{
  "id": 5223,
  "url": "https://arxiv.org/abs/2604.26522v1",
  "title": "AGEL-Comp: A Neuro-Symbolic Framework for Compositional Generalization in Interactive Agents",
  "summary": "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 Program",
  "authors": "Mahnoor Shahid, Hannes Rothe",
  "category": "research",
  "topics": "agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-29T10:42:02.000Z",
  "fetched_at": "2026-07-14T16:31:35.575Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5223",
  "original_url": "https://arxiv.org/abs/2604.26522v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}