Author

Shuang-Quan Guo

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Preprint Sep 2026

Towards Strategy-Level RSI for Skill-Augmented Agents: Learning When to Reuse Skills from Execution Feedback

Long-running agents accumulate reusable Skills, but a Skill that is semantically relevant to a task is not necessarily worth loading in the current state. We study the applicability question that arises once a candidate Skill is known: should it be loaded in the current state? We propose SkillApt, which uses matched WI...

Shuang-Quan Guo, Xiao-Bo Sui, Fei Jiang et al. · 2 citations

Preprint Sep 2026

SkillApt: Learning When to Activate Agent Skills from Counterfactual Evidence

Large language model agents increasingly retrieve reusable Skills and inject them into the active context. However, a retrieved Skill can be relevant yet unnecessary, costly, or even harmful in the current execution state. We present SkillApt, a post-retrieval activation framework that decides whether a retrieved Skill...

Shuang-Quan Guo · 2 citations