让智能体从交互中提炼出带逻辑的可复用技能，提升长任务规划能力。
Lifting Traces to Logic: Programmatic Skill Induction with Neuro-Symbolic Learning for Long-Horizon Agentic Tasks
- 将交互痕迹转化为带条件判断的模块化程序
- 少样本下即可学会新技能，并灵活应对未知目标
- 适合需要长期规划与自适应决策的任务场景
基于基础模型的智能体在长周期规划任务中常因纯提示推理的短暂性而表现不佳。现有技能归纳方法虽通过参数化脚本提炼经验，但难以捕捉动态环境中的条件逻辑。本文提出神经符号技能归纳（NSI）框架，将交互轨迹提升为模块化、逻辑驱动的程序。通过显式控制流与动态变量绑定，使智能体能自主判断“何时”和“为何”执行动作。该范式支持高效泛化，仅需少量示例即可学习新技能，并灵活适配未见目标。在一系列智能体任务上的实验表明，NSI持续优于现有最优基线，使智能体具备自我演进为逻辑驱动技能架构师的能力。
原文摘要 · Abstract (English)
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.
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