This paper studies the problem of discrete super-resolution. Existing stability guarantees rely on the fact that certain sep- aration conditions are satisfied by the true support. However, such structural conditions have not been exploited in the cor- responding algorithmic designs. This paper proposes a novel Bayesian approach based on the model aggregation idea that can generate an exact sparse estimate, and maintain the re- quired structures of the support. The proposed method is implemented within the MCMC framework and empirically provides better support recovery than available algorithms.