{
  "id": 2226,
  "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1818923",
  "title": "A benchmark for assessing large language models on molecular-to-food and food-to-molecular prediction tasks",
  "summary": "Large Language Models (LLMs) have demonstrated remarkable proficiency in general-purpose tasks, yet their capacity for fine-grained reasoning in knowledge-intensive domains (KIDs) remains largely unexplored. This study addresses this gap by investigating LLM performance in the specialized field of food chemistry. We introduce a novel benchmark comprising two core tasks: Molecular-to-Food Prediction (MFP) and Food-to-Molecular Prediction (FMP). To support this benchmark, we curated and standardiz",
  "authors": "Jin Pan",
  "category": "research",
  "topics": "children-education,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-10T00:00:00.000Z",
  "fetched_at": "2026-07-14T16:11:46.979Z",
  "source_slug": "x-frontiers-in-artificial-intelligence",
  "source_name": "Frontiers in Artificial Intelligence",
  "source_homepage": "https://www.frontiersin.org/journals/artificial-intelligence",
  "ethics_ai_record_url": "https://ethics.ai/record/2226",
  "original_url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1818923",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}