Beyond the Query: Corpus-conditioned Predictive Retrieval for Underspecified Queries

Users search because they may lack the knowledge needed to describe the information they seek. Query expansion can help, but may emphasize only some concepts or relations relevant to an underspecified query. We introduce CPR (Corpus-conditioned Predictive Retrieval), which uses corpus documents as partial representations of possible intents. Motivated by the posterior-predictive principle, CPR averages corpus-based relevance predictions under a support prior constructed from semantic and lexical evidence to refine candidate support, without additional training. Experiments on reasoning-intensive retrieval show that better prior rankings need not yield better document predictions. Composition with query expansion recovers shared failures, while weaker direct retrievers can still provide useful document relations. These findings support evaluating evidence through the predictions it enables and using corpus evidence to complement generated queries and stronger embeddings.