs) show promise as tool-using agents but remain limited in long-horizon tasks that require remembering, organizing, and reusing knowledge.
SkillLens introduces a hierarchical skill-evolution framework that organizes skills into a four-layer graph of policies, strategies, procedures, and primitives, allowing retrieval at mixed granularity. The system first retrieves semantically relevant skill seeds, expands them via a degree‑corrected random walk, and uses a verifier to decide whether to accept, decompose, rewrite, or skip each visited unit. This approach enables agents to reuse compatible subskills while locally adapting mismatched components, and theoretical analysis shows sublinear cost under sparse mismatch assumptions, with empirical results on MuLocbench and ALFWorld demonstrating consistent improvements over strong baselines.
arXiv:2606. 16769v1 Announce Type: new Abstract: Agent skills are commonly distributed as SKILL.
SKILL.state is a new runtime architecture for large language model agents that replaces the traditional append‑only conversational history with an explicit, mutable execution state. At each step the model receives only the immutable skill specification, the current structured state, and the latest observation, discarding intermediate reasoning after validating state updates. Experiments across datasets, models, and environments show that SKILL.state improves task accuracy and significantly reduces cumulative token consumption, proving that explicit execution state is a scalable, architecture‑agnostic abstraction for long‑horizon agent skills.
arXiv:2608. 06880v1 Announce Type: new Abstract: General-purpose skills promise reusable procedural knowledge for language agents, yet semantic relevance does not guarantee execution utility: a retrieved skill may encode assumptions that conflict with the current task, execution environment, or other retrieved skills.
arXiv:2607. 25853v1 Announce Type: new Abstract: Skills have become an important abstraction for enabling large language model (LLM) agents to reuse past experience in long-horizon interactive tasks.
arXiv:2610.15492v1 Announce Type: new Abstract: Skill internalization aims to convert externally specified procedural skills into persistent model capabilities, allowing language-model agents to exec...
The paper introduces Boundary-Aware Skill Memory (BASM), a method that enriches skill memories for large language model agents with explicit boundary fields such as applicability conditions, risk cues, avoidance rules, and recovery notes. This approach transforms retrieved skills from unconditional templates into state‑conditioned guidance, preventing the Skill Imitation Trap where more skills lead to incorrect tool usage. Experiments on three agent benchmarks and four model scales show that BASM improves task success rates, accuracy, and reduces attack success while cutting average steps compared to memory‑free baselines.
SkillBrowser is a Meta‑Skill‑managed framework that allows large language model agents to identify their current capability needs, formulate targeted queries, and selectively load skills on demand. By providing relevant procedural content only when needed, it reduces context pollution and misaligned skill exposure that can disrupt reasoning. Experiments on six SRA‑Bench subsets and five LLMs show that SkillBrowser improves aggregate accuracy over static retrieval and LLM‑based selection, especially for multi‑step tool use, while mitigating accuracy degradation on already‑solvable instances.