Parallel Tempering

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Parallel tempering runs multiple copies of a sampler or search process at different temperatures, then exchanges their states so exploratory replicas can help cooler ones cross barriers. Methods range from replica-exchange MCMC and Hamiltonian or bouncy-particle samplers to learned transport maps and tempering embedded in diffusion, energy-based, or discrete-search models. The approach is used for Bayesian inference and molecular simulation, and increasingly for combinatorial optimization, prompt search, and generative reasoning. A central challenge is making exchanges and extra replicas computationally worthwhile while preserving reliable target sampling or balancing broad exploration against focused search.

Latest papers

14 papers3 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Posterior sampling by source-space MCMC via prior-based few-step transport maps
Oct 1, 2026Hoang Phuc Hau Luu, Marcelo Hartmann, Zhongjian WangMarkov Chain Monte Carlo Bayesian Inference
Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore. · Department of Computer Science, Faculty of Science, University of Helsinki, Finland.
Explore Broadly, Reason Sharply: Push Small Models toward the Frontier via Sampling
Sep 29, 2026Panagiotis Theodoropoulos, Nan Jiang, Xintong Duan +4Efficient Language Model Reasoning Parallel Tempering
Georgia Institute of Technology · Morgan Stanley · University of Texas at El Paso +1
Parallel Tempering for Diffusion-Based Combinatorial Optimization
Sep 29, 2026Arman Mielke, Uwe Bauknecht, Thilo Strauss +1Combinatorial Optimization Neural Combinatorial Optimization
ETAS Research · University of Stuttgart · Max Planck Research School for Intelligent Systems (IMPRS-IS) +2
Nonlocal Transition Kernel for Efficient Learning of Restricted Boltzmann Machines
Aug 18, 2026Kaiji Sekimoto, Muneki YasudaRestricted Boltzmann Machines Markov Chain Monte Carlo
Graduate School of Science and Engineering, Yamagata University, Japan
A Direct Route to Markov Chain Convergence via Asymptotic Equivalence with the Target
Aug 4, 2026Patrick ForréMarkov Chain Monte Carlo Convergence Analysis
AI4Science Lab Korteweg-de Vries Institute for Mathematics University of Amsterdam
Equilibrium Training of Energy-Based Models with Parallel Trajectory Tempering
Jul 29, 2026Nicolas Béreux, Aurélien Decelle, Cyril Furtlehner +1Markov Chain Monte Carlo Energy-Based Models
Université Paris-Saclay, CNRS, INRIA, LISN, 91190 Gif-sur-Yvette, France · Escuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid, 28006 Madrid, Spain · Departamento de Física Teórica & IPARCOS, Universidad Complutense de Madrid, 28040 Madrid, Spain.
Parallel Noising in Neural Markov Logic Networks
Jul 21, 2026Peter Jung, Giuseppe Marra, Ondrej KuzelkaMarkov Chain Monte Carlo Graph Generation
Czech Technical University, Prague, Czech Republic · Department of Computer Science, KU Leuven, Leuven, Belgium
BayesPO: Bayesian Prompt Optimization via Parallel-Tempered Gradient-Guided Discrete MCMC
Jul 17, 2026Junjie Zhou, Zhijian OuMarkov Chain Monte Carlo LLM Prompting
Speech Processing and Machine Intelligence (SPMI) Lab Tsinghua University, China
Towards Diverse Scientific Hypothesis Search with Large Language Models
Jun 9, 2026Haorui Wang, Parshin Shojaee, Kazem Meidani +7Automated Algorithm Discovery Scientific Hypothesis Generation
Georgia Institute of Technology · Virginia Tech · Carnegie Mellon University +3
Parallel Tempering Initial Sampling in Inference-Time Reward Alignment
May 29, 2026Myeongjun Oh, Gwangho Kim, Sungyoon LeeDiffusion Model Alignment Markov Chain Monte Carlo
Department of Artificial Intelligence 2Department of Computer Science Hanyang University, Seoul, Korea
Solving Integer Linear Programming with Parallel Tempering
May 28, 2026Kyuil Sim, Sanghyeok Choi, Jinkyoo ParkMarkov Chain Monte Carlo Mixed-Integer Linear Programming
1KAIST · University of Edinburgh
Generative Replica-Exchange: A Flow-based Framework for Accelerating Replica Exchange Simulations
Mar 18, 2026Shengjie Huang, Sijie Yang, Jianqiao Yi +6Molecular Dynamics Simulation Parallel Tempering
State Key Laboratory of Bioactive Molecules and Druggability Assessment, International Cooperative Laboratory of Traditional Chinese Medicine Modernization and Innovative Drug Discovery of Chinese Ministry of Education (MOE), School of Pharmacy, Jinan University, #855 Xingye Avenue, Guangzhou, 510632, China · Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA · Howard Hughes Medical Institute, NYU Grossman School of Medicine, New York, NY 10016, USA +1
Refined Gradient-Based Temperature Optimization for the Replica-Exchange Monte-Carlo Method
Jan 20, 2026Tatsuya Miyata, Shunta Arai, Satoshi TakabeParallel Tempering Constrained Optimization
Institute of Science Tokyo, Ookayama, Tokyo 152-8550, Japan
Bouncy particle sampler with infinite exchanging parallel tempering
Sep 2, 2025Yohei Saito, Shun Kimura, Koujin TakedaMarkov Chain Monte Carlo Parallel Tempering
Center for Mathematics and Data Science, Gunma University · Graduate School of Science and Engineering, Ibaraki University

Oct 1, 2026J/K move · Enter open · S save

Posterior sampling by source-space MCMC via prior-based few-step transport maps

Hoang Phuc Hau Luu, Marcelo Hartmann, Zhongjian Wang

Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore. · Department of Computer Science, Faculty of Science, University of Helsinki, Finland.

Markov Chain Monte Carlo Bayesian Inference Posterior Sampling Parallel Tempering Inference-Time Guidance

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Bayesian inference increasingly uses informative but implicit priors represented only by samples, such as historical ensembles, simulator outputs, and pretrained generative models. The same computational problem appears in the test-time guidance task (generalized Bayes), where an explicit positive weight, e.g., an exponentiated reward, tilts an implicit prior. We develop a framework for source-space generalized Bayesian inference that combines inexpensive few-step prior transports with posterior stability guarantees. Specifically, we represent the prior using a one- or few-step improved MeanFlow (iMF) map and perform posterior sampling in its Gaussian source space. We establish Wasserstein error bounds between the exact and learned posteriors in terms of the joint population iMF and auxiliary-velocity loss, decomposed into training suboptimality and model-class approximation error. In the iMF source space, we adopt parallel tempering with preconditioned Crank-Nicolson updates and introduce a hybrid variant that incorporates split Hamiltonian Monte Carlo to improve sampling efficiency. Synthetic experiments show that the proposed framework can approximate posterior distributions accurately and efficiently, while CLIP-guided ImageNet experiments demonstrate its ability to steer a pretrained iMF image prior toward text-specified preferences.