Recycle only when you have to

AlphaFold 3-like models (e.g., Boltz, Protenix, etc.) have pushed the frontier of protein binder design. This architecture is used both for the generation of binder candidates (e.g., by converting these models into backbone or full atom generative models) as well as for the screening of these candidates for valid binders. In this paper, we focus on the large-scale screening problem. We start from the observation that AlphaFold 3's confidence metrics (ipSAE, ipTM) as well as structural consistency metrics (scrmsd) can be predicted directly from the trunk even at early recycling. Because our proposed predictor is cheap, differentiable, and available at every cycle, it can be used in two distinct ways: as a decision signal to wisely spend the recycling budget for screening and as an objective for hallucination-based design. We show that both applications can lead to increased performance at significantly reduced computational cost.