{"paper":{"title":"Lattice Configuration Generation with a Self-Learning Diffusion Model","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cond-mat.str-el"],"primary_cat":"hep-lat","authors_text":"Akio Tomiya","submitted_at":"2026-07-14T09:58:51Z","abstract_excerpt":"We show that a diffusion sampler for lattice-field configurations can be trained without preparing training data by an external Monte Carlo calculation. Starting from exactly sampled configurations at $\\beta=0$, we construct a self-bootstrap sampler, SLDiffusion, in which periodic Gaussian proposals with a fixed learned score are Metropolis--Hastings corrected, at each $\\beta$, against the same physical target at every noise level, and only replay configurations from the resulting chain are used to train the score at the next stage. In the two-dimensional compact XY model, self-training procee"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.12587","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2607.12587/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}