PWO is a trust-region optimizer for autoregressive NQS that improves stability over Adam and stochastic reconfiguration methods while scaling to 1.5B-parameter models on spin systems.
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A differentiable pipeline uses continuous atom occupancy and gradient descent plus a neural network to optimize short-range order in multi-element alloys directly for target stiffness properties.
Hybrid simulation and non-Euclidean elasticity theory demonstrate that clathrin coats develop adaptive rigidity and memory during growth, producing flat, stalled, or closed outcomes through two energy-landscape gates and matching experiments without fitted parameters.
HST/WFC3 UV imaging of SR 12 c measures accretion luminosity of 1.65 ± 0.19 × 10^{-5} L_⊙ and rate of 8 ± 2 × 10^{-12} M_⊙ yr^{-1}, placing it at the end stages of giant planet assembly with a full UV-to-sub-mm SED.
Uncorrected Gaussian residual penalties in full-space sampling converge after marginalization to the graph-lifted reduced posterior multiplied by the inverse absolute determinant of the state Jacobian, requiring explicit determinant corrections for equivalence.
COTHROM applies a Potts Hamiltonian representation of constitutional mandates, MCMC/simulated annealing optimization, and Pareto/MCDA analysis to improve Irish constituency boundaries over existing legal ones in County Cork for proportionality and compactness across weightings.
Introduces global and local contraction coefficients under E_γ-divergence to derive explicit mixing-time bounds for projected Langevin Monte Carlo and independent Metropolis-Hastings, including heavy-tailed cases.
Neutron diffraction detects intrinsic chemical short-range order in CoCrNi via a diffuse peak at 1.85 Å^{-1}, enhanced by aging, with simulations and SANS confirming nanoscale Ni-rich domains.
Derives non-asymptotic error bounds for standard, defensive, and self-normalized importance sampling with random KDE proposals from geometrically ergodic Markov chains, separating n^{-1/2} Monte Carlo error from MIAE/MISE proposal error.
SRM generates statistically representative periodic microstructures with up to 10^7 particles at near-linear cost and produces both equilibrium-like and strongly non-equilibrium arrangements, including platelet networks.
gemlib.mcmc supplies composable kernel abstractions for Metropolis-within-Gibbs sampling via writer monads, allowing concise expression and reuse of complex MCMC algorithms for partially observed epidemic models.
A homotopy-plus-MCMC data-generation pipeline trains a mass-conditioned diffusion model that yields 40% more feasible initial costates and a better Pareto front for multiobjective indirect low-thrust transfers than adjoint-control-transformation baselines.
EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
Transformer networks sample up to 180x180 2D Ising systems and 64x64 Edwards-Anderson systems by generating spin groups with probability approximations, yielding ~20x higher effective sample size than prior neural samplers at criticality.
Reactive graphs enable efficient MCMC inference in probabilistic programming languages by automatically tracking and selectively recomputing data dependencies during sampling.
Coupling-Grouped XY-QAOA enables joint anomaly-feature selection via a constraint-preserving grouped-angle QAOA variant, achieving 45.9-61.3% circuit depth reduction and larger feasible executions (64 qubits at p=2) on IBM Heron hardware compared to standard approaches.
Exact mapping of Ising-Heisenberg model on extended Lieb lattice to square-lattice Ising model identifies continuous and discontinuous thermal phase transitions confirmed by simulations.
Monte Carlo layer-ratio reconstruction via fixed-layer Markov chains produces the estimate M(10) ≈ 8.936 × 10^78 with uncertainty from cross-n scaling calibrated on known smaller values.
AI4BayesCode generates validated modular stateful MCMC samplers from natural language Bayesian model descriptions via LLM translation, modular blocks, and recursive stateful composition.
Recasts sampling-based nonconvex optimization as smoothed gradient descent to obtain non-asymptotic convergence guarantees and introduces the DIDA annealed algorithm that converges to the global optimum.
E-value sequential tests enable early stopping of MCMC sampling in Bayesian deep ensembles, often needing only a fraction of the full budget while improving over standard deep ensembles.
FluxMC integrates flow matching with parallel tempering MCMC to converge in under five hours on high-fidelity IMRPhenomHM waveforms for massive black hole binaries, where standard methods fail after hundreds of hours and produce two to three orders of magnitude higher distributional error.
dynesty is an open-source Python package for dynamic nested sampling that improves efficiency in Bayesian posterior and evidence estimation compared to MCMC on certain problems.
A dynamic load balancer for UM-Bridge achieves near-millisecond average node idle time on heterogeneous tsunami simulation workloads in Bayesian inversion without prior workload assumptions.
citing papers explorer
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One More Time: Revisiting Neural Quantum States from a Reinforcement Learning Perspective
PWO is a trust-region optimizer for autoregressive NQS that improves stability over Adam and stochastic reconfiguration methods while scaling to 1.5B-parameter models on spin systems.
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Differentiable inverse design of short-range order in high-entropy alloys: from target sro to target property
A differentiable pipeline uses continuous atom occupancy and gradient descent plus a neural network to optimize short-range order in multi-element alloys directly for target stiffness properties.
-
Pathway variability, coat stiffening and mechanical adaptation during clathrin-mediated endocytosis
Hybrid simulation and non-Euclidean elasticity theory demonstrate that clathrin coats develop adaptive rigidity and memory during growth, producing flat, stalled, or closed outcomes through two energy-landscape gates and matching experiments without fitted parameters.
-
Ultraviolet Imaging of SR 12 c with HST/WFC3: Accretion and Variability of a Giant Planet at the End Stages of Growth
HST/WFC3 UV imaging of SR 12 c measures accretion luminosity of 1.65 ± 0.19 × 10^{-5} L_⊙ and rate of 8 ± 2 × 10^{-12} M_⊙ yr^{-1}, placing it at the end stages of giant planet assembly with a full UV-to-sub-mm SED.
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Constraint residuals, graph posteriors, and determinant-corrected full-space targets in Bayesian inverse problems
Uncorrected Gaussian residual penalties in full-space sampling converge after marginalization to the graph-lifted reduced posterior multiplied by the inverse absolute determinant of the state Jacobian, requiring explicit determinant corrections for equivalence.
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Constituency Optimisation Through Hamiltonian Representation Of Mandates (COTHROM): Algorithmic Redistricting of Irish Election Boundaries
COTHROM applies a Potts Hamiltonian representation of constitutional mandates, MCMC/simulated annealing optimization, and Pareto/MCDA analysis to improve Irish constituency boundaries over existing legal ones in County Cork for proportionality and compactness across weightings.
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Local and Global Contraction Principles for MCMC Mixing
Introduces global and local contraction coefficients under E_γ-divergence to derive explicit mixing-time bounds for projected Langevin Monte Carlo and independent Metropolis-Hastings, including heavy-tailed cases.
-
Direct Observation of Chemical Short-Range Order in CoCrNi Alloy Using Neutron Diffraction
Neutron diffraction detects intrinsic chemical short-range order in CoCrNi via a diffuse peak at 1.85 Å^{-1}, enhanced by aging, with simulations and SANS confirming nanoscale Ni-rich domains.
-
Error Bounds for Importance Sampling with Estimated Proposal Distributions
Derives non-asymptotic error bounds for standard, defensive, and self-normalized importance sampling with random KDE proposals from geometrically ergodic Markov chains, separating n^{-1/2} Monte Carlo error from MIAE/MISE proposal error.
-
Efficient generation of large-scale non-equilibrium distributions of particles
SRM generates statistically representative periodic microstructures with up to 10^7 particles at near-linear cost and produces both equilibrium-like and strongly non-equilibrium arrangements, including platelet networks.
-
gemlib.mcmc: composable kernels for Metropolis-within-Gibbs sampling schemes
gemlib.mcmc supplies composable kernel abstractions for Metropolis-within-Gibbs sampling via writer monads, allowing concise expression and reuse of complex MCMC algorithms for partially observed epidemic models.
-
Transfer Learning of Multiobjective Indirect Low-Thrust Trajectories Using Diffusion Models and Markov Chain Monte Carlo
A homotopy-plus-MCMC data-generation pipeline trains a mass-conditioned diffusion model that yields 40% more feasible initial costates and a better Pareto front for multiobjective indirect low-thrust transfers than adjoint-control-transformation baselines.
-
Evaluating LLMs on Large-Scale Graph Property Estimation via Random Walks
EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
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Sampling two-dimensional spin systems with transformers
Transformer networks sample up to 180x180 2D Ising systems and 64x64 Edwards-Anderson systems by generating spin groups with probability approximations, yielding ~20x higher effective sample size than prior neural samplers at criticality.
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Reactive Graphs for Efficient Markov Chain Monte Carlo Inference in Probabilistic Programming Languages
Reactive graphs enable efficient MCMC inference in probabilistic programming languages by automatically tracking and selectively recomputing data dependencies during sampling.
-
Coupling-Grouped XY-QAOA for Joint Anomaly-Feature Selection
Coupling-Grouped XY-QAOA enables joint anomaly-feature selection via a constraint-preserving grouped-angle QAOA variant, achieving 45.9-61.3% circuit depth reduction and larger feasible executions (64 qubits at p=2) on IBM Heron hardware compared to standard approaches.
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Continuous and discontinuous transitions in the Ising-Heisenberg model on the extended Lieb lattice in a magnetic field
Exact mapping of Ising-Heisenberg model on extended Lieb lattice to square-lattice Ising model identifies continuous and discontinuous thermal phase transitions confirmed by simulations.
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Finite-n Estimate of Dedekind Numbers by Layer-Ratio Monte Carlo
Monte Carlo layer-ratio reconstruction via fixed-layer Markov chains produces the estimate M(10) ≈ 8.936 × 10^78 with uncertainty from cross-n scaling calibrated on known smaller values.
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AI4BayesCode: From Natural Language Descriptions to Validated Modular Stateful Bayesian Samplers
AI4BayesCode generates validated modular stateful MCMC samplers from natural language Bayesian model descriptions via LLM translation, modular blocks, and recursive stateful composition.
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Global Convergence of Sampling-Based Nonconvex Optimization through Diffusion-Style Smoothing
Recasts sampling-based nonconvex optimization as smoothed gradient descent to obtain non-asymptotic convergence guarantees and introduces the DIDA annealed algorithm that converges to the global optimum.
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Towards E-Value Based Stopping Rules for Bayesian Deep Ensembles
E-value sequential tests enable early stopping of MCMC sampling in Bayesian deep ensembles, often needing only a fraction of the full budget while improving over standard deep ensembles.
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FluxMC: Rapid and High-Fidelity Inference for Space-Based Gravitational-Wave Observations
FluxMC integrates flow matching with parallel tempering MCMC to converge in under five hours on high-fidelity IMRPhenomHM waveforms for massive black hole binaries, where standard methods fail after hundreds of hours and produce two to three orders of magnitude higher distributional error.
-
dynesty: A Dynamic Nested Sampling Package for Estimating Bayesian Posteriors and Evidences
dynesty is an open-source Python package for dynamic nested sampling that improves efficiency in Bayesian posterior and evidence estimation compared to MCMC on certain problems.
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Dynamic Load Balancing for Uncertainty Quantification with Applications in Bayesian Inversion
A dynamic load balancer for UM-Bridge achieves near-millisecond average node idle time on heterogeneous tsunami simulation workloads in Bayesian inversion without prior workload assumptions.
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Quantum Statistics and Structural Topology Govern Thermal Transport in Two-Dimensional Monolayer Amorphous Carbon
Quantum thermal conductivity of 2D monolayer amorphous carbon ranges 3.5-10 W/m/K at room temperature and is less than half the classical value, with distinct mode polarization behavior.
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Sampling Data with Chains of Forward-Backward Diffusion Steps
U-turn chains are Markov chains formed by short forward-backward diffusion steps that remain on the learned manifold and, with Metropolis-Hastings, sample from energy-modified targets, exhibiting an ergodicity-breaking transition on fragmented manifolds.
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JWST Advanced Deep Extragalactic Survey (JADES) Data Release 5: stellar population catalogue for galaxies in GOODS-N and GOODS-S
JADES DR5 delivers a public catalog of Bayesian-inferred stellar masses, SFRs, SFHs, dust, metallicities, and AGN contributions for ~500k galaxies via Prospector with an evolving SFMS prior.
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Euclid preparation: Testing multi-field inflation with galaxy power spectrum and bispectrum
Validates redshift-space power spectrum and bispectrum analysis on Abacus-PNG mocks to recover unbiased f_NL constraints for Euclid spectroscopic sample.
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Variational Autoregressive Networks with probability priors
Incorporating probability priors into variational autoregressive networks reduces training burden and enables larger system sizes for sampling in the Ising and Edwards-Anderson models.
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Inferring Asteroseismic Parameters from Short Observations Using Deep Learning: Application to TESS and K2 Red Giants
Deep learning infers Δν and ν_max from one-month TESS and K2 observations of red giants with reliable results for ~50% of Kepler/K2 samples and ~23% of TESS stars, plus ΔΠ1 for ~200 K2 young red giants that match known patterns.
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Bayesian Rain Field Reconstruction using Commercial Microwave Links and Diffusion Model Priors
Bayesian inverse problem with diffusion model priors for CML-based rain field reconstruction outperforms baselines by preserving rainfall statistics better than Gaussian processes.
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Turing patterns on non-fluctuating surfaces under mechanical stresses
Turing patterns on non-fluctuating lattices under mechanical stress modeled by Finsler geometry respond to external forces similarly to those on fluctuating membranes.
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Magnetotransport and Phase competition in three-dimensional Hubbard-Holstein model at half-filling
Simulations of the 3D Hubbard-Holstein model at half-filling find antiferromagnetic and charge-ordered insulators separated by a first-order transition with no intervening metal, plus metallic bipolaronic states and pseudogap features at higher temperatures.
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Bathymetry Reconstruction by Bayesian Inference
Bayesian inference reconstructs bathymetry from point water height measurements, improving NRMSE over adjoint optimization on real wave flume data while quantifying uncertainty.
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Determining the Host Stars of Planets in Binary Star Systems with Asterodensity Profiling: Investigating the Canonical Radius Gap
Probabilistic host-star assignments via asterodensity profiling suggest the exoplanet radius gap is less empty in binary systems once possible circumsecondary planets are included.
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Auto-Adaptive PINNs with Applications to Phase Transitions
An auto-adaptive sampling technique for PINNs is introduced and tested on Allen-Cahn equations to better resolve interfacial regions compared to residual-adaptive methods.
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SPT-3G D1: Axion Early Dark Energy with CMB experiments and DESI
CMB-only data give f_EDE < 0.07 at 95% CL with no strong AEDE signal, while CMB+DESI yields f_EDE = 0.055^{+0.024}_{-0.047} at 68% CL and lowers Hubble tension to 2.6 sigma.
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Assessment of normalizing flows for parameter estimation on time-frequency representations of gravitational-wave data
GP15 maps BBH spectrograms to parameter posteriors via residual networks and normalizing flows, producing results consistent with LVK analyses on GWTC-2.1 and GWTC-3 events while running in seconds.
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Role of SKA in Advancing Remote Measurements of Magnetic Fields of Solar Coronal Mass Ejections
SKA's higher sensitivity and bandwidth will enable fuller exploitation of radio methods for measuring CME magnetic fields and improving space weather predictions.
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Pointwise is Pointless? A Multimodal Ablation Study for Precipitation Nowcasting with Graph Neural Networks
A multimodal GNN ablation for Nordic precipitation nowcasting shows sparse point observations improve station and onset scores while NWP and CRPS losses improve radar-grid performance, indicating local and field skills are distinct targets.
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Quantum-Inspired Hamiltonian Optimization, Stochastic Tensor Networks and Adaptive Congestion Routing for Large-Scale QKD Networks
A quantum-inspired framework using effective Hamiltonians, Metropolis annealing and stochastic tensor-network compression is proposed for adaptive multi-demand routing in large-scale QKD networks.
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A computational model of spatial politics: Hotelling-Downs model as statistical physics
Simulations show two-party systems moderate policy positions while multiparty systems increase polarization, with turnout and activists further driving extremes.
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Fast Monte-Carlo
An eigenvalue-based small-sample approximation to MCMC reduces required paths from up to 1,000,000 to as few as 10 while producing comparable steady-state distributions by Wasserstein distance and lower variance.
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Bayesian Inference of Nonlinear Malaria Dynamics in Ghana via an Ensemble Markov Chain Monte Carlo Sampler
Bayesian ensemble MCMC fits a cubic-plus-damped-oscillatory model to Ghana malaria data, reports R² > 0.995, and extrapolates rising case counts through 2026 with widening uncertainty.
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Six Open Questions in Machine-Learned Interatomic Potential Foundation Models
This perspective article develops a definition of foundational MLIPs and poses six open questions that the authors believe will define future research in machine-learned interatomic potentials.
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Quantum effects in plasmas
Quantum effects govern behavior in warm dense matter and inertial fusion plasmas and are best modeled by combining quantum methods through downfolding from first-principles simulations.
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Lattice methods for students at a formal TASI
Lecture notes on lattice methods for formal TASI students covering basics, confinement, chiral fermions, and case studies in the 3D Ising model and QCD.
- Kardashev's Conundrum: Statistical Falsification of Exponential Energy Growth, the Non-Observation of Type II Technosignatures, and the Kardashev-Sagan-Nakamoto Resolution