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Adaptive interleaving of vision and language thoughts in robot planning leads to significant improvements in task execution and reasoning efficiency.
Despite advances in multimodal models, they still struggle to understand spatial relationships from an egocentric perspective, as shown by a 37.66% performance gap on the new SAW-Bench benchmark.
