arXiv Machine Learning
ENCORE: Exact Non-equilibrium COntrol with Replica Exchange for Diffusion Generation
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arXiv Machine Learning
Sep 17
METALICA: METAdynamics and repLICA exchange for enhanced diffusion sampling
METALICA is a new method that combines Metadynamics with Replica Exchange on pretrained diffusion models to improve sampling of rare conformational states in proteins. It builds a bias potential along a chosen Collective Variable, pushes new samples away from previously visited ones, and reweights them to recover the unbiased distribution. By running one replica per diffusion level and allowing inter‑replica communication, METALICA can generate long Markov chains that uncover rare events, as demonstrated on a bimodal test system and on protein unfolding where it reveals a second free‑energy minimum that other methods miss.
arXiv Machine Learning
Aug 4
Sampling Decisions: Exact Path-Space Control for Physics-Informed Generative Sampling
arXiv:2503. 14549v4 Announce Type: replace Abstract: Scientific generative models must turn tractable local decisions into globally correlated samples that respect physical constraints.
arXiv Machine Learning
Jun 10
Rare Event Analysis via Stochastic Optimal Control
arXiv:2604. 13213v2 Announce Type: replace-cross Abstract: Rare events such as conformational changes in biomolecules, phase transitions, and chemical reactions are central to the behavior of many physical systems, yet they are extremely difficult to study computationally because unbiased simulations seldom produce them.
arXiv Machine Learning
Aug 17
Stochastic Control Policies for Robust Molecular Transition Path Sampling
arXiv:2608. 13800v1 Announce Type: new Abstract: Transition path sampling (TPS) aims to efficiently generate rare molecular transition trajectories between metastable states and is essential for understanding biomolecular mechanisms.
arXiv Statistics ML
4d ago
Error-Corrected Inference-Time Scaling for Imperfect Diffusion Models
The paper introduces the Energy-based Feynman-Kac Corrector (EBFKC), a method that improves inference-time scaling for diffusion models that are imperfect due to data and training limitations. EBFKC derives Feynman-Kac dynamics to exactly track a prescribed path in continuous time, then approximates these dynamics with sequential Monte Carlo and variance‑controlling guidance. Experiments on Gaussian mixtures, particle systems, and molecular simulations demonstrate that EBFKC closely matches target distributions and free‑energy profiles, outperforming standard inference‑time scaling baselines that still exhibit significant sampling errors.
arXiv Machine Learning
6d ago
Iterative Exact Discrete Guidance for Energy-Based Sampling
arXiv:2609.37043v1 Announce Type: new Abstract: Sampling from unnormalized distributions over large discrete state spaces becomes difficult when a multimodal target is far from a tractable reference....