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Grounded Iterative Language Planning: How Parameterized World Models Reduce Hallucination Propagation in LLM Agents

arXiv cs.AI · 4d ago Cached

This paper introduces Grounded Iterative Language Planning (GILP), a method that combines a small parameterized world model with LLM-based reasoning to reduce hallucination propagation in LLM agents. Experiments show GILP reduces hallucinated-state rate from 0.176 to 0.035 and raises task success from 0.668 to 0.838 on graph-structured planning benchmarks.

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