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MIT researchers developed VLMFP, a two-stage generative AI approach combining vision-language models with formal planning software to achieve 70% success rate on complex visual planning tasks like robot navigation, nearly 2.3x better than existing baselines. The method automatically translates visual scenarios into planning files that classical solvers can process, enabling effective long-horizon planning in novel environments.