AlphaFold 3‑like models such as Boltz and Protenix have pushed protein binder design to new limits. These architectures serve both to generate binder candidates—via hallucination or by converting them into backbone/full‑atom generative models—and to screen candidates for validity. We observed that AlphaFold 3’s confidence scores (ipSAE, ipTM) and structural consistency metrics can be predicted directly from the trunk network even after early recycling steps. Our proposed predictor is cheap, differentiable, and available at every recycling cycle. It enables two distinct uses: first, as a decision signal to allocate recycling budget wisely and improve screening efficiency; second, as an objective for hallucination‑based design, steering generation toward more reliable binders. Experiments show that both applications boost performance while markedly reducing computational cost.\n\nReview: This study highlights the practical value of early confidence estimation within the model, offering an efficient pathway for protein design.