Scalable Inference-Time Annealing with Surrogate Likelihood Estimators
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 training data is generated via importance sampling during inference-time annealing. Unfortunately, these methods require computing a divergence over the score field to estimate importance weights, rendering them intractable for larger systems. Here we present scalable inference-time annealing (SI...
| Authors | Daniel Peñaherrera, Rishal Aggarwal, David Ryan Koes |
|---|---|
| Primary category | cs.LG |
| Published | 29 May 2026 |
| Last updated at source | 8 June 2026 |
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