Clustering as Reasoning: A $k$-Means Interpretation of Chain-of-Thought Graph Learning 事件

Clustering as Reasoning: A $k$-Means Interpretation of Chain-of-Thought Graph Learning arXiv:2605.24867v1 Announce Type: cross Abstract: Chain-of-Thought (CoT) prompting has shown promise in enhancing the reasoning capabilities of large language models (LLMs) on text-attributed graphs (TAGs). This work reframes CoT-based graph learning through the principle of clustering as reasoning, offering a $k$-means interpretation of how iterative reasoning operates over graph-structured data. We observe