OmniFood-Bench: Evaluating VLMs for Nutrient Reasoning and Personalized Health Advice
The rapid integration of Large Vision-Language Models (VLMs) into critical infrastructure promises to revolutionize personalized healthcare and dietary management. However, in the domain of food systems, autonomous agents face a unique and persistent challenge: the "Systemic Information Asymmetry" between visual appearance and intrinsic nutritional composition. Existing benchmarks primarily focus on coarse-grained classification tasks, such as food category recognition, which fail to evaluate the intricate reasoning chain required for real-world dietary management -- specifically, the ability to traverse from identifying hidden ingredients to estimating physical mass, and finally synthesi...
| Authors | Qian Jiang, Zhecheng Shi, Jingpu Yang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 9 July 2026 |
| Last updated at source | 17 July 2026 |
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