OmniFood-Bench: Evaluating VLMs for Nutrient Reasoning and Personalized Health Advice

• URL: http://arxiv.org/abs/2607.08423v1
• Date: Thu, 09 Jul 2026 12:46:00 GMT
• Title: OmniFood-Bench: Evaluating VLMs for Nutrient Reasoning and Personalized Health Advice
• Abstract summary: We introduce OmniFood-Bench, a comprehensive benchmark constructed from the MM-Food-100K dataset.<n>We evaluate six state-of-the-art Large Vision-Language Models (VLMs)<n>Models achieve near-human accuracy in naming dishes, but exhibit catastrophic failure in mass estimation and frequently hallucinate benign advice for high-risk diabetic profiles.
• Score: 4.592259709047762
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 synthesizing safety-critical medical advice. In this paper, we introduce OmniFood-Bench, a comprehensive benchmark constructed from the MM-Food-100K dataset. Unlike previous works, OmniFood-Bench evaluates VLMs across three progressive capabilities: Basic Perception (Ingredients & Cooking Methods), Quantitative Reasoning (Portion Size & Nutritional Profiling), and Safety-Critical Advisory (Disease-Specific Recommendations). We evaluate six state-of-the-art VLMs, including gpt-5.1, gemini-3-flash, and qwen3-vl-8B. Our extensive experiments reveal a startling "Semantic-Physical Gap": while models achieve near-human accuracy in naming dishes, they exhibit catastrophic failure in mass estimation and frequently hallucinate benign advice for high-risk diabetic profiles. This work establishes a rigorous standard for trustworthiness in autonomous agents deployed for public health. The code and datasets are available in: https://anonymous.4open.science/r/OmniFood-Bench-7D0B

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