{
  "id": 5367,
  "url": "https://arxiv.org/abs/2605.00877v2",
  "title": "OceanPile: A Large-Scale Multimodal Ocean Corpus for Foundation Models",
  "summary": "The vast and underexplored ocean plays a critical role in regulating global climate and supporting marine biodiversity, yet artificial intelligence has so far delivered limited impact in this domain due to a fundamental data bottleneck. Specifically, ocean data are highly fragmented across disparate sources and inherently exhibit multi-modal, high-noise, and weakly labeled characteristics, lacking unified schemas and semantic alignment. Although Multimodal Large Language Models (MLLMs) have achi",
  "authors": "Yida Xue, Ningyu Zhang, Tingwei Wu, Zhe Ma, Daxiong Ji, Zhao Wang et al.",
  "category": "research",
  "topics": "bias-fairness,regulation,safety-alignment,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-25T14:53:37.000Z",
  "fetched_at": "2026-07-14T16:31:44.622Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5367",
  "original_url": "https://arxiv.org/abs/2605.00877v2",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}