Scalable Inference-Time Annealing with Surrogate Likelihood Estimators

ArXi:2605.31498v1 Announce Type: new A long standing challenge in computational chemistry and biophysics is efficiently sampling the Boltzmann distribution of molecules. Advances in generative modeling have been proposed to address the limitations of conventional sampling techniques by eliminating the computational cost of simulation. A promising direction is iteratively finetuning diffusion models along a temperature ladder whereby