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This paper introduces a novel paradigm, VLM-as-probabilistic-grounder, which models uncertainty in vision-language model groundings as probability distributions for symbolic belief-space planning, enhancing robustness in partially observable settings.
The paper proposes the Belief-State Engine, an inference module that maintains a Bayesian posterior over hidden states to augment LLM agents for principled planning under partial observability, demonstrating improved task performance and decision consistency.