Neuro-Symbolic AI for LEED compliance: Document-Centric Benchmarking, Deterministic Numeric Checking, and When Multimodal Hurts
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
Record details
Published: 17 July 2026
Source: arXiv
Category: Research
Topics: Regulation · Finance, VC & PE
Retrieved: 20 July 2026
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How to cite this record
ethics.ai (17 July 2026), “Neuro-Symbolic AI for LEED compliance: Document-Centric Benchmarking, Deterministic Numeric Checking, and When Multimodal Hurts,” evidence record 11775, https://ethics.ai/record/11775 (originally published by arXiv).
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