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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Biometrika , year = 1970, month = apr, volume =
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representative citing papers
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.
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.
LATTE coordinates LLM agent teams with an evolving shared task graph, cutting token use, time, and failures while matching or beating accuracy of MetaGPT, leader-worker, and static methods.
Reactive graphs enable efficient MCMC inference in probabilistic programming languages by automatically tracking and selectively recomputing data dependencies during sampling.
A hierarchical Bayesian framework pools information across sparse dynamical system datasets via a shared population distribution to improve parameter inference and prediction over unpooled approaches.
LLM embeddings condition a generative transformer to enable faster convergence, better performance, and generalization to unseen LHC processes using a single model.
Latent diffusion model parameterization allows MCMC and SMC to outperform latent-space ESMDA in data mismatch and uncertainty reduction for 3D subsurface DA, while model-space ESMDA produces unrealistic posteriors.
MCMC analysis of 1965-2024 energy data yields a 2.01% growth rate, falsifying Kardashev's 1%, and shows no realistic trajectory reaches Type II, motivating a new information-energy metric (KarNak).
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.
GWTC-5 chirp-mass peaks form a ~1.9-spaced ladder with a new ~19 M⊙ rung matching predicted 2G+3G mergers, unifying prior 1G+2G spin-transition groups under one hierarchical scenario.
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.
Theoretical analysis of multiproposal MCMC in the infinite proposal limit using involutive theory yields new methods and inter-method relationships.
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.
Validates redshift-space power spectrum and bispectrum analysis on Abacus-PNG mocks to recover unbiased f_NL constraints for Euclid spectroscopic sample.
Extends KMS-detailed balance constructions from open quantum systems to prepare microcanonical ensembles and other stationary states with criteria for efficient implementation.
GP15, a ResNet plus normalizing-flow model trained on stacked spectrograms, recovers most binary black hole parameters in agreement with LVK posteriors on 33 three-detector events, with generation of 10,000 posterior samples in about one second.
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.
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.
Discovery of an eclipsing neutron star LMXB with clocked bursts, orbital period 6.483 hr, companion mass 0.6-0.8 Msun, and helium burst indications from energy ratio 120-130.
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.
Describes the methods for producing the fifth gravitational-wave transient catalog (GWTC-5.0) from O4b data of LIGO, Virgo and KAGRA.
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.
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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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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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.
-
Improving the Efficiency of Language Agent Teams with Adaptive Task Graphs
LATTE coordinates LLM agent teams with an evolving shared task graph, cutting token use, time, and failures while matching or beating accuracy of MetaGPT, leader-worker, and static methods.
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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.
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Learning Dynamical Systems from Multiple Sparse Datasets: A Hierarchical Bayesian Modeling Approach
A hierarchical Bayesian framework pools information across sparse dynamical system datasets via a shared population distribution to improve parameter inference and prediction over unpooled approaches.
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One Generator, Any Process: LLM-Conditioning for the LHC
LLM embeddings condition a generative transformer to enable faster convergence, better performance, and generalization to unseen LHC processes using a single model.
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Data assimilation for subsurface flow using latent diffusion model parameterization: performance of ensemble-Kalman and Monte Carlo techniques
Latent diffusion model parameterization allows MCMC and SMC to outperform latent-space ESMDA in data mismatch and uncertainty reduction for 3D subsurface DA, while model-space ESMDA produces unrealistic posteriors.
-
Kardashev's Conundrum: Statistical Falsification of Exponential Energy Growth, the Non-Observation of Type II Technosignatures, and the Kardashev-Sagan-Nakamoto Resolution
MCMC analysis of 1965-2024 energy data yields a 2.01% growth rate, falsifying Kardashev's 1%, and shows no realistic trajectory reaches Type II, motivating a new information-energy metric (KarNak).
-
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.
-
The Chirp-Mass Ladder: A New Rung Emerges
GWTC-5 chirp-mass peaks form a ~1.9-spaced ladder with a new ~19 M⊙ rung matching predicted 2G+3G mergers, unifying prior 1G+2G spin-transition groups under one hierarchical scenario.
-
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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Mad Props: Parallelism in Markov Chain Monte Carlo Through the Lens of the Infinite Proposal Limit
Theoretical analysis of multiproposal MCMC in the infinite proposal limit using involutive theory yields new methods and inter-method relationships.
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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.
-
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.
-
Dissipative microcanonical ensemble preparation from KMS-detailed balance
Extends KMS-detailed balance constructions from open quantum systems to prepare microcanonical ensembles and other stationary states with criteria for efficient implementation.
-
Assessment of normalizing flows for parameter estimation on time-frequency representations of gravitational-wave data
GP15, a ResNet plus normalizing-flow model trained on stacked spectrograms, recovers most binary black hole parameters in agreement with LVK posteriors on 33 three-detector events, with generation of 10,000 posterior samples in about one second.
-
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.
-
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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Einstein Probe discovery of EP J171159.4-333253: an eclipsing neutron star low-mass X-ray binary with clocked bursts
Discovery of an eclipsing neutron star LMXB with clocked bursts, orbital period 6.483 hr, companion mass 0.6-0.8 Msun, and helium burst indications from energy ratio 120-130.
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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.
-
GWTC-5.0: Methods for Identifying and Characterizing Gravitational-wave Transients
Describes the methods for producing the fifth gravitational-wave transient catalog (GWTC-5.0) from O4b data of LIGO, Virgo and KAGRA.
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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.
- Period spacings and global seismic parameters for K2 red giants using deep learning
- White dwarf + M dwarf Detached Binaries in Long Period Radio Transients: Observed Binary Parameters, Evolution, and Population Constraints