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SITA (Scalable Inference-Time Annealing) introduces a method for efficiently sampling molecular Boltzmann distributions by retraining flow-based models along a temperature ladder using energy-based surrogate likelihoods, avoiding costly divergence computations. The approach achieves state-of-the-art performance on Alanine Dipeptide and Tripeptide benchmarks.