Learning Graph Rationales to Compress Long Chains of Thought in Multimodal Reasoning
GRACE is proposed, a Graph-based Rationalized CoT Compression framework that transforms long CoTs into reasoning graphs to enable efficient CoT compression, where reasoning steps are represented as nodes and semantic dependencies as edges.