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This paper introduces SGR-BIM, a graph-driven semantic reasoning framework that dynamically aligns regulatory intent with BIM geometry to automate geometry-intensive compliance checks, achieving 84.3% accuracy on fire safety code queries.
This paper explores using visual graph mind maps as reasoning scaffolds for LLMs, finding that visual guidance remains effective even without direct answer hints, while textual flattening of graphs loses benefits.
TRN-R1-Zero introduces a post-training framework that enables LLMs to perform zero-shot reasoning on text-rich networks using only reinforcement learning, without supervised fine-tuning or chain-of-thought data.