{
  "id": 3823,
  "url": "https://arxiv.org/abs/2605.24503v1",
  "title": "FoodMonitor: Benchmarking MLLMs for Explainable Compliance Analysis",
  "summary": "As AI-powered compliance monitoring becomes increasingly important in public governance and industrial safety, the ability to provide verifiable evidence and traceable accountability signals is essential. However, existing video anomaly detection datasets focus on event-level binary classification, lacking the rule-driven, explainable analysis required for real-world compliance scenarios. We introduce FoodMonitor, a benchmark for explainable compliance analysis in commercial kitchen surveillance",
  "authors": "Ruihao Xu, Xingming Shui, Jingxuan Niu, Yiqin Wang, Jilin Yu, Haoji Zhang et al.",
  "category": "research",
  "topics": "regulation,privacy-surveillance,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-23T10:19:41.000Z",
  "fetched_at": "2026-07-14T16:30:31.922Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3823",
  "original_url": "https://arxiv.org/abs/2605.24503v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}