NutriBench-Kitchen: Benchmarking Embodied AI for Nutrition Management

An embodied kitchen assistant must do more than recog-nize food in isolated frames. It must track ingredient states over timeand integrate visual observations with recipe and nutritional knowledgeto support constraint-aware decision-making. We formalize this capa-bility as Embodied Nutrition Management: perceiving nutrition-relevantevents, maintaining a persistent food state, and using it for knowledge-grounded planning. Existing benchmarks evaluate static food under-standing or embodied cooking actions, but do not measure whether anagent can continuously update and use nutrition-relevant states in dy-namic kitchens. To fill this gap, we introduce NutriBench-Kitchen,a benchmark containing 1,500 manually verified question–answer pairsfrom 160 cooking videos. It covers five task families: Ingredient Entry,Memory Management, Recipe Query, Long-Term Planning, and Short-Term Planning, spanning food-state construction, maintenance, knowl-edge retrieval, and decision-making across different planning horizons.Evaluations of proprietary and open-source large vision-language modelsreveal a substantial gap from human performance, particularly in quan-titative ingredient estimation, long-term state tracking, and reasoningunder interacting constraints. We further introduce Nutri-Vgent, a di-agnostic long-video agent with separate episodic, food-state, and recipememories. Its consistent improvements demonstrate the value of explicitstate representations and structured memory for nutrition management.Together, NutriBench-Kitchen and Nutri-Vgent provide a testbed forstudying persistent state tracking and knowledge-grounded reasoning indynamic kitchens. Code is available at https://github.com/V1ol1n/NutriBench-Kitchen.