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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.31410 |
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| _version_ | 1866917548130304000 |
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| author | Mao, Mingyang Medisetti, Bhargav Rishi Grover, Utkarsh Ibrahim, Tanvir Li, Wenyan Zhang, Tingting Lin, Xiaomin |
| author_facet | Mao, Mingyang Medisetti, Bhargav Rishi Grover, Utkarsh Ibrahim, Tanvir Li, Wenyan Zhang, Tingting Lin, Xiaomin |
| contents | Food-as-Medicine requires models to reason beyond what a dish is or what nutrition it contains: they must decide whether a concrete food choice is appropriate for a specific health condition. Existing food AI benchmarks primarily evaluate dish recognition, recipe understanding, nutrient estimation, or general nutrition question answering, leaving this health-aware decision layer largely untested. We introduce FAM-Bench, a multi-modal Food-as-Medicine benchmark with 2500 nutrition-expert-verified instances across 13 diet-related health conditions. The benchmark contains two complementary tasks: dish-level suitability assessment, where models judge whether a dish is suitable for a condition from its image and ingredient list, and comparative dish analysis, where models rank four candidate dishes by condition-specific suitability. Both tasks require integrating ingredient evidence, visual preparation cues, and clinical nutrition constraints, providing a standardized testbed for grounded health-aware reasoning in language and vision-language models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_31410 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | FAM-Bench: A Multimodal Benchmark for Condition-Aware Food-as-Medicine Reasoning Mao, Mingyang Medisetti, Bhargav Rishi Grover, Utkarsh Ibrahim, Tanvir Li, Wenyan Zhang, Tingting Lin, Xiaomin Artificial Intelligence Food-as-Medicine requires models to reason beyond what a dish is or what nutrition it contains: they must decide whether a concrete food choice is appropriate for a specific health condition. Existing food AI benchmarks primarily evaluate dish recognition, recipe understanding, nutrient estimation, or general nutrition question answering, leaving this health-aware decision layer largely untested. We introduce FAM-Bench, a multi-modal Food-as-Medicine benchmark with 2500 nutrition-expert-verified instances across 13 diet-related health conditions. The benchmark contains two complementary tasks: dish-level suitability assessment, where models judge whether a dish is suitable for a condition from its image and ingredient list, and comparative dish analysis, where models rank four candidate dishes by condition-specific suitability. Both tasks require integrating ingredient evidence, visual preparation cues, and clinical nutrition constraints, providing a standardized testbed for grounded health-aware reasoning in language and vision-language models. |
| title | FAM-Bench: A Multimodal Benchmark for Condition-Aware Food-as-Medicine Reasoning |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2605.31410 |