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#sampling

7 curated events
papersTODAY 04:00 UTC

Quenched Ensemble Sampling Tackles Phase-Transition Bottlenecks in Sampling

A new arXiv preprint introduces Quenched Ensemble Sampling, a method aimed at the difficulty of drawing samples from physical energy functions near phase transitions, where the density of states shifts sharply and many existing samplers slow or stall. The work builds on nested sampling, a particle-based approach that traverses such landscapes. The abstract indicates the technique is designed to keep sampling efficient in these abrupt regime changes.

sampling/nested-sampling/physics/research
papersTODAY 04:00 UTC

Paper Examines Temperature Sensitivity in LLM Truncation Sampling

A new arXiv paper analyzes how the temperature parameter in large language model decoding interacts with truncation samplers such as top-p and min-p, which remove the least likely tokens from the candidate pool. The authors describe "temperature fragility" and argue that truncation can offer conditional benefits depending on how sampling is configured. The work is a theoretical and empirical study of decoding strategies rather than a released model or product.

llm/sampling/research/decoding
papersTODAY 04:00 UTC

Thin-shell stability yields faster logconcave sampling from a cold start

A new arXiv paper proves that logconcave probability measures lying along the Gaussian cooling path satisfy a thin-shell stability property, extending the classical thin-shell theorem. This stability result translates into better complexity bounds for the core task of drawing samples from an arbitrary logconcave distribution, including when the process begins from a cold start rather than a warm one. The work sits in the theory of Markov chain Monte Carlo and sampling algorithm analysis.

sampling/logconcave/theory/optimization
papersTODAY 04:00 UTC

IsingFormer: Learned Proposals Augment Parallel Tempering for MCMC

A new arXiv paper proposes adding a global proposal move to parallel tempering, in which a learned model suggests state changes across finite temperatures. The aim is to improve mixing in Markov chain Monte Carlo sampling and optimization, where generative models have shown promise but remain difficult to integrate with standard MCMC. The authors present this as a way to combine learned proposals with established tempering methods rather than replace them.

mcmc/sampling/generative-models/research
papersTODAY 04:00 UTC

Attention-Discounted Adaptive Sampler Proposed for Masked Diffusion Language Models

A new arXiv paper introduces an adaptive sampling method for masked diffusion language models that decides which tokens to commit during each denoising step. The approach targets a known failure mode where individually confident positions become unsafe when decoded in parallel, aiming to preserve accuracy while still reducing the number of inference iterations. The work is a revision of an earlier preprint and has not been peer reviewed.

diffusion-models/inference/sampling/research
papersTODAY 04:00 UTC

Marked Edge Walk: New MCMC Method for Sampling Graph Partitions

A new arXiv paper introduces the Marked Edge Walk, a Markov Chain Monte Carlo algorithm designed to sample graph partitions more effectively. The work targets redistricting analysis, where large ensembles of plans are generated by treating districts as graph partitions, and positions itself against existing methods such as Reversible Recombination. The abstract indicates the approach is intended to address limitations in current sampling techniques.

mcmc/redistricting/graph-partitioning/sampling
papersSEP 10 04:00 UTC

New Paper Addresses Mode Coverage Gaps in Normalizing Flow Boltzmann Generators

A new arXiv study examines why normalizing flow Boltzmann generators trained with forward KL divergence can miss portions of a target distribution when the available training samples are biased or incomplete. The authors propose using variation in the log-ratio of importance weights as a signal to detect when a flow's samples fail to cover target modes. The approach aims to improve the reliability of flow-based samplers used in statistical physics and molecular simulation.

generative-models/sampling/physics/machine-learning