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This paper introduces a neuro-symbolic pipeline for automating LEED v4.1 BD+C compliance verification using small locally deployed language models and deterministic numeric checking. Experiments on four university buildings show that a 4B model outperforms an 8B model, and the deterministic checker corrects arithmetic errors on key credits, though multimodal inputs reduce accuracy.
The article identifies a structural flaw in voice agents where they cannot detect when they over-promise across multiple conversation turns, and describes building a deterministic checker that flags contradictions without relying on LLM evaluation.