GraphMind models multi-step reasoning as an evolving heterogeneous graph, using GNN encoding and semantic matching to select theorems and generate conclusions iteratively, reporting performance gains over baselines on QA datasets.
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GraphMind: Theorem Selection and Conclusion Generation Framework with Dynamic GNN for LLM Reasoning
GraphMind models multi-step reasoning as an evolving heterogeneous graph, using GNN encoding and semantic matching to select theorems and generate conclusions iteratively, reporting performance gains over baselines on QA datasets.