arXiv AI July 28, 2026 By Michael Chertkov, Sungsoo Ahn, Hamidreza Behjoo

Sampling Decisions: Exact Path-Space Correction, Prior Cancellation and Local-Boltzmann Guidance

arXiv:2503. 14549v3 Announce Type: replace-cross Abstract: How can a cheap but biased sequential, finite-horizon sampler over a discrete space be corrected so that its terminal output follows a prescribed Gibbs distribution?

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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.

By Michael Chertkov, Hamidreza Behjoo, Sungsoo Ahn

arXiv Machine Learning

Jul 20

Neural Non-Equilibrium Hamiltonian Monte Carlo for Corrected Boltzmann Sampling

arXiv:2607. 15682v1 Announce Type: new Abstract: Sampling from an unnormalized Boltzmann density requires proposals that move probability mass globally while retaining enough path-probability information for statistical correction.

By Moxian Qian

arXiv Machine Learning

3d ago

FluxLite: Inference-Time Proposal Control for Discrete Diffusion Models

arXiv:2609.35947v1 Announce Type: new Abstract: Many inference-time tasks for pretrained discrete diffusion models and diffusion language models reduce to drawing samples from a tilted version of the...

By Yinuo Ren, Haoxuan Chen, Grant M. Rotskoff, Jiequn Han, Lexing Ying

llms diffusion benchmarks

arXiv Machine Learning

3d 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....

By Yuwen Qian, Yidong Ouyang, Zhengyan Wan, Hongyuan Zha

multimodal

arXiv Machine Learning

3d ago

Interacting particle guidance for sampling reward-tilted generative priors

arXiv:2609.37227v1 Announce Type: new Abstract: Inference-time steering adapts pretrained diffusion and flow-based models to new tasks, e.g., to generate samples from a conditional distribution or sa...

By Adhithyan Kalaivanan, Zheng Zhao, Jens Sj\"olund, Fredrik Lindsten

diffusion safety

arXiv Machine Learning

Jun 16

Amortized mean-shift interacting particles

arXiv:2606. 15871v1 Announce Type: cross Abstract: Bayesian inference for inverse problems is run to evaluate integrals -- posterior expectations, tail probabilities, and risks -- across a stream of observations.

By Ali Siahkoohi

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Sampling Decisions: Exact Path-Space Control for Physics-Informed Generative Sampling

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FluxLite: Inference-Time Proposal Control for Discrete Diffusion Models

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Amortized mean-shift interacting particles