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Simulation experiments demonstrate that our LLM-informed model-based planning approach outperforms the baseline planning strategy that fully relies on LLM and optimistic strategy with as much as 11.8% and 39.2% improvements respe","title":"Object Search in Partially-Known Environments via LLM-informed Model-based Planning and Prompt Selection","url":"https://arxiv.org/abs/2603.23800","vendor":"arxiv_cs_ai"},"summary":"arXiv:2603.23800v2 Announce Type: replace-cross \nAbstract: We present a novel LLM-informed model-based planning framework, and a novel prompt selection method, for object search in partially-known environments. Our approach uses an LLM to estimate statistics about the likelihood of finding the target object when searching various locations throughout the scene that, combined with travel costs extracted from the environment map, are used to instantiate a model, thus using the LLM to inform planning and achieve effective search performance. 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