Search reranking is regarded as a common way to boost retrieval precision. The problem nevertheless is not trivial especially when there are multiple features or modalities to be considered for search, which often happens in image and video retrieval. This paper proposes a new reranking algorithm , named circular reranking, that reinforces the mutual exchange of information across multiple modalities for civilizing search performance, following the philosophy that strong performing modality could learn from weaker ones, while weak modality does beneath from interacting with stronger ones. Technically,circular reranking conducts multiple runs of random walks through exchanging the ranking scores among different aspect in a cyclic manner. Random walk performs self-reranking through identifying documents with similar patterns based on inter-image similarity and initial rank score. In this paper, we study several properties of circular reranking, including how and which order of information propagation should be shaped to fully exploit the potential of modalities for reranking. Encouraging results are reported for both image and video retrieval on Microsoft Research Asia Multimedia image dataset and TREC Video Retrieval Evaluation 2007-2008 datasets, respectively.