May 2, 2026, cs.AIJ/K move · Enter open · S save
Jie-Jing Shao, Haiyan Yin, Yueming Lyu, Xingrui Yu+4
State Key Laboratory of Novel Software Technology, Nanjing University, China · Centre for Frontier AI Research, and Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore · School of Intelligence Science and Technology, Nanjing University, China+2
Foundation model-driven agents often struggle with long-horizon planning due to the transient nature of purely prompting-based reasoning. While existing skill induction methods mitigate this by distilling experience into state-blind parameterized scripts, they fail to capture the conditional logic required for robust execution in dynamic environments. In this paper, we propose Neuro-Symbolic Skill Induction (NSI), a framework that lifts interaction traces into modular, \textit{logic-grounded} programs. By synthesizing explicit control flows and dynamic variable binding, NSI empowers agents to discover \textit{when} and \textit{why} to act. This paradigm enables the efficient generalization, allowing agents to induce skills from few-shot examples and flexibly adapt to unseen goals. Experiments on a series of agentic tasks demonstrate that NSI consistently outperforms state-of-the-art baselines, empowering agents to self-evolve into architects of logic-grounded skills.