{
  "id": 11775,
  "url": "https://arxiv.org/abs/2607.15647v1",
  "title": "Neuro-Symbolic AI for LEED compliance: Document-Centric Benchmarking, Deterministic Numeric Checking, and When Multimodal Hurts",
  "summary": "LEED v4.1 BD+C certification remains a document-intensive process that requires reviewers to read hundreds of pages of project evidence and apply credit-specific threshold logic by hand. This paper investigates whether small, locally deployed language models can perform meaningful screening of LEED documentation and how deterministic symbolic components should share that work. A neuro-symbolic pipeline is introduced that aligns project PDFs to LEED credit sections, retrieves evidence with credit",
  "authors": "Aritro De, Juliana Felkner",
  "category": "research",
  "topics": "regulation,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-17T05:45:02.000Z",
  "fetched_at": "2026-07-20T05:10:09.534Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/11775",
  "original_url": "https://arxiv.org/abs/2607.15647v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}