Biological function in RNA is encoded across scales, from local sequence motifs to long-range structural dependencies. Conventional RNA foundation models couple parameter capacity and inference depth within a fixed architecture, prescribing a fixed computation graph for each inference. Here we present Eco-RNA, an 80M-parameter RNA foundation model that makes inference depth adjustable through learned iterative refinement. Its shared, input-conditioned Transformer core refines sequence representations through repeated application. The iteration count is selected after training and controls effective depth at fixed model parameters. We train across recurrent depths with masked reconstruction, state regularization, and a one-sided loss that anchors deeper reconstruction to a shallow reference. On the 70-assay RNAGym fitness benchmark, increasing refinement from one to four iterations raises aggregate absolute Spearman correlation from 0.242 to 0.286, with the gain retained through eight iterations. At four iterations, Eco-RNA surpasses the current SOTA Evo 2 40B on all three aggregate fitness metrics despite 500x fewer parameters. Its iteration configurations advance the compute-performance Pareto frontier among the evaluated models. It also outperforms the evaluated baselines in non-coding RNA family classification, RNA secondary-structure prediction, and chemical probing profile prediction. These results establish internal inference computation as an effective scaling dimension for RNA foundation models.

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