{"ID":6267187,"CreatedAt":"2026-07-10T01:11:38.759438437Z","UpdatedAt":"2026-07-13T01:02:08.706470581Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.08423","arxiv_id":"2607.08423","title":"OmniFood-Bench: Evaluating VLMs for Nutrient Reasoning and Personalized Health Advice","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 \u0026 Cooking Methods), Quantitative Reasoning (Portion Size \u0026 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","short_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 appear...","url_abs":"https://arxiv.org/abs/2607.08423","url_pdf":"https://arxiv.org/pdf/2607.08423v1","authors":"[\"Qian Jiang\",\"Zhecheng Shi\",\"Jingpu Yang\",\"Zirui Song\",\"Miao Fang\"]","published":"2026-07-09T12:46:00Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Language Model\"]","project_urls":"[\"https://anonymous.4open.science/r/OmniFood-Bench-7D0B\"]","has_code":false}