{"ID":2839425,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.15196","arxiv_id":"2511.15196","title":"Particle Monte Carlo methods for Lattice Field Theory","abstract":"High-dimensional multimodal sampling problems from lattice field theory (LFT) have become important benchmarks for machine learning assisted sampling methods. We show that GPU-accelerated particle methods, Sequential Monte Carlo (SMC) and nested sampling, provide a strong classical baseline that matches or outperforms state-of-the-art neural samplers in sample quality and wall-clock time on standard scalar field theory benchmarks, while also estimating the partition function. Using only a single data-driven covariance for tuning, these methods achieve competitive performance without problem-specific structure, raising the bar for when learned proposals justify their training cost.","short_abstract":"High-dimensional multimodal sampling problems from lattice field theory (LFT) have become important benchmarks for machine learning assisted sampling methods. We show that GPU-accelerated particle methods, Sequential Monte Carlo (SMC) and nested sampling, provide a strong classical baseline that matches or outperforms...","url_abs":"https://arxiv.org/abs/2511.15196","url_pdf":"https://arxiv.org/pdf/2511.15196v1","authors":"[\"David Yallup\"]","published":"2025-11-19T07:31:46Z","proceeding":"stat.ML","tasks":"[\"stat.ML\",\"cs.LG\",\"hep-lat\"]","methods":"[]","has_code":false}