Art der Publikation: Beitrag in Sammelwerk

AGEL-Comp: A Neuro-Symbolic Framework for Compositional Generalization in Interactive Agents

Autor(en):

Shahid, Mahnoor; Rothe, Hannes

Titel des Sammelbands:

IntelliSys 2026

Ort(e):

Amsterdam

Veröffentlichung:

2026

Digital Object Identifier (DOI):

doi:10.48550/arXiv.2604.26522

Link zum Volltext:

https://arxiv.org/abs/2604.26522

Zitation:

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Kurzfassung

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.