AI RESEARCH
DUPLEX: Agentic Dual-System Planning via LLM-Driven Information Extraction
arXiv CS.AI
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ArXi:2603.23909v1 Announce Type: new While Large Language Models (LLMs) provide semantic flexibility for robotic task planning, their susceptibility to hallucination and logical inconsistency limits their reliability in long-horizon domains. To bridge the gap between unstructured environments and rigorous plan synthesis, we propose DUPLEX, an agentic dual-system neuro-symbolic architecture that strictly confines the LLM to schema-guided information extraction rather than end-to-end planning or code generation.