arXiv:2608. 10363v1 Announce Type: new Abstract: AI agents can accelerate nutrition research, but their analyses inherit the identity, semantic, and release ambiguities of the underlying data.
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arXiv:2607. 08423v1 Announce Type: new Abstract: The rapid integration of Large Vision-Language Models (VLMs) into critical infrastructure promises to revolutionize personalized healthcare and dietary management.
arXiv:2609.22099v1 Announce Type: new Abstract: Cooking is a complex process that transforms raw ingredients into delicious and nutritious dishes, yet the recipes that encode this process remain larg...
arXiv:2607. 23273v1 Announce Type: cross Abstract: Computational nutrition needs precise ingredient data, but current databases are incomplete, inconsistent, and built for human reference rather than automated reasoning.
Large language models (LLMs) and agents are now widely used tools in code development, with data typically sent to third-party cloud-based models. Their adoption in research using personal data is constrained by governance requirements that typically prohibit data transmission to external services.
arXiv:2606. 29746v1 Announce Type: new Abstract: Navigating the deluge of heterogeneous medical data, from academic literature (PubMed) to clinical guidelines (Web) and private knowledge bases, remains a critical bottleneck for evidence-based medicine.
arXiv:2603. 19005v2 Announce Type: replace-cross Abstract: Data science plays a critical role in transforming complex data into actionable insights across numerous domains.