LLM adaptive exploration via runtime code execution outperforms static query generation for information extraction from heterogeneous BIM models on the new ifc-bench v2 benchmark.
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P4IR applies supervised fine-tuning followed by GRPO reinforcement learning to reduce tree edit distance by up to 23.8% and Levenshtein distance by up to 38.6% versus SFT baselines while outperforming several frontier LLMs on code structure and semantics for automated building code compliance.
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BIM Information Extraction Through LLM-based Adaptive Exploration
LLM adaptive exploration via runtime code execution outperforms static query generation for information extraction from heterogeneous BIM models on the new ifc-bench v2 benchmark.
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Reinforcement learning to improve large language model-based automated code compliance systems
P4IR applies supervised fine-tuning followed by GRPO reinforcement learning to reduce tree edit distance by up to 23.8% and Levenshtein distance by up to 38.6% versus SFT baselines while outperforming several frontier LLMs on code structure and semantics for automated building code compliance.