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
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: this https URL