Large Language Model (LLM)-based agents exhibit systemic failures in compositional generalization, limiting their robustness in interactive environments. This work introduces AGEL-Comp, a neuro-symbolic AI agent architecture designed to address this challenge by grounding actions of the agent. AGEL-Comp integrates three core innovations: (1) a dynamic Causal Program Graph (CPG) as a world model, representing procedural and causal knowledge as a directed hypergraph; (2) an Inductive Logic Programming (ILP) engine that synthesizes new Horn clauses from experiential feedback, grounding symbolic knowledge through interaction; and (3) a hybrid reasoning core where an LLM proposes a set of candidate sub-goals that are verified for logical consistency by a Neural Theorem Prover (NTP). Together, these components operationalize a deduction--abduction learning cycle: enabling the agent to deduce plans and abductively expand its symbolic world model, while a neural adaptation phase keeps its reasoning engine aligned with new knowledge. We propose an evaluation protocol within the \texttt{Retro Quest} simulation environment to probe for compositional generalization scenarios to evaluate our AGEL agent. Our findings clearly indicate the better performance of our AGEL model over pure LLM-based models. Our framework presents a principled path toward agents that build an explicit, interpretable, and compositionally structured understanding of their world.

翻译：基于大语言模型的智能体在组合泛化任务中表现出系统性缺陷，限制了其在交互环境中的鲁棒性。本文提出AGEL-Comp——一种通过锚定智能体行为来解决该问题的神经符号人工智能架构。该架构融合三项核心创新：（1）动态因果程序图作为世界模型，以有向超图形式表征程序性与因果知识；（2）归纳逻辑编程引擎，从经验反馈中综合新霍恩子句，通过交互锚定符号知识；（3）混合推理核心，由大语言模型生成候选子目标集，经神经定理证明器验证其逻辑一致性。这些组件协同运作构成演绎-溯因学习循环：使智能体既能演绎规划，又能溯因地扩展其符号世界模型，同时通过神经适应阶段保持推理引擎与新知识对齐。我们在\texttt{Retro Quest}仿真环境中设计评估协议，构造组合泛化场景对AGEL智能体进行测评。实验结果表明，AGEL模型性能显著优于纯大语言模型。本框架为构建具有显式、可解释且组合化世界理解的智能体提供了系统性路径。