NN-Former introduces a novel approach to neural architecture representation by combining the strengths of Graph Neural Networks and transformers while addressing their limitations. It emphasizes the importance of sibling nodes in the architecture topology and proposes new mechanisms for predicting accuracy and latency, achieving improved performance in learning Directed Acyclic Graph topology.