Lifting Traces to Logic: Programmatic Skill Induction with Neuro-Symbolic Learning for Long-Horizon Agentic Tasks
For foundation model-driven agents, NSI addresses the bottleneck of robust long-horizon planning by replacing state-blind scripts with conditional logic, enabling better generalization in dynamic environments.
NSI lifts interaction traces into logic-grounded programs with explicit control flows and dynamic variable binding, enabling agents to induce skills from few-shot examples and adapt to unseen goals, consistently outperforming state-of-the-art baselines on long-horizon agentic tasks.
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.