{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:FWFS6KBXGAKX2RK7WLFS3QGFVC","short_pith_number":"pith:FWFS6KBX","schema_version":"1.0","canonical_sha256":"2d8b2f283730157d455fb2cb2dc0c5a8aa10a0ba2d358efe22fd239e279e67be","source":{"kind":"arxiv","id":"2211.07541","version":1},"attestation_state":"computed","paper":{"title":"Aspects of scaling and scalability for flow-based sampling of lattice QCD","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.stat-mech","cs.LG"],"primary_cat":"hep-lat","authors_text":"Aleksandar Botev, Alexander G. D. G. Matthews, Ali Razavi, Daniel C. Hackett, Danilo J. Rezende, Denis Boyda, Fernando Romero-L\\'opez, Julian M. Urban, Kyle Cranmer, Michael S. Albergo, Phiala E. Shanahan, Ryan Abbott, S\\'ebastien Racani\\`ere","submitted_at":"2022-11-14T17:07:37Z","abstract_excerpt":"Recent applications of machine-learned normalizing flows to sampling in lattice field theory suggest that such methods may be able to mitigate critical slowing down and topological freezing. However, these demonstrations have been at the scale of toy models, and it remains to be determined whether they can be applied to state-of-the-art lattice quantum chromodynamics calculations. Assessing the viability of sampling algorithms for lattice field theory at scale has traditionally been accomplished using simple cost scaling laws, but as we discuss in this work, their utility is limited for flow-b"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2211.07541","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"hep-lat","submitted_at":"2022-11-14T17:07:37Z","cross_cats_sorted":["cond-mat.stat-mech","cs.LG"],"title_canon_sha256":"6b2050d0200be7b698a1ed7f0bdfdd6e84d683894c595a76cedbcfd94bf539c1","abstract_canon_sha256":"99dc810089f6e22a6658c201252e3c38c71f578f4ce6a6f075d73da1dddf9b65"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:15:54.837910Z","signature_b64":"LvNTxUFSe812mvj1FexPlOCHCioyQCPzBR7gE3X7SLKOcC6imQT0lqnmDvEWfmibfG/brT6nMREh9E5kRrazDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2d8b2f283730157d455fb2cb2dc0c5a8aa10a0ba2d358efe22fd239e279e67be","last_reissued_at":"2026-07-05T05:15:54.837367Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:15:54.837367Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Aspects of scaling and scalability for flow-based sampling of lattice QCD","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.stat-mech","cs.LG"],"primary_cat":"hep-lat","authors_text":"Aleksandar Botev, Alexander G. D. G. Matthews, Ali Razavi, Daniel C. Hackett, Danilo J. Rezende, Denis Boyda, Fernando Romero-L\\'opez, Julian M. Urban, Kyle Cranmer, Michael S. Albergo, Phiala E. Shanahan, Ryan Abbott, S\\'ebastien Racani\\`ere","submitted_at":"2022-11-14T17:07:37Z","abstract_excerpt":"Recent applications of machine-learned normalizing flows to sampling in lattice field theory suggest that such methods may be able to mitigate critical slowing down and topological freezing. However, these demonstrations have been at the scale of toy models, and it remains to be determined whether they can be applied to state-of-the-art lattice quantum chromodynamics calculations. Assessing the viability of sampling algorithms for lattice field theory at scale has traditionally been accomplished using simple cost scaling laws, but as we discuss in this work, their utility is limited for flow-b"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.07541","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/2211.07541/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2211.07541","created_at":"2026-07-05T05:15:54.837431+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.07541v1","created_at":"2026-07-05T05:15:54.837431+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.07541","created_at":"2026-07-05T05:15:54.837431+00:00"},{"alias_kind":"pith_short_12","alias_value":"FWFS6KBXGAKX","created_at":"2026-07-05T05:15:54.837431+00:00"},{"alias_kind":"pith_short_16","alias_value":"FWFS6KBXGAKX2RK7","created_at":"2026-07-05T05:15:54.837431+00:00"},{"alias_kind":"pith_short_8","alias_value":"FWFS6KBX","created_at":"2026-07-05T05:15:54.837431+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2510.25704","citing_title":"Scaling flow-based approaches for topology sampling in $\\mathrm{SU}(3)$ gauge theory","ref_index":83,"is_internal_anchor":false},{"citing_arxiv_id":"2604.10209","citing_title":"Scalable Generative Sampling and Multilevel Estimation for Lattice Field Theories Near Criticality","ref_index":52,"is_internal_anchor":false},{"citing_arxiv_id":"2604.12416","citing_title":"Machine learning for four-dimensional SU(3) lattice gauge theories","ref_index":15,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FWFS6KBXGAKX2RK7WLFS3QGFVC","json":"https://pith.science/pith/FWFS6KBXGAKX2RK7WLFS3QGFVC.json","graph_json":"https://pith.science/api/pith-number/FWFS6KBXGAKX2RK7WLFS3QGFVC/graph.json","events_json":"https://pith.science/api/pith-number/FWFS6KBXGAKX2RK7WLFS3QGFVC/events.json","paper":"https://pith.science/paper/FWFS6KBX"},"agent_actions":{"view_html":"https://pith.science/pith/FWFS6KBXGAKX2RK7WLFS3QGFVC","download_json":"https://pith.science/pith/FWFS6KBXGAKX2RK7WLFS3QGFVC.json","view_paper":"https://pith.science/paper/FWFS6KBX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.07541&json=true","fetch_graph":"https://pith.science/api/pith-number/FWFS6KBXGAKX2RK7WLFS3QGFVC/graph.json","fetch_events":"https://pith.science/api/pith-number/FWFS6KBXGAKX2RK7WLFS3QGFVC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FWFS6KBXGAKX2RK7WLFS3QGFVC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FWFS6KBXGAKX2RK7WLFS3QGFVC/action/storage_attestation","attest_author":"https://pith.science/pith/FWFS6KBXGAKX2RK7WLFS3QGFVC/action/author_attestation","sign_citation":"https://pith.science/pith/FWFS6KBXGAKX2RK7WLFS3QGFVC/action/citation_signature","submit_replication":"https://pith.science/pith/FWFS6KBXGAKX2RK7WLFS3QGFVC/action/replication_record"}},"created_at":"2026-07-05T05:15:54.837431+00:00","updated_at":"2026-07-05T05:15:54.837431+00:00"}